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All Journal International Journal of Electrical and Computer Engineering Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTEK: Informatika dan Teknologi Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Applied Technology and Computing Science Journal Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Journal of Innovation Information Technology and Application (JINITA) Journal of Community Development Jurnal Ekonomi dan Teknik Informatika Jurnal Abdi Panca Marga Pengabdian Jurnal Abdimas Damhil: Jurnal Pengabdian kepada Masyarakat
Nur Wachid Adi Prasetya
Politeknik Negeri Cilacap
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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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.
Pelatihan Pengolahan Limbah Kayu Mahoni (Swietenia Mahagoni) Pada BUMDes Banjarwaru Radhi Ariawan; Nur Akhlis Sarihidaya Laksana; Unggul Satria Jati; Roy Aries Permana Tarigan; Bayu Aji Girawan; Linda Perdana Wanti; Nur Wachid Adi Prasetya; Ganjar Ndaru Ikhtiagung
Journal of Community Development Vol. 5 No. 2 (2024): December
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i2.258

Abstract

Banjarwaru village is one of the villages in Cilacap district with the potential for wooden broom handle craft commodities. The processing of mahogany wood (Swietenia mahagoni) into broom handles produces wood waste that has not been utilized optimally. The accumulation of wood waste is caused by a lack of knowledge among the Banjarwaru village woodworking community regarding the potential dangers, benefits, and economic value of wood waste. Therefore, training in wood waste processing into value-added products such as particleboard is needed. The Community service team together with BUMDes Banjarwaru carried out mentoring activities as a solution method for utilizing wood waste. The mentoring activities carried out consist of educating the negative effects of wood waste on the environment, educating the potential and benefits of wood waste in another processed forms, and training in wood waste processing into particleboard. These series of activities succeeded in increasing the woodworking community understanding of wood waste by 81.6%. The success rate of mentoring reached 91,67% shown by 11 out of 12 mentoring participants understood the potential danger, benefits, and other processed forms of wood waste.
Co-Authors Aditya Saputra, Muhammad Bintang Agus Susanto Alif Iftitah Alif Iftitah Ananta, Akmal Fauzan Andesita Prihantara Andriansyah Zakaria Andriansyah Zakaria Annas Setiawan Prabowo Annisa Romadloni Awaludin, Ihza Bayu Aji Girawan Dwi Krisbiantoro, Dwi Dwi Kumara, Dimas Maulana Dwi Novia Prasetyanti Dwi Novia Prasetyanti Eka Tripustikasari Fadillah Fadillah Furi, Syamaidzar Nadifa Ganjar Ndaru Ikhtiagung Girawan, Bayu Aji Hastuti, Hety Dwi Hijriah Fajar Muhammad Insan Ike Yunia Pasa Ikhtiagung, Ganjar Ndaru Ilham Aditya Pangestu Indonesia Isa Bahroni, Isa Jati, Unggul Satria Johanna, Anne Kristiningsih, Ari Laura Sari Lina Puspitasari Linda Perdana Wanti Linda Perdana Wanti Listyaningrum, Rostika Muhammad Nur Faiz Muhammad Ramadani Mulyadi, Rahmat Nafisa, Zahrun Nur Akhlis Sarihidaya Laksana Nur Akhlis Sarihidaya Laksana, Nur Akhlis Sarihidaya Nur Wahyu Rahadi Oman Somantri Perdana Wanti, Linda Prih Diantono Abda`u Purwaningrum, Santi Puspitasari, Lina Radhi Ariawan Rahmat Hidayat Rahmat Mulyadi Ramadani, Muhammad Ratih Hafsarah Maharrani Riyadi Purwanto Roy Aries Permana Tarigan Santi Purwaningrum Sari, Laura Sarihidaya Laksana, Nur Akhlis Sarihidaya, Nur Akhlis Sri Kusumadewi Unggul Satria Jati Wanti, Linda Perdana Wittriansyah, Khoeruddin Wittriansyah, Khoerudin Zahrun Nafisa