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Assegie, Tsehay Admassu. "Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model." Indonesian Journal of electronics, electromedical engineering, and medical informatics 3, no. 1 (2021): 9–14. http://dx.doi.org/10.35882/ijeeemi.v3i1.2.
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Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.
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Srinivasulureddy, Ch, and N. S. Kumar. "Analysis and Comparison for Innovative Prediction Technique of Breast Cancer Tumor using k Nearest Neighbor Algorithm over Support Vector Machine Algorithm with Improved Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 878–94. http://dx.doi.org/10.18137/cardiometry.2022.25.878884.
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Aim: The main objective of this study is to compare the efficiency of the k-Nearest Neighbor (KNN) and Support vector machine (SVM) algorithms in detecting breast cancer tumors and to examine their improved accuracy, sensitivity, and precision. Materials and Methods: The data for the research of Innovative breast cancer prediction using machine learning algorithms is taken from UCI Machine Learning Repository. The sample size of the innovative technique involves two groups KNN (N=20) and SVM (N=20) according to clincalc.com by keeping alpha error-threshold at 0.05, confidence interval at 95%, enrollment ratio as 0:1, and power at 80%. The accuracy, sensitivity, and precision are calculated using MATLAB software. Result: Accuracy (%), sensitivity (%), precision (%) are compared using SPSS software using an independent sample t-test tool. The accuracy of the k-Nearest Neighbor is 93.38% (p<0.001) while the accuracy of the Support vector machine is 97.50%. The sensitivity rate is 90.85% (p<0.001) for k-Nearest Neighbor whereas the results of Support vector machine sensitivity is 95.83%. The precision of k-Nearest Neighbor is 98.48% (p<0.001) whereas the results of Support vector machine precision is 100%. Conclusion: The support vector machine algorithm appears to have performed better than the k-Nearest Neighbor with improved accuracy in Innovative breast cancer prediction.
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Solikin, Steven, Angga Dwinovantyo, Henry Munandar Manik, Sri Pujiyati, and Susilohadi Susilohadi. "Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data." Journal of Applied Geospatial Information 7, no. 2 (2023): 898–903. http://dx.doi.org/10.30871/jagi.v7i2.6363.
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Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093.
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Umar, Rusydi, Imam Riadi, and Dewi Astria Faroek. "A Komparasi Image Matching Menggunakan Metode K-Nearest Neightbor (KNN) dan Support Vector Machine (SVM)." Journal of Applied Informatics and Computing 4, no. 2 (2020): 124–31. http://dx.doi.org/10.30871/jaic.v4i2.2226.
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Pencocokan gambar adalah proses menemukan gambar digital yang memiliki tingkat kesamaan. mencocokkan gambar menggunakan metode klasifikasi. Dalam mengukur pencocokan gambar, gambar yang digunakan adalah gambar logo asli dan gambar logo hasil manipulasi. Perbandingan algoritma klasifikasi dari dua metode yaitu K-Nearest Neighbor (KNN) dan Support Vector Machine dengan optimasi Sequential Minimal Optimization (SMO) yang digunakan untuk menghitung kecocokan berdasarkan nilai akurasi. Metode klasifikasi K-Nearest Neighbor (KNN) didasarkan pada kedekatan atau perhitungan K sedangkan metode klasifikasi Support Vector Machine (SVM) mengukur jarak antara hyperplane dan data terdekat. Nilai kecocokan gambar diukur dengan Precision, Recall, F1-Score, dan Accuracy. Langkah-langkah pencocokan gambar mulai dari persiapan pemrosesan data, ekstraksi fitur dan bentuk warna HSV, kemudian tahap klasifikasi. Gambar digital digunakan sebanyak 10 gambar yang terdiri dari satu logo asli dan 9 logo yang dimanipulasi. Pada tahap pengujian klasifikasi, menggunakan aplikasi WEKA dengan menerapkan metode validasi silang 10 kali lipat. Dari hasil tes yang dilakukan bahwa metode klasifikasi k-neighbor (KNN) terdekat adalah 80% dan memiliki k = 0,889 yang cukup baik dalam mengukur kedekatan, sedangkan metode klasifikasi SVM adalah 70%. Hasil perbandingan pencocokan gambar ini dapat disimpulkan bahwa metode klasifikasi K-Nearest Neighbor bekerja lebih baik daripada SVM untuk pencocokan gambar.
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Tjikdaphia, Nadya Bethry Balqies, and Sulastri Sulastri. "COMPARISON OF NBC, SVM, KNN CLASSIFICATION RESULTS IN SENTIMENT ANALYSIS OF MOBILE JKN." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, no. 4 (2023): 665–72. http://dx.doi.org/10.33330/jurteksi.v9i4.2539.
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Abstract: The JKN Mobile application is a mobile application created to facilitate healthcare administration in Indonesia since 2017. The application has been downloaded by over 10 million users and has received 484,000 diverse reviews, including positive, negative, and neutral feedback. The average rating given by users is 4.5 out of 5 stars. This research aims to perform sentiment analysis on user reviews found in the Google Play Store review column. The methods used for sentiment analysis are Naive Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM). The test results show that with a 10% test data and 90% training data proportion, the SVM method achieves the highest accuracy of 95%. Naive Bayes follows with an accuracy of 87%, and K-NN with an accuracy of 75%. Keywords: JKN mobile application, sentiment analysis, naive bayes, k-nearest neighbor (K-NN), support vector machine (SVM). Abstrak: Aplikasi Mobile JKN adalah sebuah aplikasi yang dibuat untuk mempermudah administrasi kesehatan di Indonesia sejak tahun 2017. Aplikasi ini telah diunduh lebih dari 10 juta pengguna dengan 484 ribu ulasan beragam positif, negatif, dan netral. Rata-rata rating yang diberikan pengguna adalah 4,5 bintang dari 5 bintang. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan pengguna yang terdapat di kolom review Google Play Store. Metode yang digunakan untuk analisis sentimen adalah Naive Bayes, K-Nearest Neighbor (K-NN), dan Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa dengan menggunakan proporsi data uji sebesar 10% dan data training sebesar 90%, metode SVM mencapai akurasi tertinggi sebesar 95%. Diikuti oleh Naive Bayes dengan akurasi 87%, dan K-NN dengan akurasi 75%. Kata kunci: JKN mobile, analisis sentimen, naïve bayes, k-nearest neighbor (K-NN), support vector machine (SVM).
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Sopiatul Ulum, Rizal Fahmi Alifa, Putri Rizkika, and Chaerur Rozikin. "Perbandingan Performa Algoritma KNN dan SVM dalam Klasifikasi Kelayakan Air Minum." Generation Journal 7, no. 2 (2023): 141–46. http://dx.doi.org/10.29407/gj.v7i2.20270.
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Air menjadi kebutuhan mendasar bagi kelangsungan makhluk hidup dan pembangunan. Saat ini, kesadaran masyarakat terhadap pola konsumsi air yang berkualitas dan bermutu semakin tinggi sehingga diperlukan penelitian terhadap kelayakan air. Dalam penelitian air tersebut menggunakan metode klasifikasi objek. Pada penelitian ini membahas perbandingan antara 2 metode Machine Learning yaitu K-Nearest Neighbors (K-NN) dengan Support Vector Machine (SVM) berdasarkan parameter yang telah ditentukan. Penelitian ini menghasilkan tingkat akurasi algoritme K-Nearest Neighbors (K-NN) sebesar 65,341% dan algoritme Support Vector Machine (SVM) menghasilkan akurasi sebesar 69,764%. Dari hasil tersebut, dapat disimpulkan bahwa algoritme Support Vector Machine (SVM) memiliki akurasi lebih tinggi daripada algoritme K-Nearest Neighbors (K-NN).
