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Jurnal Kecerdasan Buatan dan Teknologi Informasi
Published by CV. Ninety Media Publisher
ISSN : 29636191     EISSN : 29642922     DOI : https://doi.org/10.69916
Core Subject : Science,
Computer Science & IT
Jurnal Kecerdasan Buatan dan Teknologi Informasi or abbreviated JKBTI is a national journal published by the Ninety Media Publisher since 2022 with E-ISSN : 2964-2922 and P-ISSN : 2963-6191. JKBTI publishes articles on research results in the field of Artificial Intelligence and Information Technology. JKBTI is committed to becoming the best national journal by publishing quality articles in Indonesian and English and becoming the main reference for researchers. All submissions are blind and reviewed by peer reviewers. All papers can be submitted in BAHASA INDONESIA or ENGLISH. Scope : Neural Networks, Machine Learning, Deep Learning, Data Mining, Big Data, Decision-Making System, Information System, Mobile Application, Data Warehouses, Database, Internet of Thing, Expert System.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 101 Documents
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ARTIFICIAL INTELLIGENCE-BASED GOVERNMENT MANAGEMENT TRANSFORMATION IN IMPROVING THE QUALITY OF PUBLIC SERVICES IN THE ERA OF DIGITAL GOVERNANCE Lalu Ahmad Murdhani
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 3 (2025): September 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i3.465

Abstract

This study aims to analyze the transformation of government management based on artificial intelligence in improving the quality of public services in the era of digital governance. The study focuses on the use of AI in public-service delivery, administrative decision-making, and bureaucratic efficiency, while also examining the need for accountability and public ethics in its implementation. This research uses a qualitative method with an exploratory-descriptive and conceptual model-building approach. Data were collected through secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, and regulatory materials related to AI governance, digital government, public administration, and public-service innovation. The data were analyzed using thematic analysis to identify key patterns related to AI utilization, organizational readiness, decision-support systems, ethical risks, and accountability mechanisms. The findings show that AI can improve public services through automation, intelligent citizen interaction, complaint classification, document verification, and predictive service delivery. AI also supports bureaucratic efficiency by reducing repetitive administrative tasks, improving data-based decision-making, and strengthening service monitoring. The main contribution of this study is the formulation of an adaptive AI-based government management model consisting of five dimensions: AI-enabled service innovation, data-driven decision support, bureaucratic workflow redesign, human oversight, and ethical-accountable governance. This model emphasizes that AI transformation in government must be supported by institutional capacity, transparent procedures, human supervision, and public-value orientation.
BIG DATA AND ARTIFICIAL INTELLIGENCE IN LOCAL GOVERNMENT DISASTER RISK MANAGEMENT: TOWARD RESPONSIVE AND ADAPTIVE GOVERNANCE Lalu Ahmad Murdhani
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 1 (2026): January 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i1.466

Abstract

This study aims to analyze the integration of big data and artificial intelligence in local government disaster risk management to support more responsive and adaptive governance. The research focuses on the use of big data and AI for disaster mitigation, early warning, emergency response, and post-disaster aid distribution. This study employs a qualitative method with an exploratory-descriptive approach and conceptual model development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, disaster management guidelines, institutional reports, and regulatory materials related to digital governance, disaster risk reduction, big data analytics, artificial intelligence, and local government management. The data were analyzed using thematic analysis by classifying findings into key themes: data integration, AI-supported risk prediction, early warning, emergency coordination, aid distribution, institutional readiness, ethical risks, and public accountability. The findings show that big data can improve disaster governance by integrating geospatial, meteorological, population, infrastructure, social media, public complaint, and social assistance data. AI strengthens this process through predictive analytics, damage estimation, urgent-needs classification, evacuation support, misinformation detection, and assistance prioritization. The study contributes by proposing an integrated big data and AI-based local government disaster risk management model that links digital technology with mitigation, early detection, emergency response, and post-disaster recovery. The study implies that local governments must strengthen data governance, inter-agency coordination, human-resource capacity, transparency, privacy protection, and human oversight to ensure that AI-based disaster management remains accountable, ethical, and oriented toward public safety.
THE UTILIZATION OF BIG DATA IN REGIONAL DEVELOPMENT PLANNING: A STUDY ON STRENGTHENING EVIDENCE-BASED POLICY IN LOCAL GOVERNMENT Mujahidin
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 3 (2025): September 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i3.467

