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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 137 Documents
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Development and Implementation of the Primakara Virtual Assistant Based on Generative Artificial Intelligence Putra, Made Adi Paramartha; Suyasa, I Putu Buda; Artana, I Made; Utami, Nengah Widya
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.v6i3.1407

Abstract

The growing need for efficient, accessible, and context-aware academic support systems has led to the exploration of Generative AI (GenAI) technologies in educational settings. However, existing virtual assistants often lack contextual relevance, adaptability, and user-friendly interaction, limiting their effectiveness in higher education environments. This study proposes a GenAI-based Virtual Assistant (VA) tailored for university-related applications, combining voice recognition, natural language understanding, and text-to-speech technologies to create an interactive and intelligent support system. The proposed work was evaluated through four key testing stages: black-box functionality testing, response similarity analysis, inference time measurement, and user acceptance testing. Black-box testing validated the system’s ability to process speech input, generate accurate audio responses, and provide responsive UI/UX feedback. A TF-IDF cosine similarity analysis across 11 academic departments yielded an average similarity score of 81.86%, demonstrating semantic alignment with institutional content. The system also maintained an average response time of 3.88 seconds. User feedback from 25 participants revealed high satisfaction levels, with scores exceeding 4.0 across all indicators and large T-statistic value. These results confirm the system’s potential as an effective, real-time academic assistant.
Comparative Analysis of ARIMA and Fourier Series Methods for Air Temperature Forecasting in Surabaya Salsabiila, Annas Thasya Haafizhah; Permata, Regita Putri; Hidayati, Sri
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.v6i3.1415

Abstract

Urban climate change, particularly rising temperatures and the Urban Heat Island (UHI) phenomenon, poses challenges for cities like Surabaya, Indonesia. This study compares the forecasting performance of ARIMA and ARIMA-Fourier models using daily air temperature data from 2020 to 2024. The analysis involved stationarity testing, model estimation, and evaluation across four forecasting horizons. ARIMA models (especially ARIMA(0,1,1) and ARIMA(1,1,0)) showed reliable short-term forecasts, but were less effective in capturing seasonal patterns. To address this, Fourier terms were integrated into the ARIMA framework. The ARIMA-Fourier model achieved better accuracy and higher R² values in short- and medium-term forecasts, particularly with an oscillation parameter of k = 150. However, its performance declined in long-term predictions due to overfitting risks. Overall, the ARIMA-Fourier model is more adaptive for capturing complex temperature seasonality and can support more accurate urban climate forecasting in Surabaya.
Comparison of Transfer Learning Using VGG16, MobileNetV2, and ResNet50 for Pornography Image Detection SihWardana, Christopher Ade; Isa, Sani Muhamad
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.v6i3.1418

Abstract

The rapid growth of digital technology is vital for the Indonesian Scout to reach and interact with its members. The National Indonesian Scout (Kwarnas) uses the “Ayo Pramuka” social media application to support this. However, such platforms risk exposing users, especially teenagers, to harmful content like pornography. This research applies Computer Vision and Transfer Learning Convolutional Neural Networks (CNNs) to detect pornographic images automatically. The objective is to identify the CNN model (VGG16, MobileNet V2, ResNet 50) with the highest detection accuracy and determine the impact of color space preprocessing. The method includes two stages first, image preprocessing by converting RGB images to HSV and YCbCr second, feature extraction using pre-trained CNNs with freezing and fine-tuning. A dataset of 4060 images was used for training and testing. Without preprocessing, VGG16 achieved the best accuracy of 99.01%. When RGB images were converted to HSV, ResNet 50 produced the highest accuracy of 99.51%. The findings show that combining color space transformation and Transfer Learning CNN significantly improves pornographic content detection in the “Ayo Pramuka” Application, enhancing safe digital engagement for Indonesian Scouts.
Ambidextrous AI Governance for Driving SmartCo’s Digital Transformation Using COBIT 2019 Traditional and DevOps Azzahra, Octamevia Inkaputri; Mulyana, Rahmat; Adi, Taufik Nur
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.v6i3.1419

