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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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The Role of Non-State Actors in Climate Governance: Contributions, Challenges and Future Directions Islam, Md Mujahidul; Jahan, Nusrat
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1054

Abstract

Since anthropogenic causes accelerate rapid climate change with intensifying the adverse impacts of climate induce hazards, Non-State Actors (NSAs) have emerged as pivotal actors in climate governance. The aim of this research is to explore the diverse roles and contributions of NSAs in climate governance and analyze the challenges and institutional barriers they encounter with proposing some recommendations to strengthen their impact. It employs a qualitative approach where data were collected through KII method. Thematic analysis reveals some meaningful role of NSAs in climate governance including advocating for climate justice, raising awareness, promoting sustainable technologies, enhancing community adaptation and resilience, and collaborating across sectors. Digital awareness campaign of Greenpeace during the Copenhagen and Paris Conference and BRAC's climate-resilient housing and rainwater harvesting initiatives in Bangladesh can be placed as notable examples of NSAs’ roles. Despite their significant contributions, several persistent challenges such as poor coordination among NSAs and with state actors, legitimacy deficits, governance gaps, lack of institutional support and insufficient financing impedes them to realize their full potential. To overcome these challenges, this study recommends the need for legal inclusion of NSAs’ roles, inclusive participation, incorporating intersectionality, stronger accountability mechanisms and sustainable financial frameworks. Furthermore, this study offers actionable recommendations for policymakers and practitioners seeking to enhance the effectiveness of non-state engagement in climate action.
Digital Mapping of Fermented Foods for the Advancement of Gastronomy Tourism in Indonesia Singgalen, Yerik Afrianto; Kartikawangi, Dorien; Winayu, Birgitta Narindri Rara
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1055

Abstract

This research introduces a pioneering digital mapping framework for Indonesian fermented foods that integrates geospatial technologies with traditional gastronomic knowledge systems. Employing Rapid Application Development methodology on the Oracle APEX platform, the study establishes a comprehensive documentation infrastructure capturing the geographical distribution, production methodologies, and cultural significance of diverse fermentation practices across Indonesia's archipelagic landscape. The resulting prototype offers multifunctional capabilities through an intuitive interface design that serves preservation imperatives and tourism development objectives. Findings demonstrate that systematic digital documentation of fermented food traditions creates measurable economic opportunities through enhanced destination competitiveness, specialized culinary tourism routes, and improved market visibility for artisanal producers. The community-driven documentation protocols position local knowledge-holders as primary content contributors, while the system architecture establishes essential connections between geographical contexts and traditional fermentation techniques. This research addresses critical documentation gaps while establishing standardized protocols applicable beyond Indonesia to other regions with significant fermentation heritage. The digital mapping system ultimately functions as both a cultural preservation mechanism and a strategic asset for sustainable gastronomy tourism development, offering a replicable model for transforming endangered culinary knowledge into economically viable digital assets that benefit traditional food-producing communities.
Design and Implementation of a Stock Purchase System for Printing Businesses Using the Waterfall Method Ginting, Mega Henia Br; Lee, Francka Sakti
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1057

Abstract

Efficient stock availability is essential for the seamless operation of business processes within a company. However, stock management often encounters several critical challenges, including discrepancies between warehouse inventory and logbook records, as well as mismatches between ordered and received quantities. These issues frequently lead to overstocking or stockouts overstocking increases operational costs and risks quality degradation or expiration of goods, while stockouts disrupt sales and customer service. To address these challenges, this study proposes the design of a stock purchasing management application aimed at optimizing inventory tracking and enhancing operational efficiency within a printing shop. The system is developed using the Waterfall methodology, a structured software development model that helps minimize errors throughout the design process. To validate the system's functionality, black box testing is employed, ensuring that the application performs as intended. The resulting application offers an effective solution to stock management issues, reducing inventory imbalances and supporting more efficient business operations.
Digital Forensic Analysis of UAV Flight Data Using Static and Dynamic Methods in Coal Mining Area Halim, Muhammad Yusuf; Luthfi, Ahmad
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1061

