Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Informatic Research and Software Engineering Journal of Student Research Exploration
Biyantoro, Arell Saverro
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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

Found 2 Documents
Search

 1 
Penerapan Decision Tree untuk Klasifikasi Status Kesehatan dengan perbandingan KNN dan Naive Bayes: Application of Decision Tree for Health Status Classification, Compared to KNN and Naive Bayes Biyantoro, Arell Saverro; Prasetyo, Budi
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 4 No. 1 (2024): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v4i1.1342

Abstract

Fokus penelitian ini adalah pengujian algoritma machine learning untuk klasifikasi status kesehatan, dengan penekananpada penggunaan Algoritma Decision Tree, K-Nearest Neighbor (KNN), dan Naive Bayes. Metode penelitian inimenggunakan dataset dummy bertema kesehatan untuk melakukan tahapan perencanaan, pengumpulan data,preprocessing, instruksi, dan analisis hasil. Hasil penelitian menunjukkan bahwa algoritma machine learning DecisionTree memberikan akurasi tertinggi (95.45%) dalam memprediksi kesehatan berdasarkan variabel seperti usia (Age) danintensitas olahraga (ExerciseHours). Intensitas olahraga lebih dari 2,5 jam per minggu dianggap sebagai faktor pentingdalam menentukan kesehatan yang baik, sementara usia dan durasi olahraga tertentu mempengaruhi kategori kesehatan yang dihasilkan. Penelitian ini memberikan wawasan penting tentang penggunaan machine learning untuk membantu prediksi status kesehatan menggunakan variabel karakteristik individu dalam dataset.
Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews Biyantoro, Arell Saverro; Prasetiyo, Budi
Journal of Student Research Exploration Vol. 3 No. 2 (2025): July 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i2.396

Abstract

Dana is a mobile electronic wallet application available for download on Google Play Store. Users can rate and comment on this application directly through the review section on the platform. By utilizing these user reviews, research can be conducted to identify the main complaints experienced by Dana application users. This research uses Support Vector Machine (SVM) sentiment analysis to classify reviews and Latent Dirichlet Allocation (LDA) to map negative comment topics. LDA extracts several representative words or tokens that are grouped to form specific themes. The findings show that the most common sources of user complaints are related to transaction issues, premium features, and app updates. These insights can provide valuable input for developers to improve the overall quality and user experience of the Dana app.
Co-Authors Budi Prasetiyo, Budi Budi Prasetyo