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Bahtiar Imran
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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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SHORTEST ROUTE SEARCH TO ACCOMMODATIONS NEAR MANDALIKA CIRCUIT USING DIJKSTRA'S ALGORITHM AND ANDROID-BASED LOCATION-BASED SERVICE Moch. Syahrir; Ahmad Subandi Azmi; Kurniadin Abd. Latif; Pahrul Irfan
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.v5i2.443

Abstract

The development of mobile technology, particularly on the Android platform, has created significant opportunities for real-time, location-based applications. One important implementation is the use of Location Based Service (LBS) in the tourism sector to help tourists efficiently find strategic locations. This study focuses on developing an Android-based LBS application that integrates the Dijkstra Algorithm to determine the shortest route to accommodations around the Mandalika Circuit area, Kuta Beach, Lombok, a leading destination for MotoGP events in Indonesia. The system development adopts the waterfall model, consisting of requirement analysis, system design, implementation, and testing. In the analysis phase, user needs related to accommodation information and route navigation are identified. The design phase includes system architecture, user interface, and digital map integration. Implementation is carried out by developing an Android application capable of accessing real-time location data and processing route calculations using the Dijkstra Algorithm to produce the most efficient path. The resulting application displays the distribution of nearby accommodations, provides travel distance information, and offers optimal route guidance that can be directly accessed by users. System testing shows that the application runs according to the defined functional requirements. Additionally, evaluation using a Likert-scale questionnaire indicates a user satisfaction level of 84%, reflecting good acceptance and usability. In conclusion, this research successfully implements LBS technology combined with the Dijkstra Algorithm in a mobile application, providing practical solutions for tourists visiting the Mandalika Circuit area.
AN ANALYSIS OF FEAR OF MISSING OUT (FOMO) AS A DRIVER OF HOAX DISSEMINATION IN THE PRABOWO ERA USING MLP Saputra, Irfan; Agustina Heryati; Hendra Di Kesuma
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.v5i2.444

Abstract

The development of social media over the past decade has accelerated the spread of information, including hoaxes, which impact public perception and political stability. One psychological factor contributing to the impulsive spread of information is Fear of Missing Out (FOMO), defined as the feeling of anxiety experienced when individuals believe they are missing important information or events. This study aims to analyze the relationship between the FOMO phenomenon and the tendency to spread political hoaxes related to the Prabowo administration on social media. The research data was obtained through comment crawling techniques on the TikTok platform and then processed using the following stages: preprocessing text (e.g., cleaning, case folding, tokenizing, filtering, stemming) and labeling of FOMO, Non-FOMO, Hoax, and Non-Hoax classes. The Multi-Layer Perceptron (MLP) model is used to classify user behavior patterns. FOMO plays a role in increasing the spread of fake news in the political sphere, and this demonstrates that a combination of psychological factors and machine learning techniques can help understand the dynamics of disinformation on social media.
DESIGN AND DEVELOPMENT OF AN EARLY WARNING SYSTEM THROUGH CONTINUOUS AUDITING AND CONTINUOUS MONITORING IN PUBLIC SECTOR PROCUREMENT R Wisnu Prio Pamungkas; Rakhmi Khalida
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.v5i2.445

Abstract

The background of this research is the challenge in supervising goods and services procurement in the public sector, which is still dominated by traditional, reactive auditing methods conducted after transactions are finalized. The primary issue is the high volume of transactions and data complexity, which hinders early fraud detection. This research aims to design and develop an early warning system using Continuous Auditing and Continuous Monitoring (CACM) methods to enhance the effectiveness of fraud detection. The research method involves system development based on data integration from the Electronic Procurement System (SPSE) and other supporting monitoring systems. By utilizing data analytics, the system is designed to automatically identify risk indicators based on tender winner patterns and bidding behavior. The results indicate that CACM implementation enables real-time anomaly identification, providing early warning signals for auditors to take preventive measures before broader irregularities occur. In conclusion, the application of the CACM system transforms the internal oversight paradigm into a more proactive approach, strengthening fraud detection capabilities while improving accountability and transparency in government procurement processes
COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR MACHINE FOR FOOD CALORIE LEVEL CLASSIFICATION Oktaviadi Resmiranta, Dading; Tanwir; I Gede Yogi Pratama; Naufal Hanif; Azral Satriani; Khairan Marzuki
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.v5i2.450