Abstract

Unmanned Aerial Vehicles (UAV) have become vital tools in industrial sectors such as coal mining for site inspections and operational monitoring. However, unauthorized UAV flights present security risks that necessitate forensic investigation. This study examines a forensic case involving a DJI Mini 3 UAV suspected of crossing company boundaries. Using the Conceptual Digital Forensics Model for the Drone Forensic Field, both static and dynamic forensic acquisition methods were applied. Static acquisition recovered 53 photographs, 11 videos, 11 audio files, 10 deleted photos, 4 deleted videos, and 3 unidentified log files. Dynamic acquisition yielded 64 media files including 63 photographs (.JPG and .jpg) with 10 deleted, 14 videos (.MP4, .MOV, .SWF) with 6 deleted, 11 audio files, 4 plain text files, 31 deleted files, 3 EXIF metadata records containing GPS coordinates, and 3 unidentified log files. The GPS data from EXIF metadata was visualized in Google Earth to map flight paths and confirm boundary violations. These findings demonstrate that dynamic acquisition retrieves a more comprehensive artifact set than static acquisition. This study highlights the importance of UAV digital forensics in supporting security investigations and ensuring compliance with industrial UAV policies.
The Trajectory of Scaled Agile Research: A Bibliometric Analysis and Visualization Approach Khoza, Lucas Thulani; Maphosa, Mfowabo
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1062

Abstract

Modern project management in organisations is moving towards Scaled Agile to achieve success. Scaled Agile refers to a set of organisational structures and processes for implementing agile practices that are applied on an enterprise scale. This study explores Scaled Agile growth and impact by analysing 238 publications obtained from the Scopus databases using bibliometric analysis. The results show that publications on Scaled Agile have steadily increased, with more contributions from developed nations than developing countries. In terms of the geographic distribution of publications, Germany is the leading followed by Sweden and the United States. The results also show that Scaled Agile is being applied across different fields, but is dominated by computer science, engineering, and business. We visualized the high-frequency terms using a word cloud and the keyword co-occurrence map, and a density map using VOSViewer. The h-index of 21 for the analysed articles indicates the significant scholarly impact of the publications. The study identified the following key themes: team dynamics, organisational structures, and practical applications of Scaled Agile. The study also identifies the major challenges associated with Scaled Agile, namely cultural issues and scalability issues, effective organisational design, and change management strategies. The findings of this study offer valuable insights into the current state of Scaled Agile that appeal to industry practitioners and academics interested in Scaled Agile research and implementation.
Integrating Artificial Intelligence and Social Media for English as a Foreign Language (EFL) Learning: A Study on Meta-AI’s Influence on Reading Comprehension Azmaien, Umme
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1070

Abstract

This study explores the integration of Artificial Intelligence (AI) and social media, particularly Meta-AI-enhanced WhatsApp, in enhancing reading comprehension among English as a Foreign Language (EFL) learners. Despite the growing use of AI and social media in education, there is a notable lack of empirical research examining their combined effect on language learning. To address this gap, a systematic review was conducted following the PRISMA 2020 framework. A total of 850 studies were initially identified from databases such as PubMed, Scopus, Web of Science, and Google Scholar. After applying strict inclusion and exclusion criteria, 140 studies were included in the review, with 20 selected for in-depth analysis. The findings reveal that Meta-AI-supported platforms provide personalized learning paths, adaptive feedback, and enhanced engagement, contributing significantly to the improvement of reading skills. However, challenges such as ethical concerns, reduced human interaction, and technology accessibility were also noted. This study offers valuable insights for educators and policymakers on effectively integrating AI and social media tools into EFL instruction, suggesting that technology-enhanced environments can surpass traditional methods in promoting reading comprehension and learner motivation.
EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition Akbar, Ahmad Taufiq; Saifullah, Shoffan; Prapcoyo, Hari; Rustamadji, Heru; Cahyana, Nur Heri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1071

Abstract

Facial expression recognition (FER) remains a challenging task due to the subtle visual variations between emotional categories and the constraints of small, controlled datasets. Traditional deep learning approaches often require extensive training, large-scale datasets, and data augmentation to achieve robust generalization. To overcome these limitations, this paper proposes a hybrid FER framework that combines EfficientNet B0 as a deep feature extractor with an L2-regularized Support Vector Machine (L2-SVM) classifier. The model is designed to operate effectively on limited data without the need for end-to-end fine-tuning or augmentation, offering a lightweight and efficient solution for resource-constrained environments. Experimental results on the JAFFE and CK+ benchmark datasets demonstrate the proposed method’s strong performance, achieving up to 100% accuracy across various hold-out splits (90:10, 80:20, 70:30) and 99.8% accuracy under 5-fold cross-validation. Evaluation metrics including precision, recall, and F1-score consistently exceeded 95% across all emotion classes. Confusion matrix analysis revealed perfect classification of high-intensity emotions such as Happiness and Surprise, while minor misclassifications occurred in more ambiguous expressions like Fear and Sadness. These results validate the model’s generalization ability, efficiency, and suitability for real-time FER tasks. Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classification
A Data-Driven Framework for Optimizing Propranolol Dosage Using Support Vector Regression and Reinforcement Learning Njoku, Felix Anayo; Awofisayo, Sunday Olajide; Ekpar, Frank Edughom; Ozuomba, Simeon
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1075