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Minarno, Agus Eko, Fauzi Dwi Setiawan Sumadi, Hardianto Wibowo, and Yuda Munarko. "Classification of batik patterns using K-Nearest neighbor and support vector machine." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 1260–67. http://dx.doi.org/10.11591/eei.v9i3.1971.
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This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
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Agus, Eko Minarno, Dwi Setiawan Sumadi Fauzi, Wibowo Hardianto, and Munarko Yuda. "Classification of batik patterns using K-Nearest neighbor and support vector machine." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 1260–67. https://doi.org/10.11591/eei.v9i3.1971.
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This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and support vector machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include energy, entropy, contras, correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
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Basedt, Ngabdul, Eko Supriyadi, and Agus Susilo Nugroho. "Perbandingan Algoritma Klasifikasi dalam Analisis Sentimen Opini Masyarakat tentang Kenaikan Harga Bbm." Joined Journal (Journal of Informatics Education) 6, no. 2 (2024): 219. http://dx.doi.org/10.31331/joined.v6i2.2893.
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Kenaikan harga bahan bakar minyak (BBM) telah menjadi permasalahan yang cukup kompleks dan kontroversial . Peningkatan harga BBM memengaruhi berbagai aspek ekonomi dan sosial, termasuk inflasi, biaya produksi, dan tarif transportasi di Indonesia. Klasifikasi sentimen menggunakan algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan metode algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan algoritma klasifikasi sentimen menghasilkan akurasi yang paling tinggi didapatkan oleh algoritma Naive Bayes dengan akurasi sebesar 80,28%. Kedua adalah algoritma Support Vector Machine (SVM) dengan akurasi sebesar 73,89%. Algoritma yang memiliki nilai akurasi paling kecil adalah algorima K-Nearest Neighbor (KNN) dengan akurasi sebesar 50,00%.
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Alamsyah, Riza, Iman Permana, and Maharani Siti Aulieza. "Perbandingan Cacat Ubin Keramik dengan Metode K-Nearest Neighbor dan Support Vector Machine." Techno.Com 22, no. 4 (2023): 805–11. http://dx.doi.org/10.33633/tc.v22i4.8897.
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Penentuan kualitas ubin keramik sudah dilakukan secara otomatis dalam beberapa tahun terakhir. Kendala saat penentuan ubin keramik bercacat dapat berpengaruh terhadap penurunan kualitas produk akhir. Isu yang menjadi fokus dalam penelitian yaitu perbandingan metode antara KNN dengan SVM untuk mendeteksi cacat pada ubin keramik untuk mencapai hasil yang lebih akurat. Untuk mengatasi isu ini, proses yang dilakukan meliputi pengumpulan data gambar dari ubin keramik, yang kemudian diikuti oleh tahap preprocessing dan ekstraksi fitur berdasarkan tekstur. Data gambar tersebut kemudian diklasifikasikan dengan metode KNN dan SVM. Temuan dari penelitian ini menunjukkan bahwa pengklasifikasian dengan metode KNN pada k = 3 mampu memberikan hasil yang lebih unggul, yaitu mencapai akurasi 98.947%, sedangkan pengklasifikasian dengan metode SVM hanya mencapai akurasi 85.263%.
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Andryani, Ade. "Deteksi Email Spam dan Non-Spam Berdasarkan Isi Konten Menggunakan Metode K Nearest Neighbor dan Support Vector Machine." Syntax Idea 6, no. 2 (2024): 1–14. http://dx.doi.org/10.46799/syntax-idea.v6i2.3058.
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Terhadap banyaknya kasus penyalahgunaan email yang berpotensi merugikan orang lain. Email yang disalahgunakan ini biasa dikenal sebagai email spam yang mana email tersebut berisikan iklan, scam, bahkan malware. Penelitian ini bertujuan untuk mendeteksi email spam dan non-spam berdasarkan isi konten menggunakan metode K-Nearest Neighbor dan Support Vector Machine nilai terbaik dari algoritma K-Nearest Neighbor dengan pengukuran jarak Euclidean Distance. Support Vector Machine dan K-Nearest Neighbor dapat mengklasifikasi dan mendeteksi spam email atau non-spam email, K-Nearest Neighbor menggunakan perhitungan jarak Euclidean Distance dengan nilai K = 1,3, dan 5. Hasil evaluasi menggunakan confusion matrix yang menghasilkan bahwa motode K-Nearest Neighbor dengan nilai k=3 mendapatkan tingkat akurasi sebesar 92%, tingkat presisi sebesar 91%, recall sebesar 100%, dan f1_score sebesar 95%. Metode Support Vector Machine mendapatkan nilai akurasi sebesar 97% dengan tingkat akurasi sebesar 97%, recall sebesar 100%, dan f1_score sebesar 98%. Hal ini menjadikan metode Support Vector Machine lebih unggal dibandingkan metode K-Nearest Neighbor dalam penelitian ini. Selain itu model yang dibangun juga sudah dapat digunakan untuk memprediksi spam dan non spam dari isi konten email baru.
 
 Kata Kunci: Confusion Matrix, Email, KNN, Spam, SVM
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Putra, Raymond Chandra. "Pembangunan Perangkat Pendeteksi Jenis Gerakan Raket Bulu Tangkis Dengan Algoritma KNN dan SVM." Teknika 9, no. 2 (2020): 113–20. http://dx.doi.org/10.34148/teknika.v9i2.291.
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Internet of Things (IoT) dapat diaplikasikan untuk banyak bidang, salah satunya pada latihan olahraga bulu tangkis. Pada olahraga bulu tangkis, terutama bagi pemain pemula mengalami kesulitan untuk mengetahui apakah gerakan yang dilakukan sudah benar atau belum. Pada penelitian ini, dibangun sebuah embedded system yang dipasang pada raket yang berfungsi mengambil data gerakan pukulan. Data pukulan ini dikirim ke sebuah perangkat lunak yang dapat mendeteksi jenis gerakan raket bulu tangkis. Embedded system terdiri dari Arduino dan sensor accelerometer dan gyroscope. Data pukulan disimpan ke dalam basis data melalui web service. Perangkat lunak dibangun dengan memanfaatkan prinsip pembelajaran mesin terarah yaitu klasifikasi. Algoritma klasifikasi yang digunakan adalah algoritma k-Nearest Neighbor dan membandingkan hasilnya dengan algoritma lain yaitu Support Vector Machine. Pengujian dilakukan dengan mengumpulkan data latih yang digunakan oleh algoritma klasifikasi untuk memprediksi gerakan. Kinerja dari kedua algoritma klasifikasi diukur dan dibandingkan. Dari hasil pengujian, maka disimpulkan bahwa algoritma Support Vector Machine menghasilkan kinerja yang lebih baik dari k-Nearest Neighbor dalam mendeteksi gerakan raket. Selain itu kinerja algoritma Support Vector Machine yang lebih baik tersebut dihasilkan dengan data latih yang lebih sedikit dibandingkan k-Nearest Neighbor.
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A'yuni, Qurrata, and Billy Hendrik. "Literature Review: Analisis Komparatif Algoritma CNN, KNN, dan SVM untuk Klasifikasi Penyakit Kelapa Sawit." Journal of Education Research 5, no. 4 (2024): 6589–96. https://doi.org/10.37985/jer.v5i4.1983.
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Kelapa sawit adalah salah satu komoditas perkebunan yang populer di dunia dan di Indonesia, serta memiliki peran penting dalam subsektor perkebunan dalam meningkatkan perekonomian negara, akan tetapi penyakit pada tanaman kelapa sawit menghambat produksi optimal. Dengan tujuan untuk mendapatkan algoritma yang tepat untuk klasifikasi penyakit pada tanaman kelapa sawit, penelitian ini menggunakan metode Systematic Literature Review (SLR) dengan melakukan perbandingan terhadap beberapa algoritma Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), dan Support Vector Machine (SVM) untuk meninjau literatur yang ada dengan memberikan analisis komprehensif. Hasil analisis menunjukkan bahwa algoritma yang paling populer dan paling efektif dengan tingkat akurasi diatas 90% adalah Convolutional Neural Network (CNN) dibandingkan K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM). Metode yang banyak digunakan untuk pengujian keakuratan hasil adalah Confusion Matrix.
