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International Journal of Computer Science and Humanitarian AI
Published by Universitas Bina Nusantara
ISSN : 30644372     EISSN : -     DOI : https://doi.org/10.21512/ijcshai.v2i2.14418
Core Subject : Humanities, Science, Engineering,
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
International Journal of Computer Science and Humanitarian AI (IJCSHAI) is an international journal published biannually in February and October. The Journal focuses on various issues: Computer Science, Artificial Intelligence (AI), Fuzzy Systems, Expert Systems, Geo-AI, Machine Learning, Deep Learning, Humanitarian AI, Data Science, Computer Vision, Natural Language Processing (NLP), Information Systems, Psychoinformatics, Computational Intelligence, Recommender Systems, Robotics, Robot Vision and Control Systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 20 Documents
 1   2 
The Framework of Vehicle Detection and Counting System for Handling of Toll Road Congestion using YOLOv8 Widodo Budiharto; Heri Ngarianto
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.13020

Abstract

The Global COVID-19 pandemic and the increasing number of vehicles have exacerbated traffic congestion, particularly in developing countries. In Jakarta, Indonesia, congestion on toll roads is a significant issue that needs to be addressed through an Intelligent Transportation System (ITS). One of the key solutions proposed is vehicle detection and traffic prediction on toll roads. This study introduces a computer vision-based approach utilizing YOLOv8 to detect, track, and count vehicles to predict traffic congestion. The system operates by identifying vehicles (cars and trucks), preprocessing the data, and calculating the total number of vehicles within the camera’s range. If the vehicle count surpasses the threshold set by the toll road provider, the system updates the traffic status (normal or congested) and triggers a warning. The vehicle detection system can identify cars and trucks within a range of up to 150 meters. Experimental results using test videos demonstrate that the YOLOv8-based system achieves an accuracy of 98% with an average detection speed of 83.6 milliseconds, ensuring highly efficient performance. With its high accuracy and speed, this system can be effectively integrated into traffic management solutions to alleviate congestion and enhance transportation efficiency in Jakarta.
A Literature Review on AI and DSS for Resilient and Sustainable Humanitarian Logistics Maria Loura Christhia; Olivia Oktariska Timbayo; Ahmad Ardi Wahidurrijal; Abimanyu Bagarela Anjaya Putra
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.13028

Abstract

Disaster response is a critical component of disaster management, requiring effective strategies to reduce exposure and vulnerability to hazards. Rising global temperatures and extreme weather events have intensified the need for adaptive disaster relief systems. Humanitarian logistics, a vital subset of the supply chain, plays a central role in disaster preparedness, response, and recovery phases but often faces challenges such as resource constraints, inefficient communication, and  unpredictable  crises.  This  study  employs a systematic literature review (SLR) using the PRISMA methodology to explore the application of Artificial Intelligence (AI) and Decision Support Systems (DSS) in humanitarian logistics from 2019 to 2024. SCOPUS served as the primary database, identifying 1,171 documents, with 52 studies selected for in-depth analysis. These studies highlight the potential of AI techniques, including machine learning and clustering algorithms, and DSS implementations for resource allocation, stakeholder coordination, and real-time decision- making. Findings demonstrate that integrating AI and DSS can optimize emergency vehicle routing, improve relief distribution, and enhance stakeholder collaboration. Advanced technologies such as Radio Frequency Identification (RFID), the Internet of Things (IoT), and Digital Twins improve logistics efficiency and scalability. Despite these advancements, challenges like data integration and algorithmic reliability persist. The study recommends prioritizing transparent systems, hybrid simulations, and addressing algorithmic constraints to advance disaster management practices.
Editorial, Foreword, and Table of Content Widodo Budiharto
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

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Abstract

The Utilization of Generative AI in Designing Data Analytics and Visualization Workshop (Case Study: GDGoC at Universitas Negeri Malang) Refiana Andiyah; Ence Surahman; Herlina Ike Oktaviani
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.13519

