cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ai Munandar
Contact Email
ijitcsa@gmail.com
Phone
+62+6282111152015
Journal Mail Official
ijitcsa@gmail.com
Editorial Address
International Journal of Information Technology and Computer Science Applications (IJITCSA) Sekretariat Jejaring Penelitian dan Pengabdian Masyarakat (JPPM) : Ranau Estate Blok D.3, Kel. Panggungjati, Kp. Pantogan Kec. Taktakan - Kota Serang, Provinsi Banten, e-mail : jitcsa@jejaringppm.org web : www.jejaringppm.org
Location
Kota serang,
Banten
INDONESIA
International Journal of Information Technology and Computer Science Applications (IJITCSA)
Published by Jejaring Penelitian dan Pengabdian Masyarakat
ISSN : 29643139     EISSN : 29855330     DOI : https://doi.org/10.58776/ijitcsa.v1i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering
he Journal of Information Technology and Computer Science Applications (JITCSA) is an information technology and computer science publication. Applications from both fields for solving real cases are also welcome. JITCSA accepts research articles, systematic reviews, literature studies, and other relevant ones. Several fields of science that are the focus of JITCSA include information technology and the like, computer science fields, including artificial intelligence, data science, data mining, machine learning, deep learning, and the like. IJITCSA is published three times a year, in January, May, and September. The first issue in January 2023 had eight articles. Focus and Scope International Journal of Information Technology and Computer Science Applications includes scholarly writings on scientific research or review, pure research, and applied research in the field of computer science, information systems, and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Information systems System Software Artificial Intelligence Computer Architecture Distributed Systems System & Software Engineering Genomics & Bioinformatics Internet and Web AI & Expert systems Software Process and Life Cycle Database Systems Software Testing & Quality assurance Bioinformatics Information Technology Implementation Computing Languages & Algorithms E-commerce & M-Commerce Computer Networks & Communications Computing Systems Control Systems & Engineering Systems Engineering System Security Digital Forensics Data Mining & Machine Learning Data Modeling
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 73 Documents
 4   5   6   7   8 
Healthcare Data Integration Through Enterprise Data Warehousing: Architecture, Conformance Pipeline, and Experimental Validation for Readmission Analytics La Duy Ngôn
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 1 (2026): January - April 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i1.246

Abstract

Healthcare organizations operate a fragmented digital landscape in which hospital information systems (HIS), electronic health records (EHR), laboratory systems, billing platforms, and departmental applications are optimized for transaction processing but not for integrated analysis. The resulting interoperability gaps, semantic inconsistency, duplicated records, and uneven data quality constrain enterprise reporting and limit higher-value analytics. This paper substantially proposes implementable enterprise data warehouse architecture, formalizing its data-quality and conformance mechanisms, and validating the design through experimental analytics use case. The proposed framework combines an integration layer for ETL/ELT, conformed dimensions, departmental marts, governance controls, and an analytics layer for OLAP and machine learning. To demonstrate practical value, the paper evaluates the framework on a de-identified inpatient diabetes dataset comprising 101,766 encounters and 50 raw attributes. The experimental pipeline performs profiling, conformance mapping, diagnosis grouping, missing-value treatment, and dimensional modeling before training benchmark readmission models. The best ranking performance is obtained by XGBoost with an AUROC of 0.688 and an AUPRC of 0.235, while threshold tuning improves recall-oriented operational utility. The results show that healthcare warehousing should not be framed merely as centralized storage; rather, it is an architectural mechanism for interoperability, data quality control, reproducible analytics, and decision support. The manuscript concludes with implementation guidance and limitations relevant to hospitals seeking a scalable, governance-aware warehousing program.
Revisiting the IBM Retail Data Warehouse: A Governed One-Column Architecture and Reproducible Open-Dataset Validation for Retail Analytics Nayananda Karunaratne; Pulasthi Medhananda
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 1 (2026): January - April 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i1.247

