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Journal articles on the topic '5-fold cross validation'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

OSANYINLOKUN, Oluwatoyin E. "MACHINE LEARNING MODEL FOR PREDICTING THE BENDING MOMENTS AND SHEAR FORCES IN REINFORCED CONCRETE BEAMS CONTAINING SAWDUST ASH AS A PARTIAL CEMENT SUBSTITUTE." OAUSTECH Journal of Engineering and Intelligent Technology 1, no. 1 (2025): 246–58. https://doi.org/10.36108/ojeit/5202.10.0152.

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An artificial neural network (ANN) was used to predict sawdust ash (SDA)-reinforced concrete beams’ (RCBs) bending moment (BM) and shear force (SF). With compressive strength-test results data using an ANN, a numerical maximum SF and BM-predicting concrete model for various mixtures of RC containing different percentages of SDA as a partial replacement for cement was developed and validated. The ANN used in this study was a multi-layer perceptron (MLP) with three hidden layers. The MLP was trained on the data using the back-propagation algorithm. A cross-validation technique that repeatedly sp

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Cai, Zongyou, Lun M. Wong, Ye Heng Wong, Hok-lam Lee, Kam-yau Li, and Tiffany Y. So. "Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading." Cancers 15, no. 22 (2023): 5459. http://dx.doi.org/10.3390/cancers15225459.

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Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). Methods: This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative

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Dankwa, Stephen, and Wenfeng Zheng. "Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study." Applied Sciences 9, no. 16 (2019): 3322. http://dx.doi.org/10.3390/app9163322.

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Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine l

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Kann, Bonpagna, Thodsaporn Chay-intr, Hour Kaing, and Thanaruk Theeramunkong. "Khmer Treebank Construction via Interactive Tree Visualization." IJITEE (International Journal of Information Technology and Electrical Engineering) 3, no. 3 (2019): 67. http://dx.doi.org/10.22146/ijitee.48545.

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Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their prob

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Jasmir, Jasmir, Xaverius Sika, Mulyadi Mulyadi, and Rischa Amelia. "Klasifikasi Kelayakan Pemberian Kredit Pada Calon Debitur Menggunakan Naïve Bayes." JURIKOM (Jurnal Riset Komputer) 9, no. 6 (2022): 1833. http://dx.doi.org/10.30865/jurikom.v9i6.5131.

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As a lending company, PT. PRIMA KONSUMEN FINANCE certainly has the possibility of bad credit in extending credit to its debtors which can reduce the company's income. Therefore the author performs data mining analysis on debtor data that has borrowed at PT. PRIMA KONSUMEN FINANCE to become valuable information for the company. The author uses debtor data in 2019 as many as 265 data. In conducting the analysis the author uses the WEKA Tools tool. The method used is the Naïve Bayes classification method with 9 attributes. The contribution of this research is to build a creditworthiness classific

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Asim Shahid, Muhammad, Muhammad Mansoor Alam, and Mazliham Mohd Su’ud. "Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm." PLOS ONE 18, no. 4 (2023): e0284209. http://dx.doi.org/10.1371/journal.pone.0284209.

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The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this

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Attanayake, A. M. C. H., D. D. M. Jayasundara, and T. S. G. Peiris. "AN APPLICATION OF 5-FOLD CROSS VALIDATION ON A BINARY LOGISTIC REGRESSION MODEL." Advances and Applications in Statistics 49, no. 6 (2016): 443–51. http://dx.doi.org/10.17654/as049060443.

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Sooai, Adri Gabriel, Sisilia Daeng Bakka Mau, Yovinia Carmeneja Hoar Siki, Donatus Joseph Manehat, Shine Crossifixio Sianturi, and Alicia Herlin Mondolang. "OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION." Jurnal Ilmiah Kursor 12, no. 2 (2023): 49–58. http://dx.doi.org/10.21107/kursor.v12i2.347.

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As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, wh

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Dake, Delali Kwasi, and Charles Buabeng-Andoh. "Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions." Mobile Information Systems 2022 (November 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/2670562.

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Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An at

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Zer, P. P. P. A. N. W. Fikrul Ilmi R. H., Fazli Nugraha Tambunan, Rika Rosnelly, and Wanayumini Wanayumini. "Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods." SinkrOn 8, no. 2 (2023): 939–47. http://dx.doi.org/10.33395/sinkron.v8i2.12195.

