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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Infotekmesin JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Journal of Community Development Jurnal Abdi Panca Marga Journal of Multimedia Trend and Technology UNNES International Conference on ELTLT Pengabdian Jurnal Abdimas Damhil: Jurnal Pengabdian kepada Masyarakat
Wanti, Linda Perdana
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Journal : Infotekmesin

 1 
Comparison of The Dempster Shafer Method and Bayes' Theorem in The Detection of Inflammatory Bowel Disease Wanti, Linda Perdana; Adi Prasetya, Nur Wachid; Somantri, Oman
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1797

Abstract

This study discusses the comparison of the Dempster-Shafer method and Bayes' theorem in the process of early detection of inflammatory bowel disease. Inflammatory bowel disease, better known as intestinal inflammation, attacks the digestive tract in the form of irritation, chronic inflammation, and injuries to the digestive tract. Early signs of inflammatory bowel disease include excess abdominal pain, blood when passing stools, acute diarrhea, weight loss, and fatigue. The Dempster-Shafer method is a method that produces an accurate diagnosis of uncertainty caused by adding or reducing information about the symptoms of a disease. Meanwhile, Bayes' theorem explains the probability of an event based on the factors that may be related to the event. This study aims to measure the accuracy of disease detection using the Dempster-Shafer method compared to the probability of occurrence of the disease using Bayes' theorem. The results of calculating the level of accuracy show that the Bayes Theorem method is better at predicting inflammatory bowel disease with a probability of occurrence of disease in the tested data of 75.9%.
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.
Support Vector Machine (SVM) - Based Optimization of Leukemia Cell Image Classification Wanti, Linda Perdana; Romadloni, Annisa; Muhammad, Kukuh; Supriyono, Abdul Rohman
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.v17i1.2974

Abstract

Leukemia is a type of blood cancer characterized by the uncontrolled proliferation of abnormal white blood cells that originate from the bone marrow. Early detection of leukemia poses a significant challenge in the medical field, as the conventional diagnostic process still relies on manual microscopic observation by hematologists, which is time-consuming and prone to subjective errors. This study aims to analyze the potential of the Support Vector Machine (SVM) algorithm in optimizing the classification of leukemia cell images based on morphological and texture features extracted from microscopic images. The test results show that the SVM model with the RBF kernel provides the best performance with an accuracy of 96.4%, a precision of 95.8%, a recall of 96.1%, and an F1-score of 96.0%, surpassing the results of linear and polynomial kernels. The analysis shows that the use of a combination of shape and texture features has a significant effect on improving classification accuracy.
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 Annisa Romadloni Awaludin, Ihza Dwi Krisbiantoro, Dwi Dwi Kumara, Dimas Maulana Eka Tripustikasari, Eka Fandy Setyo Utomo Furi, Syamaidzar Nadifa Girawan, Bayu Aji Hastuti, Hety Dwi Ikhtiagung, Ganjar Ndaru Isa Bahroni, Isa Jati, Unggul Satria Johanna, Anne Karyati, Titin Kholia, Renanda Sinta Nurul Kristiningsih, Ari Listyaningrum, Rostika Muhammad, Kukuh Nafisa, Zahrun Nur Akhlis Sarihidaya Laksana, Nur Akhlis Sarihidaya Nur Wachid Adi Prasetya Oman Somantri Prihantoro, Andesita Purwaningrum, Santi Radhi Ariawan Rahmat Hidayat Ramadani, Muhammad Riyadi Purwanto Roy Aries Permana Tarigan Rujianto Eko Saputro Sari, Laura Sarihidaya Laksana, Nur Akhlis Sarihidaya, Nur Akhlis Supriyono, Abdul Rohman Wittriansyah, Khoeruddin Wittriansyah, Khoerudin