Abstract

The IBM Retail Data Warehouse (RDW) correctly recognized the importance of integrated retail data, but it remained largely descriptive, did not formalize the underlying architecture, and lacked a reproducible empirical validation. This paper reconstructs and substantially extends that early proposal into a publication-ready research article. We first synthesize the historical IBM RDW, Retail Data Warehouse Model (RDWM), Retail Services Data Model (RSDM), and Retail Business Solution Template (RBST) concepts with contemporary data warehousing, data governance, and retail analytics literature. We then propose a governed, RDW-informed logical architecture that separates ingestion, quality control, conformed dimensional modeling, analytics marts, and decision-support services. To move beyond conceptual discussion, we instantiate the architecture with an open retail dataset from the UCI Machine Learning Repository containing 541,909 transactions. After governance-oriented preprocessing, the final analytical mart contains 392,692 valid rows, 18,532 orders, 4,338 customers, 3,665 products, and 37 countries. We formulate the transformation and forecasting workflow mathematically, define an end-to-end algorithmic pipeline, and evaluate a retail revenue forecasting task using naive, seasonal naive, linear regression, ridge regression, random forest, and gradient boosting baselines. On the hold-out test window, the best model (linear regression on warehouse-engineered features) achieves an RMSE of 4,302.61 GBP and R2=0.9766, while a raw, ungoverned pipeline yields a much weaker RMSE of 10,068.59 GBP. This corresponds to a 57.27% reduction in RMSE attributable to governance and dimensional integration. The results show that the practical value of an RDW-like architecture is not merely organizational; when implemented as a governed analytical platform, it measurably improves reproducibility, interpretability, and forecasting quality.
A Lakehouse-Oriented Big Data Infrastructure for Educational Analytics: Integrating Administrative and Assessment Data for Early Student Risk Prediction Bhairav Kaphle; Biswajit Shrestha
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 1 (2026): January - April 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i1.248

Abstract

Educational institutions increasingly depend on heterogeneous digital systems, yet many analytics initiatives remain fragmented across student information, registration, assessment, and learning platforms. This paper proposes a lakehouse-oriented big data infrastructure for educational analytics and validates it through a reproducible early-risk prediction study using the Open University Learning Analytics Dataset (OULAD). The study integrates five public OULAD tables student information, course registration, assessment metadata, student assessment submissions, and course presentation metadata into temporally valid feature tables aligned to the student–module–presentation level. We define a windowed feature engineering framework that constructs actionable indicators such as submission rate, weighted completion score, average submission lag, and assessment coverage gap at 30%, 50%, 70%, and 100% of the course timeline. Two supervised classifiers, logistic regression and random forest, are evaluated under a stratified 80/20 protocol. The results show that administrative data alone provides weak discrimination (AUC  0.673), whereas integrated mid-course assessment evidence substantially improves performance. At the 50% course window, the random-forest model achieves an AUC of 0.947, F1 of 0.879, and recall of 0.829; even at the 30% window the model already reaches an AUC of 0.904. These findings demonstrate that the value of educational prediction depends not only on model choice but also on data integration architecture. The paper contributes (i) a lakehouse-oriented reference architecture for higher-education analytics, (ii) a temporally constrained feature engineering strategy for early-warning systems, and (iii) an empirical ablation showing that multi-source integration yields large and operationally meaningful gains.
Application of K-Means Clustering in Grouping Customer Preferences for K-Pop Albums And Merchandise Aditiya Dwi cahyo; Wowon Priatna; Agus Hidayat
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.206

Abstract

The increasing popularity of K-Pop in Indonesia is particularly in the purchase of physical products. THJMINE Store faces challenges in inventory management and promotional strategies due to the lack of product grouping for albums and merchandise. This study applies the K-Means Clustering algorithm to 110 sales transaction data from July 2022 to January 2025. The method used in this study is the CRISP-DM approach, which consists of the following stages: business understanding, data understanding, data preparation, modeling, and evaluation discussion. The result of the study shows that the K-Means algorithm successfully formed three clusters with customer classification: loyal customers (cluster 0), general customers (cluster 1), and premium or collector customers (cluster 2). The model evaluation results in a DBI score of 0.6342, indicating good cluster quality. These clustering results can help THJMINE Store understand customer segmentation, develop more targeted marketing strategies, and improve inventory management efficiency.
Comparing Holt-Winters Variants Accuracy in Forecasting Indonesia LQ45 Stock Prices: Siang Jong Jek; Raden Gunawan Santosa
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.258

Abstract

This study applies the Holt–Winters method, an exponential smoothing approach incorporating level, trend, and seasonal components, to compare the predictive accuracy of four variants (multiplicative, additive, OR, and average) of Holt-Winter Method in forecasting stock prices of companies listed in the LQ45 index. The dataset consists of stock prices from 2016–2021 for training and January–February 2022 for testing, with forecasting accuracy evaluated using Mean Absolute Percentage Error (MAPE), visualized through boxplots, and assessed using the nonparametric Kruskal–Wallis test. The Holt–Winters computations were performed using Microsoft Excel, while boxplot visualization and the Kruskal–Wallis test were conducted using the R programming language. The results indicate significant differences in predictive performance among the four methods with p-value = 0.04059 in Kruskal-Wallis test. The Additive Holt–Winters method achieves the best performance with the lowest MAPE, while the multiplicative method performs the worst. Among LQ45 stocks, INDF records the lowest forecasting error (1.6799%), whereas TPIA exhibits the highest (83.0783%). These results suggesting that the additive Holt–Winters method is more suitable for forecasting LQ45 stock prices under the observed conditions
Association Pattern Analysis of Production Results Using the Apriori Algorithm Zacky Achmad Sholeh; Wowon Priatna; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.208