Abstract

The rapid escalation of global metabolic health concerns emphasizes the critical urgency for advanced technological solutions that facilitate precise and automated monitoring of daily caloric intake. This research conducts a rigorous comparative analysis to evaluate the predictive performance and computational efficiency of Random Forest (RF) and Support Vector Machine (SVM) algorithms in classifying food calorie levels. The methodology commenced with a comprehensive data preprocessing phase involving multi-strategy missing value imputation and the discretization of caloric values into ordinal categories. Feature selection was meticulously executed using linear regression coefficients to identify high-impact nutritional variables. To ensure a robust evaluation, the dataset was partitioned using an 80:20 ratio for training and testing, complemented by cross-validation to minimize bias and variance. Experimental results indicated that the Random Forest (RF) demonstrated superior classification capabilities, achieving a peak accuracy of 94.8% alongside balanced precision and recall scores. Statistical evaluation via confusion matrices further revealed that Random Forest exhibited enhanced generalization across high-dimensional nutritional features compared to the geometric approach of Support Vector Machine (SVM). Furthermore, the analysis of computational overhead provided critical insights into the real-time deployment feasibility of each model. Ultimately, the findings suggest that the Random Forest serves as a robust engine for personalized dietary management systems, offering a reliable framework for future developments in preventive digital healthcare. By successfully bridging machine learning with nutritional science, this study establishes a benchmark for high-accuracy food classification essential for modern health-centric mobile applications.
EFFICIENT HYBRID CNN-VISION TRANSFORMER FOR MEDICAL IMAGE CLASSIFICATION WITH LIMITED ANNOTATIONS Sudirman, San; Yani, Ahmad; Darmawan Bakti, Lalu
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.v4i3.453

Abstract

Medical image classification is a critical component of computer-aided diagnosis systems, yet its performance is often hindered by the scarcity of annotated data. This situation is common in the medical domain due to ethical, cost, and labeling constraints. Convolutional Neural Networks (CNNs) are effective at extracting local features but are suboptimal at capturing global context. Conversely, Vision Transformers (ViTs) excel at modeling long-range dependencies but require large amounts of training data. To address these limitations, this study proposes a hybrid CNN–Vision Transformer model that integrates the strengths of both to improve classification performance under limited annotation conditions. The model was tested using the OrganAMNIST dataset, consisting of 53,339 two-dimensional abdominal CT images with 11 organ classes. Experimental results show that the model achieves an accuracy of 92.3%, an F1-score of 91.8%, and an AUC of 99.5%, with only 3.67 million parameters. Compared to ResNet50, this model reduces the number of parameters by 84% and increases inference speed by up to 2.4 times. Additionally, the model demonstrates better training stability compared to baseline models such as ResNet50 and ViT-Small. The results of the study show that the integration of local and global features in a hybrid architecture can simultaneously improve accuracy and efficiency. This approach has the potential to be applied to medical diagnosis systems with limited data and computational resources.
THE USE OF EXPLAINABLE AI FOR ANALYZING SOCIOECONOMIC DETERMINANTS OF THE HUMAN DEVELOPMENT INDEX IN INDONESIA BASED ON REGRESSION MODELS Istikomah, Sintha; Purnomo Putro, Dwi
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.v4i3.456

Abstract

The Human Development Index (HDI) is a key indicator of quality of life, reflecting achievements in health, education, and a decent standard of living. Significant regional disparities in Indonesia highlight the need to analyze its determinants for effective policy formulation. This study examines the simultaneous influence of socioeconomic factors—poverty rate, GRDP per capita, life expectancy, mean years of schooling, and expenditure per capita—on HDI across 514 regencies/cities using machine learning and Explainable AI (XAI). Secondary data from the Indonesian Central Bureau of Statistics (BPS) in 2021 were utilized. The target variable (IPM_score) was constructed through feature engineering. Linear Regression, Random Forest, and XGBoost models were trained using an 80:20 split and evaluated with Mean Squared Error (MSE) and R². SHAP was applied to interpret feature contributions. Results show XGBoost achieved the best performance (R² = 0.987), outperforming Random Forest (R² = 0.974), while Linear Regression achieved R² = 1.000 due to perfect linearity. SHAP analysis identified expenditure per capita as the most dominant factor (r = 0.9996), followed by mean years of schooling (r = 0.667), while poverty showed a strong negative effect (r = -0.638). These findings emphasize that purchasing power and education are critical drivers of HDI. The use of XAI enhances model transparency and supports evidence-based policy, particularly in integrating poverty reduction with improvements in education and economic capacity.
STATE RESPONSIBILITY IN PERSONAL DATA PROTECTION WITHIN THE ELECTRONIC-BASED GOVERNMENT SYSTEM Muhammad Suhardi
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.v4i3.458

