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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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Implementing GCV and mGCV to Determine Optimal Knot in Spline Regression for East Java Life Expectancy Lestari, Amanda Ayu Dewi; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
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.v6i2.1379

Abstract

Life Expectancy is a vital indicator for evaluating population’s overall welfare and health status within a specific region. According to data published by Badan Pusat Statistik (BPS) National, East Java Province ranks 10th nationally in terms of life expectancy in 2024, with male life expectancy recorded at 70.39 years and female life expectancy at 74.4 years. This research focuses on examining four key factors that are believed to influence life expectancy in East Java during the 2024 including the Percentage of the Poor Population (X1), the Percentage of Individuals Aged 5 and Above Who Regularly Smoke Tobacco (X2), the Expected Years of Schooling (X3), and the Open Unemployment Rate (X4). To determine the optimal knot points in the nonparametric truncated spline regression model, the study utilizes Generalized Cross-Validation (GCV) and the modified Generalized Cross-Validation (mGCV) methode by minimizing their respective error values. The findings indicate that all four variables significantly impact life expectancy. Among the methods applied, the mGCV approach demonstrates good performance, achieving the lowest error value of 0.100 and a coefficient of determination of 82.91%.
Customer Transaction Clustering with K-Prototype Algorithm Using Euclidean-Hamming Distance and Elbow Method Kuswardana, Dendy Arizki; Prasetya, Dwi Arman; Trimono, Trimono; Diyasa, I Gede Susrama Mas; Awang, Wan Suryani Wan
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.v6i2.1381

Abstract

This study aims to cluster customer transactions in a Japanese food stall using the K-Prototype Algorithm with a combination of Euclidean-Hamming Distance and the Elbow method. Facing intense industry competition, this study seeks to understand customer purchasing behavior to increase loyalty and sales. From 9.721 initial entries, 9.705 cleaned and transformed records were analyzed. K-Prototype was chosen because of its ability to handle numeric features (Total Sales, Product Quantity) and categorical features (Payment Method, Order Type, Day Category and Time Category). The combination of Euclidean-Hamming distances was used for distance measurement. The optimal number of clusters was determined using the Elbow method, with the results recommending three clusters as the most optimal number. A Silhouette score of 0.6191 indicates a Good Structure clustering result, effectively identifying three distinct customer grouping: "Loyal Regulars" (49.5%), "Casual Shoppers" (42.3%), and "Premium Shoppers" (8.2%). Statistical validity was also tested using ANOVA and Chi-Square, the results showed significant differences between the clusters in numerical and categorical variables with a p-value <0.0001. The clusters are statistically valid in both numerical and categorical aspects. These insights provide an understanding of customer characteristics and reveal a strategically valuable cluster for targeted marketing.
A Hybrid Model of Graph Attention Networks and Random Forests for Link Prediction in Co-Authorship Networks Arfiani, Ika; Yuliansyah, Herman
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.v6i2.1382

Abstract

Co-authorship prediction is important in academic network analysis due to it helps to understand patterns of scientific collaboration and supports collaboration recommendation systems. Topology-based approaches, such as connectivity metrics and node distance, have been widely used to model new relationships in networks. However, these approaches often overlook relevant author attributes, such as reputation and productivity. This study develops a co-authorship prediction model by combining a Graph Attention Network (GAT) and a Random Forest. GAT is used to extract topological features from the co-authorship graph, while Random Forest leverages additional attributes such as h-index and the number of publications to improve prediction accuracy. Experiments were conducted on a co-authorship dataset comprising over 10,000 authors and 50,000 publications. The results show that GAT achieved 85% accuracy, while Random Forest reached 80%. The combination of the two yielded 90% accuracy and a higher F1-score, indicating a better balance between precision and recall. The combined model also proved more accurate in predicting collaborations involving highly productive authors. These findings suggest that a hybrid approach can more comprehensively capture the dynamics of academic collaboration and may serve as a foundation for developing more effective collaboration prediction systems in the future.
Deep Learning and Remote Sensing for Agricultural Land Use Monitoring: A Spatio-Multitemporal Analysis of Rice Field Conversion using Optical Satellite Images Wijayanto, Arie Wahyu; Zalukhu, Bill Van Ricardo; Putri, Salwa Rizqina; Wilantika, Nori; Yuniarto, Budi; Kurniawan, Robert; Pratama, Ahmad R.
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.v6i2.1385

