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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Maulana, Malik
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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KLASIFIKASI KELANCARAN PEMBAYARAN PINJAMAN KOPERASI DENGAN METODE DECISION TREE Kandayu, Raka; Maulana, Malik; Silaban, Wilfrida; Ompusunggu, Elvis Sastra
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 5 No. 1 (2021): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v5i1.1910

Abstract

Salah isatu ipenyebab ikredit ibermasalah iadalah ikurang itelitinya ipihak ikoperasi idalam isurvei idan ianalisis ipemberian ipeminjaman. iOleh ikarena iitu, ianalisis ikredit idengan iteknik idata imining iperlu idilakukan isehingga idapat imeminimalisir inasabah iterlambat imembayar ipinjaman iserta imempersingkat iwaktu ianalisis ipemberian ipinjaman. iPenelitian iini ibertujuan iuntuk membuat sistem decision tree yang dapat digunakan sebagai klasifikasi nasabah peminjaman berdasarkan nilai kelancaran pembayaran pinjamannya. Pada penelitian ini atribut yang yang digunakan sebagai acuan untuk menganalisis kelacancaran pembayaran pinjaman koperasi adalah status keanggotaan, status pernikahan, pekerjaan, pendapatan, pengeluaran, jaminan, keperluan, jumlah pinjaman dan lama pinjman. Algotitma C4.5 digunakan untuk membentu decision tree dengan cara mencari nilai entropy, gain info, split info, dan gain ratio pada setiap atribut untuk menentukan atribut akar dan cabangnya.
Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering Mufarroha, Fifin Ayu; Rahmat, Abdullah Basuki; Husni; Rachmad, Aeri; Lestari, Vivin Ayu; Dwiyanti, Tasya; Maulana, Malik
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2555

Abstract

Stunting remains a significant health issue in Indonesia due to its long-term impact on human resource quality and economic productivity. Despite various intervention programs, disparities in stunting rates between regions remain high, particularly in areas with diverse socioeconomic conditions. This study aims to identify patterns and group regions based on stunting risk levels using two machine learning approaches: Hierarchical Clustering (HC) and Deep Embedded Clustering (DEC). The data used are aggregated data from toddler measurements, including the number of toddlers measured, the number of stunting cases, and the percentage of stunting in the 2020–2024 period. The analysis was conducted by comparing the cluster results from the two methods. The HC method is implemented using an Agglomerative Clustering approach with the Ward linkage criterion, while DEC uses a layered autoencoder architecture optimized through Kullback–Leibler divergence. To assess cluster quality, the study uses the Silhouette Score metric. The results showed that HC produced the highest Silhouette score of 0.5430, while DEC reached 0.4874, with a year-on-year performance trend. These findings indicate that HC excels in clustering stability, while DEC is more adaptive to data complexity and nonlinear patterns. The combination of the two has the potential to support the formulation of more comprehensive, data-driven policies to identify and address stunting-prone areas.
Co-Authors Aeri Rachmad Dwiyanti, Tasya Fifin Ayu Mufarroha Husni Kandayu, Raka Ompusunggu, Elvis Sastra Rahmat, Abdullah Basuki Silaban, Wilfrida Vivin Ayu Lestari