Academic literature on the topic 'Computer algortihms'
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Journal articles on the topic "Computer algortihms"
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Siregar, Yunita Sari, Boni Oktaviana Sembiring, Hasdiana Hasdiana, Arie Rafika Dewi, and Herlina Harahap. "Algortihm C4.5 in mapping the admission patterns of new Students in Engingeering Computer." SinkrOn 6, no. 1 (2021): 80–90. http://dx.doi.org/10.33395/sinkron.v6i1.11154.
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University of Harapan Medan is one of the private universities in North Sumatra which has computer-based study programs such as Informatics Engineering and Information Systems. Every year this college receives many registrations from students who have completed their education at the school stage. The large number of incoming student data makes it difficult for the admin to select new students who will register. In this study using the C4.5 algorithm data mining method to map the pattern of student admissions selection in the field of Engineering and computers. The attributes used are the average value of report cards (high, enough, low), basic academic ability tests (very high, high, medium, low, very low), basic computer knowledge tests (very high, high, enough, low, very low) and interviews (good, bad). Data mining is a mathematical calculation process that uses algorithms and requires large data. While the C4.5 algorithm is an algorithm that processes data by calculating entrophy and information gain, where after the calculation process is carried out, those who get the largest information gain value will become nodes and branches. This C4.5 algorithm will describe a decision tree that will form a pattern in student selection. The results of this study indicate that in mapping the selection pattern of interview attributes into level 1 nodes, the attributes of the basic computer knowledge test become the level 1 branch, the attributes of the basic academic ability test become the level 2 branch and the attribute average value of report cards becomes the level 3 branch.
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Irfan, Miftahul, Wardhani Utami Dewi, Khoirin Nisa, and Mustofa Usman. "IMPELEMENTASI K-NEAREST NEIGHBORS, DECISION TREE DAN SUPPORT VECTOR MECHINE PADA DATA DIABETES." Jurnal Mahasiswa Ilmu Komputer 4, no. 2 (2023): 137–50. http://dx.doi.org/10.24127/ilmukomputer.v4i2.4007.
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Diabetes merupakan salah satu penyakit yang menjadi penyebab kematian terbesar didunia. Kasus kematiannya pun tercatat lebih dari 4 juta pada tahun 2019. Diabetes juga dapat menyebabkan timbulnya penyakit lainnya. Bahaya diabetes ini menjadi perhatian khusus WHO. Seiring dengan perkembangan teknologi ini, banyak sekali kolaborasi antara bidang kesehatan, statistic dan computer untuk menanggulangi berbagai macam penyakit. Algortima machine learning menjadi popular dalam proses klasifikasi data dan sudah banyak diterapkan pada data kesehatan. Dengan begitu pada artikel ini akan dilakukan perbandingan algoritma machine learning KNN, Decision Tree, dan SVM untuk melihat algortima mana yang paling cocok untuk klasifikasi data diabetes. Hasil menunjukkan bahwa KNN dan SVM memiliki akurasi yang cukup besar yaitu 81,13%. Sehingga kedua algortima tersebut dapat menjadi rekomendasi proses klasifikasi data diabetes sehingga dapat membantu dokter dalam menanggulangi penyakit diabetes. Hasil ini juga menunjukkan bahwa 8 variabel yang digunakan berpengaruh terhadap resiko diabetes
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Azura Trijayanti, Intan Aulia, Nazwa Khairunisa, Farhan Asyrof Hamadi Purba, and Indra Gunawan. "Implementasi Struktur Data Antrian Queue dalam Sistem Penjadwalan Proses pada Sistem Operasi." Jurnal Publikasi Teknik Informatika 4, no. 2 (2025): 48–53. https://doi.org/10.55606/jupti.v4i2.4170.
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Data structures are a fundamental conceptin computer science used to store, organize, and mange data within a program or computer system. The selection of the appropriate data structure plays a crucial role in optimizing both time and space efficiency in data processing. Generally, data structures are classified into two main categories: linear and non-linear structures. Linear data structures include arrays, linked lists, stacks, and qeueues, where elements are arranged in a sequential order. Non-linear data structures, on the other hand, include trees and graphs, which organize element in more complex relationship. Each type of data structure has specific characteritics and operations that can impact the performance of aplications, such as searching, insertion, and deletion of data. The choice of the right data structure is essential in algortihm design as it can significantly enhance the efficienty and effectiveness of a system or application.
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Sitanggang, Erwin Daniel. "Analisis Preboot Execution Environment Server Linux dengan Algoritma First Come First Serve." LOFIAN: Jurnal Teknologi Informasi dan Komunikasi 1, no. 1 (2021): 12–16. http://dx.doi.org/10.58918/lofian.v1i1.159.