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Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affecte

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Yunitasari, Yessi, and Andi Rahman Putera. "Analisis Sentimen Masyarakat di Twitter Terkait Pandemi Covid-19." SMATIKA JURNAL 11, no. 01 (2021): 22–26. http://dx.doi.org/10.32664/smatika.v11i01.520.

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Media sosial merupakan tempat untuk mencari pertemanan baru serta tempat untuk mengeluarkan pendapat terhadap sesuatu secara bebas. Salah satu media sosial yang banyak digunakan saat ini adalah Twitter. Banyak masyarakat yang memanfaatkan Twitter untuk mengeluarkan pendapat terhadap Pandemi Covid-19 yang terjadi di berbagai negara termasuk di Indonesia. Pandemi Covid-19 atau korona virus di Indonesia diawali dengan temuan penderita penyakit korona virus 2019 (COVID-19) pada 2 Maret 2020 hingga 8 April 2020, telah terkonfirmasi 2.738 kasus positif COVID-19, dengan 221 kasus di antaranya meningg

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Normawati, Dwi, and Dewi Pramudi Ismi. "K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining." Signal and Image Processing Letters 1, no. 2 (2019): 23–35. http://dx.doi.org/10.31763/simple.v1i2.3.

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Coronary heart disease is a disease that often causes human death, occurs when there is atherosclerosis blocking blood flow to the heart muscle in the coronary arteries. The doctor's referral method for diagnosing coronary heart disease is coronary angiography, but it is invasive, high risk and expensive. The purpose of this study is to analyze the effect of implementing the k-Fold Cross Validation (CV) dataset on the rule-based feature selection to diagnose coronary heart disease, using the Cleveland heart disease dataset. The research conducted a feature selection using a medical expert-base

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Yu, Wang, Wang Ruibo, Jia Huichen, and Li Jihong. "Blocked 3×2 Cross-Validated t-Test for Comparing Supervised Classification Learning Algorithms." Neural Computation 26, no. 1 (2014): 208–35. http://dx.doi.org/10.1162/neco_a_00532.

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In the research of machine learning algorithms for classification tasks, the comparison of the performances of algorithms is extremely important, and a statistical test of significance for generalization error is often used to perform it in the machine learning literature. In view of the randomness of partitions in cross-validation, a new blocked 3×2 cross-validation is proposed to estimate generalization error in this letter. We then conduct an analysis of variance of the blocked 3×2 cross-validated estimator. A relatively conservative variance estimator that considers the correlation between

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Zudyanti Dwi Rahma Sari, Yulia Arvita, and Jasmir Jasmir. "Penerapan Data Mining Untuk Prediksi Penyakit Diabetes Menggunakan Algoritma C4.5 Zudyanti Dwi Rahma Sari1, Ja." Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) 4, no. 1 (2024): 827–34. http://dx.doi.org/10.33998/jakakom.2024.4.1.1624.

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Kesehatan merupakan peranan terpenting dalam kehidupan. Salah satu penyakit yang dapat menyebabkan komplikasi dan kematian adalah diabetes. Diabetes merupakan penyakit yang disebabkan oleh pankreas yang tidak memproduksi insulin yang cukup untuk tubuh sehinggan kadar gula dalam darah melebihi normal. Diabetes merupakan penyakit keturunan, penyakit ini dapat diturunkan kepada anaknya dari orang tua yang mengidap penyakit diabetes, sangat disayangkan jika usia yang masih muda sudah mengidap penyakit diabetes. Pemeriksaan dalam dunia medis dilakukan dengan cara pendiagnosaan penyakit berdasarkan

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Laksana, Made Dwiki Budi, AAIN Eka Karyawati, Luh Arida Ayu Rahning Putri, I. Wayan Santiyasa, Ngurah Agus Sanjaya ER, and I. Gusti Agung Gede Arya Kadnyanan. "Text Summarization terhadap Berita Bahasa Indonesia menggunakan Dual Encoding." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 11, no. 2 (2022): 339. http://dx.doi.org/10.24843/jlk.2022.v11.i02.p13.

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Text summarization or automatic text summarization can make readers receive information quickly without having to read the entire news text, so readers can get more time in reading other news texts. Making text summarization can use two techniques, namely, extractive and abstractive techniques. Abstractive techniques have the aim of producing summary sentences with concepts as humans take the essence of a document that is read. In this study, the author builds an abstractive summarization model using the Dual Encoding method consisting of GRU. The evaluation was carried out using K- Fold Cross

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Raina Rahmah, Restu, Iwan Rizal Setiawan, and Fathia Frazna Az-Zahra. "PREDIKSI KETERLAMBATAN MAHASISWA DALAM MEMBAYAR BIAYA KULIAH MENGGUNAKAN ALGORITMA C4.5." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 5 (2024): 10792–800. http://dx.doi.org/10.36040/jati.v8i5.11145.

