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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 881 Documents
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User-Centered Design of Marker-Based Augmented Reality Learning Media to Improve Informatics Learning Effectiveness Chairunnisa Umi Kalsum; Ali Ibrahim; Ken Ditha Tania
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1704

Abstract

Computer science education in schools still relies heavily on traditional teaching methods, resulting in low student motivation, particularly in topics related to computer hardware components. This study develops an interactive Augmented Reality (AR) learning medium with Marker-Based Tracking using a User-Centered Design (UCD) approach to improve Informatics Learning effectiveness among 73 junior high school students at SMP Negeri 28 OKU. The UCD method was implemented through four stages: establishing requirements, designing, prototyping, and evaluating. Learning effectiveness was measured using N-Gain, a paired sample t-test, and Cohen's d based on pre-test and post-test scores. User experience was evaluated using the User Experience Questionnaire (UEQ), covering six dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. The N-Gain score reached 0.937, classified as "High." The paired t-test yielded a statistically significant difference between pre-test and post-test scores (p < 0.001), and Cohen's d was 1.757, indicating a "Very Large" effect size. UEQ evaluation results across all six dimensions exceeded the benchmark threshold for the "Excellent" category. These findings indicate improved learning outcomes in the evaluated class and positive user experience with the developed AR medium.
Comparative Machine Learning Models for Predicting SME Business Performance Azizah; Rahmat Gernowo; Budi Warsito; Jumi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1705

Abstract

Small and Medium Enterprises (SMEs) play an essential role in supporting economic growth, employment creation, and innovation across developing and emerging economies. To maintain competitiveness, SMEs need to improve their business performance by understanding the factors that influence organizational success. Several factors, including risk-taking, open innovation, cost leadership, proactiveness, aggressiveness, and autonomy, contribute to SME performance improvement. This study aims to develop and compare machine learning models for predicting SME business performance using these influencing factors. A dataset containing 283 SME records obtained from the Mendeley Data Repository was used in this study. Four machine learning approaches were evaluated, including Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and ANN optimized using Particle Swarm Optimization (PSO). The models were assessed using regression performance metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and computational execution time. The experimental results indicate that RF achieved the best prediction performance with an R² of 0.924, RMSE of 0.120, and MAE of 0.065, demonstrating high predictive accuracy and low error. XGBoost and ANN-PSO also showed competitive performance, while ANN achieved moderate results. Therefore, RF is recommended as an effective model for SME performance prediction. Future studies should employ larger datasets and external validation to improve model generalizability.
Residual Learning-Based Hybrid ARIMA–LSTM for Digital Retail Demand Forecasting Dwi Hartanti; Aprilisa Arum Sari
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1706

Abstract

The rapid growth of digital retail has increased the need for accurate demand forecasting to support inventory planning and operational decision-making. This study proposes and evaluates a residual learning-based Hybrid ARIMA–LSTM framework for daily retail demand forecasting. A univariate dataset containing 1,826 daily demand observations collected between January 2021 and December 2025 was preprocessed using data cleaning, Min–Max normalization, Augmented Dickey–Fuller (ADF) testing, first-order differencing, and sliding window transformation. ARIMA was employed to model the linear component of the demand series, while LSTM was used to learn nonlinear residual patterns. The proposed framework was evaluated against ARIMA, LSTM, Exponential Smoothing, Moving Average, and Naïve Forecast using MAE, MSE, and RMSE on the original demand scale after inverse normalization. The Hybrid ARIMA–LSTM achieved the lowest MSE (4668.71) and RMSE (68.32) among the evaluated forecasting models, whereas the standalone LSTM achieved a slightly lower MAE (49.87). Furthermore, residual analysis showed that the Hybrid ARIMA–LSTM produced stable forecasting performance, with residuals randomly distributed around the zero reference line, indicating minimal systematic prediction bias. This study demonstrates that a residual learning-based Hybrid ARIMA–LSTM framework can effectively improve daily digital retail demand forecasting by integrating statistical and deep learning models under identical experimental settings.
Comparative Analysis of Four Machine Learning Classifiers for Indonesian Hoax News Detection Dedi Irawan; Sudarmaji
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1710