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Hartono, Seno, Anggi Perwitasari, and Herry Sujaini. "Komparasi Algoritma Nonparametrik untuk Klasifikasi Citra Wajah Berdasarkan Suku di Indonesia." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 6, no. 3 (2020): 337. http://dx.doi.org/10.26418/jp.v6i3.43268.
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Klasifikasi merupakan metode data mining yang berfungsi untuk mengatur dan mengkategorikan data pada kelas yang berbeda-beda. Penelitian ini bertujuan untuk membandingkan dan menentukan algoritma nonparametrik terbaik dalam pengklasifikasian citra wajah. Dalam proses pengklasifikasian, penelitian ini menggunakan algoritma klasifikasi nonparametrik yaitu k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, dan AdaBoost Untuk mengklasifikasikan citra wajah penduduk Indonesia yang berasal dari suku Batak, Dayak, Jawa, Melayu, dan Tionghoa. Penelitian ini menggunakan Orange Data Mining Tool sebagai alat bantu untuk melakukan proses data mining. Dari hasil pengklasifikasian dengan menerapkan algoritma k-Nearest Neigbor, Support Vector Machine, Decision Tree, dan AdaBoost, SVM memberikan nilai akurasi yang lebih baik dibanding algoritma lainnya. Rata-rata nilai precision keempat algoritma tersebut berturut-turut adalah Support Vector Machine 37.5%, diikuti oleh algoritma k-Nearest Neighbor 31.55%, AdaBoost 30.25%, dan untuk Decision Tree 29.75%.
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KURNIA, RAHMADI, MELIA ASMITA, ROZAKY IHSAN, IKHWANA ELFITRI, and DANANG KUMARA HADI. "Perbandingan Metoda Klasifikasi K-Nearest Neighbor dan Support Vector Machine pada Pengenalan Benda Terhalang berbasis Kode Rantai." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 12, no. 3 (2024): 823. http://dx.doi.org/10.26760/elkomika.v12i3.823.
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ABSTRAKBenda yang terhalang oleh benda lain memiliki bentuk yang tidak sempurna karena sebagian sisinya tidak terlihat. Untuk mengatasi permasalahan tersebut, digunakan metoda yang dapat mengenali bentuk pada benda pada sisi yang masih nampak. Penelitian ini membandingkan metoda klasifikasi K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM) berbasis kode rantai untuk mendeteksi bentuk benda terhalang. Terdapat 15 sampel untuk lima bentuk bangun datar pada 2 jenis citra benda. Hasil untuk dua jenis citra, metoda KNN memiliki rata-rata ketepatan sebesar 89,6% sedangkan metoda SVM sebesar 88.4%. Waktu komputasi citra animasi menggunakan metoda SVM lebih cepat 0,044 detik dari pada metoda KNN dan lebih cepat 0,034 detik untuk citra riil. Rata-rata memori yang digunakan dengan metoda SVM pada citra animasi lebih sedikit 0,32 Mb dari pada metoda K-NN Pada citra riil rata-rata memori yang digunakan dengan metoda SVM lebih sedikit 0,44 Mb dari metoda K-NN.Kata kunci: transformasi Hough, kode rantai, bentuk benda, KNN, SVM ABSTRACTObject that are blocked by other objects have an imperfect shape because some of their side are not visible. To overcome this problem, we propose a comparison the K Nearest Neighbor classification (K-NN) and the Support Vector Machine (SVM) method based on chain code algorithm. We used 15 samples for each shape of the object for two kind of images. The result of KNN method classification has an average accuracy of 89,6%. The SVM method has an average accuracy of 88.4%. The average computing time for the SVM method is 0,044 seconds faster than KNN method for drawing image and 0,0034 seconds faster for real images, The average memory for drawing image using the SVM method is 0,32Mb less than K-NN. In the real images the average memory used with the SVM method is 0,44 Mb less than the K-NN.Keywords: hough transform, chain code, shape object, KNN, SVM
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Al-Thwaib, Eman, and Waseem Al-Romimah. "Support Vector Machine versus k-Nearest Neighbor for Arabic Text Classification." International Journal of Sciences Volume 3, no. 2014-06 (2014): 1–5. https://doi.org/10.5281/zenodo.3348731.
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Text Classification (TC) or text categorization can be described as the act of assigning text documents to predefined classes or categories. The need for automatic text classification came from the large amount of electronic documents on the web. The classification accuracy is affected by the documents content and the classification technique being used. In this research, an automatic Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) classifiers will be developed and compared in classifying 800 Arabic documents into four categories (sport, politics, religion, and economy). The experimental results are presented in terms of F1-measure, precision, and recall.Read Complete Article at ijSciences: V3201405505
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Ehsan, Muhmammad. "Comparison of the Predictive Models of Human Activity Recognition (HAR) in Smartphones." UMT Artificial Intelligence Review 1, no. 2 (2021): 27–35. http://dx.doi.org/10.32350/air.0102.03.
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This report compared the performance of different classification algorithms such as decision tree, K-Nearest Neighbour (KNN), logistic regression, Support Vector Machine (SVM) and random forest. The dataset comprised smartphones’ accelerometer and gyroscope readings of the participants while performing different activities, such as walking, walking downstairs, walking upstairs, standing, sitting, and laying. Different machine learning algorithms were applied to this dataset for classification and their accuracy rates were compared. KNN and SVM were found to be the most accurate of all.
 KEYWORDS— decision tree, Human Activity Recognition (HAR), K-Nearest Neighbour (KNN), logistic regression, random forest, Support Vector Machine (SVM)
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Hanafi, Muhammad, and Mhd Furqan. "Perbandingan Analisis Sentimen Presiden 2024 Menggunakan Algoritma Support Vector Machine dan K-Nearest Neighbor." CESS (Journal of Computer Engineering, System and Science) 10, no. 1 (2025): 275. https://doi.org/10.24114/cess.v10i1.67747.
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Di era digital, media sosial menjadi wadah utama bagi masyarakat untuk menyampaikan opini mereka terhadap berbagai isu, termasuk Pemilihan Presiden 2024 di Indonesia. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap pasangan calon "Prabowo-Gibran" berdasarkan 944 tweet yang dikumpulkan selama periode Maret hingga Mei 2024. Metode klasifikasi sentimen yang digunakan dalam penelitian ini adalah K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM), dengan tujuan untuk membandingkan tingkat akurasi kedua algoritma tersebut dalam mengklasifikasikan sentimen publik. Dataset yang digunakan dibagi menjadi dua bagian, yaitu 80% untuk pelatihan dan 20% untuk pengujian. Model KNN diterapkan dengan jumlah tetangga terdekat sebanyak lima (k=5) menggunakan KNeighborsClassifier(n_neighbors=5), sedangkan model SVM menggunakan kernel linear untuk memisahkan data sentimen. Proses analisis dilakukan menggunakan Python dan Google Colab, mencakup tahapan seperti pelabelan data, preprocessing teks, dan ekstraksi fitur. Evaluasi model dilakukan menggunakan Confusion Matrix, yang mengukur akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki tingkat akurasi sebesar 52%, sedangkan KNN hanya mencapai akurasi 51% berdasarkan 189 sampel data uji. Temuan ini mengindikasikan bahwa SVM lebih efektif dibandingkan KNN dalam mengklasifikasikan sentimen publik terkait Pemilihan Presiden 2024 di Indonesia. Meskipun demikian, akurasi yang diperoleh masih tergolong rendah, sehingga penelitian lebih lanjut diperlukan untuk meningkatkan performa model, misalnya dengan optimasi parameter, peningkatan kualitas dataset, atau penerapan teknik machine learning yang lebih canggih.
