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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 780 Documents
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A Framework for E-Government Service Management Implementation in Indonesia: An Actor-Network Theory (ANT) Perspective Yusuf, Muhammad; Sophan, Moch Kautsar; Muntasa, Arif; Dwi Cahyani, Andharini; Prastiti, Novi; Oluwakemi Oseni, Kazeem
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.v8i2.1542

Abstract

The Electronic Government Service Management System (EGSMS) is mandated by the Indonesian government; however, its implementation remains limited, particularly at the local government level. This study aims to examine the implementation of EGSMS in Madura, Indonesia, using Actor-Network Theory (ANT) as the theoretical lens to understand interactions among human actors, technological artefacts, institutional arrangements, and regulatory instruments. The research was conducted in the Departments of Communication and Informatics in Pamekasan and Sampang, Madura, Indonesia, during 2023 and 2024. Based on two local government case studies, this study develops an ANT-based framework for EGSMS implementation, which was subsequently reviewed by two experts to strengthen its relevance, validity, and practical applicability. The findings reveal actor-network dynamics, implementation challenges, enabling factors, and best practices in local EGSMS adoption. The novelty of this research lies in applying ANT to construct a context-sensitive EGSMS implementation framework grounded in empirical evidence. This framework is significant because it can serve as a practical guide for implementing and improving EGSMS in other local government contexts in Indonesia.
Citizen Role Representation in Digital Government Maturity Models: A Systematic Review Majid, Qurrota Ayun; Sensuse, Dana Indra
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.v8i2.1548

Abstract

This study investigates how citizen roles are represented and structurally positioned within Digital Government Maturity Models (DGMMs). Using a Systematic Literature Review guided by Kitchenham’s protocol, the review analyzed studies published between 2020 and 2025 across seven major academic databases. From 900 initial records, 14 studies met the inclusion and quality criteria. Through backward tracing, policy reference analysis, and cross-model extraction, these studies produced 77 DGMMs as the final units of analysis. The models were examined using a role-based analytical lens that classifies citizen representation into five levels: None, Limited, Implicit, Explicit, and Strong. The findings show that the Limited category remains dominant, indicating that most DGMMs still position citizens mainly as end-users or service recipients rather than active participants in digital governance processes. However, the increasing presence of Explicit and Strong models reflects a gradual shift toward participatory, collaborative, and citizen-centric digital governance. This study contributes by proposing a typology of citizen role representation that extends prior descriptive mappings into a deeper structural evaluation of how citizen participation, engagement, and co-creation are embedded within digital maturity
Machine Learning Classification of SCD, CHF, and NSR Using 15-Minute ECG-Derived HRV Features Panjaitan, Febriyanti; Ce, Win; Ramadhan, M. Fajar; Winarnie; Oktafiandi, Hery
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.v8i2.1557

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection essential for effective intervention. Heart Rate Variability (HRV) is widely used as a non-invasive marker for assessing cardiac conditions, and machine learning has shown potential in classifying heart diseases such as Sudden Cardiac Death (SCD) and Congestive Heart Failure (CHF). This study evaluates the performance of Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) using 15-minute ECG signals comprising three 5-minute segments. The dataset consists of 53 subjects, generating 159 segments, including SCD, CHF, and Normal Sinus Rhythm (NSR). To prevent data leakage, a subject-wise split (80:20) is applied for training and testing. Two evaluation scenarios are considered: per-segment classification and combined 15-minute classification. Results indicate that SVM and DT achieve consistently high, stable performance with near-perfect accuracy, precision, recall, and F1-score, whereas KNN shows lower, more variable performance, particularly in segment-based analysis. The combined 15-minute approach provides more stable results, suggesting improved HRV representation and class separability. Although the results are promising, further validation with larger, more diverse datasets is required to ensure robustness and generalizability. This study highlights the potential of HRV-based machine learning while emphasizing the importance of appropriate temporal representation and rigorous evaluation design.
Logistic Regression Modeling of Peatland Fire Hotspots in Bengkalis District Using Integrated Environmental and Anthropogenic Drivers Hayati, Nur; Sitanggang, Imas Sukaesih; Prasetyo, Lilik Budi; Syaufina, Lailan
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.v8i2.1560

