cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Edi Sutoyo
Contact Email
journalijadis@gmail.com
Phone
+62895410194922
Journal Mail Official
info@ijadis.org
Editorial Address
Indonesian Scientific Journal (Jurnal Ilmiah Indonesia) Jl. Pasar Atas No 3, Kompleks Setramas Kota Cimahi, Bandung
Location
Unknown,
Unknown
INDONESIA
International Journal of Advances in Data and Information Systems
Published by Indonesian Scientific Journal
ISSN : -     EISSN : 27213056     DOI : https://doi.org/10.25008/ijadis
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Advances in Data and Information Systems (IJADIS) (e-ISSN: 2721-3056) is a peer-reviewed journal in the field of data science and information system that is published twice a year; scheduled in April and October. The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Data Science and Information System. The Journal is published by the Indonesian Scientific Journal. Accepted paper will be available online (free access), and there will be no publication fee. The author will get their own personal copy of the paperwork. IJADIS welcomes all topics that are relevant to data science, and information system. The listed topics of interest are as follows: Data clustering and classifications Statistical model in data science Artificial intelligence and machine learning in data science Data visualization Data mining Data intelligence Business intelligence and data warehousing Cloud computing for Big Data Data processing and analytics in IoT Tools and applications in data science Vision and future directions of data science Computational Linguistics Text Classification Language resources Information retrieval Information extraction Information security Machine translation Sentiment analysis Semantics Summarization Speech processing Mathematical linguistics NLP applications Information Science Cryptography and steganography Digital Forensic Social media and social network Crowdsourcing Computational intelligence Collective intelligence Graph theory and computation Network science Modeling and simulation Parallel and distributed computing High-performance computing Information architecture
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 137 Documents
 10   11   12   13   14 
Revisiting Cyber Threats in Government Sectors: A Systematic Review of Attacks, Challenges, and Policy-Level Defenses Nuraeni, Aisyah; Nugraha, Yudhistira; Aminanto, Muhamad Erza
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1404

Abstract

This paper presents a systematic literature review (SLR) based on the PRISMA framework, synthesizing 128 peer-reviewed studies published between 2020 and 2024, drawn from major scholarly databases. The review investigates cyber threats specifically targeting government institutions and identifies phishing, ransomware, malware, and denial-of-service (DoS) attacks as the most prevalent attack vectors affecting government sector environments. In addition to these threats, the study highlights persistent institutional limitations, such as outdated infrastructure, fragmented inter-agency coordination, limited technical capacity, and regulatory gaps, which hinder effective cybersecurity governance and response. To address these challenges, the review compiles both proactive and reactive mitigation strategies, emphasizing the need for SOC design principles such as scalability, interoperability, inter-agency coordination, and resilience in cyber operations. The paper synthesizes its findings into a taxonomy of threat profiles and contextual constraints, offering a foundational reference for building government-specific SOC models. It also outlines future research directions related to operational validation, capability maturity modeling, and institutional alignment in public-sector cybersecurity architectures. 
Machine Learning-Based Prediction of Divorce Verdicts Using Posita Data and Imbalanced Data Handling: A Case Study in Padang Sidempuan Rahmadini, Rina; Santoso, Bagus Jati
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1405

Abstract

This study aims to develop a predictive model for divorce verdicts ("Granted" or "Rejected") in the Religious Courts of Indonesia using machine learning techniques. The dataset consists of 2,026 finalized divorce cases from the Religious Court of Padang Sidempuan between 2018 and 2025, incorporating structured variables and posita—narrative texts describing the plaintiff’s reasons for divorce. Keyword-based feature extraction was applied to transform these texts into interpretable indicators. To handle class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was implemented on the training data. Six classical machine learning algorithms were evaluated: Decision Tree, Naïve Bayes, K-Nearest Neighbors, Random Forest, LightGBM, and XGBoost. Performance was measured using accuracy, precision, recall, F1-score, F2-score, and AUC. The results indicate that Naïve Bayes achieved the highest recall (100%) for the “Granted” class, while LightGBM and XGBoost demonstrated the most balanced performance across both classes. Feature importance analysis revealed that mediation outcomes, domestic violence, and economic hardship were among the most influential factors in determining verdicts. The study highlights the applicability of interpretable machine learning in legal decision support and discusses limitations such as the single-court scope and challenges in predicting minority class outcomes. Future work may explore multi-jurisdictional data, deep learning approaches, and domain-specific embeddings for enhanced performance.
Multi-Task Learning for Traffic Sign Recognition using Multi-Scale Convolutional Neural Networks Akbar, Mutaqin; Susilawati, Indah; Jati, Budi Sulistiyo; Alamsyah, Nur
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1406

