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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pendidikan Fisika dan Keilmuan (JPFK) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Terapan Jurnal ICT : Information Communication & Technology EVOLUSI : Jurnal Sains dan Manajemen Jurnal Teknologi Informasi dan Pendidikan Infotekmesin Jurnal E-Komtek Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Jurnal PkM (Pengabdian kepada Masyarakat)
Riyadi Purwanto
Politeknik Negeri Cilacap
Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Pemanfaatan Algoritma Random Forest Regression dalam Memprediksi Kepuasan Mahasiswa Terhadap Dosen Rostika Listyaningrum; Riyadi Purwanto; 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 Nur Wachid Adi Prasetya; Linda Perdana Wanti; Riyadi Purwanto
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 Linda Perdana Wanti; Nur Wachid Adi Prasetya; Riyadi Purwanto; Rahmat Mulyadi; Akmal Fauzan Ananta
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 Abdul Rohman Supriyono Afrizal Abdi Musyafiq Akmal Fauzan Ananta Alina Mei Cahyaningtyas Andesita Prihantara Annas Setiawan Prabowo Antonius Agung Hartono Arif Fahrizal Betti Widianingsih Cahya Vikasari Cahya Vikasari Devi Rahmawati Devi Taufiq Nurrohman Dodi Satriawan Dwi Novia Prasetyanti Dwi Novia Prasetyanti Fadillah Fadillah Hera Susanti Isa Bahroni, Isa Kurniawan Yogi Laksono Linda Perdana Wanti Lutfi Syafirullah Nur Wachid Adi Prasetya Nur Wahyu Rahadi Nurrohman, Devi Taufiq Oman Somantri Prih Diantono Abda'u Prih Diantono Abda`u Rahmat Mulyadi Ratih Hafsarah Maharrani Rostika Listyaningrum Sari, Eka Dyah Puspita Wily Riezkiadi Zaenurrohman Zaenurrohman