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Blikon, Yohanes Balawuri. "PERBANDINGA KINERJA PENGKLASIFIKASI CITRA BUAH KAKAO SAKIT DAN SEHAT MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) DAN K-NEAREST NEIGHBORS (KNN)." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 14, no. 1 (2023): 1–8. http://dx.doi.org/10.24176/simet.v14i1.9012.
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Kakao merupakan salah satu hasil bumi dibidang perkebunan. Perkebunan kakao dengan hasilnya yaitu biji kakao dapat diolah menjadi bahan dasar tepung atau coklat. Keberadaan perkebunan ini tentu perlu mendapat dukungan teknologi atau kecerdasan buatan untuk membantu proses pensortiran secara modern jika dilakukan penerapan conveyer belt atau model pemetikan otomatis masa depan menggunakan drone pemetik buah. Proses pensortiran yang dimaksud yaitu menggunakan model pengklasifikasian untuk mendeteksi dataset buah kakao sakit dan sehat. Penelitian ini membandingkan model klasifikasi Support Vector Machine (SVM) dan k-Nearest Neighbors (KNN) dengan tujuan untuk mengetahui kinerja pengklasifikasi yang lebih persisi. Dari hasi ujicoba yang dilakukan performa dari model klasifikasi Support Vector Machine (SVM) dengan kernel RBF dan cross validation 2 mendapatkan hasil prediksi yang lebih tinggi yaitu sebesar 82,5% sedangkan model klasifikasi k-Nearest Neighbors (KNN) dengan number of neighbors 5, metric euclidean dan weight distance tingkat akurasinya sebesar 82,3%. Kata kunci: support vector machine (SVM); k-nearest neighbors (KNN); dataset buah kakao; performa klasifikasi.
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Aqliima, Aziz, Feresa Mohd Foozy Cik, Shamala Palaniappan, and Suradi Zurinah. "YouTube Spam Comment Detection Using Support Vector Machine and K–Nearest Neighbor." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 607–11. https://doi.org/10.11591/ijeecs.v12.i2.pp607-611.
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Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and KNearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems.
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Mumtazah, Binta Bailina, and Sulistyo Dwi Sancoko. "Rekomendasi Ukuran Baju Dewasa Menggunakan Algoritma K-Nearest Neighbor dan Support Vector Machine." MALCOM: Indonesian Journal of Machine Learning and Computer Science 4, no. 4 (2024): 1635–45. https://doi.org/10.57152/malcom.v4i4.1726.
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Kemunculan platform toko online telah mengubah cara masyarakat berbelanja, termasuk dalam pembelian pakaian. Kendati menawarkan kemudahan dan aksesibilitas, berbelanja pakaian secara online seringkali menghadirkan masalah ketidakcocokan ukuran, yang menyebabkan ketidaknyamanan bagi konsumen. Untuk mengatasi masalah tersebut, penelitian ini mengembangkan sistem rekomendasi ukuran baju menggunakan atribut meliputi jenis kelamin, bentuk badan, tinggi badan, berat badan, dan lingkar dada. Sistem ini menggunakan dua algoritma, yaitu K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM), yang dievaluasi untuk menentukan performa terbaik dalam memberikan rekomendasi ukuran. Hasil eksperimen menunjukkan bahwa metode SVM dengan kernel RBF dan pendekatan One-vs-Rest pada proporsi data 80:20 memberikan akurasi tertinggi sebesar 76%, presisi 78%, recall 76%, dan F1 score 76%, sehingga terpilih sebagai model yang diimplementasikan pada website rekomendasi. Pengujian fungsional pada website dengan metode black box testing menunjukkan bahwa sistem telah memenuhi seluruh persyaratan, termasuk registrasi, login, input data, rekomendasi ukuran, dan logout. Keseluruhan hasil penelitian ini menunjukkan bahwa algoritma SVM memiliki performa lebih unggul dibandingkan KNN dalam klasifikasi ukuran baju, serta memastikan keandalan fungsional sistem dalam memberikan rekomendasi yang sesuai bagi pengguna
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Niendha Biell Binna, Tatang Rohana, Hilda Yulia Novita, and Sutan Faisal. "KLASIFIKASI JENIS BUAH TOMAT MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE." Jurnal Informatika Teknologi dan Sains (Jinteks) 7, no. 2 (2025): 800–807. https://doi.org/10.51401/jinteks.v7i2.5743.
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Tomat adalah salah satu jenis komoditas hortikultura yang cukup banyak di Indonesia dengan variasi bentuk, ukuran, dan warna. Pemilahan jenis tomat secara manual oleh petani maupun pedagang masih memiliki kelemahan, seperti keterbatasan fisik dan ketidakkonsistenan dalam klasifikasi. Tujuan dari penelitian ini adalah untuk mengembangkan sistem klasifikasi untuk jenis tomat. berbasis citra digital menggunakan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN). Proses klasifikasi dilakukan dengan mengekstraksi fitur warna dari kanal Hue (HSV), tekstur dari Gray-Level Co-occurrence Matrix (GLCM), dan bentuk dari kontur objek tomat. Dataset terdiri dari tiga jenis tomat, yaitu tomat ceri, tomat hijau, dan tomat sayur. Data melalui tahapan pra-pemrosesan sebelum dilakukan pelatihan dan pengujian model. Hasil evaluasi menunjukkan bahwa algoritma KNN menghasilkan akurasi 94,44%, sedangkan SVM mencapai akurasi sempurna sebesar 97,22%. Evaluasi dilakukan menggunakan metrik confusion matrix, akurasi, precision, recall, dan F1-score. Temuan ini mengindikasikan bahwa SVM memiliki keunggulan dalam mengklasifikasikan jenis tomat secara akurat, serta menunjukkan potensi besar penerapan teknologi klasifikasi citra dalam mendukung efisiensi proses pertanian modern
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Moosavian, Ashkan, Hojat Ahmadi, Babak Sakhaei, and Reza Labbafi. "Support vector machine and K-nearest neighbour for unbalanced fault detection." Journal of Quality in Maintenance Engineering 20, no. 1 (2014): 65–75. http://dx.doi.org/10.1108/jqme-04-2012-0016.
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Purpose – The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers. Design/methodology/approach – To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were extracted from FFT amplitude of the signals. SVM and KNN were employed to classify the three different conditions. The performances of the two classifiers were obtained under different values of their parameter. Findings – The experimental results show the potential application of SVM for machine fault diagnosis. Practical implications – The results demonstrate that the proposed approach can be used effectively for detecting unbalanced condition in rotating machines. Originality/value – In this paper, an intelligent approach for unbalanced fault detection was proposed based on supervised learning method. Also, a performance comparison was made between KNN and SVM in fault classification. In addition, this approach gave a high level of classification accuracy. The proposed intelligent approach can be used for other mechanical faults.
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Hehanussa, Siti Gayatri, Sony Hartono Wijaya, and Toto Haryanto. "Penerapan Metode K-Nearest Neighbor dan Support Vector Machine untuk Klasifikasi Kematangan Buah Mengkudu." Jurnal Ilmu Komputer dan Agri-Informatika 12, no. 1 (2025): 25–37. https://doi.org/10.29244/jika.12.1.25-37.
