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All Journal Jurnal Digit : Digital of Information Technology MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Integrated System and Management Technology Indonesian Journal of Infomatics Computer Architecture and Signal Processing Software Engineering in Computing Systems Big Data Analytics and Data Science Cyber Security and Network Management
Sri Supatmi
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering

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SITAMPAN: Mobile application for planting and harvesting of horticultural crops in Garut Regency Eddy Soeryanto Soegoto; Lia Warlina; Sri Supatmi; Agis Abhi Rafdhi; Rizky Jumansyah; Herry Saputra
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 12 No. 3 (2022): Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v12i3.123-136

Abstract

Garut Regency’s agriculture sector has the most impact on the region’s GDP compared to other sectors. The Central Statistics Agency reports that the agricultural sector’s contribution was 39.11% in 2014 and will be 37.97% in 2020. The farmer’s inability to manage their crop goods is the primary cause of the contribution decline. The study aims to build a prototype of a mobile application information system for planting and harvesting. commodities based on a geospatial information system. The research method used was field surveys, interviews, and focus group discussions (FGD) with farmers, farmer group leaders, agricultural extension workers, and officials at Garut Regency Agriculture Office. The designed application is Sitampan which stands for Sistem Informasi Tanam dan Panen (Planting and Harvesting Information System). The users of this application are farmers, farmer group leaders, agricultural extension workers, and Garut Regency Agriculture Office. Each user will have a specific role and access based on their roles. The public can only see the data contained in this application. In conclusion, this app can serve as a platform for information and communication for farmers. Farmers can use this app as a decision-making tool to manage their crops, including when to sell, plant, and harvest. Hopefully, this application will enhance the welfare of farmers, particularly those in Garut district, one of the industries that have the most impact on the Indonesian agricultural sector.
ANALISIS KINERJA FUZZY LOGIC DALAM SISTEM PENETASAN TELUR OTOMATIS DENGAN FITUR MONITORING BERBASIS TELEGRAM BOT Rifqi Fahrudin; Ridho Taufiq Subagio; Petrus Sokibi; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Jurnal Digit : Digital of Information Technology Vol 16, No 1 (2026)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v16i1.459

Abstract

Temperatur dan kelembaban merupakan dua faktor utama yang menentukan keberhasilan penetasan telur. Berdasarkan referensi, temperatur optimal dalam mesin tetas yaitu 35-39°C dan kelembaban optimal yaitu 40%- 56%RH. Namun kebanyakan mesin penetas telur konvensional yang ada dipasaran hanya memperhitungkan satu faktor saja yaitu temperatur. Untuk itulah digunakan system fuzzy logic control agar kestabilan suhu dapat terjaga. Dengan menggunakan nodemcu sebagai pengontrolan utama, hasil pembacaan sensor akan diproses sesuai dengan Algortitma Fuzzy Logic yang telah ditanamkan dalam minimum sistem. Lalu akan disesuaikan dengan Set Point yang telah ditetapkan. Output dari alat berupa sinyal digital yang akan mengontrol elemen fan cooller berupa kipas 5V DC. Logika fuzzy akan berjalan sesuai suhu ruangan jika suhu didalam ruangan lebih dari 39°C kipas akan berjalan sesuai output dari fuzzy rulebase. Jika suhu melebihi setting point yaitu 37-39°C maka lampu pijar akan mati, dan akan menyala kembali jika suhu kurang dari 38°C. Dalam hal ini semua aktivitas dalam ruangan penetas telur dalam di monitoring melalui telegram.  Hasil pengujian menunjukkan bahwa sistem kendali logika fuzzy berbasis parameter suhu dan kelembapan mampu menghasilkan keluaran yang presisi. Sebagai contoh, pada kondisi suhu 32°C dan kelembapan 60%, sistem secara otomatis mengaktifkan lampu dan mengatur kecepatan kipas sebesar 50% (Level 1) untuk menjaga stabilitas kondisi ruangan.Kata kunci: fuzzy logic control, suhu, kelembaban, NodeMCU, Telegram.
Integrated and Spatiotemporal Predictive Data-Driven Narcotics Intelligence Ecosystem: Governance, Interoperability, and Analytics Pipeline for Evidence-Based Policy : Case Study: BNNP West Java, Indonesia Luki Ishwara; Agus Nursikuwagus; Ednawati Rainarli; Zainal Arifin Hasibuan; Sri Supatmi
Integrated System and Management Technology Vol. 1 No. 2 (2026): July: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i2.436