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Preboot Execusion Environment Server merupakan layanan jaringan computer yang terintegrasi dengan system operasi linux. Layanan ini merupakan suatu workstation/mesin yang dapat beroperasi tanpa adanya dukungan media penyimpanan (storage/disk) local. Hal ini tentu saja memberi manfaat dalam perkembangan teknologi jaringan computer dalam berbagai aspek. Dalam penerapannya dengan menggunakan perangkat server maupun client yang sama pada distro linux CentOS 7 dan Debian 7 dengan memperhatikan aspek efesiensi seperti waktu akses, jumlah akses dan kecepatan akses untuk meminimalkan penggunaan sumber daya dapat disimpulkan bahwa semakin banyak client maka burst time yang akan digunakan client semakin sedikit. Untuk menanggulangi permasalahan tersebut diterapkan algortima penjadwalan FSCS sehingga kecepatan masing-masing client tetap stabil. Dan hasil diperoleh perbandingan kinerja dari kedua distro linux tersebut bahwa Debian lebih unggul bila melayani client yang sedikit dan CentOS unggul dari segala aspek jika jumlah client semakin banyak.
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Gounder, Yasoda Kailasa, and Sowkarthika Subramanian. "Application of machine learning controller in matrix converter based on model predictive control algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (2023): 1489. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1489-1496.
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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.
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Grafarend, Erik W., Torben Krarup, and Rainer Syffus. "An algortihm for the inverse of a multivariate homogeneous polynomial of degree n." Journal of Geodesy 70, no. 5 (1996): 276–86. http://dx.doi.org/10.1007/s001900050018.
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Dio, Muhamad Dio Riza Pratama, Bayu Priyatna Bayu Priyatna, April Lia Hananto April Lia Hananto, and Shofa Shofiah Hilabi Shofa Shofiah Hilabi. "Deteksi Objek Kecelakaan Pada Kendaraan Roda Empat Menggunakan Algoritma YOLOv5." Teknologi 12, no. 2 (2022): 15–26. https://doi.org/10.26594/teknologi.v12i2.3260.
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Kecelakaan lalulintas khususnya kendaraan roda empat merupakan insiden yang sering terjadi dan mengakibatkan banyak kerugian bagi siapa saja yang mengalaminya. CCTV adalah alat yang digunakan untuk memantau volume kendaraan tetapi tidak bisa untuk mendeteksi kecelakaan. Oleh sebab itu tujuan pada penelitian ini adalah membangun model untuk mendeteksi kecelakaaan ataupun dampak kerusakan kendaraan roda empat melalui data angle insiden dari CCTV serta pendekatan computer vision dengan you only look once (YOLO) yang dikenal sebagai algortima deteksi objek yang memiliki akurasi cukup tinggi jika dibandingkan dengan beberapa algoritma yang ada. Data yang diambil dan digunakan pada penelitian ini memiliki jumlah data latih sebanyak 1146 gambar serta data uji sebanyak 109 gambar diperoleh akurasi model dengan mAP 90,2% dan dilakukan uji data dengan input video diperoleh mAP 84% lalu pada pengujian data acak diperoleh akurasi mAP 51,5%.
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Kusmiran, Amirin. "IMPLEMENTASI ALGORITMA DISCRETE FURIER TRANSFORM UNTUK KARAKTERISASI NADA DARI HURUF VOKAL." Jurnal TAMBORA 1, no. 2 (2016): 31–35. http://dx.doi.org/10.36761/jt.v1i2.135.
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Sinyal atau gelombang merupakan salah satu phenomena fisik yang telah banyak diaplikasi dibidang sains dan teknologi untuk mengkarakterisasi suatu bahan, seperti retakan dan kandungan dari material, dan nada. Sifat fisik yang digunakan untuk mengkarakterisasi bahan adalah frekuensi. Frekuensi yang dihasilkan oleh manusia berberda-berbeda dikarenakan tekanan, pita suara juga berbeda-beda. Penekanan suara dapat dikarakterisasi dalam domain waktu, sedangkan frekuensinya dapat dikarakterisasi dalam domain frekuensi. Untuk mengkaraterisasi, nada tersebut direkam dengan menggunakan microphone. Hasil rekaman tersebut akan tersimpan di dalam soundcard yang terintegrasi dengan personal computer (PC), kemudian dianalisis dengan menggunakan algoritma yang diimplementasi kedalam matlab. Algortima tersebut adalah algoritma recording dan discrete Fourier transform (DFT). Windows leakage dapat diminimalisasi menggunakan algoritma Blackmann dan Barthannwin modified. Frekuensi ,dan amplitudo yang dihasilkan oleh nada darihuruf vokala dalahnada I adalah 190 Hz dengan amplitudo 0,14 dB, nada o adalah 580 Hz dengan amplitudo 0,1 dB, nada u adalah 210 Hz dengan amplitudo 0,15 dB, nada e adalah 200 Hz dengan amplitudo 0,13 dB, dan nada aadalah 310 Hz dengan 0,1 dB.Noise yang dihasilkan oleh nada o pada saat pengambilan data disebabkan oleh perangkat personal computer (PC).
