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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Sains dan Informatika Prosiding Seminar Nasional Riset Kuantitatif Terapan 2017 JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Teknologi Terapan Infotekmesin Jurnal E-Komtek Bulletin of Computer Science Research Jurnal Informatika: Jurnal Pengembangan IT
Riyadi Purwanto
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Social Sciences Other

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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.
The Certainty Factor Method in An Expert System for Tuberculosis Disease Diagnosis Kumara, Dimas Maulana Dwi; Linda Perdana Wanti; Purwanto, Riyadi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.549

Abstract

Tuberculosis is an infection caused by acid-fast bacilli (AFB) and is an infectious disease that can attack anyone through the air. This disease is hazardous and chronic, with a high prevalence among individuals aged 15-35 years. The diagnosis of tuberculosis traditionally takes a long time because it involves an interview process by medical experts and testing sputum samples in the laboratory to determine whether the patient is positive or negative for this disease. This process is not only time-consuming but also requires significant resources. To overcome this problem and speed up the diagnosis process, a technology-based approach is needed, namely the Expert System with the certainty factor method. This method can handle uncertainty in medical diagnosis by providing a certainty value for each observed symptom. This article discusses in depth the application of the certainty factor method in an expert system to diagnose Tuberculosis. By using this method, the system can provide faster and more accurate diagnosis results in diagnosing tuberculosis with a confidence level of 94.6% and reduce the workload of medical personnel. The application of the certainty factor method allows the integration of various symptoms and relevant medical data to produce more precise and reliable diagnostic conclusions.
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 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 Prihantara, Andesita Rahadi, Nur Wahyu Ratih Hafsarah Maharrani Riezkiadi, Wily Rostika Listyaningrum Supriyono, Abdul Rohman Supriyono, Abdul Rohman Wahyu Rahadi, Nur Wanti, Linda Perdana