Abstract

Across BNN, police, health, corrections, and local government, Indonesia's crossagen cynarcotics control produces a lot of data yet fragmented, limiting timely identification of abuse patterns,hotspots, and resource requirements. It aims to bridge the gap between international breakthroughson the use of machine-learning–based monitoring and optimization under uncertainty and underdeveloped provincial integration of governance, interoperability, and predictive analytics in the public sector. It is about designing and assessing an integrated spatiotemporal predictive, datadriven narcotics intelligence ecosystem for BNNP West Java. The approach combines iterative information systems engineering with an embedded case study and a mixed-methods evaluation covering seven phases: requirements structuring; data governance and quality; federated/hybrid interoperability and Privacy-Preserving Record Linkage; spatiotemporal predictive pipelines with both baseline and advanced models and anomaly detection; hotspot and risk mapping; early warning and situational dashboards linked to operational protocols; and implementation assessment with institutional learning.Evaluation utilizes quantitative measurements for data quality and model performance (including lead time and false-alarm considerations) and qualitative findings evaluating governance readiness and usability. Expected outputs can comprise a four-pillar framework bridging governance and policy impact, replicable artefacts to be deployed at the provincial level, and implications for evidence-based narcotics policy under national digital-government agendas, with considerations for data-access andprivacy limitations.
The Effect of Data Imbalance on the Interpretation Stability of LIME-Based Explainable AI on Nutritional Status Prediction Models Sri Nurhayati; Hidayat Hidayat; Siti Ar-Rachmi Ningrum; Zainal Arifin Hasibuan; Sri Supatmi
Indonesian Journal of Infomatics Vol. 1 No. 2 (2026): May: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i2.438

Abstract

Data imbalance is a common challenge in nutritional status prediction because it can reduce classification performance and influence the reliability of Explainable Artificial Intelligence (XAI) interpretations. This study aims to examine the impact of data imbalance on the stability of Local Interpretable Model-Agnostic Explanations (LIME)-based interpretations. A Random Forest model was developed under two scenarios: using the original imbalanced dataset and using a balanced dataset generated through the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated and compared, followed by LIME-based interpretation and stability analysis. The results indicate that SMOTE enhanced the model’s ability to identify minority classes, with recall increasing from 0.36 to 0.55, although overall accuracy slightly declined. LIME analysis revealed changes in feature contributions between the two scenarios, reflecting the influence of data distribution on model explanations. The interpretation stability score reached 0.80, suggesting relatively consistent explanations despite variations in class balance. These findings highlight the importance of jointly evaluating predictive performance and interpretation stability in health-related machine learning applications.
Data-Driven Learning Analytics Conceptual Framework for Automated Competency Mapping in Outcome-Based Education: A Design Science Research Approach Hasbu Naim Syaddad; Zainal Arifin Hasibuan; Bobi Kurniawan S; Sri Supatmi; Agus Nursikuwagus; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.396

Abstract

The implementation of Outcome-Based Education (OBE) in higher education demands precise measurement of Graduate Learning Outcomes (CPL) and Course Learning Outcomes (CPMK). However, current conventional Learning Management Systems (LMS) remain static and centered on final performance metrics (grades), thus failing to map student academic profiles into sub-competencies in a real-time and granular manner. This study proposes a conceptual artifact in the form of an Intelligent Tutoring System (ITS) architecture based on Learning Analytics (LA) and Knowledge Graphs to automate competency mapping. Through the Design Science Research Methodology (DSRM) approach, this framework designs a data fusion pipeline that integrates high-resolution academic log data with curriculum ontologies. The proposed architecture consists of three main layers: data acquisition, predictive modeling using Machine Learning, and a recommendation engine based on Explainable AI (XAI). This conceptual framework provides a blueprint for higher education institutions to transform from reactive curriculum evaluation into precise and auditable adaptive learning governance.
Adaptive-Cognitive Smart Farming Architectures for Food Security Resilience: A Systematic Literature Review of IoT and AI-Based Approaches Ridho Taufiq Subagio; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi; Hidayat Hidayat; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.445