Abstract

Across Indonesian online platforms, fabricated news spreads faster than fact-checkers can confirm. Because much of the literature relies on resource-intensive deep models, one applied question stays unsettled: which lighter, more transparent classifier best detects Indonesian hoaxes? We assessed four algorithms, Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Extreme Gradient Boosting (XGBoost), on 1,116 Indonesian articles from the MAFINDO/TurnBackHoax repository, using one shared preprocessing pipeline and two feature schemes, Bag-of-Words (BoW) and TF-IDF, under an 80:20 stratified split. On the held-out test set, Random Forest with BoW performed best at 98.66% accuracy and 98.59% macro F1-score, missing three of 224 cases, with XGBoost next at 98.21%. Under repeated five-fold cross-validation, however, XGBoost with BoW attained a significantly higher mean (98.36% versus 97.44% macro F1), so the two ensembles are best regarded as closely competitive rather than decisively ranked. BoW also beat TF-IDF for every classifier, most sharply for Naïve Bayes (84.70% versus 70.19% F1). Because the study uses lexical features alone on a single dataset, the findings indicate that classical ensembles can rival reported deep-learning figures at far lower cost, rather than forming a universal conclusion; broader validation across other sources and periods is still needed.
Evaluating E-Commerce User Satisfaction Using an Integrated PIECES and EUCS Framework Retno Waluyo; Jeffri Prayitno Bangkit Saputra
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1716

Abstract

E-commerce in Indonesia is growing rapidly because of the increase in internet users and digital commerce activities. This study evaluates user satisfaction with e-commerce applications using a partial integration of the PIECES Framework and selected EUCS dimensions, including Performance, Information and Data, Economics, Control and Security, Efficiency, Service, Format, and Timeliness. Rather than implementing the complete EUCS model, this study incorporates only the selected dimensions considered most relevant to evaluating user satisfaction with e-commerce applications. A quantitative approach was employed using questionnaires distributed to 200 e-commerce users in Banyumas Regency through purposive sampling. The data were analyzed using SPSS, including validity and reliability tests, classical assumption tests, multiple linear regression, the coefficient of determination, and hypothesis testing. The regression model explained 56.4% of the variance in User Satisfaction (Adjusted R² = 0.564), indicating moderate explanatory power. The results show that Information and Data, Economics, Efficiency, Service, and Timeliness significantly influence user satisfaction, whereas Performance, Control and Security, and Format do not have significant effects. These findings provide practical guidance for e-commerce providers to prioritize improvements in information quality, transaction efficiency, customer service responsiveness, and system usability usabulity that are limited to respondents in Banyumas Regency.
Digital Twin and AI in Marine Systems: A Bibliometric Analysis from an Information Systems Perspective Aditya Lapu Kalua; Mochamad Agung Wibowo; Luther Alexander Latumakulita
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1717

Abstract

Marine Digital Twin (DT) and Artificial Intelligence (AI) research spans maritime engineering, environmental monitoring, and governance contexts. This study provides a Scopus-based bibliometric and scientometric mapping of 289 English-language documents published from 2020 through April 10, 2026. VOSviewer and the Bibliometrix R-package were used for keyword co-occurrence mapping and descriptive analysis. Annual output was 4, 9, 24, 37, 50, 108, and 57 documents for 2020-2026, respectively; 2026 is a partial-year observation. China (n = 78), the United Kingdom (n = 31), and the United States (n = 27) were the leading countries. University College London (n = 10), Ningbo University (n = 8), and Dr. D. Y. Patil Institute of Technology (n = 7) had the highest affiliation counts. Three clusters were identified consistently: Industrial Maritime Applications, AI and Algorithmic Methods, and Environmental Monitoring and Earth Systems. The corpus remains dominated by engineering and offshore infrastructure, while the smaller environmental cluster does not by itself demonstrate a transition toward conservation. The Information Systems contribution is framed through data governance, semantic interoperability, platform ecosystems, and decision-support systems.
Open Government Data-Based Smart Tourism Analytics: A Conceptual Governance Framework Ucu Nugraha; Murnawan; Zakiah Darajat
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1718

Abstract

Digital transformation and Open Government Data (OGD) offer opportunities for more transparent, evidence-based, and sustainable tourism governance. However, prior smart tourism and OGD studies remain fragmented, often addressing data publication, analytics, governance, visualization, or sustainability evaluation separately. This study develops an initial conceptual framework for OGD-based Smart Tourism Analytics to support sustainable destination governance. The method combines a structured Scopus-only search, PRISMA-based screening, Biblioshiny mapping, systematic literature synthesis, and layered framework derivation. Of 68 Scopus records published during 2021–2026, 32 documents were selected, comprising 19 core studies and 13 contextual evidence sources. The analysis identifies persistent gaps in OGD integration, analytics readiness, governance control, stakeholder-oriented visualization, and outcome evaluation. The proposed framework includes five layers: OGD integration, smart analytics, decision support and visualization, sustainable destination governance, and tourism outcome evaluation. Pangandaran is used only as an illustrative application context, not as empirical validation. No prototype or live API integration was tested. Accordingly, the framework remains conceptual and requires prototype development, live API integration, and field validation with local governments.
Securing Educational Financial Data Against Insider Threats: A Hybrid Blockchain Approach with Merkle Tree Aggregation Adi Alfian Hafis; Soiful Hadi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1719