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Buah mengkudu (Morinda citrifolia) merupakan salah satu komoditas ekspor buah-buahan di Indonesia yang selalu tersedia di setiap musim dan dikenal memiliki berbagai manfaat kesehatan. Buah mengkudu berasal dari wilayah Asia Tenggara, termasuk Indonesia, dan sering digunakan dalam pengobatan tradisional. Pada umumnya masyarakat menentukan kematangan buah mengkudu secara manual, yaitu dengan menggunakan penampakan visual. Hal ini menyebabkan adanya perbedaan persepsi dalam menentukan tingkat kematangan buah mengkudu. Oleh karena itu, penelitian ini bertujuan membangun model machine learning untuk klasifikasi tingkat kematangan buah mengkudu. Metode klasifikasi yang digunakan adalah K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM) dengan menggunakan ekstraksi fitur warna Hue Saturation Intensity (HSI) dan ekstraksi fitur tekstur Local Binary Pattern (LBP). Pengklasifikasian yang dilakukan pada buah mengkudu dengan algoritma KNN menghasilkan model klasifikasi yang lebih baik daripada menggunakan algoritma SVM. Akurasi terbaik yang dihasilkan oleh KNN sebesar 88.62% pada k=11, sedangkan akurasi terbaik SVM dengan kernel polynomial sebesar 87.80%, menggunakan parameter C=0.1 Gamma=1, Degree=5, dan coef0=1.0. Hasil ini didapatkan dari data latih dan data uji dengan perbandingan 80:20.
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Sujaini, Herry. "Klasifikasi Citra Alat Musik Tradisional dengan Metode k-Nearest Neighbor, Random Forest, dan Support Vector Machine." JURNAL SISTEM INFORMASI BISNIS 9, no. 2 (2019): 185. http://dx.doi.org/10.21456/vol9iss2pp185-191.
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Dalam dekade terakhir, metode non-parametrik (algoritma berbasis pembelajaran mesin) semakin banyak dipergunakan dari berbagai aplikasi berbasis pengolahan citra digital. Penelitian ini bertujuan untuk membandingkan tiga metode non-parametrik yaitu Metode k-Nearest Neighbor (kNN), Random Forest (RF), dan Support Vector Machine (SVM) terhadap klasifikasi citra alat musik tradisional di Indonesia yang populer di kalangan masyarakat yaitu : angklung, djembe, gamelan, gong, gordang, kendang, kolintang, rebana, sasando, dan serunai. Dari hasil eksperimen pengklasifikasian dengan metode kNN, RF dan SVM, metode kNN memiliki akurasi yang paling baik. Rata-rata nilai precision ketiga metode tersebut berturut-turut adalah 92,1% untuk kNN, 85,4% untuk SVM, dan 69,4% untuk RF
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Hasanah, Siti Hadijah, Muhamad Riyan Maulana, and Dian Nurdiana. "GOJEK DATA ANALYSIS THROUGH TEXT MINING USING SUPPORT VECTOR MACHINE (SVM) AND K-NEAREST NEIGHBOR (KNN)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 889–902. https://doi.org/10.30598/barekengvol19iss2pp889-902.
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The main focus of this research is to apply and test the effectiveness of SVM and KNN methods in Gojek data text analysis. This research will examine how the two methods can classify user comments and feedback and identify data sentiment analysis at the same time practically help Gojek understand user needs and improve service quality. The data obtained through scrapping is categorized into positive and negative sentiment. Data is taken from Gojek application user reviews throughout the year 2022 with a total of 1148 sentiment data with a percentage of 80% training data and 20% testing data. Evaluation of model performance using Confusion Matrix and AUC-ROC Curve shows that SVM is more effective than KNN, with accuracy on training data of 92.55% for SVM and 81.71% for KNN, as well as accuracy on testing data of 82.40% for SVM and 77,09% for KNN.
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Nusantara Habibi, Ahmad Rizky, Ilham Sufiyandi, Murni Murni, et al. "Diabetes Mellitus Disease Analysis using Support Vector Machines and K-Nearest Neighbor Methods." Indonesian Journal of Modern Science and Technology 1, no. 1 (2025): 22–27. https://doi.org/10.64021/ijmst.1.1.22-27.2025.
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Diabetes Mellitus (DM) is a chronic disease characterized by high blood sugar levels and can cause various serious complications if not treated properly. This study aims to analyze the effectiveness of Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) methods in classifying diabetes mellitus patient data. The methodology used includes collecting diabetes datasets, preprocessing data, and applying SVM and KNN algorithms to perform classification. The performance of both methods is analyzed using evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results show that the SVM method provides more optimal performance in classifying diabetes data compared to KNN, with higher accuracy and lower error rate. This finding indicates that SVM is more suitable for early detection of diabetes mellitus in the dataset used in this study.
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Tripath, Kirti, Harsh Sohal, and Shruti Jain. "COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR FEATURE-BASED CLASSIFICATION USING HEART RATE VARIABILITY." Biomedical Engineering: Applications, Basis and Communications 32, no. 02 (2020): 2050009. http://dx.doi.org/10.4015/s101623722050009x.
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This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.
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Aziz, Aqliima, Cik Feresa Mohd Foozy, Palaniappan Shamala, and Zurinah Suradi. "YouTube Spam Comment Detection Using Support Vector Machine and K–Nearest Neighbor." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 612. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp612-619.
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<p>Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems.</p>
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Ammar Tahir and Adil Pervaiz. "Hand written character recognition using SVM." Pacific International Journal 3, no. 2 (2020): 59–62. http://dx.doi.org/10.55014/pij.v3i2.98.
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Classification is one of the most important tasks for different applications such as text categorization, tone recognition, image classification, microarray gene expression, proteins structure predictions, data Classification, etc. Hand-written digit classification is a process that interprets handwritten digits by machine. There are many techniques used for HRC like neural networks and k-nearest neighbor (KNN).In this paper, a novel supervised learning technique, Support Vector Machine (SVM), is applied to blur images data. SVM is a powerful machine model use for classification for two or more classes. This paper represents pixel base detection technique for training machines on blur images. SVM is employed as classifier results are accurate nearest 80% which are comparable with state of art.
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Naveena, M., and Hemantha Kumar G. "Classification of Birds Using KNN and SVM Classifier." International Journal of Computer Science Issues 17, no. 1 (2020): 27–31. https://doi.org/10.5281/zenodo.3987114.
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This paper aims to develop a bird’s classification system based on classifiers fusion to easily identify the birds. It is based on image processing, which can control the classification, qualification and segmentation of images and hence recognize the birds. Usually from the captured images multiple shape features like area, centroid, angle at centroid, maximum angle and minimum angle can be extracted and analyzed to classify and recognize the birds. And then the extracted features are classified using KNN (K-Nearest Neighbor) Classifier and SVM (Support vector machine) classifiers.
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Desiani, Anita, Adinda Ayu Lestari, M. Al-Ariq, Ali Amran, and Yuli Andriani. "Comparison of Support Vector Machine and K-Nearest Neighbors in Breast Cancer Classification." Pattimura International Journal of Mathematics (PIJMath) 1, no. 1 (2022): 33–42. http://dx.doi.org/10.30598/pijmathvol1iss1pp33-42.
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Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.
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Vika Vitaloka Pramansah. "Analisis Perbandingan Algoritma SVM Dan KNN Untuk Klasifikasi Anime Bergenre Drama." Jurnal Informatika dan Teknologi Komputer ( J-ICOM) 3, no. 1 (2022): 49–55. http://dx.doi.org/10.33059/j-icom.v3i1.4950.
