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Triyuni Puspita Dewi
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JOKI: Journal of Computing and Informatics
Published by Laskar Karya
ISSN : -     EISSN : 30635535     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
JOKI: Journal of Computing and Informatics is a peer-reviewed journal in the field of informatics. This journal is published twice a year (June and December) by the Laskar Karya in Probolinggo , East Java. Manuscripts submitted by authors undergo a double-blind review process. Accepted papers are published both online and in print. JOKI publishes original papers in the field of informatics which include but are not limited to: Computer Vision, Software Engineering, Natural Language Processing, Human-Machine Interface, Next Network Generation, IT Governance, Information Search Engine, Multimedia Security, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Computer Network Security, Business Process, Cognitive Systems, Programming Methodology and Paradigm, Data Engineering, Knowledge Based Management System, Knowledge Discovery in Data, Digital Signal Processing, Stochastic Systems, Information Theory, Intelligent Systems, Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 27 Documents
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Design and Development of a Unity 2D Math Educational Game for Elementary Students’ Arithmetic Skills Moh Shiddiq Windiarto; Ahmed Mahdi Jubair
JOKI: Jurnal Komputasi dan Informatika Vol 2 No 2 (2025): Desember 2025
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v2i2.208

Abstract

Mathematics learning at the elementary school level often faces challenges due to students’ low motivation and difficulties in understanding abstract arithmetic concepts. Conventional teaching methods that rely heavily on textbooks and lectures tend to make students less engaged in learning activities. Therefore, an interactive and enjoyable learning medium is needed to support students in understanding basic arithmetic operations. This study aims to develop a Unity 2D–based math educational game as an alternative learning medium to improve elementary students’ arithmetic skills and learning motivation. The research employed a research and development approach using the Extreme Programming model, which consists of planning, design, coding, and testing stages. Data were collected through observation, interviews, and documentation to identify user needs and system requirements. The developed game integrates arithmetic materials such as addition, subtraction, multiplication, and division into interactive gameplay supported by visual elements, scoring systems, levels, and immediate feedback mechanisms. System testing was conducted using black-box testing to evaluate functionality and usability testing to measure user satisfaction. The results indicate that the developed educational game operates properly and provides an engaging learning experience for students. The implementation of gamification elements successfully increases students’ motivation and supports their understanding of arithmetic concepts. This study concludes that the Unity 2D–based math educational game can serve as an effective interactive learning medium for improving arithmetic skills and supporting technology-based learning in elementary education.
Development of a Web-Based Student Registration System Using Laravel Framework Faikatul Himmah; Mohamed Salah Laouar
JOKI: Jurnal Komputasi dan Informatika Vol 2 No 2 (2025): Desember 2025
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v2i2.218

Abstract

The manual student registration process at TPQ Madinah Baitis Salam Foundation caused administrative inefficiencies, including paper accumulation, delayed data processing, and increased risk of data entry errors. These limitations reduced service quality and hindered effective data management. This study aimed to develop a web-based student registration system to digitalize and streamline the admission process. The system was developed using the Laravel framework with a structured and iterative development approach. Data were collected through direct observation and interviews to identify functional and operational requirements. The system integrates online registration, user authentication, student data management, payment verification, and administrative reporting within a centralized database platform. Functional testing using black-box methods confirmed that all features operated according to system requirements. In addition, a User Satisfaction Test was conducted to evaluate system usability and performance, resulting in an overall satisfaction rate of 90%, categorized as very satisfied. The findings indicate that the developed system effectively improves administrative efficiency, enhances data accuracy, reduces paper-based processes, and increases accessibility for users. The system provides a scalable digital solution suitable for non-formal educational institutions seeking to modernize their admission management processes. 
Web-Based Rice Leaf Disease Classification Using CNN Dian Widiarti; Olabode D. Ibini
JOKI: Jurnal Komputasi dan Informatika Vol 3 No 1 (2026): June 2026
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v3i1.374

Abstract

Rice productivity is significantly affected by leaf diseases that reduce crop yield and quality. Conventional disease identification methods rely on manual observation, which is often time-consuming, subjective, and prone to misclassification due to similarities in visual symptoms. This study proposes an automated image-based classification system to detect rice leaf diseases accurately and efficiently. The system utilizes a deep learning model based on convolutional neural networks to classify rice leaf images into three disease categories: neck blast, leaf blight, and rice hispa. A dataset consisting of 3,631 images was used, with 80% allocated for training, 10% for validation, and 10% for testing. Image preprocessing techniques, including resizing, normalization, and augmentation, were applied to improve model performance and generalization. The experimental results show that the proposed model achieved a testing accuracy of 97.80%, with high precision, recall, and F1-score across all classes. The trained model was then deployed into a web-based system that enables users to upload images and obtain real-time classification results. The findings demonstrate that the proposed system provides a reliable and practical solution for early disease detection, supporting precision agriculture and improving decision-making for farmers. The system also offers potential for further development into mobile and integrated smart farming platforms.
Web-Based Decision Support System for Internship Placement Recommendations Arif Faizin; Duabari Silas Aziaka
JOKI: Jurnal Komputasi dan Informatika Vol 3 No 1 (2026): June 2026
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v3i1.375