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Yurtay, Yuksel, Nilüfer Yurtay, Arda Yuksel, and Ayca Armay. "Sample application on dramatization in education." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 7 (2017): 08–13. http://dx.doi.org/10.18844/prosoc.v3i7.1978.
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Developments on the technology have a big effect to change our lifestyle and education methods. It is obvious that effects of games based computer technology is increased on people’s life. Dramatization in Education is the one of the latest studies that is used education materials and dramatization methodologies. The idea of converting the education materials to game, is invented with changing human profiles and their interestsIn these study, it is intended that teaching the Gini Algorithm that is used with dramatization tools which is the one of the most significant usage area in data mining. The dramatization of theorical steps of Gini Algortihm is executed and it is developed with practiced on a group of student. It is measured the effects on students of classical education and dramatization on education with this application.After the measurement, results are discussed, evuluated and shared. The application is designed for mobile devices and it is coded to be used with Android Operating System. Besides, this applicaton is developed computer engineer students based. Instead of to memorize the theroical informations and formulas, the application focused on how people understand the logic.This information becomes permanent information in young minds. Application consists two stages. First one is the preparing of dataset and second stage is the operating the algorithm with the help of prepared datasets and formulas. In the last part of the application, whole main nodes provide to users as decision tree table. Keywords: Education, dramatization, data mining, gini algorithm.
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Fitria, Ainul, Salahuddin Salahuddin, and Muhammad Rizka. "Rancang Bangun Aplikasi Machine Learning Pemilihan Varietas Bibit Jagung Unggul Menggunakan Algoritma Artificial Neural Network (ANN) Berbasis Web." Journal of Artificial Intelligence and Software Engineering (J-AISE) 4, no. 1 (2024): 27. http://dx.doi.org/10.30811/jaise.v4i1.5401.
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Jagung atau dalam bahasa latin Zea Mays merupakan adalah salah satu dari jenis tanaman pangan dari keluarga rumput-rumputan yang dikelompokkan dalam tanaman biji-bijian. Jagung memiliki banyak varietas. Adapun varietas yang telah dilepas oleh Menteri Pertanian hingga Oktober tahun 2022 sebanyak 361 varietas, yaitu jagung hibrida sebanyak 298 varietas, jagung komposit sebanyak 59 varietas, dan ada sebanyak 4 varietas jagung hibrida produk rekayasa genetik (PRG). Petani jagung biasanya memilih dan menentukan bibit jagung yang akan dibudidayakan berdasarkan rekomendasi pedagang bibit jagung atau dari rekan sesama petani jagung. Namun demikian sering dijumpai hasil panen jagung tidak sesuai dengan ekspektasi dan target yang diharapkan. Bahkan, tidak jarang petani jagung mengalami gagal panen yang disebabkan oleh beberapa faktor, salah satunya dikarenakan bibit jagung yang dipilih bukan merupakan varietas bibit jagung unggul. Sistem ini dirancang untuk membantu para petani jagung khususnya di daerah Aceh dalam memilih dan menentukan bibit jagung unggul untuk dibudidayakan dengan tujuan mendapatkan hasil panen yang memuaskan. Sistem ini menggunakan algoritma Artificial Neural Network untuk melakukan pemilihan. Artificial Neural Network (ANN) adalah algoritma Machine Learning dengan model komputasi yang terinspirasi dari prinsip kerja otak manusia. Artificial Neural Network digunakan dalam aplikasi ini karena dapat melakukan prediksi dengan akurat. Hasil yang diharapkan dengan adanya sistem ini petani dapat memilih varietas bibit jagung unggul untuk dibudidayakan, sehingga dapat memenuhi kebutuhan stok dalam negeri dengan memanfaatkan komputer dalam tahapan pemilihan bibit unggul. Penerapan algortima ANN Multi Layer Perceptron pada aplikasi ini menggunakan 21 data varietas jagung dengan 504 dataset yang dimasukkan mendapatkan hasil nilai tertinggi dengan persentase akurasi 90,47%. Dengan hasil tersebut, algortima Artificial Neural Network Multi Layer Perceptron dapat digunakan untuk Aplikasi Machine Learning dalam menentukan pemilihan varietas bibit jagung unggul Abstract Corn or in Latin Zea Mays is one of the types of food crops from the grass family which is grouped into grain crops. Corn has many varieties. The varieties that have been released by the Minister of Agriculture until October 2022 are 361 varieties, namely 298 varieties of hybrid corn, 59 varieties of composite corn, and there are as many as 4 varieties of genetically modified (PRG) hybrid corn. Maize farmers usually choose their maize seeds based on recommendations from maize seed traders or fellow maize farmers. However, maize yields are often not in line with expectations and targets. In fact, it is not uncommon for corn farmers to experience crop failure caused by several factors, one of which is because the corn seeds chosen are not superior corn seed varieties. This system is designed to help corn