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Edi Sutoyo
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Indonesian Scientific Journal (Jurnal Ilmiah Indonesia) Jl. Pasar Atas No 3, Kompleks Setramas Kota Cimahi, Bandung
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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 168 Documents
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Spatio-Temporal AIS Big Data Analytics of Vessel Traffic Patterns in Kaohsiung Port Arfianto , Afif Zuhri; Santosa, Anisa Fitri; Sutrisno, Imam; Hasin, Muhammad Khoirul; Asmara, I Putu Sindhu; Riananda, Dimas Pristovani; Pambudi, Dwi Sasmita Aji
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1504

Abstract

Maritime traffic management in major ports requires a comprehensive understanding of vessel movement patterns to ensure operational efficiency and safety. This study presents a spatio-temporal analysis of vessel traffic in Kaohsiung Port, Taiwan, utilizing a 10-month snapshot of AIS data (December 2024–October 2025). Employing quantitative methods including Kernel Density Estimation (KDE) for spatial intensity mapping, grid-based discretization for traffic density quantification, and temporal resolution analysis at multiple scales, the research identifies key operational hotspots and peak traffic periods. The analysis encompasses 1,247,890 AIS records from diverse vessel types, revealing distinct spatial clustering patterns in port entrance channels, anchorage zones, and terminal areas. Temporal analysis demonstrates pronounced diurnal and weekly cyclical patterns, with peak traffic intensities occurring during daytime operational hours and weekdays, reflecting commercial shipping schedules and port operational rhythms. The KDE-based hotspot identification reveals high-density zones concentrated within 0.5 nautical miles of major container terminals, indicating critical areas requiring enhanced traffic monitoring and collision avoidance measures. Grid-based traffic density quantification provides granular insights into vessel distribution across different port sectors, enabling zone-specific risk assessment and resource allocation strategies. The findings reveal complex spatio-temporal patterns that reflect the port's role as a major container hub in the Asia-Pacific region. Despite data quality limitations such as unspecified vessel types (59.9%) and incomplete destination fields, the results provide actionable insights for port authorities to enhance safety, optimize operations, and support strategic planning. This methodological framework demonstrates scalability and transferability to other port environments, contributing to the advancement of data-driven maritime traffic management systems
Development of A Control Delay Layer for Data Transmission Stability in Remote Patient Monitoring System Rofiudin, Ahmad Sidik; Kusumasari, Tien Fabrianti; Suakanto, Sinung
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1509

Abstract

Remote Patient Monitoring (RPM) systems generate continuous health data that must be reliably processed at the backend to support timely clinical decision-making. Many real-world RPM deployments rely on synchronous request handling, which can lead to service degradation and request failures under high concurrency. This study designed and evaluated a Control Delay Layer (CDL) as an application-layer mechanism to improve backend request-handling stability in a web-based RPM system. The proposed mechanism decouples data reception from permanent storage through temporary buffering and deferred batch processing while regulating data submission behavior at the service level. System behavior before and after CDL implementation was examined using controlled load testing under identical scenarios. The evaluation employed service-level performance metrics, including request failure rate, response time distribution, and computational resource utilization. Experimental results show that the baseline monolithic system experienced an average request failure rate of approximately 14% under peak load, whereas no request failures were observed after CDL implementation. The CDL enabled system maintained consistent response-time behavior and stable resource utilization at higher concurrency levels. These findings demonstrate that backend-level request-handling control can effectively enhance system stability under high load conditions without requiring device-level modifications, providing a complementary approach for scalable and resilient digital health systems.
SPADE-LSTM: An Integrated Sequential Pattern Mining and Deep Learning for Badminton Next-Stroke Prediction Sari, Jefita Resti; Oktarina, Sachnaz Desta; Erfiani, Erfiani
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1510