Abstract

This study aims to analyze the utilization of big data in regional development planning as a strategy to strengthen evidence-based policy in local government. The research focuses on how big data can support development program planning, poverty reduction, social assistance targeting, and basic-service improvement. This study uses a qualitative method with an exploratory-descriptive approach and conceptual framework development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, regional planning materials, and regulatory documents related to big data, digital governance, evidence-based policy, local development planning, poverty alleviation, and public services. The data were analyzed using thematic analysis by classifying the findings into several themes: data integration, evidence-based program formulation, poverty and vulnerability mapping, social assistance targeting, basic-service improvement, institutional readiness, data governance, and public accountability. The findings show that big data can improve regional planning by integrating population records, poverty databases, social assistance data, geospatial information, public-service indicators, village-level data, citizen complaints, and digital feedback. The study contributes by proposing an evidence-based local development planning framework consisting of five dimensions: data integration, analytical interpretation, program prioritization, accountable implementation, and continuous evaluation. This framework emphasizes that big data must be supported by institutional coordination, analytical capacity, ethical safeguards, public participation, and accountable governance to produce more accurate, inclusive, and responsive local development policies.
ARTIFICIAL INTELLIGENCE AS AN INSTRUMENT FOR LOCAL GOVERNMENT DECISION-MAKING: OPPORTUNITIES, RISKS, AND GOVERNANCE CHALLENGES Mujahidin
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 1 (2026): January 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i1.468

Abstract

This study aims to analyze artificial intelligence as an instrument for supporting local government decision-making, with particular attention to its opportunities, risks, and governance challenges. The study focuses on AI use in public services, licensing administration, and community-needs analysis, while emphasizing that AI must not replace the role of authorized public officials in governmental decision-making. This research uses a qualitative method with an exploratory-descriptive approach and conceptual governance framework development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, regulatory materials, and scholarly works related to AI, automated decision-making, digital governance, local government administration, explainable AI, and public-sector ethics. The data were analyzed using thematic analysis by classifying findings into AI opportunities, algorithmic risks, human oversight, explainability, administrative accountability, institutional readiness, and ethical safeguards. The findings show that AI can support bureaucratic decisions by improving document screening, service-priority classification, licensing risk assessment, complaint analysis, eligibility recommendation, and identification of community needs. The study also finds that AI may create risks of algorithmic bias, opacity, privacy violation, automation bias, and administrative exclusion. The main contribution of this study is the formulation of a human-supervised AI decision-support framework consisting of data governance, AI-based administrative analysis, human verification, accountable decision-making, and citizen redress mechanisms.
GOVERNMENT BIG DATA GOVERNANCE MODEL TO IMPROVE THE EFFECTIVENESS OF INTEGRATED PUBLIC SERVICES Muhammad Kautsar; Mujahidin
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 3 (2025): September 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i3.470

Abstract

This study aims to analyze a government big data governance model for improving the effectiveness of integrated public services. The research focuses on inter-agency data integration, system interoperability, data security, and the use of public data in integrated service delivery. This study employs a qualitative method with an exploratory-descriptive approach and conceptual governance model development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, digital government guidelines, regulatory materials, and scholarly works related to big data governance, data-driven government, interoperability, data security, and public-service innovation. The data were analyzed using thematic analysis by classifying findings into inter-agency data integration, system interoperability, data quality, data security, institutional coordination, collaborative governance, public-service effectiveness, and accountability. The findings show that integrated public services require more than digital applications or one-stop service portals. Effective integration depends on shared data standards, interoperable systems, secure data exchange, reliable data quality, and coordinated institutional responsibility. The study contributes by proposing a cross-sector government big data governance model consisting of institutional coordination, data integration, system interoperability, data quality assurance, data security, and collaborative service use. This model emphasizes that big data must be governed as a strategic public asset to improve service speed, accuracy, accessibility, transparency, and accountability.
CIVIL SERVANTS’ READINESS IN FACING THE IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN LOCAL GOVERNMENT BUREAUCRACY Marzuki; Mujahidin
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 1 (2026): January 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i1.471

