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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 761 Documents
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Data-Driven Traffic for Infrastructure Planning: An LSTM Approach Using Indonesian Road-Vehicle Trends Aria Hendrawan; Nabilah Putri
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1516

Abstract

The rapid growth of motorized vehicles in Indonesia, unmatched by proportional expansion in road infrastructure, has intensified pressure on the national transportation system. This study examines the application of a Long Short-Term Memory (LSTM) model to analyze and forecast the national traffic load ratio, defined as the ratio of total motorized vehicles to total road length. Annual aggregate data from the Indonesian Central Bureau of Statistics (BPS) for the period 2016–2023 were used in the analysis. The results indicate that the model achieved a strong fit on the training data, with RMSE = 0.3652 and MAE = 0.3617, but performed substantially worse on the test data, with RMSE = 1.7585 and MAE = 1.7585. This discrepancy suggests overfitting, largely attributable to the extremely limited sample size. As such, the findings should be interpreted as exploratory rather than as evidence of reliable forecasting performance. Despite these limitations, the model projects a continued upward trend in national infrastructure pressure over the next five years. These findings provide an initial data-driven indication that transportation infrastructure demand in Indonesia is likely to intensify, while also underscoring the need for future research using larger datasets and baseline model comparisons before policy-level application can be justified.
A Multi-Algorithm Approach for Predicting OSCE Exam Passing Status Zulkifli; Panji Bintoro; Fitriana; Muhammad Galih Ramaputra; Hafsah Mukaromah
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1518

Abstract

This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams. Five machine learning algorithms Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (kNN) are assessed experimentally in this study. A dataset of 439 clinical competency data from Aisyah Pringsewu University midwifery students was used to create the model. Eight clinical skill factors were used as input, including baby massage, newborn care, and family planning services. To guarantee result stability, the 5-fold cross-validation approach was used for model validation. According to the test findings, every algorithm performs well, with an accuracy of more than 90%. On this particular dataset, SVM achieved a 100% classification accuracy, whereas Random Forest and SVM showed the most efficacy. With an average validation accuracy of 95%, neural networks also demonstrated excellent performance. This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams.
A Hybrid Certainty Factor–XGBoost Approach for Cyberattack Detection Using the TON_IoT Dataset Adiva Dwi Aprianto; Ratih Hafsarah Maharrani; Indi Cahya Ratna Auliya; Vania Rizky Alifiah
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1519

Abstract

Computer networks are vital to modern organizations, yet growing digital dependence has increased both the frequency and complexity of cyberattacks. To address this challenge, this study proposes an interpretable cyberattack detection framework that combines rule-based reasoning with machine learning through a hybrid Certainty Factor (CF)–XGBoost model. The framework integrates CF confidence scores and XGBoost probability outputs within a meta-learning classifier, enabling strong predictive performance while preserving explainability. Experiments conducted on the TON_IoT dataset using an 80:20 stratified split demonstrate that XGBoost achieved the highest accuracy at 99.61%, followed closely by the hybrid model at 99.42%, whereas the standalone CF model reached 76.31%. Although the hybrid approach produced a slightly lower accuracy than XGBoost alone, it substantially enhanced interpretability by connecting predictions to explicit rule-based reasoning. This makes the proposed framework especially suitable for Security Operations Center (SOC) environments, where transparent decision-making is essential. Overall, the findings suggest that the hybrid CF–XGBoost model offers a practical and explainable solution for cyberattack detection, though further validation on more diverse datasets is necessary before real-world deployment.
Data Mining Analysis for KIP Scholarship Eligibility Using Integrated DBSCAN and TOPSIS Imam Akbar; Chyquitha Danuputri; Rahma; Ita Sarmita Samad
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1534

Abstract

This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.
IS/IT Strategic Planning for Digital Maturity Development in an FMCG Skincare Enterprise Devi Yurisca Bernanda; Johanes Fernandes Andry; Francka Sakti Lee; Neti Amalia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1535

Abstract

In this study, an IS/IT strategic plan will be developed for FMCG Skincare Enterprises, a firm that operates within the oral care and personal care sector, with a high regard for innovation in its products. This research adopts a qualitative descriptive case study methodology is employed, whereby the study deliberately narrows down the scope of investigation to a single organization. Within this framework, the Ward and Peppard model is used to examine the external and internal environments of the business, in order to determine the information needs of the organization. Besides, the McFarlan Strategic Grid is applied to categorize and prioritize IS/IT initiatives in terms of their strategic importance. Based on the results, it is evident that the firm enjoys numerous competitive advantages, such as having innovative products and employing shoppertainment marketing strategies using digital technology to widen the market coverage. Nonetheless, the results also point out some strategic problems that the organization faces, such as having low production capacity and being heavily reliant on a specific digital marketing channel, TikTok Shop. This study proposes a hybrid system development strategy method as the best way to incorporate digital systems. The IS/IT strategic roadmap proposed will enable the firm to improve its digital structure, increase efficiency, and ensure that it transforms into a competitive organization in the beauty industry through time.
A Hybrid Ensemble Stacking Framework Integrating Long Short-Term Memory and Random Forest for Bitcoin Price Forecasting Akhlis Munazilin; Mochamad Agung Wibowo; Rizky Parlika
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1537