Abstract

This study explores the use of Generative Artificial Intelligence (Generative AI) in designing a Data Analytics and Visualization workshop within the Google Developer Groups on Campus (GDGoC) community at the State University of Malang. Employing a qualitative case study approach, data were gathered through in-depth interviews, observations, and document analysis involving key informants directly engaged in the planning and execution of the workshop. The findings reveal that Generative AI significantly enhanced the efficiency, effectiveness, and quality of the workshop program. The technology was utilized across various stages, from conceptualizing the event and gathering references to preparing presentation materials. Respondents noted that Generative AI facilitated faster and more systematic material organization, supporting prior research on its ability to improve productivity and efficiency in educational settings. Nevertheless, the study also identified challenges, including reliance on AI, difficulties in generating appropriate prompts, and the necessity of validating AI-generated content. In the context of Human-Computer Interaction (HCI), Generative AI was perceived to offer favorable usability and user experience, although adequate digital literacy is essential to ensure its ethical and effective use. In conclusion, Generative AI presents considerable potential as a tool for developing training programs, with human involvement remaining critical to ensure the relevance and accuracy of the generated information.
Developing Intelligent GeoDashboard Platform for the Downstream of Nickel, Bauxite, Cobalt, and Silica: Systematic Literature Review Andrea Sutanto; Raditya Tamam; Alexander Agung Santoso Gunawan
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.14415

Abstract

Indonesia possesses abundant natural resources, including nickel, bauxite, cobalt, and silica, which are essential for industries such as battery production, construction, and green technology. To maximize their economic value, the Indonesian government has implemented downstream policies that require domestic processing before export. Effective resource management is crucial for the success of these policies and the national economy. This study conducts a systematic literature review to examine how downstream policies are implemented in different countries (RQ1), analyze cases of downstream disputes and their solutions (RQ2), and explore the impact of technology and Global Value Chains (GVCs) on these policies (RQ3). A structured methodology is used to collect and analyze relevant literature, highlighting best practices and key challenges. Findings show that countries with strong regulations and technological innovation achieve better downstream outcomes. Past disputes reveal the need for strategic policymaking and technological adaptation to avoid risks. In this context, the PetaHilirisasi platform offers a smart solution by integrating geospatial technology and artificial intelligence to monitor and manage mineral resources efficiently. This platform helps optimize downstream processes, improve operational efficiency, and reduce environmental impact. PetaHilirisasi demonstrates the potential of digital solutions in strengthening Indonesia’s downstream sector. By leveraging technology, Indonesia can enhance the value of its natural resources while promoting sustainable development in the mineral industry,
Implementation of Random Forest Algorithm in Handling Imbalanced Data: A Study on Default Models and Hyperparameter Tuning Ivan William Lianata; Kang Nicholas Darren Nugroho; Yosua Nathanael; Neilson Christopher; Edy Irwansyah
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.14417

Abstract

The healthcare industry has benefited greatly from the quick development of artificial intelligence, especially machine learning (ML). Unbalanced data is a significant problem in medical classification, as it can impair model performance, particularly when it comes to identifying important minority classes like patients with particular diseases. The purpose of this research is to evaluate how well two ensemble-based algorithms—Random Forest and Gradient Boosting—perform when dealing with data imbalance in diabetes prediction. Age, body mass index, smoking history, HbA1c level, blood glucose level, and other demographic and medical variables are included in the dataset, which was acquired from Kaggle. Data preprocessing, train-test splitting, model implementation with default parameters, and hyperparameter tuning with Grid Search and Cross Validation comprise the methodology. Accuracy, precision, recall, F1-score, and AUC-ROC metrics were used to assess the model's performance. Both models achieved high accuracy above 97%, according to the results. Following tuning, Random Forest achieved 97.06% accuracy, 0.974 AUC, and 0.99 positive-class precision; however, recall somewhat declined, possibly resulting in underdiagnosis. Gradient Boosting, on the other hand, showed consistent performance with an AUC of 0.9791 and an F1-score of 0.81. These results demonstrate that model performance can be enhanced by hyperparameter tuning; however, algorithm selection should be based on the needs of the application, especially in medical settings where striking a balance between sensitivity and diagnostic precision is crucial.
Design and Implementation of Chatbot Pancasila for Teaching Pancasila and Character Building for University’s Students Karina Dwinovera Mulia; Widodo Budiharto; Heri Ngarianto; Frederikus Fios
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.14418