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Biaya kuliah merupakan sumber pendanaan utama yang penting bagi peningkatan mutu pendidikan dan pembangunan infrastruktur. Keterlambatan pembayaran dapat mengganggu alur keuangan institusi, mempengaruhi proses belajar mengajar, dan menghambat pengembangan infrastruktur. Untuk mengatasi masalah ini, diperlukan prediksi keterlambatan pembayaran mahasiswa. Algoritma C4.5, salah satu algoritma machine learning, digunakan untuk membangun model prediktif yang mampu mengidentifikasi mahasiswa yang kemungkinan terlambat membayar dengan akurasi yang baik. Evaluasi model dilakukan menggunakan confusion

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Sulaiman, Sarina, Nor Amalina Abdul Rahim, and Andri Pranolo. "Generated rules for AIDS and e-learning classifier using rough set approach." International Journal of Advances in Intelligent Informatics 2, no. 2 (2016): 103. http://dx.doi.org/10.26555/ijain.v2i2.74.

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The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisti

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Kim, Kyung-Su, Byung Kil Kim, Myung Jin Chung, Hyun Bin Cho, Beak Hwan Cho, and Yong Gi Jung. "Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation." PLOS ONE 17, no. 2 (2022): e0263125. http://dx.doi.org/10.1371/journal.pone.0263125.

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Background This study aims to develop artificial intelligence (AI) system to automatically classify patients with maxillary sinus fungal ball (MFB), chronic rhinosinusitis (CRS), and healthy controls (HCs). Methods We collected 512 coronal image sets from ostiomeatal unit computed tomography (OMU CT) performed on subjects who visited a single tertiary hospital. These data included 254 MFB, 128 CRS, and 130 HC subjects and were used for training the proposed AI system. The AI system takes these 1024 sets of half CT images as input and classifies these as MFB, CRS, or HC. To optimize the classif

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Yang, Dongru, Yi Lin, Jianwen Wei, et al. "Assisting Heart Valve Diseases Diagnosis via Transformer-Based Classification of Heart Sound Signals." Electronics 12, no. 10 (2023): 2221. http://dx.doi.org/10.3390/electronics12102221.

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Background: In computer-aided medical diagnosis or prognosis, the automatic classification of heart valve diseases based on heart sound signals is of great importance since the heart sound signal contains a wealth of information that can reflect the heart status. Traditional binary classification algorithms (normal and abnormal) currently cannot comprehensively assess the heart valve diseases based on analyzing various heart sounds. The differences between heart sound signals are relatively subtle, but the reflected heart conditions differ significantly. Consequently, from a clinical point of

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Jabaru, Saheed Olalekan, Waheed Babatunde Yahya, and Kamoru Jimoh. "Comparison of Non-Convex Variable Selection Criteria in High-dimensional Data with Count Response." Kasu Journal of Computer Science 1, no. 2 (2024): 183–99. http://dx.doi.org/10.47514/kjcs/2024.1.2.004.

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to the large number of predictors and potential collinearity. While significant work has been done on selecting predictors from high-dimensional data with metrical covariates and Gaussian responses, there is a gap in comparing the performances of SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty) across different tuning parameters for count responses. Such comparisons, especially with k-fold cross-validation, are rare, highlighting the need for this study. Aim: This study aims to compare the effectiveness of SCAD and MCP in high-dimensional datasets where the response

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Azis, Huzain. "Assessing the Performance of Logistic Regression in Heart Disease Detection through 5-Fold Cross-Validation." International Journal of Artificial Intelligence in Medical Issues 2, no. 1 (2024): 1–11. http://dx.doi.org/10.56705/ijaimi.v2i1.137.

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This study explores the effectiveness of Logistic Regression in predicting heart disease using a dataset derived from multiple international databases. Employing a 5-fold cross-validation method, the research aimed to evaluate the model's accuracy, precision, recall, and F1-score. Results indicated that Logistic Regression performs robustly, with accuracy ranging from 80% to 88.29%, and high recall rates, highlighting its potential as a valuable tool in medical diagnostics. Despite some variability in precision, which may lead to higher false positive rates, the model's high recall is crucial

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Zaidi, Shujaat Ali, Varin Chouvatut, Chailert Phongnarisorn, and Dussadee Praserttitipong. "Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation." Intelligence-Based Medicine 11 (2025): 100230. https://doi.org/10.1016/j.ibmed.2025.100230.