Abstract

Bitcoin is a non-linear and non-stationary digital asset that has become a highly volatile asset challenging the usual prediction models. In this paper, the authors present a problem-specific Hybrid Ensemble Stacking approach, the proposed approach, which combines the benefits of Long Short-Term Memory (LSTM) in terms of capturing long-term temporal variations with the power of Random Forest (RF) to process complex technical characteristics. The model follows a two-tier structure with a split ratio of 90:10 using BTC/USD historical data of Yahoo Finance and Binance (20102025) to combine the predictions of base learners with the use of a Linear Regression meta-learner. Findings show that pure LSTM has a low RMSE and MAE, but the Hybrid model has the best Mean Absolute Percentage Error (MAPE) of 3.54%. This means that the stacking mechanism will provide a more balanced error percentage, that is, it will enhance stability in forecasting at the phases of price discovery. It is novel in the sense that it uses macro-technical indicators to stabilize predictions in the face of market anomalies as a stacking scheme. These results have real-life implications on developers of financial systems in creating consistent crypto-asset risk management instruments.
Large Language Models for Intelligent Decision Support in Inventory and Supply Chain Operations: A Systematic Literature Review Yusuf Durachman; Eva Khudzaeva; Naura Aulia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1540

Abstract

Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), is increasingly explored to strengthen decision support in supply chain and inventory management by improving interpretability and access to analytics. However, prior work is scattered across optimization, simulation, logistics, and governance discussions, limiting clear system design guidance. This study conducts a Systematic Literature Review (SLR) following PRISMA 2020 across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar, yielding 200 records, of which 34 studies were included in qualitative synthesis. Results show that LLMs are predominantly positioned as orchestration and explanatory layers operating alongside structured components such as optimization solvers, simulation engines, and digital twins, rather than as autonomous decision-makers. Governance, organizational readiness, and trust emerge as central considerations for operational deployment. This review provides an evidence map linking LLM roles and integration architectures across supply chain and inventory contexts. While LLMs offer strong augmentation capabilities, direct empirical validation for specific contexts such as web-based inventory systems remains limited; design implications for such systems are derived from the broader corpus, underscoring the need for standardized evaluation benchmarks and targeted empirical studies.
Sentiment Analysis in Electronic Health Records for Patient-Centric Care: A Systematic Literature Review of Methods, Applications, and Challenges Caroline Mhlanga; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1545

Abstract

This study examines the role of sentiment analysis in EHR narratives for enhancing patient-centred care, focusing on methodological approaches, application domains, and implementation challenges in clinical settings. A systematic literature review (SLR) was conducted in accordance with PRISMA guidelines. Relevant studies were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed. The search, conducted in September 2025, included peer-reviewed articles published between 2021 and September 2025. The findings reveal a clear shift from rule-based and traditional machine learning approaches to transformer-based models. Sentiment analysis is increasingly applied in areas such as mental health, oncology, and patient experience monitoring. However, most implementations remain domain-specific and are not fully integrated into routine clinical workflows. This study provides a structured synthesis of sentiment analysis in EHRs and identifies key gaps between methodological advancements and real-world implementation. It advances a socio-technical perspective that integrates analytical performance, clinical applicability, and governance considerations, offering a consolidated lens for understanding sentiment-aware healthcare systems. Despite rapid methodological progress, the impact of sentiment analysis in EHRs remains constrained by limited scalability and insufficient integration into clinical practice.
Federated Learning for Privacy-Preserving Sentiment Analysis in Distributed Electronic Health Record Environments: A Systematic Literature Review Frederick Mlungisi Dandure; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1546

Abstract

Federated learning (FL) has emerged as a privacy-preserving approach for distributed healthcare analytics, yet its application to sentiment analysis of unstructured electronic health record (EHR) narratives remains limited. This systematic review examined the empirical maturity, methodological trends, and governance implications of federated sentiment-aware learning in distributed EHR settings. Following PRISMA 2020, searches were conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, and PubMed on January 5, 2026, covering peer-reviewed studies published from January 2021 to January 2026. After screening and eligibility assessment, 29 empirical implementation studies were included in the qualitative synthesis, while conceptual and survey papers were reviewed contextually but excluded from the core analysis. The evidence shows that FL in healthcare is advancing mainly in structured prediction and privacy-preserving infrastructure. By contrast, sentiment-aware learning on unstructured clinical narratives remains at an early stage, with limited implementation and validation. This review distinguishes empirical from conceptual contributions and proposes a governance-aware, literature-derived framework to guide future implementation-focused research.
Sensor-Driven Nutrient Monitoring Using a Two-Layer Machine Learning Model for Sugarcane Fertilization Recommendation Fadiana; Didi Supriyadi; Daniel Yeri Kristiyanto; Isnaeni Nurul Agita
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1547

Abstract

The growth of sugarcane requires optimal environmental conditions and the availability of balanced nutrients. However, fulfilling nutrition is a challenge because it requires targeted observation. The study proposes a machine learning-based decision support model using a predictive empirical approach to monitor nutrient needs and recommend fertilizer dosages. The proposed approach integrates field data with a two-layer modeling framework to support fertilization decision-making. The classification model predicts the status of nutrient adequacy, while the regression model estimates the level of fertilizer application. The target label (y) is generated through feature extraction using a rule-based empirical formula derived from the threshold of agronomic parameters. The nutrients analyzed included macronutrients (nitrogen, phosphorus, potassium) and micronutrients (iron, zinc, copper). Model development involves selecting the best-performing algorithm using recall for classification and RMSE and R² for regression. The results of the cross-validation showed that the Gradient Boosting algorithm achieved the most consistent performance, with a recall of 0.99 during training and >0.98 in holdout testing. The regression model also showed low RMSE and high R² values, especially for micronutrient estimation. The proposed model contributes to data-driven fertilization optimization.

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