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Terdapat banyak genre anime seperti drama, aksi, romansa, komedi, dan lain sebagainya. Namun, dikarenakan genre anime itu banyak, penonton cukup kesulitan untuk mencari anime yang genrenya mereka sukai seperti genre drama yang menceritakan kehidupan manusia sehari-hari yang sifatnya cukup ringan. Dari permasalahan tersebut, maka dibutuhkan suatu metode klasifikasi untuk mengklasifikasikan anime yang tergolong ke dalam genre drama. Klasifikasi dalam data mining merupakan metode yang umum, suatu objek yang sebelumnya belum diketahui kelas/labelnya dapat melalui metode klasifikasi agar kelasnya dapat diperkirakan [7]. Klasifikasi memiliki beberapa algoritma diantaranya Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN). Penggunaan algoritma SVM dan KNN telah banyak digunakan dan tingkat akurasinya yang baik. Dalam penelitian ini akan menganalisa perbandingan diantara kedua algoritma tersebut pada dataset yang digunakan berjumlah 12.294 data dan 2 kelas genre yaitu drama dan non drama, atribut dataset anime berjumlah 7. Hasil penelitian ini, menunjukkan bahwa algoritma dengan K-Nearest Neighbors (KNN) yang menghasilkan nilai akurasi training sebesar 100% dan nilai akurasi testing sebesar 84%. Dan juga hasil dari algoritma Support Vector Machine (SVM)menghasilkan nilai akurasi training sebesar 83% dan nilai akurasi testing sebesar 82%. Hasil nilai akurasi kedua algoritma tersebut menunjukkan bahwa algoritma K-Nearest Neighbors (KNN) memiliki akurasi testing yang lebih baik dari Support Vector Machine (SVM) dengan selisih keduanya cukup tipis.
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Rajini N, Hema. "Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN." International research journal of engineering, IT & scientific research 3, no. 1 (2017): 36–44. http://dx.doi.org/10.21744/irjeis.v3n1.895.
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A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demonstrated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks.
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Pamungkas, Adji Surya, and Nuri Cahyono. "Analisis Sentimen Review ChatGPT di Play Store menggunakan Support Vector Machine dan K-Nearest Neighbor." Edumatic: Jurnal Pendidikan Informatika 8, no. 1 (2024): 1–10. http://dx.doi.org/10.29408/edumatic.v8i1.24114.
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The ChatGPT application for Android was launched on July 25, 2023, and the language model from OpenAI achieved a rating of 4.8 until early 2024. Despite the majority of positive reviews, user reports stating that ChatGPT provides inaccurate answers raise concerns about the reliability of this application. This research aims to compare the models of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms in analyzing the sentiment of ChatGPT application reviews. Utilizing text mining methods to extract information from text, data was collected from Google Play Store reviews using data scraping techniques and analyzed with Support Vector Machine and K-Nearest Neighbor algorithms. Cross-validation with 5 folds and data split using 80% training and 20% testing data were applied to evaluate the performance of both algorithms. The sentiment classification results showed that the Support Vector Machine algorithm achieved an average accuracy of 80%, while K-Nearest Neighbor reached 71%. SVM excels due to its ability to overcome KNN's limitations regarding less relevant features that do not significantly contribute to predictions. The findings of this study are expected to help developers understand and respond to user feedback regarding the reliability of ChatGPT.
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Dwinanto, Ramadya Wahyu, Arif Setia Sandi A, and Rian Ardianto. "Klasifikasi Berisiko Stunting pada Balita: Perbandingan K-Nearest Neighbor, Naïve Bayes, Support Vector Machine." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 8, no. 2 (2024): 264–73. http://dx.doi.org/10.46880/jmika.vol8no2.pp264-273.
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Stunting in children under five is a significant health problem that impacts child development. This study aims to develop a classification model to predict stunting risk using SVM, KNN, and Naïve Bayes algorithms. Data from the Jatilawang Health Center included 523 under-fives with variables such as age, weight, length, arm circumference, z-score, parental education, and maternal health history. Following the CRISP-DM steps, the data was processed through handling missing data, feature selection, and dividing the data into training and testing sets with a ratio of 80:20. Results showed SVM had the highest accuracy of 90%, followed by KNN 89%, and Naïve Bayes 85%. This research produces a stunting risk prediction model that is implemented in a simple website, supporting early intervention and decision-making in stunting prevention efforts.
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Baita, Anna, Yoga Pristyanto, and Nuri Cahyono. "Analisis Sentimen Mengenai Vaksin Sinovac Menggunakan Algoritma Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN)." Information System Journal 4, no. 2 (2021): 42–46. https://doi.org/10.24076/infosjournal.2021v4i2.687.
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Pandemi COVID-19 yang bermula di Wuhan, Tiongkok, saat ini menjadi pandemi yang terjadi di berbagai negara di seluruh dunia. Upaya vaksinasi dilakukan untuk mengurangi tingkat penyebaran dari virus COVID-19. Pemberian vaksin memberikan dampak yang berbeda-beda, sehingga menimbulkan berbagai opini terhadap pemberian vaksin ini. Sentimen analisis dapat digunakan untuk mengalisa opini masyarakat terhadap pemberian vaksin ini. Dalam penelitian ini menggunakan algoritma SVM dan KNN untuk melakukan analisa mengenai sentimen masyarakat terhadap pemberian vaksin ini. Adapun opini di dapatkan dari aplikasi twitter dengan keyword sinovac. Dataset yang digunakan merupakan cuitan dalam bahasa Inggris. Proses pelabelan teks dilakukan secara otomatis menggunakan textblob. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki performa yang lebih baik jika dibandingkan dengan algoritma KNN. Akurasi algoritma SVM sebesar 0.7, sedangkan akurasi algoritma KNN sebesar 0.56.
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Alimjan, Gulnaz, Tieli Sun, Hurxida Jumahun, Yu Guan, Wanting Zhou, and Hongguang Sun. "A Hybrid Classification Approach Based on Support Vector Machine and K-Nearest Neighbor for Remote Sensing Data." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (2017): 1750034. http://dx.doi.org/10.1142/s0218001417500343.
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Analysis and classification for remote sensing landscape based on remote sensing imagery is a popular research topic. In this paper, we propose a new remote sensing data classifier by incorporating the support vector machine (SVM) learning information into the K-nearest neighbor (KNN) classifier. The SVM is well known for its extraordinary generalization capability even with limited learning samples, and it is very useful for remote sensing applications as data samples are usually limited. The KNN has been widely used in data classification due to its simplicity and effectiveness. However, the KNN is instance-based and needs to keep all the training samples for classification, which could cause not only high computation complexity but also overfitting problems. Meanwhile, the performance of the KNN classifier is sensitive to the neighborhood size [Formula: see text] and how to select the value of the parameter [Formula: see text] relies heavily on practice and experience. Based on the observations that the SVM can contribute to the KNN on the problems of smaller training samples size as well as the selection of the parameter [Formula: see text], we propose a support vector nearest neighbor (abbreviated as SV-NN) hybrid classification approach which can simplify the parameter selection while maintaining classification accuracy. The proposed approach is consist of two stages. In the first stage, the SVM is performed on the training samples to obtain the reduced support vectors (SVs) for each of the sample categories. In the second stage, a nearest neighbor classifier (NNC) is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the NNC identifies the category with minimum distance. To evaluate the effectiveness of the proposed approach, firstly experiments of classification for samples from remote sensing data are evaluated, and then experiments of identifying different land covers regions in the remote sensing images are evaluated. Experimental results show that the SV-NN approach maintains good classification accuracy while reduces the training samples compared with the conventional SVM and KNN classification model.
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Cahyani, Denis Eka. "PENERAPAN MACHINE LEARNING UNTUK PREDIKSI PENYAKIT STROKE." Jurnal Kajian Matematika dan Aplikasinya (JKMA) 3, no. 1 (2022): 15. http://dx.doi.org/10.17977/um055v3i12022p15-22.