Abstract

Traffic signs are an essential component of road infrastructure. According to the Department of Transportation, Indonesia has over 300 distinct traffic signs, categorized based on their functions and purposes. TSR systems have been widely integrated into various intelligent transportation technologies, such as Driver Assistance Systems (DAS), Advanced Driver Assistance Systems (ADAS), and Autonomous Driving Systems (ADS). The output generated by TSR serves as a critical input for DAS, ADAS, ADS, and other intelligent systems. This article presents a CNN-based classification for traffic sign recognition using multi-task learning (MTL), focusing on traffic signs in Indonesia. The dataset was collected from direct capture with the help of a cellphone camera, indirect capture by utilizing screenshots on a digital map application, and they are captured from several different angles, during the day and at night. The proposed CNN architecture incorporates multi-scale within an MTL framework. The use of a multi-scale approach will hopefully enhance the model’s ability to recognize traffic signs in varied and complex environments. And the integration of MTL will enable the model to handle multiple related tasks concurrently, sharing learned features across tasks. During the training stage, the MS-CNN outperformed a standard CNN model by demonstrating lower initial loss, higher starting accuracy, and achieving 100% accuracy by the 8th epoch with a minimal error rate of just 0.003. In the testing stage, the model achieved exceptional results, as shown by the confusion matrix, it successfully classified all traffic sign types (10 classes) and accurately categorized each sign into one of two categories—warning or prohibition. All performance metrics, including precision, recall, and F1-score, reached 100% for both output tasks, confirming the robustness and reliability of the model.
Improving Credibility of Digital Evidence Investigation in E-Commerce Fraud Cases using ISO/IEC 27037 Syahida Alawi, Hanna; Riadi, Imam; Sunardi, Sunardi
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1408

Abstract

TikTokShop fraud is an emerging challenge in e-commerce investigations, demanding robust digital forensic approaches. This study tackles the complexities of investigating such fraud within the TikTokShop platform, focusing on the acquisition, preservation, and validation of multifaceted digital evidence, including screenshots, payment records, account data, videos, and communication logs. Adhering to ISO/IEC 27037 for evidence handling, Magnet and Oxygen forensic tools were used for systematic evidence acquisition. The analysis using Oxygen Forensic recovered 100% of relevant artifacts, which is slightly higher compared to Magnet Axiom, which recovered 38.46% of artifacts, although both tools were effective in retrieving critical artifacts such as image metadata, account information, and data transfers. Due to image compression by the TikTokShop application, discrepancies in hash values emerged, requiring supplementary validation. Optical Character Recognition (OCR) and Levenshtein distance algorithms quantified textual similarity within image-based evidence, while the Forensically platform enabled advanced image forensic analyses to detect potential tampering and authenticity. This rigorous, multi-layered forensic framework complements traditional hash verification by providing corroborative content-level authentication. Findings confirm that although hash inconsistencies arise from application-induced compression, integrating OCR, Levenshtein, and forensic image analysis enhances the reliability of digital evidence. The novelty of this research lies in its robust synergy of ISO/IEC 27037-compliant handling with advanced digital content verification, contributing to the advancement of digital forensic practices in complex social commerce fraud scenarios.
Analysis of User Experience Usage on the Sardjito Hospital and Yogyakarta Regional Public Hospital Websites Using the User Experience Questionnaire (UEQ) Wahyu Setyaningsih, Putry; Yakobus Chandra, Albert
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1411

