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All Journal JSR : Jaringan Sistem Informasi Robotik Journal of Software Engineering and Information System (SEIS)
Marta, Puji Pramudya
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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MODEL PREDIKSI PENERIMA BANTUAN SOSIAL BERBASIS ALGORITMA RANDOM FOREST Sukma, Siti Hatmara; Suarna, Nana; Bahtiar, Agus; Marta, Puji Pramudya; Anam, Khaerul
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.10526

Abstract

Inaccurate targeting of social assistance beneficiaries remains a critical issue at the village level due to subjective and inconsistent manual verification processes. This study aims to develop a predictive model for determining social assistance eligibility using the Random Forest algorithm based on 2021 SDGs Village microdata from Cibeureum Village. The research involves data preprocessing, model training, and hyperparameter optimization, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The proposed model achieved an accuracy of 94.34%, indicating strong and stable classification performance. Feature importance analysis shows that housing conditions, access to clean water, and asset ownership are the most influential socioeconomic indicators. These findings demonstrate that Random Forest can effectively support data-driven decision-making and improve the accuracy of social assistance distribution at the village level.
Algoritma LightGBM dengan SMOTE & ADASYN untuk Prediksi Risiko Serangan Jantung Sugianto, Nanda Putri; Purnamasari, Ade Irma; Pratama, Denni; Marta, Puji Pramudya; Wijaya, Yudhistira Arie
JSR : Jaringan Sistem Informasi Robotik Vol 10, No 1 (2026): JSR : Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v10i1.633

Abstract

Ketidakseimbangan data merupakan tantangan utama dalam pemodelan prediksi medis, termasuk prediksi serangan jantung, karena jumlah kasus positif jauh lebih sedikit dibandingkan kasus negatif sehingga menurunkan kemampuan model dalam mendeteksi pasien berisiko tinggi. Penelitian ini bertujuan untuk membandingkan efektivitas dua teknik oversampling, yaitu Synthetic Minority Oversampling Technique (SMOTE) dan Adaptive Synthetic Sampling (ADASYN), dalam meningkatkan performa algoritma Light Gradient Boosting Machine (LightGBM) untuk prediksi risiko serangan jantung. Dataset berjumlah 1.319 sampel dengan sembilan fitur klinis dan dianalisis melalui tahapan pra-pemrosesan, normalisasi, penanganan class imbalance, pembangunan model, serta evaluasi menggunakan Accuracy, Precision, Recall, F1-Score, dan AUC-ROC. Hasil menunjukkan bahwa model baseline memiliki akurasi tinggi namun sensitivitas terhadap kelas positif masih rendah. Setelah diterapkan oversampling, model mengalami peningkatan signifikan. LightGBM-SMOTE memperoleh F1-Score terbesar (0.9876) dan AUC-ROC 0.9853, sedangkan LightGBM-ADASYN mencapai F1-Score 0.9855 dan AUC-ROC 0.9861. Temuan ini menunjukkan bahwa SMOTE memberikan peningkatan performa yang lebih stabil dalam mendeteksi kelas minoritas. Dengan demikian, teknik oversampling khususnya SMOTE terbukti efektif untuk meningkatkan akurasi dan sensitivitas model prediksi serangan jantung.
Co-Authors Khaerul Anam Pratama, Denni Purnamasari, Ade Irma Suarna, Nana Sugianto, Nanda Putri Sukma, Siti Hatmara Yudhistira Arie Wijaya