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Stroke is a global health problem and one of the leading causes of adult disability. Early detection and prompt treatment are needed to minimize further damage to the affected brain area and complications to other parts of the body. Machine learning techniques can be used to predict stroke detection. Machine learning algorithms such as Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree are compared in this study to obtain the best performance in predicting stroke. The implementation stages in this research consist of the pre-processing data, the application of the algorithm and the evaluation and analysis. The Naïve Bayes algorithm obtains better Accuracy, Precision, Recall, and F1-Measure values compared to other algorithms. The values of Accuracy, Precision, Recall, and F1-Measure obtained by Naïve Bayes are 93.93%, 88.23%, 93.93%, and 91.00%, respectively. So the conclusion of this study is that the Naïve Bayes algorithm has the best performance compared to the SVM, KNN and Decision Tree algorithms in predicting stroke.Keywords: decision tree, klasifikasi, k-nearest neighbor, naïve bayes, stroke, support vector machine
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Akbar, Alvin Tarisa, Novanto Yudistira, and Achmad Ridok. "Identifikasi Gagal Ginjal Kronis dengan Mengimplementasikan Metode Support Vector Machine beserta K-Nearest Neighbour (SVM-KNN)." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 2 (2023): 301. http://dx.doi.org/10.25126/jtiik.20231026059.
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<p class="Judul2">Ginjal merupakan bagian vital bagi manusia karena berfungsi untuk menyaring atau membersihkan cairan yang kita minum agar dapat dikonsumsi oleh tumbuh secara normal. Gagal ginjal adalah situasi dimana ginjal mengalami penurunan funsionalnya secara terus-menerus yang mana dapat mengakibatkan ketidakmampuan ginjal untuk berfungsi untuk semestinya. Untuk membantu pasien yang terjangkit penyakit gagal ginjal kronis hal yang terlebih dahulu dilakukan adalah mengindentifikasi penyakit tersebut. Indentifikasi gagal ginjal kronis dengan menggunakan <em>dataset</em> yang dibuat oleh L.Jerlin Rubini dkk. sudah dilakukan dengan berbagai metode klasifikasi, contohnya adalah implementasi metode klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbour (KNN). Salah satu kelemahan dari SVM adalah bila data terlalu dekat dengan <em>hyperplane</em> adanya potensi untuk salah mengklasifikasi. Lalu salah satu kelemahan dari KNN adalah berpotensi mengalami penuruan akurasi bila nilai k terlalu tinggi atau terlalu rendah yang mana masing-masing mengakibatkan banyaknya <em>noise</em> data atau terlalu kecil data yang digunakan sebagai pembanding. Untuk penelitian ini, kami mengimplementasikan penggabungan metode SVM dengan KNN yang dikenal dengan SVM-KNN yang menggunakan optimasi <em>Simplified Sequential Minimal Optimization</em> (<em>Simplified</em> SMO). Metode ini mencoba untuk menutupi kelemahan dari SVM dan KNN. Penelitian ini melakukan percobaan pada beberapa nilai parameter yang digunakan untuk mendapatkan akurasi pada metode klasifikasi SVM-KNN terbaik. Parameter yang diuji adalah <em>cost</em>, <em>tolerance</em>, <em>gamma</em>, dan <em>bias</em> pada metode SVM, parameter <em>k</em> pada metode KNN, serta parameter <em>miu</em> pada metode SVM-KNN. Nilai rata-rata akurasi terbaik didapatkan dengan menggunakan SVM-KNN dengan nilai 94,25% dan terbukti lebih baik dari pada SVM dengan 94,09% dan KNN dengan 91,73%.</p><p class="Judul2"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"><em><br /></em></p><p class="Judul2"><em>Kidneys are a vital part for humans because they function to filter or clean the fluids we ingest so that they can be consumed safely. Kidney failure is a situation where the kidneys experience a continuous decline in function which can result in the inability of the kidneys to function properly. To help patients with chronic kidney failure, the first thing to do is to identify the disease. Identification of chronic kidney failure using the dataset created by L.Jerlin Rubini et. al. had been tested with various classification methods, for example the implementation of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). One of the weaknesses of SVM is that if the data is too close to the hyperplane there is the potential for misclassification. Then one of the weaknesses of KNN is that it has the potential to experience a decrease in accuracy if the value of k is too high or too low which results in a lot of noise data or too little data used as a comparison respectively. For this research, we implemented a hybrid of SVM with KNN known as SVM-KNN which was optimized using Simplified Sequential Minimal Optimization (Simplified SMO). This study conducted experiments on several parameter values used to obtain the best accuracy in SVM-KNN. The parameters tested are cost, tolerance, gamma, bias on SVM, parameter k on KNN, and miu on SVM-KNN. The average value of accuracy was obtained using SVM-KNN with 94.25% and proved better than SVM with 94.09% and KNN with 91.73%.</em><em></em></p>
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Naufal, Mohammad Farid, Selvia Ferdiana Kusuma, Kevin Christian Tanus, et al. "Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19." Teknika 10, no. 2 (2021): 96–103. http://dx.doi.org/10.34148/teknika.v10i2.331.
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Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.
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Aprilliandhika, Wahyu, and Ferian Fauzi Abdulloh. "COMPARISON OF K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE ALGORITHM OPTIMIZATION WITH GRID SEARCH CV ON STROKE PREDICTION." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 991–1000. https://doi.org/10.52436/1.jutif.2024.5.4.1951.
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Stroke ranks second as the leading cause of death globally, with disability being the primary accompanying factor. The cause of death in stroke patients is due to the lack of an optimal stroke prediction system; therefore, identifying whether a patient is experiencing a stroke or not becomes the focus of this research. Thus, the objective of this study is to compare the performance of stroke prediction using two classification models, namely K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), with and without using the GridSearchCV optimization technique. In this experiment, the dataset is processed and divided into training and testing data using the SMOTE oversampling technique. Initial testing is conducted without GridSearchCV. The results of the initial testing show that the KNN model performs better than SVM, with accuracies of 91% and 83%, respectively. After optimizing parameters using GridSearchCV, both models experience a significant performance improvement. The KNN model increases accuracy to 95% with precision of 91% and recall of 98%, while the SVM model increases accuracy to 94% with precision of 90% and recall of 99%. These results indicate that using GridSearchCV to optimize parameters of KNN and SVM models can significantly enhance stroke prediction performance. There are differences in precision and recall between KNN and SVM. The KNN model tends to have higher recall, while the SVM model has higher precision, and for accuracy, the KNN algorithm outperforms SVM in stroke prediction.
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Prayoga, Putri Regina, Purnawansyah Purnawansyah, Tasrif Hasanuddin, and Herdianti Darwis. "Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor." Edumatic: Jurnal Pendidikan Informatika 7, no. 1 (2023): 160–68. http://dx.doi.org/10.29408/edumatic.v7i1.17521.
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Indonesia is a rich country in herbal plants that can be used as traditional medicine. Leaves are one of the main components of herbal plants that are difficult to distinguish in texture and shape. This study aims to classify two types of herbal leaves, namely Sauropus androgynus and Moringa leaves using the K-nearest neighbor (KNN) and Support vector machine (SVM) with fourier descriptor (FD) feature extraction on texture and shape features. The research uses primary data collected through a smartphone camera as much as 480 image data with light and dark scenarios which are then divided into 80:20 training and testing data. Based on the research that has been done, it is found that the KNN for light scenario data and dark scenarios get 92% and 94% accuracy respectively. The test results using SVM with FD feature extraction obtain an accuracy of 96% for light and dark scenarios. Thus, SVM is more recommended in the classification of herbal leaf images.
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Cheng, Hao. "KNN-SVM Classifiers in Complex Diagnosis." Journal of Physics: Conference Series 2694, no. 1 (2024): 012081. http://dx.doi.org/10.1088/1742-6596/2694/1/012081.