Abstract

Artificial Intelligence (AI) plays a vital role in accelerating digital transformation within the technology sector. This study investigates SmartCo, a technology company seeking to enhance the security and governance of AI implementation using an ambidextrous COBIT 2019 framework that integrates people, processes, and technology. The research adopts a Design Science Research (DSR) methodology, utilizing interviews, questionnaires, and internal document analysis until data saturation was achieved. Governance and Management Objectives (GMOs) were prioritized using design factors, DevOps practices, relevant regulations (ICT Minister Regulation No. 5/2021 and SOE Minister Regulation No. PER-2/MBU/03/2023), and previous studies. DSS05 (Managed Security Services) was selected as the primary focus, reflecting the organization’s priority on data protection and secure AI operations. The capability maturity assessment revealed gaps in security leadership, documentation, and process automation, indicating the need for more adaptive and integrated governance. Targeted improvements were implemented, including formalizing governance structures, enhancing security training, and adopting supportive technologies, which increased the DSS05 maturity level from 3.00 to 3.86. A comprehensive roadmap guides further enhancements in security-focused governance. This study provides practical insights for organizations aiming for secure, AI-enabled digital transformation. In addition, it contributes to the theoretical foundation of ambidextrous COBIT 2019 governance frameworks by demonstrating their application in a regulated technology environment.
Passenger and Revenue Estimation for New Rail Transit Lines Under Construction: A Demographic Approach Alifianti, Tarisma Dwi Putri; Ni’mah, Rifdatun; Permata, Regita Putri
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.v6i2.1420

Abstract

This study proposes a data-driven approach to estimate passenger volume and revenue for new rail transit lines under construction, addressing the challenge of limited historical data. Principal Component Analysis (PCA) was used to reduce 29 demographic variables into three principal components, which collectively captured up to 85% of the variance. These components informed a Fuzzy C-Means (FCM) clustering process that grouped new stations with existing ones based on demographic similarity. The clustering yielded a Fuzzy Partition Coefficient (FPC) of 0.913, indicating high cluster validity and low overlap between clusters. Transition probabilities of passenger flows between stations were modeled using Markov Chains. The expanded transition matrix, incorporating new stations through demographic analogy, demonstrated rapid convergence to a stationary distribution within 5–10 iterations, validating the model’s stability. Simulation results project a 57% increase in weekday passengers and a 74% increase in weekend passengers, with estimated daily revenue peaking at Rp1.216 billion. The evaluation results confirm the robustness and reliability of the combined FCM–Markov model for long-term passenger and revenue forecasting in new transit infrastructure planning.
Spatiotemporal Clustering of Key Food Commodity Prices Using Multivariate Time Series Tsabitah, Dhiya Ulayya; Angraini, Yenni; Sumertajaya, I Made
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.v6i3.1422

Abstract

Food price stabilization remains a critical challenge in economic development planning and food security, particularly in developing countries like Indonesia, which exhibit high spatial and temporal diversity. To develop an efficient and adaptive predictive approach for understanding food commodity price dynamics, this study integrates multivariate time series clustering using a Dynamic Time Warping-based K-Means algorithm with a hybrid forecasting model that combines Vector Error Correction Model with Exogenous Variables and Long Short-Term Memory. The clustering evaluation results indicate reasonably cohesive group structures, with a silhouette score of 0.45 and a Davies-Bouldin Index of 0.67. Each cluster profile reveals significant differences in price trends, volatility, and anomaly patterns. Model validation using the Wilcoxon signed-rank test shows that the differences between cluster-level forecasts and individual-level actual values are generally statistically insignificant. These findings suggest that the proposed integrative approach can accurately capture regional price patterns and serve as a foundation for more data-driven and responsive policymaking in food price stabilization efforts. The 30-period forecasts for rice, eggs, and red onions reflected dynamic variations aligned with spatial characteristics: rice shows relatively stable behavior, eggs exhibit strong seasonal patterns, and red onions display the highest price volatility.
Enhancing GERD Disease Prediction using Extra Tree Classifier Tuned by Komodo Mlipir Algorithm Purba, Diya Namira; Fariani, Rida Indah
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.v6i3.1428

