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All Journal Journal of Big Data Analytic and Artificial Intelligence
Rosmini Rosmini
STMIK PPKIA Tarakanita Rahmawati
Computer Science & IT

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Implementasi Algoritma K-Medoids Dalam Mengelompokkan Siswa Berdasarkan Keaktifan Dalam Proses Pembelajaran Noor Oktavia Ih’Diati; Anto Anto; Rosmini Rosmini
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.38

Abstract

Grouping students based on their level of engagement is an effective strategy to improve the quality of learning. SMP 9 Tarakan currently does not have a system that can group students based on their engagement in the learning process, which could assist in evaluating learning outcomes. In the initial stage of applying this method, the data collected came from the report card grades of 8th-grade students (Class VIII I) in the 2nd semester (Even Semester) of the 2022/2023 academic year. The characteristics used in the analysis include grades in Religion, Civic Education (PPKn), Mathematics, Science (IPA), Social Studies (IPS), Indonesian Language, English, Physical Education (Penjaskes), and Cultural Arts and Skills, with a total of 31 data points analyzed. The second step is to determine the number of clusters. The third step involves randomly selecting clusters with an initial medoid. The fourth step is to calculate the distance for each student using the Euclidean distance method, then mark the nearest distance and calculate the total distance. The fifth step is to calculate the total deviation (S) and use the Davies-Bouldin Index (DBI) to find the optimal value of k by conducting tests five times with k=3. Based on the calculation results, the analysis of student data grouping produced three clusters using Euclidean distance and Davies-Bouldin Index calculations. The results show that 3 students fall into the Highly Interested cluster, 4 students into the Interested cluster, and 24 students into the Less Interested cluster.
Klasifikasi Penyakit Karies Gigi Menggunakan Algoritma Modified K-Nearest Neighbor Arnold Kalalo; Rosmini Rosmini; Anto Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 2 (2024): JBIDAI Desember 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.60

Abstract

Dental caries, commonly known as tooth cavities, is a disease where bacteria damage the structure of tooth tissues such as enamel, dentin, and cementum. The primary cause of dental caries is the demineralization of tooth surfaces caused by organic acids from sugary foods. If dental caries is not promptly treated or checked from the beginning, the damage can worsen to the point where the tooth must be extracted. To facilitate identifying the severity of caries, a dental caries classification system was developed using the MKNN (Modified K-Nearest Neighbor) algorithm. The MKNN method is an enhancement of the KNN method, with the main differences being in the calculation of training data validity and the weight voting process. In this study, there are three different classes of dental caries and six symptoms or variables. The stages of the MKNN method used are: distance calculation using Euclidean distance, testing the validity of training data, determining k based on distance calculation, and weight voting calculation in KNN. The test results show that the k value, the number of training data, and the number of test data affect the classification results. The classification results from the test using 20 training data, 10 test data, and k=3 are as follows: 1 patient classified with superficial caries, 5 patients with media caries, and 3 patients with profunda caries. The diagnosis produced by the application is consistent with the expert (doctor) diagnosis.
Co-Authors Anto Anto Arnold Kalalo Noor Oktavia Ih’Diati