Abstract

This study aims to analyze association patterns in production data at CV. Sinar Agung Teknik using the Apriori algorithm. The company faces challenges in identifying co-produced product relationships, which complicates production pattern recognition. The research adopts the Knowledge Discovery in Databases (KDD) approach, comprising data selection from three months of daily production, data cleaning, transformation into transactional format, application of the Apriori algorithm, and result visualization. Key parameters applied in the mining process include support, confidence, and lift. The analysis was conducted from 1-itemset to 5-itemset combinations to determine product co-occurrence frequencies. The results revealed several significant association rules. One notable rule shows that the production of Karet Membran TT, Panel Pressure Destec, and Plat C Starcam is followed by Join Tuas Starcam and Karet Membran COM, with a confidence of 90% and a lift value of 2.25. A lift greater than 1 indicates a strong correlation among the products. These findings are expected to provide data-driven insights that can support decision-making in warehouse management, inventory control, and the strategic arrangement and retrieval of products
Clustering and Sales Prediction Using K-Means and Simple Linear Regression Tia Aulia; Wowon Priatna; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.209

Abstract

CV. Cipta Usaha Selaras faces challenges in identifying customer purchasing patterns and accurately projecting sales values. The importance of this research lies in the company’s need for data-driven marketing strategies and efficient operational planning. This study employs the K-Means algorithm to cluster customers based on purchase frequency and total transaction value, as well as Simple Linear Regression to predict total purchases based on transaction frequency. The data analyzed consists of 358 sales transaction entries from the year 2024. The clustering results reveal three customer segments with distinct characteristics, with a Silhouette Score of 0.7913, indicating good segmentation quality. The regression model produced an equation with a coefficient of determination (R²) of 0.6910, a MAE of IDR 213 million, and a MSE of IDR 206 trillion. These results indicate that the applied approach provides a reasonably representative overview of customer purchasing behavior. This research offers a significant contribution to data-driven decision-making within the company, particularly in the development of marketing strategies and estimation of potential revenue.
Comparison of Naïve Bayes and K-Nearest Neighbor for Iphone 16 Youtube Sentiment Anisya Wulandari; Wowon Priatna; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.210

Abstract

Sentiment analysis plays an important role in understanding public opinion toward technological products, particularly in the context of social media such as YouTube. This study aims to analyze the sentiment of user comments on an iPhone 16 review video published by the GadgetIn YouTube channel, as well as to compare the performance of the Naïve Bayes and K-Nearest Neighbor classification algorithms. The data were collected through a crawling process, resulting in 2,499 comments, which were then split into training data 80% and testing data 20%. The methodology includes text cleaning, tokenization, normalization, and term weighting using the TF-IDF method. The experimental results show that the Naïve Bayes algorithm achieved an accuracy of 73%, with precision, recall, and F1-score each reaching 72%, outperforming KNN, which only achieved 65% accuracy. Most comments were neutral; positive comments generally focused on design and performance, while negative comments mainly highlighted price and comparisons with other products. These findings indicate that the Naïve Bayes algorithm is more suitable for sentiment analysis of unstructured YouTube comment data.
Toward Rigorous Zero-Shot and Few-Shot Benchmarking of Time-Series Foundation Models Under Domain Shift: A Leakage-Aware Benchmark Specification, Governance Framework, and Executable Pilot Instantiation Ibezimako Chiazagomekpere
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.254