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Abstract In many applications, classification plays an indispensable role due to its powerful detection and diagnosis function. Especially in real data on disease, the detection of important factors and the diagnosis of the result usually bring huge contributions to patients. Simultaneously, complex problems in real data such as imbalanced data and missing data also lead to more challenges and difficulties. The ignorance of missing data will undermine study efficiency, and sometimes introduce substantial bias. Imbalanced data tends to be overwhelmed by the majority classes and ignoring the minority ones. The paper develops new support vector machine classifiers using k-nearest neighbors’ information (KNN-SVM), to impute missing data by calculating k-nearest neighbors’ statistical characteristic values and to interpolate some new samples between k-nearest minority class examples. As comparisons, the paper uses different kernel functions in KNN-SVM classifiers to show the different performances in disease diagnosis accuracy.
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Nurmalasari, Dewi, Teguh Iman Hermanto, and Iman Ma'ruf Nugroho. "Perbandingan Algoritma SVM, KNN dan NBC Terhadap Analisis Sentimen Aplikasi Loan Service." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 3 (2023): 1521. http://dx.doi.org/10.30865/mib.v7i3.6427.
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According to data on the number of credit card users in Indonesia, it has decreased from late 2019 to 2021 oneThe reason is because of the Covid-19 pandemic that hit. Because of this condition, many people are starting to switch to digital credit because they are considered to minimize transmission of viruses and the process is felt to be more efficient than having to use a credit card. This study aims to compare the level of accuracy between the three algorithms, namely the naïve Bayes classifier, k-nearest neighbor and support vector machine for digital credit applications or often called loan services, namely Kredivo. Akulaku, and Indodana in Indonesia by classifying it into two classes namely positive and negative by using the help of the Python programming language to analyze a sentiment by going through text preprocessing and weighting processes said TF-IDF. The results for the accuracy of the Kredivo application using K-NN get a score of 84%, Naïve Bayes 88%, and SVM get 89%. For the application of the K-NN method, it gets 79%, Naïve Bayes 86%, and SVM 87%. As for the indodana application, the K-NN method gets 81%, Naïve Bayes 88%, and SVM 88%. From the results of this accuracy it can be concluded that the Support Vector Machine method has better accuracy results compared to the K-Nearest Neighbor and Naïve Bayes methods.
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Han, Bo, Hongpeng Ding, Yanxia Zhang, and Yongheng Zhao. "Improving Accuracy of Quasars' Photometric Redshift Estimation by Integration of KNN and SVM." Proceedings of the International Astronomical Union 11, A29A (2015): 209. http://dx.doi.org/10.1017/s1743921316002830.
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AbstractCatastrophic failure is an unsolved problem existing in the most photometric redshift estimation approaches for a long history. In this study, we propose a novel approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. Experiments based on the quasar sample from SDSS show that the fusion approach can significantly mitigate catastrophic failure and improve the accuracy of photometric redshift estimation.
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Razat, Agarwal, and Parth Sagar Prof. "A Comparative Study of Supervised Machine Learning Algorithms for Fruit Prediction." Journal of Web Development and Web Designing 4, no. 1 (2019): 14–18. https://doi.org/10.5281/zenodo.2621205.
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<em>In this paper, machine learning techniques have been applied for the fruit image classification and prediction over a large dataset. In the implemented work, five models have been developed and their performances are compared in predicting the fruit names. These models are based on five supervised learning techniques i.e., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, and Softmax. The experimental results show that Support Vector Machine algorithm performs the best for large datasets and also Support Vector Machine is the best for small datasets. The results also reveal that reduction in the number of fruits reduces the accuracy’s of each algorithm.</em>
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Adiansyah, Arman, and Wahyudin. "Analisis Sentimen Pada Ulasan Aplikasi Home Credit Dengan Metode SVM dan K-NN." Jurnal Komputer Antartika 1, no. 4 (2023): 174–81. http://dx.doi.org/10.70052/jka.v1i4.50.
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Dalam era teknologi, mencari pembiayaan finansial semakin mudah melalui aplikasi mobile seperti Home Credit. Aplikasi ini telah diunduh oleh lebih dari 10 juta pengguna Android dengan peringkat keseluruhan 4,4 di Google Play Store. Untuk membantu meninjau aplikasi, pengguna dapat memberikan ulasan dan penilaian di Google Play Store. Namun, dengan banyaknya ulasan, diperlukan analisis sentimen untuk mempermudah pemaha man. Dalam penelitian ini, dilakukan analisis sentimen menggunakan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) pada data ulasan dari Google Play Store. Data yang diambil berjumlah 2.845 dengan informasi tentang skor dan komentar. Sentimen positif dan negatif ditentukan berdasarkan skor, dengan skor 4 dan 5 untuk sentimen positif, serta skor 1, 2, dan 3 untuk sentimen negatif. Setelah tahap preprocessing dan penghitungan tf-idf, dilakukan perhitungan menggunakan algoritma SVM dan KNN. Hasilnya menunjukkan bahwa metode SVM memiliki presisi 89%, recall 86%, F1-score 87%, dan akurasi 88%. Sementara metode KNN memiliki presisi 79%, recall 80%, F1-score 79%, dan akurasi 79%. Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode Support Vector Machine lebih baik dalam melakukan analisis sentimen dalam penelitian ini. In the age of technology, finding finance has never been easier through mobile apps like Home Credit. The app has been downloaded by over 10 million Android users with an overall rating of 4.4 on the Google Play Store. To help review the app, users can leave reviews and ratings on the Google Play Store. However, with so many reviews, sentiment analysis is needed to facilitate understanding. In this study, sentiment analysis using the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) methods was conducted on review data from the Google Play Store. The data taken amounted to 2,845 with information about scores and comments. Positive and negative sentiments are determined based on scores, with scores of 4 and 5 for positive sentiments, and scores of 1, 2, and 3 for negative sentiments. After the preprocessing stage and tf-idf calculation, calculations are performed using the SVM and KNN algorithms. The results show that the SVM method has 89% precision, 86% recall, 87% F1-score, and 88% accuracy. While the KNN method has 79% precision, 80% recall, 79% F1-score, and 79% accuracy. Based on these results, it can be concluded that the Support Vector Machine method is better at performing sentiment analysis in this study.
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Apriani, Elsa, and Nunik Pratiwi. "Analisis Sentimen Game Show Clash of Champions Ruangguru dengan Algoritma KNN dan SVM." Jurnal Pendidikan dan Teknologi Indonesia 5, no. 1 (2025): 39–51. https://doi.org/10.52436/1.jpti.556.
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Penelitian ini menganalisis sentimen publik terhadap acara Clash of Champions (CoC) oleh Ruangguru menggunakan algoritma K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM). Data dianalisis melalui proses evaluasi menggunakan Confusion Matrix dengan metrik evaluasi berupa accuracy, precision, recall, dan F1-Score. Hasil menunjukkan bahwa algoritma KNN memiliki accuracy tertinggi sebesar 74.01%, sedangkan SVM memiliki accuracy 73.68%. Dengan performa yang lebih stabil, KNN terbukti lebih unggul dalam mendeteksi sentimen positif dan negatif dibandingkan SVM. Penelitian ini menunjukkan bahwa algoritma KNN lebih efektif untuk analisis sentimen pada acara edukatif seperti CoC. Hasil ini diharapkan dapat memberikan masukan bagi pengembang program untuk meningkatkan kualitas acara di masa depan.
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Ghosh, Madhumita, and Ravi Gor. "STOCK PRICE PREDICTION USING SUPPORT VECTOR REGRESSION AND K-NEAREST NEIGHBORS: A COMPARISON." International Journal of Engineering Science Technologies 6, no. 4 (2022): 1–9. http://dx.doi.org/10.29121/ijoest.v6.i4.2022.354.
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Supervised Learning is an important type of Machine learning. It includes regression and classification problems. In Supervised learning, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) can be used for classification and regression. Here, both algorithms are used for regression problem. The stock data is trained by SVR and KNN respectively to predict the stock price of the next day using python tool. Both algorithms are compared and it is observed that the price predicted by SVR is closer as compared to KNN.