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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Journal of Information Systems Engineering and Business Intelligence JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal E-Komtek JIKA (Jurnal Informatika) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Teknika JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan IJCOSIN : Indonesian Journal of Community Service and Innovation KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Informatika Jurnal Masyarakat Madani Indonesia eProceedings of Engineering Jurnal Informatika: Jurnal Pengembangan IT Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Jnanadharma Transaction on Informatics and Data Science Jurnal Teknokes KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Yohani Setiya Rafika Nur
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Optimization Of Extreme Learning Machine Models Using Metaheuristic Approaches For Diabetes Classification Sulaeman, Gilang; Nur, Yohani Setiya Rafika Nur; Paramadini, Adanti Wido; Aldo, Dasril; Fathoni, M. Yoka
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4690

Abstract

Proper classification of diabetes is a significant challenge in contemporary healthcare, especially related to early detection and clinical decision support systems. This study aims to optimize the Extreme Learning Machine (ELM) model with a metaheuristic approach to improve performance in diabetes classification. The data used was an open dataset containing the patient's medical attributes, such as age, gender, smoking status, body mass index, blood glucose level, and HbA1c. The initial process includes data cleansing, one-hot coding for categorical features, MinMax normalization, and unbalanced data handling with SMOTE. The ELM model was tested with four activation functions (Sigmoid, ReLU, Tanh, and RBF) each combined with three metaheuristic optimization strategies, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm. The results of the evaluation showed that the combination of the Tanh activation function with GA optimization obtained the highest accuracy of 87.98% and an F1-score of 0.5489. Overall, GA optimization appears to be superior to all other measurement configurations in consistent classification performance. The main contribution of this study is to offer a systematic approach to select the best combination of activation functions and optimization algorithms in ELM, as well as to provide empirical evidence to support the application of metaheuristic strategies to improve the accuracy of disease classification based on health data. This research has direct implications for the development of a more precise and data-based medical diagnostic classification system for diabetes.
EXPERT SYSTEM WITH DEMPSTER-SHAFER METHOD FOR EARLY IDENTIFICATION OF DISEASES DUE TO COMPLICATIONS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME Wido Paramadini, Adanti; Dasril Aldo; Yoka Fathoni, M.; Yohani Setiya Rafika Nur; Dading Qolbu Adi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2021

Abstract

Systemic Inflammatory Response Syndrome (SIRS) is a generalized inflammatory condition, triggered by various factors such as infection or trauma, which can lead to serious complications if not treated quickly. This condition is characterized by symptoms such as fever or hypothermia, tachycardia, tachypnea, and changes in white blood cell count. Complications that can arise from SIRS include Acute Respiratory Distress Syndrome (ARDS), which results in fluid in the alveoli and requires mechanical ventilation; acute encephalopathy, which leads to brain dysfunction; Asidosis Metabolik, indicating liver damage; hemolysis, which results in the breakdown of red blood cells; and Deep Vein Thrombosis (DVT), which is at risk of causing pulmonary embolism. To overcome this diagnostic challenge, this study implements the Dempster-Shafer method in an expert system, where it allows the aggregation and combination of various sources of evidence to produce degrees of belief and degrees of plausibility for each diagnostic hypothesis. By accounting for uncertainties and contradictions in the data, the system improves diagnostic accuracy through dynamically weighting and updating beliefs based on available evidence. This process allows early and accurate identification of SIRS complications, supporting appropriate medical intervention. System evaluation showed diagnostic accuracy of 93%, confirming the potential of expert systems in supporting rapid and precise clinical decision-making in managing SIRS complications.
Expert System for Diagnosing Autoimmune Diseases Using Dempster–Shafer and Fuzzy Logic: A Case Study of Prof. Dr. Margono Soekarjo Regional Hospital Rahmadani, Ragil Putri; Nur, Yohani Setiya Rafika; Utami, Annisaa
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5585