Abstract

This study examines the responsibility of the state in protecting personal data within Indonesia’s Electronic-Based Government System. The objective is to analyze the legal obligations of government institutions as personal data controllers in digital public services and to formulate a normative framework that integrates the Personal Data Protection Law, SPBE governance, and the protection of citizens’ constitutional rights. This research employs a qualitative legal method with a normative-juridical and conceptual approach. Data were collected through documentary study of Indonesian legal instruments concerning personal data protection, public services, government administration, electronic systems, and electronic-based government, supported by relevant scholarly literature on data governance and digital public administration. The findings show that Indonesia already has an important legal foundation for personal data protection and digital government, but the Personal Data Protection Law and SPBE framework have not yet been fully integrated. This regulatory fragmentation creates risks related to unclear institutional responsibility, excessive data processing, weak citizen notification, inaccurate data use, data breaches, and limited remedies. The study proposes the concept of the state as a constitutional data controller, meaning that government responsibility extends beyond technical compliance toward the protection of privacy, dignity, legal certainty, equality, and access to public services. This study contributes to strengthening a rights-based model of SPBE in Indonesia.
REGULATORY MODEL OF ARTIFICIAL INTELLIGENCE IN DIGITAL GOVERNMENT: BETWEEN SOFT LAW, ETHICS, AND THE NEED FOR BINDING LAW IN INDONESIA Muhammad Suhardi
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.v5i1.460

Abstract

This study examines the regulatory model of Artificial Intelligence (AI) in Indonesia’s digital government, focusing on the relationship between ethical guidelines, soft law, and the need for binding legal regulation. The objective is to analyze the legal limitations of Indonesia’s AI Ethics Circular Letter and to formulate a stronger regulatory framework for AI use in public administration. This research employs a qualitative legal method with normative-juridical, conceptual, and regulatory-comparative approaches. Data were collected through documentary study of Indonesian legal instruments, including the AI Ethics Circular Letter, the Personal Data Protection Law, and the Electronic-Based Government System framework, supported by scholarly literature on AI regulation, soft law, public-sector AI governance, and administrative accountability. The findings show that Indonesia’s current AI governance remains at an early and transitional stage. The AI Ethics Circular Letter provides an important ethical foundation, but it lacks binding obligations, risk classification, mandatory audit mechanisms, institutional liability, sanctions, and remedies for citizens affected by AI systems. This study proposes a hybrid regulatory model that combines ethical principles with binding legal rules, public-sector-specific obligations, sectoral standards, institutional supervision, and accessible remedies. The study contributes to the development of rights-based and accountability-oriented AI governance in Indonesia’s digital government.
ALGORITHMIC TRANSPARENCY IN DIGITAL PUBLIC SERVICES: AN ADMINISTRATIVE LAW PERSPECTIVE Abdul Wahab; Muhammad Suhardi
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.v5i1.461

Abstract

The increasing use of artificial intelligence and automated decision-making systems in digital public services has created new challenges for administrative law, particularly regarding transparency, accountability, and citizens’ procedural rights. This study examines algorithmic transparency as a legal obligation of government institutions in AI-based public service delivery. Using a normative juridical method with statutory, conceptual, and comparative approaches, this article analyses how the right to explanation can be constructed as part of administrative due process, reason-giving, and good administration. The findings show that the use of algorithmic systems does not reduce the government’s responsibility to provide lawful, reasonable, and reviewable decisions. Instead, the complexity of AI-based decision-making strengthens the need for meaningful explanations that are understandable, case-relevant, and useful for citizens affected by public decisions. This study argues that the right to explanation should not be limited to technical disclosure of algorithmic models, but should include information on whether AI was used, how it influenced the decision, what data and criteria were considered, and what remedies are available. The novelty of this article lies in positioning algorithmic transparency within the doctrinal framework of administrative law, rather than treating it solely as an ethical or technological issue. The study contributes to the development of accountable, citizen-centred, and legally grounded AI governance in digital public administration.
CYBERSECURITY GOVERNANCE IN ELECTRONIC-BASED GOVERNMENT SYSTEMS: AN ANALYSIS OF THE GOVERNMENT’S LEGAL RESPONSIBILITY FOR PUBLIC DATA BREACHES Erfan Wahyudi; Muhammad Suhardi
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.v4i3.462

Abstract

The increasing digitalisation of public administration has made cybersecurity governance a central issue in electronic-based government systems. Public data breaches in government digital platforms are no longer merely technical incidents, but also raise questions of administrative responsibility, public service continuity, and citizens’ legal protection. This study examines the government’s legal responsibility for public data breaches within the framework of cybersecurity governance and electronic-based government systems. Using a normative juridical method with statutory, conceptual, and analytical approaches, this article analyses cybersecurity as part of the state’s duty to provide secure, reliable, and accountable digital public services. The findings show that government responsibility can be constructed through three layers: preventive responsibility, responsive responsibility, and restorative responsibility. Preventive responsibility requires risk-based cybersecurity standards, institutional coordination, security audits, and adequate backup systems. Responsive responsibility requires rapid incident detection, containment, reporting, and transparent public communication. Restorative responsibility requires service recovery, breach notification, institutional evaluation, and remedies for affected citizens. The novelty of this study lies in integrating cybersecurity governance, electronic-based government systems, and administrative-law responsibility into a single analytical framework. The study argues that public data protection is not only a technical obligation, but also a legal manifestation of due care, accountability, good administration, and public service responsibility. Therefore, cybersecurity governance must be positioned as an essential requirement for lawful, secure, and citizen-centred digital government.

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