Abstract

Internship placement is a critical component of vocational education aimed at improving students’ competencies and readiness for professional environments. However, internship placement processes in many institutions are still conducted manually, resulting in mismatches between student competencies and institutional requirements, reduced placement accuracy, and inefficient administrative procedures. This study proposes a web-based decision support system to improve the accuracy, objectivity, and efficiency of internship placement. The system integrates structured student data, institutional requirements, and evaluation criteria into an automated matching mechanism to generate placement recommendations. The development process includes data collection, system design, implementation, and testing to ensure functionality and usability. The system was evaluated using functional testing and user acceptance evaluation involving administrators, teachers, and students. The results demonstrate that the system operates effectively in managing data, calculating compatibility scores, and generating accurate placement recommendations. User evaluation indicates that the system improves administrative efficiency, transparency, and placement accuracy compared with manual methods. The proposed system provides a practical solution for managing internship placement and supports data-driven decision-making in educational institutions. The findings also indicate the potential for further development through integration with predictive analytics and broader institutional deployment to support digital transformation in educational management.
Mobile-Based Alumni Management System for Digital Engagement in Educational Institutions Ahmad Homaidi; Prapti Deshmukh
JOKI: Jurnal Komputasi dan Informatika Vol 3 No 1 (2026): June 2026
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v3i1.376

Abstract

Alumni management plays an important role in maintaining institutional relationships, supporting information exchange, and strengthening collaboration between graduates and educational institutions. However, many institutions still manage alumni data manually, resulting in inefficient data processing, limited communication, and difficulties in maintaining long-term engagement. This study proposes a mobile-based alumni management system designed to improve data management efficiency and enhance communication between alumni and institutions. The system integrates alumni registration, profile management, information sharing, and interactive communication within a centralized platform accessible through mobile devices and a web-based administrative panel. The development process included requirement analysis, system design, implementation, and system testing to ensure functionality and usability. Functional testing confirmed that all features operated as expected, while user acceptance evaluation involving alumni participants indicated a feasibility score of 92.5%, categorized as very good. These results demonstrate that the proposed system improves accessibility, administrative efficiency, and alumni engagement compared with manual management methods. The developed platform provides a practical and scalable solution for managing alumni data and communication in educational institutions. Future enhancements may include integration of advanced analytics, expanded communication features, and broader institutional deployment to support sustainable alumni engagement and digital transformation in education management.
Deep Learning-Based Classification of Toxic Ornamental Plants for Real-Time Identification Hoiriyah; Lailatul Masrurah
JOKI: Jurnal Komputasi dan Informatika Vol 3 No 1 (2026): June 2026
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v3i1.377

Abstract

Ornamental plants are widely used in residential and public environments due to their aesthetic value, yet some species contain toxic compounds that may pose risks to humans and animals. The visual similarity between toxic and non-toxic ornamental plants makes manual identification difficult for the general public, increasing the potential for accidental exposure. This study proposes an automated image-based classification system to identify toxic and non-toxic ornamental plants accurately and efficiently. The system utilizes deep learning-based image classification techniques to analyze plant images and categorize them into five classes: lily, daffodil, caladium, sansevieria, and non-toxic ornamental plants. A dataset of 3,320 images was prepared and divided into training, validation, and testing subsets. Image preprocessing and augmentation were applied to improve data quality and model generalization. Experimental results show that the proposed model achieved a testing accuracy of 98.25%, outperforming the comparative model and demonstrating stable classification performance across all classes. The best-performing model was integrated into a web-based application that enables users to upload plant images and receive real-time identification results. The findings indicate that the proposed system provides an accurate, accessible, and practical solution for identifying toxic ornamental plants and supporting public safety awareness. Future development may focus on expanding plant categories and deploying mobile-based implementations for broader accessibility.
Real-Time Eggplant Leaf Disease Diagnosis Using Image Classification Abu Tholib; Moh Ainol Yaqin
JOKI: Jurnal Komputasi dan Informatika Vol 3 No 1 (2026): June 2026
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v3i1.378

Abstract

Eggplant is an important horticultural crop whose productivity is often affected by various leaf diseases that reduce crop quality and yield. Manual identification of plant diseases relies heavily on human observation and experience, making it time-consuming and prone to misclassification, especially when symptoms appear visually similar. The system employed a convolutional neural network/transfer learning model to identify eggplant leaf diseases accurately and efficiently. The system utilizes a deep learning model trained on a dataset of 3,551 leaf images categorized into seven disease classes and one healthy class. Image preprocessing and augmentation techniques were applied to improve model performance and generalization. Experimental evaluation showed that the proposed model achieved a testing accuracy of approximately 82% with balanced precision and recall across all categories, indicating stable classification performance. The trained model was integrated into a web-based application that allows users to upload leaf images and obtain real-time diagnostic results along with recommended handling information. The findings demonstrate that the proposed system provides a practical and reliable solution for early disease detection and supports more efficient agricultural management. Future development may include expanding dataset diversity, improving model robustness, and deploying mobile-based applications to enhance accessibility and scalability in precision agriculture.

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