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International Journal of Information Engineering and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481902     EISSN : 30481953     DOI : 10.62951
Core Subject : Engineering,
Engineering
The scope of the this Journal covers the fields of Information Engineering and Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 38 Documents
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Implementation of IT Governance in the Design of Expert Systems for Improving Library User Services Muhammad Wahyudi; Darmeli Nasution
International Journal of Information Engineering and Science Vol. 1 No. 4 (2024): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i2.117

Abstract

The integration of IT Governance and expert system design offers transformative benefits for enhancing library user services. This research employs the COBIT 5.0 framework to align IT strategies with library objectives while developing an expert system tailored for personalized recommendations. The findings indicate that the expert system significantly improves operational efficiency, service accuracy, and user satisfaction by using user profiles to recommend relevant materials and streamline the borrowing process. Testing revealed high user satisfaction levels, with 96.6% finding the system effective and 100% confirming its efficiency. Additionally, IT Governance ensures strategic integration between technological infrastructure and service quality objectives, enabling data-driven decision-making. The study also highlights challenges, such as the need for robust data management and user training, suggesting areas for future improvement. Recommendations include incorporating machine learning to enhance system intelligence, conducting regular evaluations to maintain system relevance, and testing the scalability of this approach across various types of libraries. By integrating IT Governance with an expert system, this research sets a strong foundation for modernizing library services to better meet user expectations in the digital era.
Automatic Passenger Counting System on Public Buses Using CNN YOLOv8 Model for Passenger Capacity Optimization Ari Dian Prastyo; Sharfina Andzani Minhalina; Surya Agung; Denty Nirwana Bintang; Muhammad Yordi Septian; Endang Purnama Giri; Gema Parasti Mindara
International Journal of Information Engineering and Science Vol. 1 No. 4 (2024): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i4.121

Abstract

This study presents the development and evaluation of an automatic passenger counting system for public buses using the YOLOv8 algorithm based on Convolutional Neural Networks (CNN). Accurate passenger counting plays a crucial role in optimizing public transportation operations, as it enables effective capacity management, reduces operational costs, and improves overall passenger comfort. Conventional manual counting methods are often inefficient, time-consuming, and prone to human error, particularly in high-density urban transportation environments. Therefore, an automated and intelligent solution is required to support real-time monitoring and operational decision-making. The proposed system employs deep learning-based object detection to identify and count passengers from video streams captured by cameras installed inside buses. Two camera positions, namely front and rear views, were evaluated to assess system performance under different visual conditions. The experimental results show that the system achieves high detection accuracy in the front camera view, with a confidence score of 0.82, indicating reliable performance in scenarios with minimal object occlusion. In contrast, the rear camera view demonstrates slightly lower accuracy, with a confidence score of 0.76, mainly due to increased object overlap and variations in lighting conditions. These findings emphasize the importance of appropriate camera placement and environmental consideration in improving detection reliability. In addition, the implementation of the proposed system enables real-time monitoring of passenger flow, which supports dynamic scheduling, demand-based route planning, and efficient fleet management. Accurate passenger data allows transportation operators to optimize service allocation, reduce congestion, and enhance overall service quality. Overall, this study contributes to the development of intelligent transportation systems by demonstrating the practical applicability of deep learning-based passenger counting solutions. The proposed approach offers strong potential for real-world deployment in smart city environments, supporting the creation of more sustainable, efficient, and passenger-oriented public transportation services.
Parking Slot Scanning for Maximum Efficiency Using Python Faras, Algyon; Andisa, Gany; Nashwandra, Nakula Bintang; Nadhifah, Jauza; Widhiwipati, David Reza; Giri, Endang Purnama; Mindara, Gema Parasti
International Journal of Information Engineering and Science Vol. 2 No. 1 (2025): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i4.122

