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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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.
Performance Evaluation and Optimization of an IoT-Based Fish Smoking Monitoring System for Ensuring Product Quality Syafirullah, Lutfi; Mahardika, Fajar; Purwanto, Riyadi; Prasetyanti, Dwi Novia
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15736

Abstract

Fish smoking is a widely used preservation method; however, the quality of smoked fish is highly dependent on the stability of temperature, humidity, and smoking duration. Manual control of these parameters has limitations and may reduce product quality. Existing studies on fish smoking monitoring systems primarily focus on temperature control without providing quantitative evaluation of how multi-parameter process stability affects product quality and shelf life. This study aims to design and implement an Internet of Things (IoT)-based monitoring system for fish smoking equipment to ensure the quality of smoked fish. The research method used is Research and Development (R&D), which includes needs analysis, system design, development, testing, and evaluation stages. The system integrates temperature and humidity sensors, a microcontroller, and an IoT platform for real-time monitoring. The test results show that the system is capable of monitoring the smoking chamber temperature within a range of 60–80 °C with an average error of ±1.5 °C compared to a standard measuring instrument, and maintaining an optimal temperature of 70 °C during the smoking process. Quality testing of the smoked fish indicates uniform doneness, a golden-brown color, firm texture, and an average moisture content reduction of 35%. Shelf-life testing shows that the smoked fish can last up to 7–10 days at room temperature and up to 21 days under cold storage without significant changes in aroma and texture. Unlike previous works, this study provides quantitative evidence that improved stability of multiple smoking parameters through IoT-based monitoring significantly enhances product quality consistency and extends the shelf life of smoked fish.
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.
Improving Diagnostic Accuracy on Prescription Text Data Using SMOTE-Optimized SVM Wanti, Linda Perdana; Prasetya, Nur Wachid Adi; Purwanto, Riyadi; Mulyadi, Rahmat; Ananta, Akmal Fauzan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7441

Abstract

Disease classification based on drug prescription data plays a crucial role in helping healthcare professionals understand patient health conditions and supporting clinical decision-making. Drug prescription data actually contains a wealth of information regarding disease indications, but is generally presented in unstructured, free-text form. Furthermore, the data distribution across disease classes is often imbalanced, with some diseases receiving less data than others. This can lead to inaccurate classification models that favor disease classes with more data. This study aims to enhance the performance of disease classification based on drug prescription data by combining text mining approaches, the Synthetic Minority Oversampling Technique (SMOTE), and the Support Vector Machine (SVM) algorithm. The research process begins with text preprocessing, which includes case folding, tokenization, stopword removal, and stemming, to clean and normalize the prescription data. Next, the text data is converted into numeric features using the Term Frequency–Inverse Document Frequency (TF-IDF) method to enable processing by machine learning algorithms. To address the class imbalance issue, the SMOTE method is applied to training data by generating synthetic data for a limited number of disease classes. A classification model was then built using the SVM algorithm, known to be effective in handling high-dimensional text data. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the application of SMOTE and parameter optimization in Support Vector Machine significantly improved classification performance, with an accuracy of 92.6%, a precision of 91.8%, a recall of 93.4%, and an F1-score of 92.6%. The increased recall value in the class of patients diagnosed with diabetes indicates that the model is able to correctly identify most diabetes cases based on medical prescription data.
Co-Authors Afrizal Abdi Musyafiq Alina Mei Cahyaningtyas Ananta, Akmal Fauzan 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. Mulyadi, Rahmat 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