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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sains Informatika Terapan (JSIT)
Siti Aisyah
Universitas Insan Cita Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Fuzzy Steering Control For Wall-Following Behavior Of A Mobile Robot In Webots Simulation Danu Jaya Saputro; Siti Aisyah
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.1001

Abstract

Navigation logic plays a crucial role in enabling mobile robots to move autonomously and safely within structured environments. This study presents the development and simulation of a wall-following mobile robot designed to navigate along predefined boundaries while avoiding frontal obstacles. The simulation is implemented in the Webots environment, where the robot employs infrared proximity sensors integrated with a fuzzy logic–based control algorithm. Crisp distance measurements obtained from sensors PS5, PS6, and PS7 are fuzzified into linguistic variables, namely Near, Medium, and Far. Based on the defined input and output membership functions, the fuzzy controller determines appropriate steering actions, including strong turning and forward motion. The proposed approach evaluates the relationship between proximity sensor thresholds and the resulting steering velocity control, demonstrating the effectiveness of fuzzy inference in regulating wall-following behavior.
Identification of Mental Health for Generation Z Using Machine Learning Algorithm Sri Retnowati; Siti Aisyah
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Mental health issues such as stress, anxiety, and trauma have become significant challenges, particularly among Generation Z. The lack of effective early detection tools has hindered efforts to address these problems promptly and accurately. This study aims to develop a machine learning-based classification model to detect potential mental health conditions using standardized psychological instruments: DASS-21, STAI, and ACE. Data were collected from 733 youths aged 17–24, of whom 212 exhibited signs of risk. After cleaning and preprocessing, 58 features were retained from the initial 92. Several machine learning models such as Logistic Regression, Support Vector Machine (SVM), and Random Forest were evaluated using class balancing techniques including SMOTE and class weighting. Evaluation metrics are included accuracy, recall, precision, F1-score, and ROC AUC. Logistic regression achieved the highest performance, with 94% accuracy, 100% recall, 82% precision, and an F1-score of 0.90. The ROC AUC reached 99.5%, indicating excellent discriminative ability. This research highlights the effectiveness of machine learning for early detection of mental health conditions and supports its integration into scalable, technology-based mental health screening tools, particularly for at-risk youth populations.
Co-Authors Danu Jaya Saputro Sri Retnowati