Abstract

Badminton rallies consist of complex and rapid stroke transitions that reflect players’ tactical decision-making. While prior studies have examined stroke patterns descriptively or applied standalone predictive models, limited research integrates interpretable sequential pattern mining with deep learning for next-stroke prediction. This study proposes an integrated SPADE–LSTM framework to analyze and predict badminton stroke sequences using a 10-class scheme (drive, dropshot, lob, netting, and smash for two athletes). Match data were transformed into structured stroke sequences and contextual features, then divided into training, validation, and test sets using a match–set–rally grouping strategy to prevent information leakage. Sequential patterns were first extracted using the Sequential Pattern Discovery using Equivalent Classes (SPADE) algorithm to capture frequent tactical transitions. These pattern-based features were subsequently used to train a Long Short-Term Memory (LSTM) model for multi-class classification. The proposed model achieved an accuracy of 88.68%, with weighted precision, recall, and F1-score of 0.9075, 0.8868, and 0.8851, respectively. Misclassifications were mainly observed in tactically similar stroke transitions and minority classes. The results indicate that integrating interpretable sequential pattern mining with deep learning provides both strong predictive performance and meaningful tactical insights for badminton performance analysis.
Identification of Drug Material Melting Conditions from Hot-Stage Microscopy Images Using Active Contour and Support Vector Machine Methods Reski, Julia Mega; Ramadhani, Muhammad
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1513

Abstract

The hospital pharmacy installation plays an essential role in ensuring the quality of pharmaceutical supplies. One important stage in drug production is raw material analysis, particularly melting point determination as a purity indicator. Conventional methods, such as capillary tubes, are limited in accuracy and prone to subjectivity. This study aims to develop an automated image-based monitoring system integrated with Hot Stage Microscopy (HSM) to objectively detect real-time morphological changes in pharmaceutical materials. The system was designed using digital image processing stages consisting of image acquisition, processing, and output. Images were captured using a binocular microscope and processed on an Odroid XU4 mini-computer. Phase boundaries were identified using the Active Contour segmentation method, while texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM) at four orientation angles. Classification was performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. The results showed that the Active Contour method effectively detected melting phases, and the SVM achieved an accuracy of 91.67%, precision of 91.89%, sensitivity of 91.67%, and an F1-score of 91.66%. The system successfully distinguished pure Paracetamol from mixtures with Gallic Acid and Ferulic Acid.
Hybrid Relevance and Sentiment Classification of Indonesian Gold Tweets Using Machine Learning for Market Risk Signal Extraction Kamalia, Antika Zahrotul; Indra, Indra; Wibowo, Arief; Riwurohi, Jan Everhard; Hassan, Shiza
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1517

Abstract

This study proposes a hybrid relevance–sentiment classification framework to analyze public opinion on physical Antam gold from Indonesian Twitter data and to support exploratory market-risk signal extraction. Tweets were collected during February–November 2025, after preprocessing and text-normalized deduplication, 1,271 unique tweets were retained. The approach combines weak supervision (rule-/lexicon-based silver labels) with TF-IDF-based machine learning in two stages: (1) relevance classification to separate tweets genuinely discussing physical Antam gold from non-relevant contexts (e.g., ANTM stock/capital-market discussions), and (2) two-class sentiment classification (positive vs negative) applied to relevance-filtered tweets. Random Forest achieved the strongest relevance performance (Accuracy = 0.984; macro-F1 = 0.943; 5-fold CV macro-F1 = 0.928 ± 0.033). For sentiment classification, performance was moderate and close across models; the most stable model under cross-validation (Logistic Regression/Naive Bayes) was used for downstream aggregation. Sentiment outputs were aggregated into a monthly sentiment index for descriptive comparison with gold prices; the observed association was weak, indicating that the index is better interpreted as a risk-perception proxy rather than a direct price predictor.
A Problem-Driven User Experience Model for Evaluating Government Transparency Platforms: Evidence from A Regional Command Center Yuniarto, Dwi; Subiyakto, A'ang; Rahman, Aedah Binti Abd.
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1519