Abstract

Rice is a staple food for over half of the global population, making its production crucial for food security, especially in Indonesia, the world's third-largest rice consumer. Population growth and urban expansion have led to agricultural land conversion, necessitating efficient monitoring methods. Traditional approaches, such as area sample frameworks and tile surveys, are costly and time-consuming, prompting the need for remote sensing and deep learning solutions. This study utilizes medium-resolution Sentinel-1, Sentinel-2, and Landsat-8 optical satellite imagery from 2013 and 2021 to analyze land cover changes in West Bandung and Purwakarta Regencies, key agricultural regions in Indonesia. A deep learning model is developed to classify land cover, validated through ground-truth evaluation, and applied to assess spatio-multitemporal land use conversion, paddy field estimation, and conversion rates. Results show that deep learning models effectively classify land cover with high accuracy, revealing significant agricultural land loss due to urban expansion. This research contributes to artificial intelligence (AI)-driven land monitoring, particularly in tropical regions, and supports policymakers in sustainable food agriculture land management. The findings highlight the potential of integrating remote sensing and deep learning for cost-effective agricultural monitoring, ensuring food security and sustainable land use. Future research should explore higher-resolution imagery and advanced AI techniques to enhance predictive accuracy and decision-making.
Indonesian Sign Language (BISINDO) Classification Using Xception Transfer Learning Architecture Amelia, Meisya Vira; Saputra, Wahyu Syaifullah Jauharis; Hindrayani, Kartika Maulida; Riyantoko, Prismahardi Aji
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.v6i2.1392

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 94% during testing.
Integrating Satellite Imagery and Multicriteria Decision Analysis for High-Resolution Flood Vulnerability Mapping: A Case Study of Jakarta, Indonesia Pindarwati, Atut; Wijayanto, Arie Wahyu; Rosyani, Perani; Maghfiroh, Meilinda F. N.
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.v6i2.1388

Abstract

Jakarta, Indonesia, ranks among the most flood-prone megacities in the world, with hydrometeorological factors placing up to 98% of its area at flood risk. Its population density—approximately 15,900 individuals per square kilometer—compounds the impacts of flooding through intensified exposure and socio-economic vulnerability. This study presents a novel, data-driven methodology for flood vulnerability assessment in the Jakarta Special Capital Region (DKI Jakarta), integrating satellite remote sensing and geospatial analysis with a Multicriteria Decision Analysis (MCDA) framework. Employing the Analytical Hierarchy Process (AHP) to systematically weight environmental and socio-economic criteria, a Flood Vulnerability Index (FVI) was developed and spatially modeled at a 500-meter grid resolution. The resulting FVI map categorizes vulnerability into five levels: very low, low, moderate, high, and very high. Findings indicate an index range between 0.36 and 0.70, highlighting predominantly moderate to high vulnerability zones across the region. This high-resolution assessment provides actionable insights for disaster risk reduction, urban resilience planning, and targeted policy interventions to mitigate flood-related hazards in Jakarta.
Prediction of Rice Harvesting During the Rainy Season in Kabupaten Lamongan Using Stochastic Frontier Analysis Ningrum, Imelda Widya; Prasetya, Dwi Arman; Trimono, Trimono; Kassim, Anuar bin Mohamed
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.v6i2.1393

Abstract

The agricultural sector plays a critical role in ensuring national food security, yet it faces challenges in achieving technical efficiency due to limited land and input resources. This study aims to model and predict the technical efficiency of rice production in Lamongan Regency during the rainy season using a data science-driven Stochastic Frontier Analysis (SFA) approach. The dataset includes key inputs such as land area, labor, fertilizer, and environmental variables. The methodology involved data preprocessing, feature selection based on Pearson correlation and VIF thresholds, and model validation using metrics like R-squared, MAPE, and log-likelihood. The SFA model demonstrated high predictive capability, with R² values exceeding 0.91 in cross-validation and MAPE under 15%. The low gamma value (? = 0.0100) indicates minimal yet consistent inefficiency. The results suggest that integrating SFA with data science techniques provides an effective framework for identifying inefficiencies and can serve as a decision-support system for evidence-based agricultural policy.
Ambidextrous Cloud Governance Approach to Enhance TelCo's Digital Transformation Using COBIT 2019 Traditional and DevOps Andriani, Novi; Mulyana, Rahmat; Saedudin, Rd. Rohmat
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.v6i2.1398