Abstract

The user experience of healthcare websites is crucial for ensuring accessibility, usability, and engagement among diverse stakeholders, including patients, caregivers, and healthcare professionals. This study evaluates the RS Sardjito and RS Jogja websites using six key dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Both websites excel in Attractiveness and Perspicuity, showcasing visually appealing and user-friendly platforms. RS Sardjito demonstrates strength in Efficiency, enabling effective task completion, while RS Jogja outperforms in Dependability and Novelty, reflecting higher reliability and innovation. However, areas for improvement include Novelty and Dependability for RS Sardjito and Efficiency for RS Jogja, with both platforms requiring enhancements in Stimulation to deepen user engagement through interactive features. These findings offer actionable insights for driving policy development in healthcare website design and functionality, addressing key areas such as accessibility, usability, efficiency, reliability, and innovation. Policies should prioritize user-centered design principles, implement robust security measures to strengthen reliability, and encourage creative approaches to foster innovation. Additionally, regular benchmarking and user feedback mechanisms should be institutionalized to ensure continuous improvement. By systematically addressing these dimensions, healthcare organizations can optimize digital platforms to improve access to healthcare services, enhance patient engagement, and advance the overall quality of healthcare delivery, contributing to the growing body of research on healthcare website optimization and aligning user experience with organizational goals.
Decentralized Electronic Health Record Management with Semantic-Aware Hierarchical Encryption Kurniyanto, Firdaus Putra; Santoso, Bagus Jati
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1413

Abstract

The rising incidence of cyberattacks targeting electronic health records (EHR) in Indonesia necessitates a robust and context-aware data protection scheme. This paper proposes a decentralised EHR management system that leverages blockchain, IPFS, and a novel Semantic-Aware Hierarchical Encryption (SAHE) algorithm. SAHE enables multi-level access control based on data sensitivity semantics, ensuring privacy while maintaining usability for medical professionals. The system was implemented in a prototype environment and evaluated through stress testing with up to 200 users, achieving an average CPU usage of 55% and a memory consumption of 80.2 MB. Differential cryptanalysis demonstrated a strong avalanche effect (~50%), with no vulnerabilities found via OWASP ZAP scanning. This architecture offers a promising solution for privacy-preserving, patient-controlled EHR systems, particularly in regions with limited infrastructure.
Web-Based Monitoring System for Automatic Coffee Drying in a Smart Dryer Dome Nofriyanti, Duwi; Handayani, Ade Silvia; Suroso, Suroso; Novianti, Leni; Rakhman, M Arief; Asriyadi, Asriyadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1416

Abstract

This study developed a web-based monitoring system integrated into a smart dryer dome for automatic coffee drying. The system utilized the RN-GZWS-RS485 sensor to measure critical drying parameters: temperature, humidity, and light intensity. Data acquisition relied on an ESP32 microcontroller, transmitting real-time measurements to a server using the MQTT protocol, while sensor-actuator interactions operated through the Modbus protocol. Actuator performance adhered to predefined threshold values, maintaining drying temperature within 45–50?°C and relative humidity between 20–40%. Real-time monitoring and system status visualization were implemented via a Laravel-based web interface. Experimental tests demonstrated that 71.76% of temperature readings, 64.71% of humidity readings, and 68.24% of light intensity readings consistently fell within optimal ranges. Low standard deviation values confirmed the system’s effectiveness in maintaining stable drying conditions. Additionally, the integration of solar power facilitated system deployment in remote locations without conventional electricity infrastructure. These findings highlight the system's potential to improve the reliability, accuracy, and efficiency of automatic coffee drying processes.
Enhancing Medical Data Security Through Blockchain Smart Contract and Decentralized Application Herman, Herman; Salji, Rinday Zildjiani; Yuliansyah, Herman
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1387