Abstract

The growing number of vehicles in major cities has posed significant challenges in parking lot management. Motorists often have difficulty finding empty parking slots quickly, which not only wastes time but also aggravates traffic congestion and increases air pollution. This research develops a Python-based smart parking system by utilizing the OpenCV library to detect the status of parking slots in real-time. The system uses a camera as the main sensor and processes the image using techniques such as grayscale, Gaussian blur, and adaptive threshold to identify the parking slot status, whether empty or occupied, with good accuracy. The parking slot coordinate data is stored in CSV format to ensure efficient data management. Experimental results with video recordings show that the system is able to operate well in various parking conditions. The system proved to be cost-effective and easy to implement, making it an ideal solution for parking managers who want to improve management efficiency without being burdened with high costs. This research offers a practical solution to help motorists and parking managers optimize parking space usage, reduce search time, and minimize negative impacts such as congestion and carbon emissions.
Expert System for Autoclave Damage Detection Using the Fuzzy Logic Method Ilham M Rusdiyanto; Sri Arttini Dwi Prasetyowati; Eka Nuryanto Budisusila
International Journal of Information Engineering and Science Vol. 2 No. 1 (2025): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i4.124

Abstract

The reliability of sterilization equipment, such as autoclaves, is essential to ensure patient safety, infection control, and operational continuity in healthcare facilities. Damage or malfunction of autoclaves may disrupt sterilization processes and pose significant risks to medical services. This study aims to develop an expert system for autoclave damage detection using the fuzzy logic method to support faster and more accurate diagnostic decision-making. The proposed system applies fuzzy inference to evaluate the level of damage based on input symptoms provided by users. By handling uncertainty and varying symptom intensities, the fuzzy logic approach enables proportional assessment rather than rigid rule-based classification. The system was designed through knowledge acquisition from technical experts and implemented using fuzzy membership functions and inference rules to determine damage severity levels. Experimental testing was conducted to evaluate system performance and diagnostic accuracy. The results indicate that the expert system successfully generated diagnosis outputs for all tested scenarios, achieving functional diagnostic accuracy within the defined test cases. The system was also able to calculate a quantified damage severity value of 11.6235981% based on the given symptoms, demonstrating its capability to assess damage levels numerically and objectively. Furthermore, the developed system significantly reduces the time required for damage detection compared to manual diagnostic procedures. Automating the evaluation process, it assists electromedical technicians in identifying faults more efficiently and taking preventive or corrective actions promptly. Overall, the implementation of a fuzzy logic-based expert system provides an effective, accurate, and practical solution for improving autoclave maintenance management and supporting healthcare service reliability.
Black Box Testing on the Wingpos Website Using the Equivalence Partitioning Technique Nadhifah, Jauza; Muhammad Al Amin; Capriandika Putra Susanto; Muhammad Galuh Gumelar; Anka Luffi Ramdani; Mindara, Gema Parasti; Wicaksono, Aditya
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i4.128

Abstract

In the digital business environment, web-based Point of Sale (POS) systems play a vital role in supporting transaction processing, inventory management, and operational decision-making. Ensuring the functional reliability of such systems is essential, particularly in critical authentication features that regulate user access. However, undetected functional errors within login and registration modules may disrupt operations, compromise data integrity, and reduce user experience quality. Therefore, this study aims to evaluate the functionality of the Wingpos website, focusing specifically on the login and registration features. The research applies the Black Box Testing approach using the Equivalence Partitioning technique, which enables systematic functional validation by classifying input data into representative valid and invalid partitions without requiring access to source code. The testing process involved designing structured test cases, executing input scenarios, and comparing actual system outputs with expected results. The findings reveal that most authentication processes function in accordance with system specifications, as seven out of ten test scenarios produced expected outcomes. Nevertheless, three discrepancies were identified, including inconsistent error message validation in the login feature and the system’s inability to properly verify invalid email domains during registration. These results indicate that while the system demonstrates general functional reliability, certain validation mechanisms require refinement. In conclusion, the application of Black Box Testing with the Equivalence Partitioning technique proves effective in identifying functional gaps and supporting quality assurance processes in web-based POS systems, particularly in strengthening authentication feature reliability and improving overall system performance
The Role of Evaloexam in Fostering Technological Innovation and Entrepreneurship in Education Ekky Mulia Lasardi; Saniyyah Wafa Nurjihan; Dimas Akbar Tama; Wien Kuntari
International Journal of Information Engineering and Science Vol. 2 No. 1 (2025): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i1.132