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Chandrasekhar, Dr N. "A Machine Learning Approach to Heart Disease Prediction: 5-Fold Cross Validation and Hyperparameter Optimization." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 2199–203. https://doi.org/10.61137/ijsret.vol.11.issue2.345.

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Uzer, Mustafa Serter. "Deep Learning-Based Classification Consisting of Pre-Trained Models and Proposed Model Using K-Fold Cross-Validation for Pistachio Species." Applied Sciences 15, no. 8 (2025): 4516. https://doi.org/10.3390/app15084516.

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Pistachio is a nut originating from the Middle East, and the main varieties grown and exported in Turkey are Kirmizi and Siirt pistachios. Due to their strategic importance in the agricultural economy, they need to be classified correctly. This study aims to classify Kirmizi and Siirt pistachios using various deep learning-based models and k-fold cross-validation. For this purpose, the seven convolutional neural network models trained by transfer learning and the proposed MSU-CNN model are used for classification with k-fold cross-validation. The dataset used in this study consists of 2148 ima

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Rizki, Yoze, Reny Medikawati Taufiq, Harun Mukhtar, and Dinia Putri. "Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN." IT Journal Research and Development 5, no. 2 (2021): 215–25. http://dx.doi.org/10.25299/itjrd.2021.vol5(2).5831.

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Motif tenun melayu sangat beragam. Keberagaman ini membuat sulit membedakan motif-motif kain tenun tersebut. Klasifikasi data diperlukan untuk mengidentifikasi karakteristik objek yang terkandung dalam basis data agar kemudian dikategorikan ke dalam kelompok yang berbeda. Tujuan penelitian yang dicapai dalam penelitian ini yaitu untuk mengetahui performa pengenalan dan klasifikasi motif tenun melayu menggunakan Faster R-CNN dengan model arsitektur VGG, dengan cara mengukur persentase dari tingkat akurasi, presisi, dan recall yang akan divalidasi menggunakan K-Fold Cross Validation. Penelitian

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Minh Tuan Nguyen, Le Anh Dang Tran, Tuan Anh Vu, and Duy Nguyen. "Convolutional neural network-based emotion recognition using recursive feature elimination." International Journal of Science and Research Archive 13, no. 1 (2024): 2494–501. http://dx.doi.org/10.30574/ijsra.2024.13.1.1913.

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Emotion detection plays a crucial role in fields such as biomedical applications, smart environments, brain-computer interfaces, communication, security, and safe driving. In this paper, we present a novel approach for detecting emotions using electroencephalogram signals. The method employs convolutional neural network (CNN) as the classifier, which is chosen from a variety of intelligent algorithms. Discrete wavelet transform is used to decompose the signals into four frequency bands including theta, alpha, beta, and gamma. These bands are then utilized for feature extraction. Out of a total

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Aprihartha, Moch Anjas, and Idham Idham. "Optimization of Classification Algorithms Performance with k-Fold Cross Validation." EIGEN MATHEMATICS JOURNAL 7, no. 2 (2024): 61–66. http://dx.doi.org/10.29303/emj.v7i2.212.

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Supervised learning is a predictive method used to make predictions or classifications. Supervised learning algorithms work by building a model using training data that includes both independent and dependent variables. Several methods for building classification include Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), decision tree, etc. The lack of capacity of a classification algorithm to generalize certain data can be associated with the problem of overfitting or underfitting. K-fold cross-validation is a method that can help avoid overfitting or underfitting and produce a algor

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Hosseinzadeh, Mahdi, Arman Gorji, Ali Fathi Jouzdani, Seyed Masoud Rezaeijo, Arman Rahmim, and Mohammad R. Salmanpour. "Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems." Diagnostics 13, no. 10 (2023): 1691. http://dx.doi.org/10.3390/diagnostics13101691.

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Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). Methods: 297 patients were selected from the Parkinson’s Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as

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Siregar, Alda Cendikia, Sucipto, and Ilham Gunawan. "Prediction of the Population of Kapuas Hulu District Based on Gender Using the Backpropagation Method." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 604–7. https://doi.org/10.59934/jaiea.v4i2.709.