Abstract

This study aims to analyze civil servants’ readiness in facing the implementation of artificial intelligence in local government bureaucracy. The study focuses on civil servants’ capacity, digital literacy, technological competence, organizational culture, and bureaucratic resistance to AI-based transformation. This research uses a qualitative method with an exploratory-descriptive approach and conceptual framework development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, regulatory materials, and scholarly works related to AI adoption, digital transformation, civil-service competence, public-sector innovation, organizational culture, and bureaucratic resistance. The data were analyzed using thematic analysis by classifying findings into digital literacy, technological competence, organizational culture, leadership support, bureaucratic resistance, ethical awareness, and institutional support. The findings show that AI implementation in local government depends not only on technological infrastructure, but also on civil servants’ ability to operate digital systems, interpret algorithmic recommendations, evaluate data quality, and maintain public accountability. The study also finds that bureaucratic resistance may arise from fear of job displacement, loss of authority, weak technical confidence, rigid work culture, and lack of training. The main contribution of this study is the formulation of a human-centered AI readiness framework consisting of digital literacy, technological competence, adaptive organizational culture, ethical awareness, and institutional support. This framework emphasizes that civil servants are the key actors of successful AI transformation in local government bureaucracy.
PEARLVISION AI: AN AUTOMATED PEARL QUALITY GRADING SYSTEM BASED ON MORPHOLOGICAL FEATURES AND ENSEMBLE LEARNING Karim, Muh. Nasirudin; Muhammad Masjun Efendi; Imran, Bahtiar
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 3 (2025): September 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i3.472

Abstract

Conventional pearl quality assessment remains heavily reliant on manual visual inspection, which is subjective and inconsistent. This study develops PearlVision AI, an automated system for grading Lombok pearls using morphological feature extraction and ensemble learning. The dataset comprises 361 South Sea pearl images (Pinctada maxima) labeled into three commercial grades: A (n=120), AA (n=120), and AAA (n=120). The proposed pipeline integrates hybrid segmentation (Hough Circle Transform + Convex Hull) for robust object isolation, extraction of four geometric descriptors (circularity, eccentricity, area, perimeter), and comparative evaluation of four classification algorithms: Random Forest, Gradient Boosting, K-Nearest Neighbor, and SVM (RBF). Results demonstrate that Random Forest achieved optimal performance with a test accuracy of 97.22% and a 5-fold cross-validation score of 91.68%, consistently maintaining precision, recall, and F1-score >0.95 across all grade classes. Feature importance analysis revealed that size-related features (area and perimeter) contributed more significantly to class discrimination than shape-based metrics (circularity), reflecting the natural correlation between pearl diameter and commercial value in this dataset. With an inference time of <0.5 seconds per image, PearlVision AI offers an objective, efficient, and reproducible solution for reducing manual grading bias and enhancing quality control consistency in the pearl industry
WALMART PRICE PREDICTION USING HOLT-WINTERS FORECASTING Melani Indriasari; Soleh, Muhamad; Muhamad Ramli; Sunarto; Sumiarti Andri
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.438