Abstract

Innovation in the education sector has become increasingly important alongside the rapid advancement of digital technology, which has enabled the emergence of various platforms and applications designed to enhance teaching and learning quality. One area significantly impacted by this transformation is educational evaluation, where digital solutions offer more efficient, accurate, and data-driven assessment processes. This study aims to identify and analyze the role of the Evaloexam application in driving educational innovation while fostering the development of technological entrepreneurship among its users. The research employed a quantitative methodology using a survey approach involving educators and students at the senior high school (SMA) level who actively utilize the Evaloexam platform. A total of 100 respondents were selected through random sampling techniques, and data were collected over one month using structured online questionnaires. The collected data were analyzed using both descriptive and inferential statistical methods to measure user perceptions, system effectiveness, and innovation impact. The findings indicate that Evaloexam plays a significant role in facilitating educational evaluation processes by streamlining exam administration, automating grading, and providing analytical insights into student performance. These capabilities contribute to improved efficiency, reduced administrative workload, and more objective assessment practices. Furthermore, the application encourages the development of technological entrepreneurship by stimulating user interest in digital product innovation, educational technology development, and technology-based problem solving. The results are consistent with prior studies emphasizing the positive relationship between technology integration and improvements in educational effectiveness and operational efficiency. Practically, this research suggests that Evaloexam and similar digital evaluation platforms hold strong potential for broader implementation within educational systems to support innovation ecosystems and technology-driven learning environments. However, this study is limited by its relatively small sample size, focus on a single application, and short data collection timeframe. Future research is recommended to involve larger and more diverse populations, extended study durations, and comparative analyses across multiple evaluation platforms to obtain more comprehensive findings.
Information System Audit on the Catatmak Application on the Web and Playstore Using the Cobit Framework for Financial Recording : Study Case : Application Note Fariz Nur Fikri Zaki; Putri Awaliatuz Zahra; Vidia Alma Cyrilla; Wahyu Latifatun; Jeffri Prayitno Bangkit Saputra
International Journal of Information Engineering and Science Vol. 2 No. 1 (2025): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i1.135

Abstract

PT Jadi Kaya Raya Bersama, founded in 2024 in Banyumas, Indonesia, focuses on providing reliable financial recording solutions for Micro, Small, and Medium Enterprises (MSMEs) through fintech-based applications. The platform is designed to support transaction recording, financial monitoring, and reporting processes to improve MSME financial management. Despite its significant potential, several technical issues have hindered the application’s performance and service quality. Key problems identified include disruptions in the WhatsApp Bot API, user authentication errors, and the lack of integration with banking systems and digital wallet services. These challenges affect transaction recording accuracy, operational efficiency, and the security of user financial data. To identify the root causes of these issues and propose appropriate solutions, a system audit was conducted using the COBIT framework as a governance and management evaluation tool. The audit process involved assessing system performance, control mechanisms, and IT service management practices. The results indicate that API disruptions were primarily caused by network instability and configuration errors, which led to interruptions in automated transaction recording services. Meanwhile, authentication problems were associated with weak login mechanisms and insufficient identity verification processes. In addition, the application’s inability to integrate with banking and e-money services created limitations in transaction synchronization and reduced overall user convenience. Based on these findings, several strategic recommendations are proposed. These include optimizing API performance, strengthening authentication systems through the implementation of Two-Factor Authentication (2FA), and developing integration capabilities with banking institutions and digital wallet platforms. The implementation of these improvements is expected to enhance system efficiency, data security, and service quality. Ultimately, strengthening the fintech application’s performance will support MSMEs financial management and contribute to sustainable digital economic growth in Indonesia.
Optimizing Shortest Job First (SJF) Scheduling through Random Forest Regression for Accurate Job Execution Time Prediction Aditya Putra Ramdani; Achmad Solichan; Basirudin Ansor; Muhammad Zainudin Al Amin; Nova Christina Sari; Kilala Mahadewi
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.138

