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All Journal Jurnal Energi Dan Manufaktur Mechatronics, Electrical Power, and Vehicular Technology Simetris Jurnal Simetris Transformasi: Jurnal Pengabdian Masyarakat Compiler INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Teknik Mesin Jurnal Informatika dan Rekayasa Elektronik Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknik Informatika (JUTIF) Prima Abdika: Jurnal Pengabdian Masyarakat International Journal for Applied Information Management LOGIC : Jurnal Ilmu Komputer dan Pendidikan Jurnal Ilmiah Informatika dan Komputer Fokus Abdimas The Indonesian Journal of Computer Science
Wahyusari, Retno
Sekolah Tinggi Teknologi Ronggolawe Cepu
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
SYSTEMATIC REVIEW OF EXPERT SYSTEM FOR DETECTING MENTAL HEALTH DISORDERS IN COLLEGE STUDENTS Widyassari, Adhika Pramita; Carreon, Jonathan Rante; Wahyusari, Retno
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4089

Abstract

There is an urgent need to detect and manage mental health disorders among college students, who often face psychological challenges due to academic pressures and significant life changes. In this context, expert systems emerge as a potential tool to assist in the diagnosis and management of mental health problems. The purpose of this study is to present the results of a systematic review of expert systems for detecting mental health disorders in college students through the systematic literature review (SLR) method. By asking four research questions covering types of mental health disorders, methods used, comparisons between methods, and testing techniques, this study limits its review to studies published in the last five years, from 2019 to 2024. This review covers various types of mental health disorders, such as depression, anxiety, stress disorders and other mental health disorders that are often experienced by the college student population. As well as evaluating and comparing methods such as forward chaining, backward chaining, certainty factor and fuzzy logic methods to identify the advantages and disadvantages of each method. Certainty Factor emerged as the most accurate method with an accuracy of 96.09% and the recommendation for combining methods for this study is certainty factor and forward chaining with an accuracy result of 100%. In addition, this study also discusses the testing process to ensure the effectiveness and accuracy of the resulting diagnosis. The findings of this systematic review are expected to provide valuable insights for the development of more effective expert systems in supporting college students' mental health.
Comparative Analysis Of Ant Lion Optimization And Jaya Algorithm For Feature Selection In K-Nearest Neighbor (Knn) Based Electricity Consumption Prediction Wahyusari, Retno; Sunardi, Sunardi; Fadlil, Abdul
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.2025.6.3.4692

Abstract

The increase in demand for electrical energy is in line with increasing population, urbanization, industrial deployment, and technology. Accurate prediction of electrical energy consumption plays an important role in planning, analyzing, and managing electricity systems to ensure sustainable, safe, and economical electricity supply. K-Nearest Neighbors (KNN) is a simple and fast prediction algorithm based on the quality and relevance of the features used. This research proposes to improve the accuracy of energy consumption prediction through feature selection based on metaheuristic algorithms, namely Genetic Algorithm (GA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and Jaya Algorithm (JA). The dataset used is Tetouan City Power Consumption, with a preprocessing process of time feature extraction, min-max scaling normalization, and feature selection. The ALO+KNN and JA+KNN combinations delivered the best and most stable prediction performance, while TLBO+KNN performed poorly. GA+KNN showed the worst overall results among all combinations. The evaluation of model performance was based on RMSE, MAPE, and R² metrics. These findings highlight the importance of selecting a feature selection algorithm that aligns well with the characteristics of the model and dataset to enhance prediction accuracy.
Co-Authors ., Suprawikno Abdul Azis Abdul Fadlil Achmad Furqon Nur Fitriadhi Adhika Pramita Widyassari Agung D.N.P, Muhammad Ahmad Rifa'i Aji, Doni Kurnia Al Hadi, Fahreza Amrozi, M Ali Anang Widianto Aris Rakhmadi Astika AyuningTyas, Astika Ayu, Anggi Mutia Bambang Haryoko Carreon, Jonathan Rante Devi Tiana Kartikasari Dyah Ayu Safitri Aprilia Eka Y, Sandy Pramudya Endang Kurniawati, Endang Esa Adistiya Fajarwati Mahdum Eva Hertnacahyani Herraprastansi Figo SW, Denni Ghifari, Muhammad Al Gunawan, Helmi Hamzah, Fahmi Aliefuddin Hilda Nur Maziyah Korawan, Agus Dwi Kuncoro, Bagus Tri Lastoni Wibowo Lies Indriyatni Masuri, Masuri Mudjijanto, Mudjijanto Muhamad Nur Subkan Muhammad Abdimas Khalifuddin Nabila, Zahara Nagari, Bintang Putra Nugraha, Anung Satria Putro, Dwi Purnomo Rahmasari, Nagita Ratna Dwi Rahayu Rochmadi, Tri Safiq Rosad Salsabilla, Dea Sarmini Setiawan, David Harry Shofia, Aina Sulistiyo, Yogi Sunardi Sunardi suryani, puput eka Suryanto, Hendri Suryanto, Hendri Sushananto W, Denni Figo Teguh Adi Saputro Teguh Yuwono Teguh Yuwono Wiranti, Dwiana Asih Wulan Sutriyani