Abstract

Autoimmune diseases, particularly lupus, pose a major challenge in healthcare because their symptoms are highly variable and often mimic other medical conditions. Delayed diagnosis can worsen patient outcomes, increase the risk of severe complications, and even lead to death, especially in healthcare facilities with limited autoimmune subspecialists, such as Prof. Dr. Margono Soekarjo Regional Hospital. This study aims to develop a web-based expert system to support early screening for lupus by combining the Fuzzy Tsukamoto method and the Dempster-Shafer theory. The Fuzzy Tsukamoto method is used to represent symptom uncertainty through fuzzification, while the Dempster-Shafer theory is used to combine evidence from individual symptoms to produce confidence levels for possible diagnoses. The research process included a literature review, expert interviews, construction of a symptom–disease knowledge base, design of fuzzy rules, implementation of mass function calculations, and development of a web-based diagnostic application. Testing was conducted using ten patient test cases with confirmed expert diagnoses. The test results showed an accuracy of 100%, with all system diagnoses matching the experts’ diagnoses. The strength of this research lies in the integration of two inference methods to improve the accuracy of evidence calculation, and in the use of symptom uniqueness and occurrence parameters that were validated directly by experts. This system has the potential to serve as an effective early screening tool for healthcare providers and patients, particularly in resource-limited settings. From an informatics perspective, this study contributes to the development of intelligent decision support systems by demonstrating the effectiveness of a hybrid reasoning approach in handling uncertainty in medical diagnosis. The integration of Fuzzy Tsukamoto and Dempster–Shafer methods enhances diagnostic consistency and reliability, making the proposed system relevant for research in expert systems and medical informatics.
Co-Authors Adanti Wido Paramadini Ade Prasetyo, Ade Adriano, Riftian Dimas Afifatul Fajri, Nabila Ajeng Dyah Kurniawati Al Faiz, M. Hanif Alfonsus Simbolon Alika, Shintia Dwi Amalia Beladinna Arifa Aminatus Sa’adah Andre Citro Febriliyan Lanyak Audrey Hillary Auliya Burhanuddin Azmi, Wifqi Wifakul Bachrul Restu Bagja Bidayatul Masulah Bita Parga Zen Christantie Effendy Christian Tambunan, Gerry Claudio Felle, Roland Dading Qolbu Adi Dasril Aldo Dedi Rahman Habibie Dedy Agung Prabowo Deni Romadan, Muhamad Dwi Putro Wicaksono, Aditya Dyah Kurniawati, Ajeng Edelin Gultom Endraswari, Putri Mentari Eryan Ahmad Firdaus Faisal Dharma Adhinata Faiz, M. Hanif Al Faizah Faizah Fathan, Faizal Burhani Ulil Fau, Andrew Filfimo Yulfiz Ahsanul Hulqi Fiqrian, Muhammad Nafal Firmansyah, Muhammad Raafi'u Gusla Nengsih, Yeyi Gusnita Linda Harald Riandi Rantetana Purukan Hasan, Faiz Hidayat, Afifah Naurah Imam Ghozali J. Manurung, Barnes Kristanto, Joshua Putra Fesha Lina Fatimah Lishobrina Luqman Wahyudi M Yoka Fathoni Maulana, Ihsan Maulana, ⁠Ihsan Melinda Br Ginting Miftahul Ilmi Muadin, Dika Alim Muhamad Azrino Gustalika Muhammad Nazmi Al Faiz Muhammad Zaky Mubarok Nadia Ayu Isroh Nia Annisa Ferani Tanjung Nur Ghaniaviyanto Ramadhan Nurhaeka Tou Nurul Latifasari Pamuji, Yanuar Ikhsan Paradise Rahmadani, Ragil Putri Ramadhani, Rima Dias Rania Nur Hikmah Rianto Putra, Frederick Ridho Rahmadi Sa'adah, Aminatus Sahara Sahara Sapta Eka Putra Sulaeman, Gilang Suprapto, Amelia Rut Suryani, Ajeng Ayu Syahputra, Dio Trihastuti Yuniati Ulumiddiin, Ichya Ummi Athiyah Usman, Muhammad Lulu Latif Utami, Annisaa Wahyu Adi Prabowo Wanda Ilham Warto Widya Lelisa Army Yasin, Feri Yehezekiel Ramasyah Putra Haloho Yoka Fathoni, M. Yuan Sa'adati Zahirah, Regina Putri Wanda