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All Journal Jurnal Teknik Informatika dan Teknologi Informasi
Muhammad Yasin
Universitas Asahan
Computer Science & IT

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Penerapan Data mining untuk Clustering Kondisi Sosial Ekonomi Berdasarkan Kepemilikan Jaminan Kesehatan Menggunakan Algoritma K-Means Rika Sari; Muhammad Yasin
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6082

Abstract

This study discusses the application of the K-Means Clustering algorithm in analyzing the socioeconomic conditions of communities in North Sumatra Province based on health insurance coverage. The background of this study stems from the continuing gap in access to and coverage of health insurance, which is influenced by differences in socioeconomic conditions between regions. The purpose of this study is to identify community groups with different patterns of health insurance ownership, analyze their influence on socioeconomic conditions, and explain the application of the K-Means algorithm as a data clustering method. The data used was obtained from the North Sumatra Provincial Statistics Agency (BPS) for the 2021–2023 period, with variables including BPJS Health Insurance Premium Assistance Recipients, BPJS Health Insurance Non-Premium Assistance Recipients, Private Insurance, and Insurance from Companies/Offices. The analysis process was carried out through the stages of variable selection, initial centroid determination, distance calculation using Euclidean Distance, and iteration. The results of the study show that there are two main clusters, namely clusters with high health insurance ownership rates and clusters with low ownership rates. In terms of the BPJS Health Insurance Variable for Contribution Assistance Recipients, the cities of Medan, Samosir, Tanjungbalai, Asahan, Batu Bara, Binjai, and Toba are included in the high cluster, while Deli Serdang is in the low cluster. These findings are expected to provide an overview for local governments in formulating policies to improve access to health insurance in a more targeted and data-driven manner.
Implementasi Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Jumlah Penduduk Menurut Jenis Kelamin dan Kabupaten di Sumatera Utara Nur Saida; Muhammad Yasin
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6083

Abstract

North Sumatra Province has a large population and is spread across various districts, so an effective system is needed to manage and analyze population data. This research aims to implement the Learning Vector Quantization (LVQ) method in classifying population based on gender and district in North Sumatra. The LVQ method was chosen because of its ability to perform classification based on supervised learning that utilizes vector prototypes. The data used is sourced from the Central Bureau of Statistics (BPS) of North Sumatra in 2022 and analyzed using customized parameters in RapidMiner software. This research involves several stages, starting from data collection, UML-based system design, variable selection, to the application and testing of classification models. The results showed that the LVQ method was able to classify the population based on gender and district accurately and efficiently. It is expected that this classification system can be the basis for decision-making in regional development planning and accelerate government programs related to population distribution.
Pengaplikasian Algoritma C4.5 untuk Menganalisis Hubungan Kebiasaan Harian Siswa terhadap Prestasi Akademik Muhammad Zikri Ansyari; Muhammad Yasin
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6364

Abstract

This study aims to analyze the relationship between students’ daily habits and their academic performance using the C4.5 algorithm. The daily habit variables examined include study duration and intensity, sleep quality and patterns, frequency and type of gadget use, attendance consistency, and students’ engagement in learning activities both inside and outside the classroom. The data were collected through questionnaires and combined with students’ academic grades as indicators of performance. The C4.5 algorithm was employed to construct a decision tree model capable of identifying the daily habit attributes that have the most significant influence on academic achievement. The findings reveal that study intensity, sleep quality, and attendance consistency serve as dominant factors in predicting students’ performance levels. Furthermore, the resulting decision tree model demonstrates a relatively high accuracy level, making it a useful tool for evaluation, student development planning, and decision-making processes by educators. These results confirm that the application of the C4.5 algorithm is effective in uncovering the relationship patterns between daily habits and academic achievement and has the potential to serve as a reference for efforts to improve students’ learning quality.
Co-Authors Muhammad Zikri Ansyari Nur Saida Rika Sari