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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) 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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Fuzzy Expert System for Decission Support to Diagnosis Leukemia Linda Perdana Wanti; Nur Wachid Adi Prasetya; Zahrun Nafisa; Rahmat Mulyadi; Muhammad Ramadani
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2349

Abstract

Leukemia is a cancer of the blood and bone marrow. In leukemia, the bone marrow produces too many abnormal white blood cells. These abnormal cells cannot fight infections well and can displace healthy blood cells, which can cause anemia and bleeding. In this study, a fuzzy method will be implemented to diagnose leukemia and the results will later be compared with expert diagnoses. Fuzzy logic was chosen because it allows for degrees of truth between 0 (completely false) and 1 (completely true) and it is suitable for situations where human expertise relies on experience and judgment rather than fixed rules. Fuzzy systems can analyze large amounts of data quickly, thereby accelerating the diagnosis and decision-making process, especially when used in medical decision support systems. This study produced a leukemia diagnosis accuracy of 88.83% when compared with the results of expert diagnoses using the same symptom and sample data.
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 Aditya Saputra, Muhammad Bintang Agus Susanto Alif Iftitah Alif Iftitah Andesita Prihantara Andriansyah Zakaria Andriansyah Zakaria Annas Setiawan Prabowo Annisa Romadloni Awaludin, Ihza 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 Nafisa, Zahrun 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 Wanti, Linda Perdana Wittriansyah, Khoeruddin Wittriansyah, Khoerudin Zahrun Nafisa