Abstract

Financial information systems in educational institutions face insider threats where privileged administrators can manipulate database records undetected by conventional security. This study proposes a Hybrid Blockchain architecture integrating Merkle Tree Aggregation and a Reversal Entry mechanism to establish a tamper-evident financial audit trail and address these data integrity gaps. Developed via the Design Science Research Methodology (DSRM), the system implements three cryptographic layers: a SHA-256 Recursive Transaction Hash Chain for local integrity, a Keccak-256 Merkle Tree for daily aggregation, and public Ethereum anchoring. Empirical evaluations demonstrate successful cryptographic integrity verification across all five database manipulation attack scenarios with zero false positives only under the five tested normal operational scenarios, relying strictly on deterministic hashing rather than AI-based anomaly detection. Computational latency remains consistent at 3.45 ms per transaction. Based on Sepolia testnet data under mainnet-equivalent projections, the 1,000:1 aggregation compression yields 99.90% cost efficiency compared to pure public blockchains, with sensitivity analysis confirming financial viability across volatile gas prices and exchange rates. These findings indicate the technical and economic feasibility of adopting a Hybrid Blockchain for detecting tampering and preserving data integrity in educational institutional financial data under the evaluated scenario.
Hybrid CNN-RNN Architecture with MFCC-LFCC Features for Audio Deepfake Detection Muh. Hajar Akbar; Nurfitria Ningsi; Aldi; Muhammad Na’im Al Jum’ah; Ilcham
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1723

Abstract

The proliferation of sophisticated audio deepfake technology poses a significant threat to digital voice authentication and forensic verification systems. This research addresses this challenge by developing and evaluating a lightweight hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) architecture for audio deepfake detection. The proposed model integrates a CNN for spatial feature extraction with a bidirectional RNN for temporal dependency modeling, utilizing an early vertically fused feature set of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) stabilized via utterance-level Cepstral Mean and Variance Normalization (CMVN). We assessed the proposed framework on the official ASVspoof 2019 Logical Access (LA) evaluation benchmark dataset (71,237 trials). Comprehensive evaluation on the official evaluation set demonstrated promising performance within the evaluated benchmark, achieving a Global Equal Error Rate (EER) of 8.24% and an Area Under the Receiver Operating Characteristic (ROC-AUC) of 0.9680, while maintaining an internal validation EER of 0.33% on known attacks. While showing high sensitivity to bona fide speech and robust resilience against advanced Neural Text-to-Speech synthesis (EER < 0.25% for A07–A10), the framework exhibits notable vulnerability to phase-preserving voice conversion attacks. Consequently, without real-world forensic operational testing, the model serves as an initial diagnostic screening approach rather than a fully operational forensic solution, and still requires further cross-repository and noisy-condition validation.
User Acceptance of E-Kinerja for Employee Additional Income Policy: A TAM Study in Central Lombok Baiq Amelia Suroyani; Sofiansyah Fadli; Baiq Yulia Fitriyani
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1725

Abstract

The increasing emphasis on professionalism, accountability, and performance management among State Civil Apparatus (ASN) has encouraged government institutions to adopt digital performance systems. This study examines user acceptance of the e-Kinerja application supporting the Employee Additional Income (TPP) policy at the Environmental Agency of Central Lombok Regency using the Technology Acceptance Model (TAM). A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0.9.9 was applied to questionnaire data from 41 ASN employees. The model included Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual Use (AU), with AU measured through self-reported indicators rather than system-generated logs. The measurement model demonstrated satisfactory reliability and convergent validity, although several HTMT values exceeded the recommended threshold. Structurally, PEOU significantly affected PU (β = 0.826; p < 0.001), PU affected ATU (β = 1.070; p < 0.001), ATU affected BI (β = 0.842; p < 0.001), and BI affected AU (β = 0.823; p < 0.001), whereas PEOU did not significantly affect ATU (β = −0.346; p = 0.212). The model explained 68.2% of PU, 65.3% of ATU, 70.9% of BI, and 67.7% of AU. The findings indicate that e-Kinerja acceptance is primarily driven by perceived usefulness, attitudes, and behavioral intention. Practical improvements should emphasize TPP transparency, real-time feedback, data accuracy, simplified reporting, user training, and technical assistance to support adoption.

Page 86 of 89 | Total Record : 881

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