Article Per Year (5 Year)
p-Index From 2021 - 2026
3.409
P-Index
This Author published in this journals
All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Sains dan Informatika Prosiding Seminar Nasional Riset Kuantitatif Terapan 2017 JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Terapan Infotekmesin Jurnal E-Komtek Bulletin of Computer Science Research Jurnal Informatika: Jurnal Pengembangan IT
Riyadi Purwanto
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search
Journal : Infotekmesin

 1   2 
Evaluasi Kinerja Model Machine Learning dalam Klasifikasi Penyakit THT: Studi Komparatif Naïve Bayes, SVM, dan Random Forest Prasetya, Nur Wachid Adi; Wanti, Linda Perdana; Purwanto, Riyadi; Bahroni, Isa; Listyaningrum, Rostika
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2798

Abstract

Classification of Ear, Nose, and Throat (ENT) diseases is essential to support faster and more accurate diagnosis. However, no prior studies have specifically compared the performance of Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms in ENT cases. This study aims to evaluate and compare the three classification models in identifying ENT diseases with or without comorbidities. Medical record data were processed through preprocessing, feature selection using ANOVA, and class balancing with SMOTE. The results showed that SVM outperformed the other models with the highest accuracy (59%), followed by Random Forest (57%), and Naïve Bayes (48%). SVM demonstrated superior performance due to its consistent scores across all evaluation metrics. The study concludes that the choice of classification model significantly impacts the accuracy of ENT disease diagnosis.
Pemanfaatan Algoritma Random Forest Regression dalam Memprediksi Kepuasan Mahasiswa Terhadap Dosen Listyaningrum, Rostika; Purwanto, Riyadi; Dwi Novia Prasetyanti; Cahya Vikasari; Artdhita Fajar Pratiwi
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2808

Abstract

Student satisfaction with lecturers is a key indicator in assessing the quality of higher education. However, commonly used evaluation approaches remain largely descriptive and subjective, making them less effective in supporting sustainable quality improvement. Moreover, the comprehensive use of lecturer competency indicators in predictive models is still limited. This study addresses the gap by developing a student satisfaction prediction model using the Random Forest Regression algorithm, optimized through grid search and feature selection using the Recursive Feature Elimination (RFE) method combined with 5-fold cross-validation. Data were collected from the EDOM system of Politeknik Negeri Cilacap, involving 24 indicators based on national lecturer competency standards, and analyzed using R software. The best model was achieved with parameters mtry = 1 and ntree = 300, yielding RMSE = 0.0222, MAE = 0.0118, and R² = 0.9959. The three most influential indicators identified were structured assignments, diversity of teaching methods, and punctuality. These findings are expected to inform policies for improving the quality of higher education.
Studi Perbandingan Kinerja Support Vector Machine Pada Klasifikasi Diabetes Mellitus Menggunakan Fitur Regular Expression dan Non-Regular Expression Prasetya, Nur Wachid Adi; Wanti, Linda Perdana; Purwanto, Riyadi
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3125

Abstract

Diabetes mellitus is a rapidly progressing non-communicable disease that significantly affects quality of life. Clinical information in electronic medical records, such as prescriptions and laboratory results, often appears as unstructured text and therefore requires text-mining techniques for accurate classification. This research compares the performance of the Support Vector Machine (SVM) classifier on diabetes mellitus data processed with and without feature extraction using Regular Expressions (Regex). The workflow includes data preprocessing, feature extraction, TF-IDF weighting, model training, and evaluation using accuracy, precision, recall, and F1-score. Results show that both approaches achieve high accuracy (98.8–98.9%), with the non-Regex model performing slightly better at 98.93% compared to 98.83% for the Regex-based model. These findings indicate that SVM is effective for classifying text-based clinical data, while Regex provides potential benefits but requires further optimization to ensure its suitability for various medical text contexts.
Co-Authors Afrizal Abdi Musyafiq Alina Mei Cahyaningtyas Annas Setiawan Prabowo Cahya Vikasari Cahya Vikasari, Cahya Dodi Satriawan, Dodi Dwi Novia Prasetyanti Dwi Novia Prasetyanti Hera Susanti, Hera Ida Ayu Putu Sri Widnyani Isa Bahroni, Isa Kumara, Dimas Maulana Dwi Linda Perdana Wanti Listyaningrum, Rostika Lutfi Syafirullah Mahardika, Fajar Maharrani, Ratih Hafsarah Muh. Yamin, Muh. Novia Prasetyanti, Dwi Nur Wachid Adi Prasetya Nurrohman, Devi Taufiq Nur’Aini, Ummu Habibah Prasetyanti, Dwi Novia Prih Diantono Abda’u3 Prih Diantono Abda`u Prihantara, Andesita Rahadi, Nur Wahyu Rahadi, Nur Wahyu Ratih Hafsarah Maharrani Riezkiadi, Wily Rostika Listyaningrum Supriyono, Abdul Rohman Supriyono, Abdul Rohman Wahyu Rahadi, Nur Wanti, Linda Perdana