Abstract

Cloud computing plays a crucial role in accelerating digital transformation within the telecommunications sector by enhancing operational efficiency, scalability, and service innovation. However, TelCo faces difficulties in aligning its cloud adoption with effective governance, particularly in ensuring continuous service delivery and resilience. This study proposes a cloud governance framework based on the ambidextrous integration of COBIT 2019 Traditional components and DevOps Focus Area. Employing a Design Science Research methodology, data were collected through semi-structured interviews guided by a structured questionnaire and triangulated with internal documents until data saturation was achieved. Governance and Management Objectives were prioritized using the ambidextrous COBIT 2019 lens, supported by regulatory guidelines from the SOE Minister No. PER-2/MBU/03/2023 and the ICT Minister No. 5/2021, as well as relevant prior studies. The analysis highlighted DSS04: Managed Continuity as the most critical focus area. A capability gap assessment identified vacant leadership roles, overlapping responsibilities, and the lack of Infrastructure as Code (IaC) implementation in cloud services. Recommended improvements include formal assignment of leadership positions, clarification of IT responsibilities, and the adoption of IaC practices. These enhancements are expected to raise the capability maturity of DSS04 from 3.25 to 3.87, representing a 0.62 increase in business continuity readiness. This study contributes to research by extending ambidextrous governance theory to cloud continuity management and provides practical guidance for telecommunications providers seeking to improve operational resilience and manage risks effectively in their digital transformation efforts.
Implementation of Transfer Function ARIMA Model for Stock Price Prediction Azizah, Alisa Jihan; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida; Fahrudin, Tresna Maulana
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.v6i2.1396

Abstract

Dynamic economic growth requires stable financing sources, one of which is through the capital market. In stock investment activities, risk and return are two fundamental aspects that are interrelated and must be carefully considered. The volatility of ASII stock prices, influenced by various factors including exchange rates, can create uncertainty in investment decision-making. This study aims to predict the stock price of PT Astra International Tbk (ASII) using a transfer function model approach that integrates the influence of the Indonesian rupiah to US dollar exchange rate as an external variable. The transfer function model is an extension of the ARIMA model that can measure the dynamic relationship between input and output variables. Based on the estimation results, the best model obtained has a transfer function order of (b,s,r) = (1,0,0) with a noise series of (p_n,q_n) = (1,0). The prediction results show that ASII stock price movements tend to be stable with a gradual decline over the next 20 days. Model evaluation demonstrates low error rates, with MAE of 84.19, RMSE of 110.37, and MAPE of 1.65%. These results indicate that the transfer function model is effective in modeling and predicting short-term stock prices with reasonably good accuracy.
A CNN-Based Information System for Balinese Dance Classification with Hyperparameter Optimization Widya Utami, Nengah; Putra, Made Adi Paramartha; Putra, I Gede Juliana Eka; Sampedro, Gabriel Avelino
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.v6i2.1402

Abstract

Balinese dance classification presents challenges due to limited datasets, complex postures, and the lack of real-world implementation. Existing studies often focus on model development while overlooking deployment aspects. This research proposes a lightweight Convolutional Neural Network  (CNN) designed for Balinese dance classification and compares it with MobileNetV2, ResNet50, and VGG16 using consistent training settings. Data augmentation was applied to enhance generalization, and training epochs were optimized based on model convergence. The proposed CNN achieved a validation accuracy of 99.00%, with a precision of 92.55%, recall of 89.88%, and F1-score of 91.1%, using only 590 thousand trainable parameters and the fastest inference time of 476 milliseconds. Although others pretrained model, MobileNetV2 slightly outperformed in some metrics, the proposed model offered a better tradeoff between performance and efficiency. The trained model was deployed in a web-based application, demonstrating practical usability. This work supports the preservation of Balinese dance through accessible and efficient AI integration.

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