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All Journal Horizon: Indonesian Journal of Multidisciplinary Jurnal Ekonomi, Manajemen, Akuntansi
Arif Hamied Nababan
Politeknik Negeri Medan
Agriculture, Biological Sciences & Forestry Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Evaluasi Algoritma Random Forest dan KNN dalam Memprediksi Risiko Diabetes Berdasarkan Fitur Klinis Siti Jamilah Br Tarigan; Alyiza Dwi Ningtyas; Arif Hamied Nababan; Devanta Abraham Tarigan; Dini Rizqi Dwikunti Siregar
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.6400

Abstract

This study aims to demonstrate the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in predicting diabetes risk based on numerical clinical data. The study used a dataset of 757 samples with eight clinical features, namely the number of pregnancies, glucose levels, blood pressure, skin thickness, insulin, body mass index (BMI), familial diabetes predisposition function, and age. The data was divided into 80% training data and 20% testing data, with data scale adjustments to support the classification process. The evaluation results showed that Random Forest produced better performance with an accuracy of 73.7% and an F1-Score of 0.623, compared to KNN with an accuracy of 72.4% and an F1-Score of 0.604. Comparison of classification results showed that Random Forest was able to provide more consistent predictions in distinguishing groups at risk of diabetes from healthy groups. The contribution of this study is to provide an empirical evaluation of the description of two classification algorithms commonly used on numerical clinical data and show that Random Forest is more suitable for the development of a decision support system for diabetes risk prediction. This research can be the basis for the development of more accurate prediction models through the use of broader datasets and other machine learning methods.
Systematic Literature Review: Adopsi Aplikasi Point of Sales (POS) dalam Peningkatan Kinerja UMKM di Indonesia Alwi Ihsan Nababan; Arif Hamied Nababan; Rizky Maulidya Afifa; Rovidatul Hikmah Tanjung
EKOMA : Jurnal Ekonomi, Manajemen, Akuntansi Vol. 5 No. 5: Juli 2026
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/ekoma.v5i5.17416

Abstract

Transformasi digital UMKM Indonesia mengalami percepatan pasca COVID-19 melalui integrasi aplikasi Point of Sales (POS). Studi ini bertujuan memetakan pola publikasi POS dalam konteks UMKM, memprofilkan aplikasi POS yang paling banyak diadopsi, serta mengeksplorasi dampak POS terhadap kinerja UMKM. Pendekatan Systematic Literature Review (SLR) berlandaskan PRISMA 2020 diterapkan pada artikel di Google Scholar periode 2020-2025. Dari 130 artikel kandidat, 25 dipertahankan setelah memenuhi kriteria inklusi. Temuan: (1) publikasi mencapai puncak pada 2024 dengan 8 artikel (32%); (2) aplikasi POS yang paling banyak dikaji adalah Moka POS (6), Kasir Pintar (5), dan Qasir (3), dengan POS custom berbasis web juga dominan (4); (3) delapan tema manfaat teridentifikasi dengan efisiensi transaksi (80%), akurasi pelaporan keuangan (72%), dan kontrol inventaris (64%) sebagai keunggulan paling dominan. Studi ini berkontribusi pada pemetaan literatur POS-UMKM di Indonesia.
Co-Authors Alwi Ihsan Nababan Alyiza Dwi Ningtyas Devanta Abraham Tarigan Dini Rizqi Dwikunti Siregar Rizky Maulidya Afifa Rovidatul Hikmah Tanjung Siti Jamilah Br Tarigan