Abstract

The rapid expansion of digital transparency initiatives has encouraged local governments to adopt data-driven platforms to enhance public accountability. However, many transparency platforms struggle to achieve sustained public engagement due to unresolved user experience issues. This study proposes and empirically validates a problem-driven user experience evaluation model for government transparency platforms by focusing on three UX problem dimensions: Extra Time or Effort, Unexpected Experience, and Evolving Limitations. Using survey data from 147 valid users of the Command Center website of Sumedang Regency collected between October and December 2025, the model was tested using Partial Least Squares Structural Equation Modeling. The results indicate that all three UX problem dimensions significantly influence user experience satisfaction and continued intention to use, both directly and indirectly, with satisfaction acting as a partial mediator. The findings demonstrate that reducing cognitive effort, ensuring experiential consistency, and addressing systemic limitations are critical for sustaining public engagement with transparency platforms. This study contributes to the e-government and UX literature by offering a problem-oriented evaluation framework that emphasizes structural usability frictions rather than interface aesthetics, providing actionable insights for improving digital governance and public transparency.
Empirical Performance of E2E Frameworks in React-Vue SPAs Using DIA Rezeki, Abdillah; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Nugroho, Radityo Adi; Abadi, Friska
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1528

Abstract

Modern web applications increasingly adopt Single-Page Application (SPA) architectures to enhance the user experience through client-side rendering and dynamic content loading. However, these characteristics introduce significant challenges for automated end-to-end (E2E) testing, including asynchronous DOM manipulation, complex state management, and timing synchronization issues. This study presents a comprehensive empirical comparison of three prominent E2E testing frameworks—Selenium WebDriver, Cypress, and Playwright—across React and Vue-based SPAs. Using a quantitative experimental approach, 25 standardized test cases were executed 15 times each across Chrome, Firefox, and Edge, for a total of 270 testing sessions. Performance evaluation focused on four key metrics: execution time, success rate, CPU usage, and memory consumption. Results demonstrate that Playwright achieved the fastest execution time (56.25 seconds on React-Chrome), while Selenium exhibited superior resource efficiency with the lowest memory consumption (196.59 MB on Vue-Chrome). The Distance to Ideal Alternative (DIA) multi-criteria decision analysis method identified Playwright-Chrome as optimal for React applications (DIA score: 0.886715) and Selenium-Chrome for Vue applications (DIA score: 0.908237), indicating that framework selection should be context-dependent based on application characteristics and deployment requirements. This research supports the conclusion that no universal "best" testing framework exists, underscoring the importance of evidence-based, application-specific tool selection in software quality assurance.
A Heterogeaneous Dataset–Driven Ensemble Learning Framework for Malicious URL Detection Sukarno, Parman; Ngah, Syahrulanuar
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - 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.v7i1.1541

Abstract

Modern cyberattacks are increasingly associated with phishing campaign, malware distribution, and website defacement, which are often delivered through malicious Uniform Resource Locator (URL) originating from diverse source. This paper examine malicious URL detection using an ensemble learning framework evaluated on large scale heterogeneous dataset composed of URL aggregated from multiple public threat intelligence source. The dataset include benign, phishing, malware, and defacement URL, thereby reflecting real world variability in attack pattern and data distribution. Three ensemble based classifier, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB), are evaluated with respect to detection accuracy and computational efficiency. In addition to classification performance, this study present a detailed analysis of training and detection time in order to identify most suitable model for practical deployment. Experimental results indicate that the DT model achieves a training time of 4.14 seconds with macro and weighted accuracies of 94.11% and 91.71%, respectively, and a per category detection time of 0.2162 seconds. The RF model attains macro and weighted accuracies of 93.64% and 90.94%, with training and detection times of 9.73 seconds and 0.2420 seconds, respectively. Although the GB model exhibits the longest training time of 45.38 seconds, it achieves the fastest per category detection time of 0.2151 seconds. Despite its comparatively lower overall accuracy of 92.48% for macro averaging and 89.42s% for weighted averaging, the rapid inference capability of GB makes it a strong candidate for real time malicious URL detection in heterogeneous cybersecurity environments.

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