Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Griya Journal of Mathematics Education and Application Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Alvin Hafiz
Unknown Affiliation
Computer Science & IT Education Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimasi Parameter Model LightGBM Menggunakan Algoritma Grey Wolf Optimizer untuk Prediksi Penyakit Ginjal Kronis Muhammad Alfin; Alvin Hafiz; Muhammad Budi Akbar; Adidtya Perdana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1263

Abstract

Chronic kidney disease is an increasingly prevalent health issue that requires more precise clinical data-based early detection methods to enable timely and appropriate treatment. This study focuses on developing a predictive model for chronic kidney disease using the Light Gradient Boosting Machine (LightGBM) algorithm and enhancing its performance through hyperparameter optimization with the Grey Wolf Optimizer (GWO). The dataset used originates from public sources and undergoes several preprocessing steps, including missing value imputation, categorical feature encoding, outlier handling, initial feature selection, and stratified data splitting to maintain model quality. Three modeling approaches were evaluated: LightGBM with default parameters, LightGBM enhanced using Random Search, and LightGBM optimized with GWO. The experimental results indicate that the baseline model already performs well, Random Search improves accuracy and F1-score, and GWO achieves the highest AUC-ROC value despite requiring longer computation time. Significance testing through cross-validation shows that the performance differences among the three models are not statistically significant, suggesting that the observed improvements are not strong enough to determine a definitively superior optimization method. The feature importance analysis highlights that clinical indicators such as creatinine levels, glomerular filtration rate, blood pressure, and urine protein contribute most prominently to the prediction. Overall, the study demonstrates that LightGBM is a reliable model for early detection of chronic kidney disease, and hyperparameter optimization still offers added value that can support the development of AI-based clinical decision-support systems
Pemodelan Graf Berbobot untuk Penentuan Prioritas Jarak dan Estimasi Waktu Distribusi MBG dari SPPG ke Sekolah Mitra di Medan Tuntungan Menggunakan Algoritma Dijkstra Evelyn Silalahi; Alvin Hafiz; Suvriadi Panggabean
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1232

Abstract

Program Makan Bergizi Gratis (MBG) membutuhkan sistem distribusi yang tepat waktu dan efisien dari Satuan Pelayanan Pemenuhan Gizi (SPPG) ke sekolah-sekolah mitra guna menjaga kualitas dan kehangatan makanan. Penelitian ini bertujuan untuk memodelkan graf berbobot dalam menentukan prioritas jarak dan mengestimasi waktu distribusi MBG dari SPPG Simpang Selayang ke 10 sekolah dasar di Kecamatan Medan Tuntungan. Metode kuantitatif diterapkan dengan mengimplementasikan Algoritma Dijkstra pada jaringan jalan nyata berbasis OpenStreetMap (OSM). Data koordinat lokasi dikumpulkan melalui web scraping dan divalidasi menggunakan bounding box administratif. Untuk memberikan gambaran operasional yang lebih realistis, analisis sensitivitas dilakukan pada tiga skenario kecepatan rata-rata, yaitu 20, 30, dan 40 km/jam. Hasil komputasi menunjukkan bahwa SD NEGERI 065012 menjadi prioritas distribusi pertama dengan jarak rute terpendek (1,219 km), sedangkan SD N 060972 berada pada prioritas terakhir (5,763 km). Pada skenario kecepatan moderat (30 km/jam), rata-rata waktu tempuh distribusi ke seluruh sekolah adalah 6,4 menit. Penelitian ini menyimpulkan bahwa penerapan Algoritma Dijkstra berbasis jaringan jalan OSM terbukti lebih efektif dan realistis dalam mengoptimalkan rute distribusi dibandingkan perhitungan jarak garis lurus (Haversine), sehingga dapat menjadi acuan pengambilan keputusan logistik yang efisien bagi pengelola program MBG.
Co-Authors Adidtya Perdana, Adidtya Evelyn Silalahi Muhammad Alfin Muhammad Budi Akbar Suvriadi Panggabean