Abstract

This research studies implementation of decentralized applications (DApps) that are combined with blockchain technology and IPFS for storing patient medical data. The goal of this research is to increase the security, transparency, and access control of stored medical data to make sure only legitimate users can access the data. The proposed system uses smart contracts on the Ethereum network to handle user rights of access (doctors, patients, and admins) and ensure data integrity through the blockchain immutability feature. Patient medical records are retained in IPFS and traced using the Content Identifier (CID). Implementation outcome reveals that the system can safely process medical information, keeping patients in full control of their information, and restricting data access only to scheduled time. This system also shows the potential of blockchain and IPFS technology-based applications in achieving a more efficient health ecosystem focused on safeguarding people's data.
Automated Oil Palm Health Assessment Using Object-Based Deep Learning and High-Resolution UAV Imagery in Indonesia Pindarwati, Atut; Wijayanto, Arie Wahyu; Karmawan, I Putu Agus; Yeza, Ardhan; Sakka, Asriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1391

Abstract

Indonesia, as the world’s largest crude palm oil (CPO) producer, faces challenges in plantation monitoring due to reliance on manual data collection methods that are time-consuming, costly, and prone to human error. This study proposes an automated approach for assessing oil palm tree health using high-resolution UAV imagery (5–10 cm) and object-based deep learning models. We evaluate five state-of-the-art detectors—YOLOv5s, Faster R-CNN, Mask R-CNN, SSD, and RetinaNet—to classify individual trees into four health categories: Healthy, Moderately Healthy, Needs Improvement, and Urgent Condition. Using a dataset of 14,749 labeled trees from Kendawangan, Indonesia, YOLOv5s achieved the highest performance with a precision of 0.784, recall of 0.752, and mAP of 0.764. Our findings demonstrate the potential of AI-driven monitoring to enhance plantation management through rapid, accurate, and cost-effective health assessments—contributing a scalable solution to support precision agriculture and sustainable CPO production.
A Value-Driven Approach to Software Project Prioritization: Integrating AHP and Value-Focused Thinking in a Messaging Service Firm Ningsih, Fauziah Firlita; Wasesa, Meditya
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1401

Abstract

As requests for development projects grow, a real-life messaging service firm faces increasing challenges in objectively prioritizing initiatives due to limited developer capacity and a high number of concurrent projects. These issues have resulted in inefficient resource allocation, delayed timelines, and declining customer satisfaction, exacerbated by unclear and unstructured project selection methods. This study proposes an integrated decision-making framework that combines the Analytical Hierarchy Process (AHP), stakeholder analysis, and Value-Focused Thinking (VFT) to address the firm’s prioritization challenges. AHP structures the decision criteria and evaluates project alternatives based on their relative importance, stakeholder analysis identifies key decision-makers and their influence, and VFT ensures alignment with organizational values and strategic goals. To uncover underlying issues and stakeholder expectations, the study employs Problem Tree Analysis, structured interviews, and questionnaires. Four typical sub-project alternatives—Custom Projects, New Features, Bug Fixing, and Optimization—are assessed against four criteria: Cost, Quality, Functionality, and Client Satisfaction. The study concludes with an implementation roadmap and actionable recommendations to improve the firm’s project selection and prioritization process.

Page 12 of 14 | Total Record : 137

 10   11   12   13   14 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System Vol. 5 No. 1 (2024): April 2024 - International Journal of Advances in Data and Information Systems Vol. 4 No. 2 (2023): October 2023 - International Journal of Advances in Data and Information System Vol. 4 No. 1 (2023): April 2023 - International Journal of Advances in Data and Information Systems Vol. 3 No. 2 (2022): October 2022 - International Journal of Advances in Data and Information System Vol. 3 No. 1 (2022): April 2022 - International Journal of Advances in Data and Information Systems Vol. 2 No. 2 (2021): October 2021 - International Journal of Advances in Data and Information System Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems Vol. 1 No. 2 (2020): October 2020 - International Journal of Advances in Data and Information System Vol. 1 No. 1 (2020): April 2020 - International Journal of Advances in Data and Information Systems More Issue