Abstract

The accurate prediction and adjustment of drug dosages requires precision to maximize therapeutic benefits while minimizing harm. This research attempts to model a hybrid machine learning framework combining Support Vector Regression (SVR) and Reinforcement Learning (RL) for individualized Propranolol dosage optimization using patient-specific clinical, enzymatic, and lifestyle data. A retrospective dataset comprising patient file, lifestyle indicators, and enzyme profile was used to train an SVR model for initial dosage prediction. Reinforcement Learning was subsequently applied to refine predictions through simulated feedback loops. Model performance was assessed using Mean Squared Error (MSE), R-squared (R²), and F1-score. Statistical comparisons between SVR predictions, RL-refined dosages, and physician-prescribed doses were performed using paired t-tests and one-way ANOVA. The SVR model achieved high predictive accuracy (MSE = 0.3554; R² = 0.9835), indicating its suitability for dosage estimation. The RL-refined model demonstrated a slight decrease in accuracy (MSE = 0.9928; R² = 0.9539). Statistical tests showed no significant improvement with RL (paired t-test: t = -1.1132, p = 0.2672; ANOVA: F = 0.0165, p = 0.9836). Mean predicted dosages across SVR, RL, and physician prescriptions were closely aligned (24.85 mg, 24.83 mg, and 24.93 mg, respectively). This study demonstrates that even standalone SVR may yield Propranolol dosage estimates with high accuracy, highlighting its prospective usefulness in clinical settings as a direct yet reliable tool for use in customized healthcare. While RL does offer some level of flexibility, the statistical value of improvements made was negligible, making RL beneficial but not necessarily critical. The proposed model shows that AI systems can aid in formulating evidence-based clinical judgments for dosing medications.
Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection Makura, Sheunesu; Dobson, Caden; Rananga, Seani
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1076

Abstract

Online banking fraud has become increasingly prevalent with the widespread adoption of digital financial services, necessitating advanced security solutions capable of detecting both known and emerging threats. This paper presents a robust machine learning framework that integrates anomaly detection with network packet analysis to mitigate fraudulent activities, focusing particularly on Distributed Denial of Service (DDoS) attacks. The key contribution is an ensemble model combining Isolation Forest and K-means clustering, which achieves 98% accuracy and 98% F1-score in anomaly detection while reducing false positives to 2% which is a critical improvement for operational deployment in banking systems. The framework’s semi-supervised architecture enables zero-day fraud detection without reliance on labeled attack data, addressing a fundamental limitation of signature-based systems. By leveraging feature optimization (PCA/t-SNE) and real-time processing capabilities, this solution offers financial institutions a practical, adaptive defense mechanism against evolving cyber threats. The results demonstrate significant potential for integration into existing banking security infrastructures to enhance fraud prevention with minimal disruption.
Implementation of a Telegram-Based Child Consultation Chatbot Using IndoBERT Whurapsari, Gusti Ayu Wahyu; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1079

Abstract

Children’s health and development are crucial aspects that require proper attention from parents. However, many parents lack easy access to immediate consultation regarding their child's health and well-being. To address this issue, this study develops a child consultation chatbot on Telegram using the IndoBERT model. The chatbot utilizes data from Halodoc and Alodokter, structured into an intent-based format with 227 tags, 5,428 patterns, and 278 responses. The dataset undergoes preprocessing, including lowercasing, text cleaning, normalization, stopword removal, and stemming. Four preprocessing scenarios are tested, including the use of term frequency-based stopwords without applying stemming, the use of NLTK stopwords without stemming, the use of term frequency-based stopwords combined with stemming, and the use of NLTK stopwords combined with stemming. The best model, trained with an 80:20 training-validation split using term frequency-based stopwords without stemming, achieves 98% accuracy, 98.5% F1-score, 98.9% precision, and 98.5% recall. The chatbot successfully classifies user intent and ensures structured interactions through a confidence-based response mechanism. This research demonstrates that an IndoBERT-based chatbot can effectively assist parents in obtaining quick and relevant information regarding their children's health and development.

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