Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Taufan, Resi
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Sentimen Analisis Twitter Terhadap Isolasi Diri Masyarakat Indonesia Akibat Dampak Covid-19 Taufan, Resi; Rivanie, Tri; Rahayu, Sri; Gata, Windu
MATICS Vol 12, No 2 (2020): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v12i2.9329

Abstract

Maraknya penyebaran virus Corona atau Covid-19 di Indonesia mengakibatkan pemerintah menganjurkan agar masyarakat indonesia melakukan isolasi diri dan social distancing. Hal ini menyebabkan banyaknya tanggapan publik khusunya di media sosia twitter tentang anjuran tersebut. Dalam penelitan ini akan dilakukan analisis sentimen masyarakat terhadap dampak yang dirasakan masyarakat mengenai isolasi diri. Ada beberapa tahap untuk melakukan analisis sentimen, diantaranya adalah tahap pengumpulan data, preprocesing data, validasi data serta pengujian dilakukan dengan tools rapidminer dengan menggunakan fitur TF-IDF untuk melakukan pembobotan pada setiap kata. Pada penelitian ini setiap tweet diklasifikasikan menjadi sentimen positif dan negative. Metode Naive Bayes dalam klasifikasi sentimen memiliki akurasi yang cukup baik untuk melakukan klasifikasi sentimen.
Evaluation of SDCA, LBFGS, LightGBM and FastTree in ML.NET for Diabetes Prediction Taufan, Resi; Ardiansyah, Fahmi; Augustia, Annisa Elfina
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 2 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i2.49517

Abstract

This study aims to develop a machine learning-based diabetes risk prediction model using the ML.NET framework. The dataset utilized is a balanced-split version of the 2015 BRFSS, consisting of 70,692 respondents and 21 health indicator variables. Two training approaches were applied to analyze model performance: a baseline with default parameters and hyperparameter tuning. The preprocessing stage involved combining variables into feature vectors, Min-Max normalization, and an 80:20 train-test data split. The models were trained using four algorithms: SDCA Logistic Regression, LBFGS Logistic Regression, LightGBM, and FastTree. Evaluation results showed that LightGBM with the hyperparameter tuning approach, delivered the most consistent performance, achieving 75.37% accuracy, 82.86% AUC, 76.22% F1-score, 72.92% precision, and 79.83% recall. Feature analysis confirmed that GenHlth, HighBP, BMI, HighChol, and Age contributed dominantly to diabetes risk, aligning with medical literature regarding metabolic factors. The best-performing LightGBM model was then integrated into a .NET-based prototype application with a Razor Pages web interface. The practical contribution of this research is proof of concept for machine learning integration into e-health systems to support early detection and digital prevention of diabetes complications in the future.
Co-Authors Ardiansyah, Fahmi Augustia, Annisa Elfina Gata, Windu Rivanie, Tri SRI RAHAYU