Abstract

Food security resilience has become an increasingly critical global concern due to the combined effects of climate change, population growth, and resource scarcity. Conventional agricultural practices are no longer sufficient to meet rising food demands, thereby necessitating the adoption of intelligent and adaptive technological solutions. Smart farming, enabled by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), has emerged as a promising approach to enhance agricultural productivity, efficiency, and sustainability. However, existing smart farming systems remain fragmented and lack adaptive and cognitive capabilities required to dynamically respond to environmental variability. This study proposes an adaptive-cognitive smart farming architecture that integrates IoT, AI, edge-fog-cloud computing, federated learning, and digital twin technologies into a unified framework. A Systematic Literature Review (SLR) is conducted to synthesize insights from 60 high-quality publications indexed in IEEE, Elsevier, and Scopus databases. The proposed architecture adopts a multi-layered design consisting of sensing, edge-fog, cloud, cognitive, and application layers, enabling real-time data processing, distributed intelligence, and adaptive decision-making. To validate the proposed model, experimental simulations are performed using key performance indicators, including accuracy, mean squared error (MSE), latency, and resource efficiency. The results indicate that the proposed approach achieves superior performance, with an accuracy of 89%, a substantial reduction in latency, and improved resource utilization. These findings demonstrate that incorporating adaptive and cognitive intelligence significantly enhances system responsiveness and decision-making capabilities. This study contributes to both theory and practice by introducing a comprehensive framework for next-generation smart farming systems, ultimately supporting food security resilience in an increasingly uncertain environment.
Success Factors of Government Digital Applications in Public Service Delivery: A Systematic Literature Review Rifqi Fahrudin; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.395

Abstract

The rapid development of digital government applications has significantly transformed public service delivery; however, their success remains inconsistent due to the complexity of multiple influencing factors. Many government digital systems experience low adoption, usability challenges, and limited impact on service quality, indicating the need for a comprehensive understanding of the determinants of success. This study aims to identify and synthesize the critical success factors of government digital applications in public service delivery. To achieve this objective, a systematic literature review (SLR) was conducted using the Scopus database, applying a predefined search strategy and PRISMA-based screening process. From an initial set of 176 articles, 44 relevant studies were selected and analyzed using a coding framework to classify success factors into four dimensions: technological, organizational, user, and governance. The results show that digital government success is inherently multidimensional, with user-related factors such as trust, usability, and satisfaction emerging as the most dominant, while technological factors function as enabling components and organizational and governance factors ensure sustainability and effectiveness. Furthermore, the findings reveal significant research gaps, particularly the lack of integrated frameworks and the fragmented treatment of success factors in existing studies. This study concludes by proposing an integrated classification framework that provides a comprehensive understanding of digital government success and offers practical guidance for policymakers in designing more effective and sustainable digital public services.
The Integration Of Non-Academic Variables In Student Risk Assessment: A Conceptual Framework Hani Irmayanti; Eddy Soeryanto Soegoto; Hidayat Hidayat; Rio Yunanto; Zainal Arifin Hasibuan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.435

Abstract

Students’ success in completing their studies on time is a vital indicator of the quality of higher education management in Indonesia. However, high dropout rates pose a major challenge, often caused by institutions’ failure to detect warning signs of academic failure in a timely manner. The main issue lies in the current evaluation approach, which is reactive and limited to conventional academic indicators such as the Grade Point Average (GPA), thereby neglecting the psychosocial factors that influence performance. This study aims to develop a more comprehensive conceptual framework for the early detection of academic failure risk by integrating academic and non-academic dimensions. The methodology used is adapted from the Design Science Research Methodology (DSRM), focusing on the stages from problem identification to the design of the model artifact. The proposed approach is a hybrid model that combines traditional academic variables with non-academic variables, including psychological stress levels, self-efficacy, and social support. The design results indicate that this framework is capable of identifying “latent pressure” as a leading indicator of failure before a decline in academic performance occurs. The synthesis of this study confirms that the integration of non-academic variables enhances the model’s transparency and provides a more meaningful and targeted interpretation of risk factors. In conclusion, this framework provides a theoretical foundation for educational institutions to transition from reactive evaluation to a system of personalized, proactive interventions. The implementation of this model is expected to improve student retention through earlier and more targeted risk mitigation.
Artificial Intelligence-Based Early Warning System for Disaster Management: A Literature Review Systematic and Bibliometric Analysis Ridwan Zulkifli; Zainal Arifin Hasibuan; Irawan Afrianto; Bella Hardiyana; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.392