Abstract

Peatland fires occur almost annually in Bengkalis District, Riau Province, Indonesia, where peatlands cover about 65% of the area and contribute significantly to carbon emissions and regional haze, highlighting the need for improved fire risk prediction. This research aims to apply a probabilistic logistic regression approach to predict peatland fire hotspot occurrence and identify its key drivers. Hotspot data from 2015–2023 were derived from VIIRS satellite observations and classified into low (l), nominal (n), and high (h) confidence levels. Then hotspot confidence levels are classified into two scenarios: (1) the nh scenario (l = 0; n–h = 1) and (2) the h scenario (l–n = 0; h = 1), representing different fire thresholds. The predictor variable was modeled using anthropogenic and environmental, with multicollinearity testing to ensure model stability. The results show that the nh scenario performs better, with Nagelkerke R² = 0.0681, Hosmer–Lemeshow χ² = 5.7663, AUC = 0.69, and accuracy = 95.19%, indicating acceptable fit and moderate discrimination. Significant predictors include plantation land use, peat characteristics, and precipitation. These findings suggest that the approach can support peatland fire risk assessment, although further refinement is required.
Comparative Performance Analysis of YOLOv12 and RF-DETR in Face Detection Hendrawan, David; Wahyuni; Adytia, Pitrasacha
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.v8i2.1561

Abstract

Face detection in dense and occluded environments remains a significant challenge in computer vision. This study compares the CNN-based YOLOv12 and the Transformer-based RF-DETR to determine the optimal balance between accuracy and latency for resource-constrained edge computing. Using the WIDER FACE dataset and an NVIDIA T4 GPU, multiple model variants were evaluated. Due to GPU memory constraints during training of the RF-DETR Medium variant, a standardized batch size of 8 was implemented across all models. To ensure methodological rigor, quantitative metrics (precision, recall, F1-score, mAP) were strictly assessed on the validation set. Concurrently, a 100-image subset of the test set was used exclusively for inference efficiency benchmarking, completely separate from detection evaluation. Results indicate YOLOv12X achieved superior overall detection performance (F1-score: 0.764, mAP@50:95: 0.440), significantly outperforming RF-DETR Medium. For real-time applications, YOLOv12M demonstrated the highest efficiency (36.17 FPS vs. 23.32 FPS). Qualitatively, YOLOv12 maintained high sensitivity in crowded scenes, whereas RF-DETR provided stable small-scale face detection despite its lower recall. Overall, under these constrained-hardware conditions, YOLOv12 appears to be a highly viable solution for surveillance systems, while RF-DETR offers a stable alternative for small-object detection when computational overhead and training budgets are less restrictive.
A Systematic Literature Review of Motivation, Trust, and Purchase Intention in Live Shopping Commerce: Toward an Integrated S-O-R Conceptual Model Mutmainah, Anisah; Nadlifatin, Reny
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.v8i2.1562

Abstract

The rapid growth of live shopping highlights the complex psychological mechanisms driving purchase intentions. However, previous research remains highly fragmented, often examining motivational or trust aspects in isolation. This study addresses this gap by developing an integrated conceptual model linking motivation and trust to consumer purchase intentions. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines. From an initial retrieval of 210 records, studies were systematically screened for relevance and appraised for quality, resulting in 40 empirical articles (2017–2025) extracted from the Google Scholar database using Publish or Perish. While relying exclusively on Google Scholar is a methodological limitation, it provides a practical baseline for this review. The synthesized literature underscores the Stimulus-Organism-Response (S-O-R) framework's dominance. Findings indicate that social and hedonic motivations act as dominant stimuli triggering social presence. Crucially, this engagement builds multidimensional trust in the streamer, which is subsequently transferred to the platform and product, reducing uncertainty and driving purchases. The proposed model integrates these effective and cognitive pathways into an evidence-based framework. Ultimately, the study demonstrates that social interaction precedes cognitive trust formation, providing a structural baseline for future empirical validation in live commerce.
Detecting Experience Debt in Internal IT Services: A Minimal Touchpoint-Based XLA for Small EdTech Startups Marcel; Marzuqi, Tubagus Ahmad
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.v8i2.1567

