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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) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan IJCOSIN : Indonesian Journal of Community Service and Innovation KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Informatika eProceedings of Engineering Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Jnanadharma Transaction on Informatics and Data Science
Yohani Setiya Rafika Nur
Telkom University
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science 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 
EXPERT SYSTEM WITH DEMPSTER-SHAFER METHOD FOR EARLY IDENTIFICATION OF DISEASES DUE TO COMPLICATIONS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME Adanti Wido Paramadini; Dasril Aldo; M. Yoka Fathoni; 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.
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.
Co-Authors Adanti Wido Paramadini Ade Prasetyo, Ade Afifatul Fajri, Nabila Ajeng Ayu Suryani 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 Edelin Gultom Endraswari, Putri Mentari Eryan Ahmad Firdaus Faisal Dharma Adhinata Faiz, M. Hanif Al Fathan, Faizal Burhani Ulil Fau, Andrew Filfimo Yulfiz Ahsanul Hulqi Firmansyah, Muhammad Raafi'u Gusla Nengsih, Yeyi Gusnita Linda Hidayat, Afifah Naurah J. Manurung, Barnes Lina Fatimah Lishobrina Luqman Wahyudi M Yoka Fathoni Maulana, Ihsan Melinda Br Ginting Miftahul Ilmi Muadin, Dika Alim Muhamad Azrino Gustalika Nadia Ayu Isroh Nia Annisa Ferani Tanjung Nur Ghaniaviyanto Ramadhan Nurhaeka Tou Pamuji, Yanuar Ikhsan Paradise Ramadhani, Rima Dias Rania Nur Hikmah Ridho Rahmadi Sa'adah, Aminatus Sahara Sahara Sapta Eka Putra Sulaeman, Gilang Suprapto, Amelia Rut Trihastuti Yuniati Ummi Athiyah Usman, Muhammad Lulu Latif Utami, Annisaa Wahyu Adi Prabowo Wanda Ilham Warto Widya Lelisa Army Yasin, Feri Yehezekiel Ramasyah Putra Haloho Yuan Sa'adati Zahirah, Regina Putri Wanda