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Rediction is a branch of science used to estimate future events based on historical data. One of the effective methods currently developing is the Backpropagation Artificial Neural Network. This study aims to determine prediction results, the developed model, and its accuracy in forecasting the population of Kapuas Hulu district by gender using the Backpropagation method. The resulting model has an architecture of 2-5-2, with 2 neurons in the input layer, 5 in the hidden layer, and 2 in the output layer. The model uses a learning rate of 0.8, an error tolerance of 0.00001, and 8000 epochs. Pre

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Hamdani, Hamdani, Rahmania Hatta Heliza, Puspitasari Novianti, Septiarini Anindita, and Henderi. "Dengue classification method using support vector machines and cross-validation techniques." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1119–29. https://doi.org/10.11591/ijai.v11.i3.pp1119-1129.

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Dengue is a dangerous disease that can lead to death if the diagnosis and treatment are inappropriate. The common symptoms that occur, including headache, muscle aches, fever, and rash. Dengue is a disease that causes endemics in several countries in South Asia and Southeast Asia. There are three varieties of dengue, such as dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). This disease can currently be classified using a machine learning approach with the input data being the dengue symptoms. This study aims to classify dengue types consisting of three classe

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FUADAH, YUNENDAH NUR, IBNU DAWAN UBAIDULLAH, NUR IBRAHIM, FAUZI FRAHMA TALININGSING, NIDAAN KHOFIYA SY, and MUHAMMAD ADNAN PRAMUDITHO. "Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, no. 3 (2022): 728. http://dx.doi.org/10.26760/elkomika.v10i3.728.

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ABSTRAKPada penelitian ini dilakukan perancangan arsitektur Convolutional Neural Network (CNN) yang terdiri dari 5 layer konvolusi dan 1-fully connected layer untuk mengklasifikasikan citra fundus kedalam kondisi normal, early, moderate, deep, dan ocular hypertension (OHT). Selanjutnya, model yang diusulkan dibandingkan dengan arsitektur AlexNet yang memiliki 5 layer konvolusi dan 3- fully connected layer. Data yang digunakan berupa citra fundus yang terdiri dari 3200 data latih, 800 data validasi, dan 1000 data uji. Optimasi model CNN dilakukan dengan melakukan pengujian hyperparameter yang t

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Chelsea, Maria Yubela, and Paulina H. Prima Rosa. "Classification of delivery type of pregnant women using support vector machine." E3S Web of Conferences 475 (2024): 02015. http://dx.doi.org/10.1051/e3sconf/202447502015.

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One of the ways to reduce maternal mortality is by diagnosing childbirth to find out whether a mother will give birth normally or not so that appropriate treatment can be done. This study aims to improve maternal safety and health by classifying delivery type of pregnant women, either Caesarean or normal types, using the Support Vector Machine method. The dataset used in this study was taken from a hospital in 2020. It consists of 25 attributes and 302 records that include information about the health conditions of pregnant women and babies. Several experiments were performed towards the datas

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Tuntun, Ritham, Kusrini Kusrini, and Kusnawi Kusnawi. "Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 4 (2022): 2111. http://dx.doi.org/10.30865/mib.v6i4.4681.

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This study aims to compare the performance of two classification data mining algorithms, namely the K-Nearest Neighbor algorithm, and C4.5 using the K-fold cross validation method. The data used in this study are iris public data with a total of 150 data and 3 label target classes, namely iris-setosa, iris-versicolor, and iris-virginica. The training data used is 97% or 145 data from 150 data, and the testing data used is 3% or 5 data, and the number of K in the K-fold cross validation is 30 or 30 times the experimental stage. The results showed that the performance of the K-Nearest Neighbor a

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Taslim, Yuhelmi, and Dafwen Toresa. "Optimasi Nilai k Pada Algoritma k Nearest Neighbor Untuk Prediksi Akademik Mahasiswa Yang Bekerja." Indonesian Journal of Computer Science 10, no. 2 (2021): 379–88. http://dx.doi.org/10.33022/ijcs.v10i2.3005.