Abstract

Stock price prediction remains a complex challenge due to the volatile, noisy, and nonlinear nature of financial markets. This study aims to evaluate the effectiveness of the Holt-Winters Exponential Smoothing (HWES) method in forecasting the stock price of Walmart Inc. (WMT) and its application in investment decision-making. Historical monthly closing price data from January 2020 to December 2024 were collected and used to build an additive Holt-Winters model. The model was validated using out-of-sample data from January to February 2025, achieving RMSE of 4.535 USD and MAE of 4.801 USD, indicating good short-term predictive performance. The model was then used to forecast stock prices from March 2025 to December 2026, revealing a consistent upward trend with moderate seasonal fluctuations. However, deviations between predicted and actual values were observed during periods of market volatility, particularly in late 2025. To further evaluate performance, the Holt-Winters model was compared with the ARIMA model. Results show that ARIMA outperformed Holt-Winters in short-term forecasting with lower RMSE (4.71), MAE (4.26), and MAPE (4.21%), while Holt-Winters was more effective in capturing seasonal patterns. An investment simulation using a Dollar Cost Averaging (DCA) strategy combined with technical analysis indicators produced a total return of 3.45%, supported by both capital gains and dividend income. These findings suggest that while Holt-Winters provides a simple and interpretable approach for long-term forecasting, its performance can be improved by integrating adaptive models and external factors such as market sentiment and macroeconomic conditions for more robust predictions.
A SYSTEMATIC LITERATURE REVIEW ON THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN INFORMATION SYSTEM REQUIREMENTS ANALYSIS Matsuka, Riski Akbar; Prayogo Bagus Sudarmaji; Zaman, Zain Nur; Ilham Albana
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.452

Abstract

Requirements analysis is a critical phase in the development of information systems, as it significantly influences the overall success of a system. However, traditional approaches to requirements analysis are often performed manually and are prone to errors, inconsistencies, and inefficiencies. The advancement of Artificial Intelligence (AI) provides new opportunities to improve the effectiveness and automation of this process. This study aims to analyze the integration of AI in requirements analysis using a Systematic Literature Review (SLR) approach. The review follows the PRISMA 2020 guidelines and examines relevant studies published between 2020 and 2025. A total of 14 selected articles were analyzed to identify commonly used AI techniques, evaluate their effectiveness, and explore existing challenges. The results indicate that various AI techniques, including Machine Learning, Deep Learning, Transformer-based models, and Large Language Models (LLMs), have been widely applied in requirements analysis tasks such as classification, ambiguity detection, information extraction, and prioritization. These techniques demonstrate improvements in accuracy, time efficiency, and consistency compared to conventional methods. Despite these advantages, several challenges remain, including data imbalance, limited model generalization, lack of explainability, and limited validation in real-world industrial environments. Therefore, further research is needed to enhance the reliability and applicability of AI-based approaches in practical settings.
ENTERPRISE ARCHITECTURE PLANNING USING TOGAF ADM FOR FUEL DISTRIBUTION OPERATIONS Kaneshia Rahmadina, Putri; Nining Ariati; Agustina Heryati
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.454

Abstract

PT Dian Aristy Energi Palembang is a company engaged in the distribution of industrial fuel oil (BBM). The current operational processes are still conducted manually and are not integrated, leading to data duplication, reporting delays, low information accuracy, and difficulties in monitoring distribution activities, which affect managerial decision-making. This study aims to develop a strategic Enterprise Architecture plan based on TOGAF ADM to improve the alignment between information systems and fuel distribution operations. The research method used is qualitative descriptive with a case study approach, with data collection techniques including interviews, observations, and documentation. The TOGAF ADM phases applied consist of Preliminary Phase, Architecture Vision, Business Architecture, Information System Architecture, and Technology Architecture. The results of this study produce an Enterprise Architecture design that describes the current condition (AS-IS) and the proposed condition (TO-BE), including business process modeling, data architecture, application architecture, and supporting technology architecture. The proposed design enables the integration of operational processes through digital systems such as purchase order processing, distribution monitoring, and complaint management. This study concludes that the implementation of Enterprise Architecture based on TOGAF ADM can improve operational efficiency, data accuracy, information transparency, and support better decision-making, as well as provide a reference for the development of integrated information systems

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