farmers, especially in the Aceh area, in choosing and determining superior corn seeds for cultivation with the aim of getting satisfactory yields. This system uses Artificial Neural Network algorithm to make the selection. Artificial Neural Network (ANN) is a Machine Learning algorithm with a computational model inspired by the working principles of the human brain. Artificial Neural Network is used in this application because it can make accurate predictions. The expected results with this system are that farmers can choose superior varieties of corn seeds to be cultivated, so that they can meet the needs of domestic stocks by utilizing computers in the stages of selecting superior seeds. The application of ANN Multi Layer Perceptron algortima in this application using 21 corn variety data with 504 datasets entered gets the highest value results with an accuracy percentage of 90.47%. With these results, the Artificial Neural Network Multi Layer Perceptron algortima can be used for Machine Learning applications in determining the selection of superior corn seed varieties.
Dissertations / Theses on the topic "Computer algortihms"
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Legrand, Hélène. "Algorithmes parallèles pour le traitement rapide de géométries 3D." Electronic Thesis or Diss., Paris, ENST, 2017. http://www.theses.fr/2017ENST0053.
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Au cours des vingt dernières années, les principaux concepts du traitement du signal ont trouvé leur homologue pour le cas de la géométrie numérique, et en particulier des modèles polygonaux de surfaces 3D. Ces traitements requièrent néanmoins un temps de calcul non négligeable lorsqu’on les applique sur des modèles de taille conséquente. Cette charge de calcul devient un frein important dans le contexte actuel, où les quantités massives de données 3D générées à chaque seconde peuvent potentiellement nécessiter l’application d’un sous-ensemble de ces opérateurs. La capacité à exécuter des opérateurs de traitement géométrique en un temps très court représente alors un verrou important pour les systèmes de conception, capture et restitution 3D dynamiques. Dans ce contexte, on cherche à accélérer de plusieurs ordres de grandeur certains algorithmes de traitement géométrique actuels, et à reformuler ou approcher ces algorithmes afin de diminuer leur complexité ou de les adapter à un environnement parallèle. Dans cette thèse, nous nous appuyons sur un objet compact et efficace permettant d’analyser les surfaces 3D à plusieurs échelles : les quadriques d’erreurs. En particulier, nous proposons de nouveaux algorithmes haute performance, maintenant à la surface des quadriques d’erreur représentatives de la géométrie. Un des principaux défis tient ici à la génération des structures adaptées en parallèle, afin d’exploiter les processeurs parallèles à grain fin que sont les GPU, la principale source de puissance disponible dans un ordinateur moderne<br>Over the last twenty years, the main signal processing concepts have been adapted for digital geometry, in particular for 3D polygonal meshes. However, the processing time required for large models is significant. This computational load becomes an obstacle in the current context, where the massive amounts of data that are generated every second may need to be processed with several operators. The ability to run geometry processing operators with strong time constraints is a critical challenge in dynamic 3D systems. In this context, we seek to speed up some of the current algorithms by several orders of magnitude, and to reformulate or approximate them in order to reduce their complexity or make them parallel. In this thesis, we are building on a compact and effective object to analyze 3D surfaces at different scales : error quadrics. In particular, we propose new high performance algorithms that maintain error quadrics on the surface to represent the geometry. One of the main challenges lies in the effective generation of the right structures for parallel processing, in order to take advantage of the GPU
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Mullen, Patrick Bowen. "Learning in Short-Time Horizons with Measurable Costs." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/808.
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Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.
Books on the topic "Computer algortihms"
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Kakde, O. G. Algortithms for Compiler Design (Electrical and Computer Engineering Series). Charles River Media, 2002.
Find full textBook chapters on the topic "Computer algortihms"
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Özçelik, Ahmet Devlet, and Ahmet Sinan Öktem. "Damage Detection on Turbomachinery with Machine Learning Algortihms." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50920-9_19.
Full textConference papers on the topic "Computer algortihms"
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Amine, Ouardi, and Mestari Mohammed. "Predicting A search algortihm heuristics using neural networks." In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2021. http://dx.doi.org/10.1109/icecet52533.2021.9698700.
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