Abstract

In the modern era, where global influences are rapidly shaping young minds, the need to maintain strong national identity and moral values is more crucial than ever. For Indonesian students, Pancasila education and character building play a central role in developing not only academic competence but also personal integrity and social responsibility.  The design approach involved analyzing student needs, integrating Pancasila-based content, as well as applying automated learning algorithms so that the chatbot can provide answers that match the context of the conversation especially Character-Building Pancasila topics. This Pancasila Character Building Chatbot is designed with Natural Language Processing (NLP) technology to provide creative, interactive, and interesting learning experiences that are easily understood by students. Implementation is done through a digital platform that allows students to interact in real-time in understanding the values of Pancasila, such as divinity, humanity, unity, democracy, and social justice. Furthermore, this innovation aims to bridge the gap between traditional moral education and modern digital learning methods. By utilizing artificial intelligence, the chatbot can adapt to different learning styles and provide personalized feedback to each student. It is hoped that the presence of Chatbot Character Building Pancasila can increase the enthusiasm for learning and curiosity of students so that learning character building Pancasila is more creative and interesting for students in university classrooms.
Adaptive Gradient Compression: An Information-Theoretic Analysis of Entropy and Fisher-Based Learning Dynamics Hidayaturrahman Hidayaturrahman
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.14533

Abstract

Deep neural networks require intensive computation and communication due to the large volume of gradient updates exchanged during training. This paper investigates Adaptive Gradient Compression (AGC), an information-theoretic framework that reduces redundant gradients while preserving learning stability. Two independent compression mechanisms are analyzed: an entropy-based scheme, which filters gradients with low informational uncertainty, and a Fisher-based scheme, which prunes gradients with low sensitivity to the loss curvature. Both approaches are evaluated on the CIFAR-10 dataset using a ResNet-18 model under identical hyperparameter settings. Results show that entropy-guided compression achieves a 33.8× reduction in gradient density with only a 4.4% decrease in test accuracy, while Fisher-based compression attains 14.3× reduction and smoother convergence behavior. Despite modest increases in per-iteration latency, both methods maintain stable training and demonstrate that gradient redundancy can be systematically controlled through information metrics. These findings highlight a new pathway toward information-aware optimization, where learning efficiency is governed by the informational relevance of gradients rather than their magnitude alone. Furthermore, this study emphasizes the practical significance of integrating information theory into deep learning optimization. By selectively transmitting gradients that carry higher information content, AGC effectively mitigates communication bottlenecks in distributed training environments. Experimental analyses further reveal that adaptive compression dynamically adjusts to training dynamics, providing robustness across various learning stages. The proposed framework can thus serve as a foundation for developing future low-overhead optimization methods that balance accuracy, stability, and efficiency, and crucial aspects for large-scale deep learning deployments in edge and cloud computing contexts.
Obstacle Avoidance Method using Stereo Camera for Autonomous Robot Nabeel Kahlil Maulana; Widodo Budiharto; Hanis Amalia Saputri
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i2.14617

Abstract

This paper presents the development and implementation of an obstacle avoidance system for an autonomous robot using a stereo camera setup. The system enables the robot to navigate its environment safely by identifying obstacles and making real-time movement decisions based on depth perception. The stereo vision configuration allows the robot to estimate distances through disparity computation and polynomial linear regression modeling. The proposed algorithm performs stereo matching, image rectification, and depth estimation to generate disparity maps representing obstacle distances. The robot uses this information to figure out if the items it sees are close, medium, or far away, and then it chooses the right move, such stopping, turning left, or turning right. The robot can find and avoid obstacles in different indoor settings, as shown by the experimental findings. The regression model employed for depth estimation attained a high degree of accuracy, evidenced by a R² value of 0.97 and a minimal mean absolute error, signifying robust reliability in distance prediction. The research validates that the amalgamation of stereo vision with regression-based distance estimate yields a resilient and economical method for autonomous navigation. This study advances the ongoing evolution of intelligent robotic systems that can execute autonomous decision-making with limited human oversight
Editorial, Foreword, and Table of Content Widodo Budiharto
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 2 (2025): IJCSHAI
Publisher : Bina Nusantara University

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Abstract

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