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Sebuah lembaga pendidikan akan selalu fokus bagaimana meningkatkan kualitas akdemik dari peserta didik mereka. Penelitian ini bertujuan untuk melakukan klasifikasi dan prediksi terhadap prestasi akademik mahasiswa terutama bagi mahasiswa yang bekerja karena mereka mempunyai beban yang lebih dibanding mahasiswa yang tidak bekerja. Hasil dari prediksi ini selanjutnya dapat digunakan sebagai salah satu bahan pertimbangan bagi pihak akademik dalam mengambil kebijakan terhadap mahasiswa yang sudah bekerja. Prediksi prestasi akademik dilakukan dengan menggunakan algoritma K nearest neighbor dengan o

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Mufti Ari Bianto and Kusrini Kusrini. "SISTEM KLASIFIKASI PENYAKIT JANTUNG BERBASIS PARTICLE SWARM OPTIMIZATION DAN NAÏVE BAYES DENGAN 5-FOLD CROSS VALIDATION." Journal of Innovation Research and Knowledge 3, no. 8 (2024): 1945–54. https://doi.org/10.53625/jirk.v3i8.9914.

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Serangan Jantung adalah salah satu penyakit yang paling mematikan tercatat di dunia, terdapat jumlah kasus baru Penyakit Jantung sebanyak 1,5% serta jumlah kematian sebanyak 14,38%. Pada tahun 2022 jumlah penderita Penyakit Jantung di Indonesaia sejumlah 139.891 orang, pada umumnya jumlah penderita penyakit ini terus meningkat dikarenakan kurangnya pengetahuan atau informasi tentang penyakit jantung tersebut. Oleh karena itu dibutuhkan sebuah sistem yang dapat memberikan informasi serta klasifikasi penyakit secara dini yang dapat digunakan untuk klasifikasi apabila seseorang ingin mengetahui i

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Nugroho, Hendro, Gusti Eka Yuliastuti, and Andrean Firman Pradana. "KLASIFIKASI DIAGNOSIS DIABETES MELITUS MENGGUNAKAN METODE NAÏVE BAYES DENGAN SELEKSI FITUR BACKWARD ELIMINATION." Networking Engineering Research Operation 8, no. 2 (2023): 97–106. https://doi.org/10.21107/nero.v8i2.21110.

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Diabetes mellitus is a dangerous disease caused by high sugar levels (hyperglycemia). Hyperglycemia can cause sufferers to experience chronic disease, damage to organs in the body. Diabetes mellitus is a dangerous disease, so it is very interesting to classify diabetes mellitus using the Naïve Bayes method with Backward Elimination (BE) feature selection. The Diabetes mellitus dataset used in the research consisted of 101 data with 5 attributes consisting of age, Current Blood Sugar (GDS), 2 hours after eating/Post Pradial (PP), Fasting Blood Sugar (GPD) levels, and Low Density Lipoprotein (LD

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Mega, Meyer, and Jasmir Jasmir. "Prediksi Masa Studi Mahasiswa Unama Jambi Menggunakan Metode Algoritma C4.5." Jurnal Manajemen Sistem Informasi 8, no. 1 (2023): 140–51. http://dx.doi.org/10.33998/jurnalmsi.2023.8.1.770.

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Every year the number of students at The University of Dynamics of the Nation jambi is always increasing but the students who graduate are different from the number of students who enter. Therefore, the author conducts a data mining analysis on student data so that it can be used by academic supervisors to find out the graduation status of students and as a warning so that students can graduate on time so as to reduce the number of delays in graduation. The author uses student data in 2016 and 2017 as training data and 2020 as testing data as many as 120 training data and 109 testing data and

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Nguyen, Duy, Minh Tuan Nguyen, and Kou Yamada. "Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform." Applied Sciences 15, no. 5 (2025): 2328. https://doi.org/10.3390/app15052328.

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Electroencephalography-based emotion recognition is essential for brain-computer interface combined with artificial intelligence. This paper proposes a novel algorithm for human emotion detection using a hybrid paradigm of convolutional neural networks and a boosting model. The proposed algorithm employs two subsets of 18 and 14 features extracted from four sub-bands using discrete wavelet transform. These features are identified as the optimal subsets of the most relevant, among 42 original input features extracted from two subsets of 8 and 6 productive channels using a dual genetic algorithm

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Nanda, Yoga Aditama Ika, and Bety Wulan Sari. "NAIVE BAYES ALGORITHM IMPLEMENTATION TO DETECT HUMAN PERSONALITY DISORDERS." Jurnal Techno Nusa Mandiri 17, no. 1 (2020): 9–16. http://dx.doi.org/10.33480/techno.v17i1.1239.