Abstract

The increasing frequency and intensity of natural disasters globally demands the development of more accurate and responsive Early Warning Systems (EWS). In recent years, Artificial Intelligence (AI) has been increasingly applied in natural disaster mitigation, but the approaches used are still diverse and spread across various domains. This study aims to present a systematic literature review on the application of AI and deep learning in natural disaster early warning systems. This review was conducted following the PRISMA 2020 guidelines by analyzing literature published during the 2020–2025 period. The selection process resulted in 102 studies meeting the inclusion criteria, with 30 full-text articles being analyzed in depth to map disaster types, AI methods, data sources, and characteristics of early warning systems developed in various regions, including Asia and Africa. The review results show the dominance of deep learning approaches, particularly time series-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), particularly in flood forecasting and land deformation prediction. More advanced architectures, such as Transformer, are beginning to be adopted to capture long-term temporal patterns, while the combination of convolutional neural networks (CNN) with remote sensing data is widely used for spatial mapping of disaster events. Furthermore, the integration of sensor data and the Internet of Things (IoT) shows potential in supporting more responsive early warning systems. However, most research remains limited to the modeling or simulation stage, with little discussion of the real-time and operational implementation of EWS. This review highlights the gap between AI model development and the implementation of reliable early warning systems and provides a conceptual foundation for the future development of more integrated AI-based disaster mitigation systems.
Classification, Prediction, and Prescription of Digital Government Governance Maturity Levels: Leveraging SPBE Index Data (2019–2024) for Evidence-Based Regional Digital Government Architecture Planning in Indonesia Andi Agus Salim; Zainal Arifin Hasibuan; Agus Nursikuwagus; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.409

Abstract

Indonesia's transition from the SPBE evaluation framework to the 2025–2029 Pemdi (Digital Government) Index marks a strategic shift toward comprehensive governance maturity. However, regional governments face significant challenges in strategic planning due to the absence of empirical models linking historical SPBE performance to future Pemdi trajectories and a lack of data-driven guidance for prioritizing governance interventions. This research aims to develop an integrated Classification-Prediction-Prescription (CPP) framework to classify, forecast, and prescribe regional digital government governance maturity levels. The proposed methodology employs machine learning algorithms (Random Forest and Gradient Boosting) to conduct multi-class classification (five maturity levels) and regression (continuous score prediction) using longitudinal SPBE data (2019–2024) from 548 Indonesian regional governments. This quantitative approach is complemented by feature importance analysis and scenario-based simulations to generate actionable insights. The models are projected to achieve over 85% classification accuracy and a regression RMSE of under 0.5. The synthesis of main findings reveals that indicators within the policy and architecture planning domains are the strongest predictors driving maturity progression. Furthermore, the study segments regional governments into four distinct trajectory clusters and formulates a tailored prescriptive recommendation matrix across multiple planning horizons. In conclusion, the CPP framework effectively translates national evaluation data into actionable intelligence, empowering regional governments to optimize resource allocation, prioritize high-impact interventions, and systematically align their digital transformation pathways with formal planning documents such as the RPJMD and Regional Action Plans.
Co-Authors Agis Abhi Rafdhi Agus Nursikuwagus Andi Agus Salim Bella Hardiyana Bobby Kurniawan Bobi Kurniawan S Bobi Kurniawan, Bobi Citra Noviyasari Eddy Soeryanto Soegoto Ednawati Rainarli Hani Irmayanti Hasbu Naim Syaddad Hernalom Sitorus Herry Saputra Hidayat Hidayat Irawan Afrianto Lia Warlina Luki Ishwara Petrus Sokibi Ridho Taufiq Subagio Ridwan Zulkifli Rifqi Fahrudin Rio Yunanto Rizki Adha Rizky Jumansyah Siti Ar-Rachmi Ningrum Sri Nurhayati Ucu Nugraha Zaenal Arifin Hasibuan Zainal Arifin Hasibuan