Abstract

Gastroesophageal reflux disease (GERD) is a prevalent gastrointestinal disorder characterized by the backward flow of gastric contents into the esophagus, often causing heartburn and regurgitation, with a global prevalence of approximately 13.98%. Early detection is essential to prevent severe complications such as esophagitis, esophageal strictures, and esophageal cancer. However, conventional diagnostic methods are often limited by inadequate healthcare resources and high cost, particularly in developing countries. On the other hand, machine learning can be implemented as a promising alternative method for disease detection, improving accuracy through data pattern identification. Machine learning has been used for several disease detection tasks, such as Breast Cancer, Diabetes, etc. This study proposed an enhanced GERD prediction model by implementing the Extra Tree classifier optimized by the Komodo Mlipir Algorithm (KMA) for hyperparameter optimization.  This study used a GERD dataset from the Harvard  Dataverse, which consists of 1200 rows with 69 features. The result shows that the Extra Tree Algorithm that KMA tuned achieved a high-performance evaluation with an F1-score of 0.97.  This highlights the effectiveness of KMA in enhancing model performance. Compared to the previous study, the proposed Extra Tree Models optimized by KMA performed improved performance, demonstrating the effectiveness of metaheuristic optimization in GERD prediction.
Enhanced Classification of Lombok Pearl Quality Based on Shape and Size Using PSO-Optimized Artificial Neural Network Anshori, Muhammad Izzul; Andono, Pulung Nurtantio; Soeleman, Arief
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.v6i3.1434

Abstract

This study aims to develop an intelligent classification model for pearl quality assessment using an integrated approach combining Gray Level Co-occurrence Matrix (GLCM), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN). Sixteen texture features were extracted from four directional orientations using GLCM. PSO was employed as a feature selection algorithm to reduce dimensionality and enhance classification performance. Two ANN models were compared: a baseline model using all GLCM features and an optimized model utilizing only PSO-selected features. The models were trained and validated using 10-fold cross-validation. Results showed that the PSO-enhanced ANN achieved an accuracy of 94.72%, outperforming the baseline model which reached only 89.17%. Further evaluations using confusion matrix, Receiver Operating Characteristic (ROC) analysis, and Principal Component Analysis (PCA) confirmed the superior discriminative capability and improved class separability of the optimized model. These findings highlight the effectiveness of combining swarm intelligence with neural networks in texture-based classification tasks, offering a robust and scalable solution for automated quality inspection in the pearl industry and related domains.
Analysis Of Healthcare Employees Acceptance Of Digital Transformation In Inpatient Department Electronic Medical Record (EMR) Application At Private Hospital In Tangerang Siagian, Ricko Sony; Tjhin , Viany Utami
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.v6i3.1436

Abstract

This study examines the acceptance of healthcare professionals toward digital transformation through the implementation of Electronic Medical Records (EMR) in the inpatient department (IPD) at a private hospital in Tangerang. The research applies a combined analytical model of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) and the Technology Acceptance Model (TAM). A total of 160 doctors, including specialists, formed the study population, with 115 respondents selected as samples. The findings indicate that healthcare professionals generally feel comfortable using the EMR system and have integrated it into their daily practice, although improvements in active utilization and system optimization are still required. Statistical analysis shows that Performance Expectancy, Effort Expectancy, System Quality, and Facilitating Conditions significantly influence Intention to Use, while Habit has no significant effect. Moreover, Intention to Use strongly impacts Use Behavior. These results highlight that enhancing system reliability, usability, and organizational support is essential to increase healthcare professionals’ adoption and behavioral use of EMR in the inpatient department of private hospitals in Tangerang.
Discovering Student Learning Paths: An Educational Process Mining Approach in Moodle Supriyanto, Supriyanto; Ismail, Taufiq; Setyawan, Fariz
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.v6i3.1437

Abstract

E-learning platforms like Moodle are critical to modern education, with their effectiveness deeply reliant on fostering optimal student engagement. A thorough understanding of how students interact with these platforms is therefore essential for enhancing the learning experience. This study aimed to analyze student interaction patterns within Moodle by employing educational process mining techniques. The core objective was to uncover hidden behavioral patterns and gain valuable insights into the underlying learning processes. To achieve this, we utilized both heuristic miner and inductive miner algorithms to analyze Moodle's extensive event log data. The effectiveness of various student activity variants was rigorously assessed through fitness checking. This study presents a novel, integrated analytical approach combining frequent sequential pattern mining, user behavior analysis, feature engineering, and process mining to comprehensively evaluate student learning effects in Moodle. While applying both Heuristic Miner and Inductive Miner algorithms to extensive Moodle event logs, we not only generated precise process models highlighting effective and ineffective student activity sequences but also uncovered unique challenges, such as the Inductive Miner's inability to accurately model the 'Tugas' (assignment) component's complex activity patterns. These findings offer distinct, actionable insights for refining Moodle course design and delivery, moving beyond general observations to pinpoint specific pedagogical interventions. Ultimately, our work advances the understanding of student behavior and academic performance within the Moodle ecosystem by providing a granular, data-driven methodology for optimization.

Page 13 of 14 | Total Record : 137

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