Abstract

One of the CPU scheduling methods that is frequently used to reduce waiting time and average execution time is Shortest Job First (SJF). However, this algorithm's accuracy is largelbravy dependent on how well the job execution time is predicted. The purpose of this study is to enhance work execution time estimates by optimizing the SJF algorithm through the use of the Random Forest Regression model. The model in this study is trained using historical job data. The test results demonstrate how Random Forest Regression may be included into SJF to greatly increase system efficiency, especially in terms of throughput and waiting time reduction.
Comparative Investigation of Activity Rendering Utilizing Eevee, Cycles, and Radeon ProRender Procedures in Blender Applications Ira Zulfa; Richasanty Septima; Iryana Rezeki; Rayuwati Rayuwati
International Journal of Information Engineering and Science Vol. 2 No. 1 (2025): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i1.147

Abstract

The rapid development of multimedia technology has significantly advanced 3D animation techniques, enabling the production of high-quality visual content across industries such as film, gaming, architecture, and product visualization. Rendering, as the final stage of the 3D production pipeline, plays a crucial role in determining both visual realism and production efficiency. This study compares the performance of three rendering engines—Eevee, Cycles, and Radeon ProRender—by evaluating rendering speed, visual quality, and memory efficiency in Blender. The objective is to provide practical insights for designers and digital content creators in selecting the most suitable rendering engine based on project requirements. In this research, three identical 3D scenes were rendered using each of the three rendering engines under controlled experimental conditions. The comparison was conducted based on several parameters, including rendering time, output file size, shadow accuracy, lighting effects, and overall visual realism. Quantitative measurements were used to evaluate render speed and memory consumption, while qualitative analysis assessed differences in shadow detail, global illumination behavior, reflection accuracy, and material realism. The results indicate that Eevee outperforms the other engines in terms of rendering speed, making it highly suitable for real-time applications and projects requiring fast previews. Cycles produces the highest level of visual realism due to its physically based path-tracing algorithm, although it requires longer rendering time and higher computational resources. Meanwhile, Radeon ProRender demonstrates competitive performance, particularly in shadow quality and lighting effects, offering a balanced alternative between realism and efficiency. Based on the findings, Blender remains a flexible and effective platform. The choice of rendering engine should depend on whether speed, graphic quality, or memory optimization is prioritized.
Detecting Phishing URLs with CNN - Decision Tree Method Reza Aminullah; Fetty Tri Anggraeny; Fawwaz Ali Akbar
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.222

Abstract

This research focuses on assessing the efficacy of a method that integrates Convolutional Neural Networks (CNN) with Decision Trees for the detection of phishing URLs. Phishing represents a major cyber threat, where cybercriminals attempt to deceive individuals into disclosing sensitive information via fraudulent websites. As the frequency of phishing attacks continues to rise, there is a pressing need for effective detection and prevention strategies. In this investigation, a dataset comprising both phishing and legitimate URLs was utilized to train a CNN-Decision Tree model. The training phase includes feature extraction from URLs using CNN, which excels at identifying intricate patterns within the data, followed by classification through Decision Trees, recognized for their capacity to deliver straightforward and comprehensible interpretations of classification outcomes. The model's performance was evaluated across nine distinct scenarios to assess its effectiveness under varying conditions. The results indicated that the hybrid CNN-Decision Tree model achieved a precision rate of 94%, a recall of 90%, and an F1-Score of 92%, with an overall accuracy of 93%. These findings suggest that the model is not only proficient in identifying phishing URLs but also maintains a commendable balance between precision and recall. This research highlights that the synergy of CNN and Decision Trees can serve as a potent solution for phishing URL detection, significantly contributing to the advancement of enhanced cybersecurity systems.

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