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All Journal Elkom: Jurnal Elektronika dan Komputer JUTECH : Journal Education and Technology
Nur Ariesanto Ramdhan
Universitas Muhadi Setiabudi Brebes
Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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OPTIMALISASI ALGORITMA SUPPORT VECTOR MACHINE UNTUK MEMPREDIKSI TES SAMAPTA PERSONEL KODIM 0713 BREBES Muhammad Agam Tamlica; Nur Ariesanto Ramdhan; Otong Saeful Bachri
Elkom: Jurnal Elektronika dan Komputer Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3754

Abstract

The Samapta Test is a component of the physical fitness assessment that is mandatory for all military personnel, including personnel of the 0713 Brebes Military District Command (Kodim), as an indicator of physical readiness to carry out duties. The high failure rate of this test encourages the need for a prediction system capable of identifying personnel at risk of failure early. This study aims to build and optimize a prediction model for passing the Samapta Test using the Support Vector Machine (SVM) algorithm. Optimization is carried out through hyperparameter tuning techniques using the Grid Search Cross-Validation method to obtain the optimal combination of kernel parameters, C values, and gamma values. The data used comes from a summary of the Samapta Test results of Kodim 0713 Brebes personnel, including attributes such as age, running, push-up, sit-up, pull-up, and swimming scores. The research stages include data collection, pre-processing, feature selection, SVM model development, parameter optimization, and model performance evaluation using accuracy, precision, recall, and F1-score metrics. The results of the study show that the optimized SVM model is able to significantly increase prediction accuracy compared to the SVM model without optimization, so that it can be used as a decision-making tool for units in designing more targeted and effective personnel physical development programs.
PENGEMBANGAN SISTEM INFORMASI GEOGRAFIS BERBASIS WEB UNTUK PEMETAAN PARTISIPASI PEMILU MENGGUNAKAN ALGORITMA K-MEANS DI KABUPATEN BREBES Ibnu Salam; Nur Ariesanto Ramdhan; Abdul Khamid
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.5200

Abstract

Penelitian ini menggunakan pendekatan deskriptif kuantitatif untuk menganalisis dan mengelompokkan tingkat partisipasi pemilih tetap (DPT) pada Pemilu 2024 di Kabupaten Brebes. Data yang dianalisis mencakup 17 kecamatan dengan total 6.063 TPS dan tingkat partisipasi berkisar antara 66% hingga 82%. Metode K-Means Clustering digunakan untuk mengelompokkan data berdasarkan variabel jumlah TPS, jumlah desa, dan tingkat partisipasi. Hasil clustering menunjukkan bahwa sebanyak 12 kecamatan tergolong dalam cluster partisipasi tinggi (?75%), dua kecamatan dalam cluster sedang (73%–74%), dan tiga kecamatan dalam cluster partisipasi rendah (?72%). Hasil pengelompokan ini kemudian divisualisasikan menggunakan Sistem Informasi Geografis (SIG) berbasis WebGIS melalui aplikasi QGIS. Visualisasi data dalam bentuk peta interaktif memudahkan analisis spasial oleh penyelenggara pemilu, sehingga strategi peningkatan partisipasi pemilih dapat difokuskan pada wilayah dengan partisipasi rendah secara tepat sasaran.
Co-Authors Abdul Khamid Ibnu Salam Muhammad Agam Tamlica Otong Saeful Bachri