Abstract

Small EdTech startups often use SLA numbers to check whether internal IT services are working well. The problem is that these numbers usually show only whether the service was restored and how long it took. They do not show what users actually experienced while the problem was happening. This study looks at that hidden part. It uses the idea of experience debt to describe small but repeated service problems that slowly make daily work harder. The study was conducted in three small EdTech startups in Jakarta. It used survey data and interview data collected in the same period. The survey produced 274 valid responses across six touchpoints and five experience dimensions. The interviews involved 18 informants and focused on the service moments they remembered as most difficult. The weakest point appeared at TP2, where users faced access problems and often felt confused about what was happening, what to do next, and whether the issue was really finished. At TP4 and TP5, the main problem was fairness. Users often felt stuck in a process they could not see, had to repeat the same explanation, or did not know who was handling their request. From these findings, the study developed a simple touchpoint-based XLA as a one-page review tool. Its purpose is to help small teams notice user experience problems that normal SLA monitoring often misses. More research is still needed in other service settings and organisations.
Climate Change Impacts, Agro-Ecosystem Stress, and Adaptive Agricultural Strategies in South Asia: A Scopus Bibliometric Analysis (1997–2026) Biswas, Dipika
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.v8i2.1574

Abstract

This study presents a bibliometric analysis of research on climate change impacts, agro-ecosystem stress, and adaptive agriculture in South Asia from 1997 to 2026. Initially, 2,260 documents were retrieved mainly from the Scopus database, and 1,580 documents were selected after applying predefined screening criteria. Using bibliometric and informatics-based analytical tools, the study examines publication trends, collaboration networks, influential contributors, and thematic evolution in the field. The findings reveal a significant increase in scholarly output, particularly after 2015, indicating growing regional and global concern over climate-induced agricultural challenges. Keyword analysis highlights the dominance of climate change, drought, and resilience, reflecting a strong focus on ecological vulnerability, food security, and livelihood sustainability. Country-level analysis shows that India leads research production, followed by Pakistan, Bangladesh, and China. Major thematic clusters include climate resilience, sustainable agriculture, food security, and environmental stress management. Despite this growth, gaps remain in the integration of information systems, interdisciplinary collaboration, and region-specific adaptation strategies. This study provides a structured overview of the intellectual landscape and offers useful insights for researchers, policymakers, and practitioners working toward climate-resilient and sustainable agriculture in South Asia.
AI in Cybersecurity: A Systematic Review and Conceptual Audit Model Rananga, Ndaedzo; Venter, H.S
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.v8i2.1579

Abstract

Technological advances, particularly in Artificial Intelligence (AI), are accelerating digital transformation while increasing system complexity and exposure to sophisticated cyber threats. These developments challenge traditional cybersecurity audit approaches, which are largely periodic, retrospective, and focused on binary control checks. In response, the adoption of generative AI (GenAI) and predictive AI (PredAI) in cybersecurity auditing is becoming increasingly important. Although AI can improve audit intelligence, scalability, timeliness, and effectiveness, its use also raises concerns about transparency, governance, and auditor independence. This study employed a two-stage methodology. First, a systematic literature review following PRISMA examined studies published between 2021 and 2026, yielding 36 eligible articles. The review found that hybrid AI approaches dominate the literature (58.3%), followed by GenAI (25.0%) and PredAI (16.7%). Despite this growing interest, the literature gives limited attention to risk-based auditing approaches that move beyond binary control confirmation toward context-aware, intelligence-driven cyber risk assessment. Second, using Design Science Research, the study developed the conceptual Anti-Sheriff cybersecurity auditing model. The model shifts auditing from compliance-driven enforcement to intelligence-supported risk governance, enabling continuous auditing, better risk prioritisation, and stronger organisational cyber resilience.
A Hybrid Feature-Enriched IndoBERT Framework for Sentiment Analysis of Ride-Hailing Service Reviews in Indonesia Triawan, Puas; Tahyudin, Imam; Purwadi
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.v8i2.1587

Abstract

This study examines sentiment classification for Indonesian ride-hailing user reviews, which often contain informal expressions, ambiguity, and strong contextual dependency. Existing studies commonly rely on either traditional machine learning or transformer-based models, while limited attention has been given to integrating heterogeneous feature representations. To address this gap, this study proposes a feature-level hybrid integration strategy combining TF-IDF and IndoBERT embeddings. This approach enables the model to capture statistical term importance and contextual semantic meaning within a unified representation. A quantitative experimental design was applied to approximately 20,000 reviews collected from Gojek, Grab, and Maxim. Sentiment labels were generated through rating-based mapping and manually validated for consistency. The dataset, which was relatively balanced across positive, neutral, and negative classes, was divided into training and testing sets using an 80:20 split. Model performance was evaluated on the test set using accuracy, precision, recall, and F1-score. The proposed hybrid model achieved the highest accuracy of 93.5%, outperforming IndoBERT (91.8%) and traditional machine learning models (78.4%–87.6%). The results show that feature-level integration improves sentiment classification performance, although neutral sentiment remains challenging due to contextual ambiguity.

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