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We live in a society that still sees problems regarding one's soul and personality as taboo, even though mental health is as important as physical health. A personality disorder itself is a disorder that can be seen from behavior, mindset, and attitude, which brings difficulties to life. Based on this problem, this study applies the method of Naive Bayes classifier as early detection of human personality disorders. Using a data set of 130 correspondences from the AMIKOM university scope with the age limit of 18-25 years and identified personality disorders is a borderline type disorder. The da

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TOPRAK, Ahmet. "A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 14, no. 3 (2022): 234–43. http://dx.doi.org/10.29137/umagd.1217754.

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miRNAs, a subclass of non-coding small RNAs, are about 18-22 nucleotides long. It has been revealed that miRNAs are responsible many diseases such as cancer. Therefore, great efforts have been made recently by researchers to explore possible relationships between miRNAs and diseases. Experimental studies to identify new disease-associated miRNAs are very expensive and at the same time a long process. Therefore, to determine the relationships between miRNA and disease many computational methods have been developed. In this paper, a new method for the identification of miRNA-disease associations

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Tamuntuan, Virginia, Kusrini Kusrini, and Kusnawi Kusnawi. "Analisis Perbandingan Kinerja Algoritma Klasifikasi Pada Mahasiswa Berpotensi Dropout." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 847–55. https://doi.org/10.47065/bits.v6i2.5658.

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This research aims to compare the performance levels of two data mining classification algorithms, namely Support Vector Machine and Neural Network Backpropagation, using the K-fold cross-validation method. The data used consists of graduates from 2019 to 2023 at STMIK Multicom Bolaang Mongondow. A total of 80% of the 200 data points were used as training data, while the remaining 20% were used as testing data. K-fold cross-validation was conducted with K set to 5. The results of the study indicate that the Support Vector Machine algorithm achieved an accuracy of 80%, recall of 80%, and precis

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Ariaeinejad, A., R. Patel, T. M. Chan, and R. Samavi. "P031: Using machine learning algorithms for predicting future performance of emergency medicine residents." CJEM 19, S1 (2017): S88. http://dx.doi.org/10.1017/cem.2017.233.

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Introduction: Background: Medical education is transitioning from a time-based system to a competency-based framework. In the age of Competency-Based Medical Education, however, there is a drastically increased amount of data that needs to be interpreted. With this data, however, comes an opportunity to develop predictive analytics. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Machine learning

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Amly, Mar`iy Romizzidi, Yusra Yusra, and Muhammad Fikry. "Penerapan Metode Naïve Bayes Classifier Pada Klasifikasi Sentimen Terhadap Anies Baswedan Sebagai Bakal Calon Presiden 2024." Jurnal Sistem Komputer dan Informatika (JSON) 4, no. 4 (2023): 621. http://dx.doi.org/10.30865/json.v4i4.6214.

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Anies Baswedan is a political figure who has been declared as a 2024 presidential candidate. Public opinion is a valuable source of information to analyze sentiment towards Anies Baswedan as a 2024 presidential candidate. Limited human power, emotional instability, and the length of time required are difficulties in analyzing sentiment on large amounts of data manually. Machine learning is utilized to provide convenience in sentiment classification. This research applies the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate

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SUPRIYADI, ANDY, and MUHAMMAD ASRI SAFI'IE. "Pemodelan Klasifikasi Lama Waktu Pencapaian Jabatan Fungsional Lektor Kepala menggunakan Optimizer Parameter Support Vector Machine." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 11, no. 4 (2023): 879. http://dx.doi.org/10.26760/elkomika.v11i4.879.

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ABSTRAKPemenuhan dosen dengan jabatan fungsional lektor kepala dan guru besar menjadi sangat penting dalam memperoleh akreditasi unggul bagi perguruan tinggi. Salah satu upaya pemenuhan dengan melakukan klasifikasi dosen dari sisi lama waktu pencapaian jabatan fungsional lektor kepala dari lektor pada Universitas Sebelas Maret dibagi menjadi tiga, yaitu cepat, sedang, dan lambat. Variabel yang digunakan dalam klasifikasi antara lain usia, tempat studi, lama studi, international research, sertifikasi dosen, jabatan structural dan bidang ilmu dari staf pengajar. Penelitian ini melakukan klasifik

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Reps, Jenna M., Patrick Ryan, and P. R. Rijnbeek. "Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data." BMJ Open 11, no. 12 (2021): e050146. http://dx.doi.org/10.1136/bmjopen-2021-050146.

