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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Christina Juliane
STMIK LIKMI, Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : Building of Informatics, Technology and Science

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Komparasi Algoritma Klasifikasi Data Mining Menggunakan Optimize Selection untuk Peminatan Program Studi Khaerul Anam; Bani Nurhakim; Christina Juliane
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2160

Abstract

The selection of a study program is a unique opportunity for a student. STMIK IKMI Cirebon is now a KIP Kuliah provider, offering three study program. The research problem is the unavailability of a model of student interest in the study program, so it is necessary to carry out an interest in the study program by applying an algorithm to the classification model. The algorithm used as a comparison is the Decision Tree algorithm (C4.5), Naive Bayes, k-Nearest Neighbor and Support Vector Machine. The classification model applies the Optimize Selection operator by looking for the dominant attribute in its influence on the specialization of the student study program. Finally, the comparison model will be tested by parametric t-test in order to test the significance of the algorithm. The results of the algorithm accuracy test obtained that the SVM algorithm has the best accuracy with a value of 80.76%. While the algorithm with the lowest accuracy is Naive Bayes with a value of 74.64%. While the other two algorithms have a sequential accuracy rate of 80.47% for Decision Tree and 76.09% for k-NN. The results of this study are used to classify study preference for students in STMIK IKMI Cirebon which is useful for predicting study interest based on the background of students
Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19 Ahmad Rifai; Fatihanursari Dikananda; Christina Juliane
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2167

Abstract

The COVID-19 pandemic has greatly affected the learning environment, with the excuse of stopping the spread of COVID-19 infection. Teaching and learning activities that have generally been completed on campus face-to-face now have to be transferred to distance learning. However, one of the drawbacks of implementing distance learning is that it makes students less active, so that KBM feels tiring. The purpose of this study is to classify student learning outcomes during the COVID-19 Pandemic. The method used is the Knowledge Discovery Database (KDD) using the K-Means Algorithm. The number of clusters selected is the number of clusters with the smallest Davies Bouldin Index (DBI). The results of this study obtained 3 clusters with a DBI value of 1.379 and a centroid distance of 0.342. Cluster_1 is the data group with the highest quality index, Cluster_2 is the data group with the second highest quality index, and Cluster_0 is the data group with the lowest quality index of all clusters. By knowing the clusters of PJJ learning outcomes, it will make it easier for universities to take improvement steps to improve the quality of learning in accordance with the characteristics of each existing cluster
Co-Authors Abraham Situmorang Ahmad Rifai Alamsyah Firdaus Arifin Arifin Bani Nurhakim Bayu Pamungkas Didin Sayhidin Fatihanursari Dikananda Firman Sukmayadi Gendhi Haris Ilpan Rusilpan Khaerul Anam Novie Hari Purnomo