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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 63 Documents
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Integration of Virtual Reality and Haptic Feedback for Realistic Training Simulations Andhika Bintang Pramadya, Yohanes
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 3 (2024): July: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Training in critical sectors like healthcare and engineering demands high realism, which conventional methods often fail to provide due to significant cost and safety risks. Virtual Reality (VR) offers an immersive solution but suffers from a fundamental limitation: the lack of physical touch. This research addresses this problem by designing, implementing, and evaluating an integrated simulation system combining VR with high-fidelity haptic feedback. The primary objective was to create a realistic training platform and quantitatively measure its effectiveness in enhancing practical skill acquisition. The research applied a Research and Development (R&D) methodology to build a prototype simulation in Unity 3D. A key feature is the decoupled system architecture, which runs a high-frequency haptic loop (at 1000 Hz) independently from the visual loop (at 90 Hz) to ensure stability. A proxy-based force rendering algorithm based on Hooke’s Law (F=k*d) was implemented to simulate realistic material resistance. System effectiveness was validated through a pre-test/post-test control group experiment (N=30). The experimental group using the VR-Haptic system showed a significant improvement in procedural accuracy (p < .05) and a 28% reduction in task completion time compared to the control group. User questionnaires also confirmed a high degree of perceived realism and immersion. This study concludes that an integrated, high-frequency visuo-haptic architecture is an effective and necessary solution for developing next-generation realistic training simulators.
Optimizing Sustainable Aquaculture via Internet of Things and Machine Learning Raffi Darrell Firmansyah
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

This research aims to design and build an integrated system utilizing the Internet of Things (IoT) and Machine Learning (ML) for the optimization of sustainable aquaculture. The primary objective is to address key aquaculture challenges, including unstable water quality, feed inefficiency, and slow disease detection. The research design involves a real-time monitoring system using IoT sensors (pH, temperature, and dissolved oxygen) connected to an ESP32 microcontroller. The methodology consists of data collection from these sensors, which is then analyzed using machine learning algorithms: Linear Regression to predict water quality and a Decision Tree to classify fish health. The main outcomes show the system successfully monitors water quality in real-time. The Linear Regression model achieved a low Mean Squared Error (MSE) of 0.042 for predictions, and the Decision Tree model achieved a 93.7% accuracy in classifying fish health conditions. The conclusion is that this system is proven to be an effective decision support tool for enhancing the productivity and sustainability of aquaculture.
Ai-Based Road Performance Prediction for Supporting Smart Infrastructure Maintenance Pane, Muhammad Syahrul
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

This research aims to develop an artificial intelligence-based road performance prediction system to support smart infrastructure maintenance. Current road maintenance systems are still traditional and reactive, leading to infrastructure degradation and high repair costs. This study uses AI methods combining Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) to analyze road condition data, traffic volume, and weather conditions. ANN is effective in detecting nonlinear patterns from statistical data, while LSTM excels in processing time-series data of historical road conditions. The system is designed using UML modeling and implements a relational database for storing road, traffic, weather, and prediction data. Based on the analysis, the proposed system successfully provides a predictive maintenance solution that is proactive rather than reactive. The system's performance demonstrates that AI-based predictions can extend road service life, optimize maintenance budget allocation, and minimize public service disruptions. However, prediction accuracy is still influenced by factors such as data quality and model parameter selection.

Page 7 of 7 | Total Record : 63

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