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ObjectiveThe internal validation of prediction models aims to quantify the generalisability of a model. We aim to determine the impact, if any, that the choice of development and internal validation design has on the internal performance bias and model generalisability in big data (n~500 000).DesignRetrospective cohort.SettingPrimary and secondary care; three US claims databases.Participants1 200 769 patients pharmaceutically treated for their first occurrence of depression.MethodsWe investigated the impact of the development/validation design across 21 real-world prediction questions. Model d

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Maesya, Aries, M. Iqbal Suriansyah, and Nizar Zulmi Ramadhan. "Facial Expression Detection Using Local Binary Pattern And K-Nearest Neighbor Methods." International Conference On Research And Development (ICORAD) 2, no. 1 (2023): 49–55. http://dx.doi.org/10.47841/icorad.v2i1.81.

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Facial Expression Detection is the recognition of a pattern where the input is a digital image and the output is a label of a person's emotions that have been made into a class, which class has been stored in the database as training data to find the closest or similar. Pattern recognition with training data or similar classes is done using artificial intelligence with various methods. This study aims to test the Local Binary Pattern and k-Nearest Neighbors methods to be implemented in facial expression detection and create a system on a computer to be able to know human facial expressions are

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Sallam, Ahmad H., Emily Conley, Dzianis Prakapenka, Yang Da, and James A. Anderson. "Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat." G3: Genes|Genomes|Genetics 10, no. 7 (2020): 2265–73. http://dx.doi.org/10.1534/g3.120.401165.

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The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population st

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Saputra, M. Reynaldi, and Hafiz Irsyad. "Klasifikasi Tingkat Kemanisan Alpukat Berdasarkan Fitur Hue Saturation Value (HSV) dengan Menggunakan Support Vector Machine (SVM)." Jurnal Algoritme 2, no. 2 (2022): 113–19. http://dx.doi.org/10.35957/algoritme.v2i2.2361.

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Dalam proses penetuan mutu atau tingkat kemanisan buah alpukat di pasaran pada umumnya dilakukan dengan dua cara yaitu menggunakan pakar-pakar untuk pemilihan / sortasi kemanisan alpukat atau menggunakan metode destruktif dengan cara pengambilan sampel, uji coba kemanisan alpukat tersebut seperti menggunakan Refractometer. Permasalahan yang terjadi pada kedua proses tersebut yaitu memiliki cost yang relatif besar dan tidak menghasilkan mutu yang seragam karena sortasi tingkat kemanisan alpukat oleh pakar bersifat subjektif dan kemungkinan terjadinya kesalahan pengamatan sangat besar. Support V

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Al Rivan, Muhammad Ezar, Molavi Arman, Hafiz Irsyad, and Reynald Dwika Prameswara. "Klasifikasi Hewan Mamalia Berdasarkan Bentuk Wajah Menggunakan Fitur Histogram of Oriented dan Metode Support Vector Machine." Jurnal Sisfokom (Sistem Informasi dan Komputer) 11, no. 1 (2022): 93–99. http://dx.doi.org/10.32736/sisfokom.v11i1.1205.

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Mammals have several characteristics that can be distinguished, such as footprints, voice, and face shape. Mammals can be recognized. To classify the face shape of mammals, the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) methods can be used. This study uses the LHI-Animal-Faces dataset which is taken as many as 15 species of mammals, where each type of mammal is selected 60 images and resized to 150x150 pixels. The image is converted into a grayscale image for the HOG feature extraction process. Furthermore, the classification process uses SVM. The kernels used are L

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Taufiq, Ilham, Taghfirul Azhima Yoga Siswa, and Wawan Joko Pranoto. "Model Optimasi Random Forest dengan PSO-CHI-SM dalam Mengatasi High Dimensional dan Imbalanced Data Banjir Kota Samarinda." Jurnal Teknologi Sistem Informasi dan Aplikasi 7, no. 3 (2024): 1267–79. http://dx.doi.org/10.32493/jtsi.v7i3.41632.

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Flooding is a natural disaster that frequently affects our country. Samarinda City, in particular, continues to experience frequent flooding events with 18 incidents in 2018, 33 incidents in 2020, and 32 incidents in 2021. To predict flood disasters, it is necessary to utilize technology known as machine learning for analyzing and classifying floods. However, classification often encounters issues with high-dimensional data and class imbalance. This study aims to determine the extent to which the accuracy of flood disaster classification improves by using the Random Forest algorithm with PSO f

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