Abstract

Time-series foundation models (TSFMs) are increasingly promoted as reusable forecasting systems that can generalize across domains with zero-shot or few-shot adaptation. That claim is scientifically consequential, but current evaluation practice remains under-specified where it matters most target-domain separation, contamination control, adaptation budget definition, shift severity characterization, and aggregation across heterogeneous deployment conditions. This paper reconstructs TSFM benchmarking as a methodological problem rather than a leaderboard problem. We formalize zero-shot and few-shot forecasting under domain shift as conditional risk estimation over governed target distributions; develop a forecasting-specific taxonomy of shift covering temporal regime, entity, resolution, schema, horizon, observation-quality, intervention, and label-formation change; and propose a six-layer benchmark architecture spanning model governance, dataset governance, deterministic shift generation, evaluation tracks, metric tensors, and reporting bundles. The contribution is primarily conceptual, but to avoid a purely rhetorical framework, we also provide an executable pilot instantiation on a public electricity-transformer forecasting setting. Because large-scale TSFM execution was not conducted in this package, the pilot uses lightweight surrogate forecasters to validate the benchmark machinery itself rather than to claim new TSFM state of the art. Even this limited pilot shows that in-domain and cross-domain rankings can diverge sharply, that adaptation gains must be interpreted jointly with cost, and that robustness to observation degradation and calibration cannot be inferred from average point error alone. The paper therefore advances a benchmark doctrine: credible TSFM claims require leakage-aware governance, severity-conditioned analysis, explicit adaptation accounting, and multi-objective reporting that aligns evidence with generalization claims.
Physics-Aware AI-Initialized Dynamical Downscaling for Regional Extreme-Weather Forecasting with Open Climate Data Mina Annetta; Shanti Purohit; Viljar Vagle; Deepak Deo
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.256

Abstract

Accurate regional forecasting of extreme precipitation remains difficult because the scales that control disaster-producing rainfall are neither fully resolved by global numerical weather prediction nor reliably preserved by current global artificial intelligence weather models. Global AI systems such as Pangu-Weather, GraphCast, GenCast, and related models have transformed medium-range forecasting skill and computational efficiency, yet they remain fundamentally constrained by coarse training targets, regression-induced smoothing, and limited direct representation of terrain-locked convection and local hydrometeorological extremes. This paper reconstructs and substantially extends an event-based manuscript on AI-driven regional forecasting into a submission-oriented framework centered on the more defensible idea of physics-aware regional extreme-weather forecasting or downscaling with open climate data. The core argument is that AI should not be treated as a wholesale substitute for high-resolution regional physics; rather, it should be used as a skillful large-scale predictor whose state can be physically harmonized and injected into a regional nonhydrostatic model. We therefore formalize an AI-initialized, physics-aware dynamical downscaling pipeline in which open global reanalysis and observation products are used to generate, constrain, and evaluate regional forecasts of extreme rainfall. The framework is instantiated using the published North China July–August 2023 extreme precipitation case, for which the original study compared WRF simulations driven by Pangu forecasts against WRF simulations driven by NCEP GFS forecasts across lead times of 0.5, 3.0, and 5.5 days. This paper contributes in three ways. First, it repositions the original study within the modern literature on AI weather forecasting, regional downscaling, and physically constrained machine learning. Second, it formulates the coupling problem mathematically, clarifies the state alignment needed to make AI forecasts dynamically usable by WRF, and introduces a coherent reliability-oriented evaluation logic based on error growth, threshold skill, and event-structure consistency. Third, it reorganizes the experiments and results into a rigorous narrative grounded in reproducibility. Using the published event-level metrics, the AI-initialized regional system outperforms the GFS-initialized counterpart at extended lead times. For the North China case, the maximum precipitation threshold retaining a Threat Score of at least 0.1 is 400 mm at 5.5-day lead for Pangu-initialized WRF, whereas the GFS-driven counterpart retains comparable skill only at 50 mm. At 0.5-day lead, both systems perform competitively, but the AI-driven system still exhibits stronger spatial correlation (0.76 versus 0.68) and lower RMSE (86.2 mm versus 96.4 mm). The evidence supports a restrained but important conclusion: physics-aware AI-initialized regional modeling is a promising route for long-lead extreme-weather forecasting, yet current evidence remains case-limited and should be interpreted as a strong event-based demonstration rather than universal proof of general superiority.

Page 7 of 8 | Total Record : 73

 4   5   6   7   8 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2026): May - August 2026 Vol. 4 No. 1 (2026): January - April 2026 Vol. 3 No. 3 (2025): September - December 2025 Vol. 3 No. 2 (2025): May - August 2025 Vol. 3 No. 1 (2025): January - April 2025 Vol. 2 No. 3 (2024): September - December 2024 Vol. 2 No. 2 (2024): May - August 2024 Vol. 2 No. 1 (2024): January - April 2024 Vol. 1 No. 3 (2023): September - December 2023 Vol. 1 No. 2 (2023): May - August 2023 Vol. 1 No. 1 (2023): January - April 2023 More Issue