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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 126 Documents
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EVALUATION OF IMBALANCE CLASS HANDLING STRATEGIES ON MACHINE LEARNING MODEL PERFORMANCE Arry Verdian; Agus Wantoro
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.v5i2.459

Abstract

Breast Cancer Dataset (BCD) represents a critical health problem due to the increasing prevalence of breast cancer and the importance of early detection of recurrence. Machine Learning (ML) approaches have been widely applied to support diagnosis and prediction; however, class imbalance remains a major challenge, where the majority class (“no-recurrence-events”) significantly outnumbers the minority class (“recurrence-events”). This imbalance can lead to biased models that fail to accurately detect recurrence cases. This study aims to evaluate the effectiveness of class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE) on several ML models, including Decision Tree, Naïve Bayes, k-Nearest Neighbors (k-NN), and Random Forest. The dataset used consists of 286 records with 9 features obtained from the UCI Machine Learning repository. Data preprocessing was performed, including handling missing values and outliers, followed by class balancing using SMOTE. Model evaluation was conducted using 10-fold cross-validation and performance metrics such as accuracy, precision, recall, and F1-score. The results show that the application of SMOTE significantly improves model performance, with an average accuracy increase of 11.85%. Among the evaluated models, Random Forest combined with SMOTE achieved the best performance, with an accuracy of 79.79%. In contrast, models such as Naïve Bayes and k-NN demonstrated relatively lower performance. Overall, this study confirms that handling class imbalance using SMOTE can enhance classification performance, particularly in improving the detection of minority classes in breast cancer recurrence prediction tasks.
IMPROVING OPERATIONAL EFFICIENCY VIA END USER DEVELOPMENT: A WEB-BASED SALES MANAGEMENT SYSTEM FOR DR. BARON POMADE M. Daud Mursal Lubis; Nurjamiyah
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.v5i2.440

Abstract

This study aims to design and implement a Web -based sales management information system for Dr. Baron Pomade  using the end user development (eud) method. The background of this research lies in sales processes that were previously conducted manually, which resulted in limited customer reach, slow transaction recording, and the absence of structured sales reports. The main objective is to develop a sales system that enhances operational efficiency, simplifies transaction management, and provides accurate information to support decision-making. The significance of this research is to offer a technological solution that replaces the manual process with an integrated digital system. Furthermore, it encourages active user participation in the system development process through the EUD approach, thereby producing an application that aligns with real operational needs. This study is also expected to serve as a reference for the development of WEB -based information systems in small and medium enterprises, particularly within the men’s cosmetic industry The findings demonstrate that the developed sales information system has been successfully implemented with key features including login, registration, product management, category management, transaction handling, reporting, and configuration settings. System testing confirmed that all features function as intended. The implementation of this system significantly improves business process efficiency, accelerates transaction recording, enhances data accuracy, and supports real-time decision-making. Thus, the study proves that applying the EUD method in sales information system development positively impacts the operational performance of the business. Keywords: Sales Information System, End User Development (Eud), Pomade .
ENHANCING ONLINE AUTOMOTIVE SPARE PARTS SALES THROUGH A WEB-BASED E-SALES SYSTEM USING RAPID APPLICATION DEVELOPMENT Muhammad Alif Fiqri Harahap; Marina Elsera
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.v5i2.441

Abstract

The rapid advancement of information and communication technology has encouraged business sectors to adopt internet-based systems to improve operational efficiency and competitiveness. In the automotive industry, particularly in the sale of used car spare parts, electronic sales (e-sales) have become an important solution for expanding market reach and facilitating transactions. However, this sector still faces several challenges, including limited product transparency, inconsistent stock management, unclear product conditions, and low consumer trust in online transactions. This study aims to develop a web-based e-sales system for used car spare parts using the Rapid Application Development (RAD) method to improve transaction efficiency, inventory management, and customer trust. The RAD approach was selected because it emphasizes rapid and iterative system development through continuous prototyping and active user involvement, allowing applications to be developed according to user needs in a shorter time. The research method consisted of four stages: requirements planning, user design, construction, and implementation. The resulting system integrates various functionalities, including product management, automatic inventory monitoring, customer registration and login, transaction processing, shipment tracking, website settings, and sales monitoring through an admin dashboard. The implementation results indicate that the developed system effectively improves operational efficiency by reducing manual recording activities and simplifying transaction management. Additionally, the system enhances transparency through detailed product information and transaction monitoring features, thereby increasing consumer confidence in purchasing used spare parts online. Overall, the implementation of the RAD method proved effective in developing an adaptive and efficient e-sales platform that supports digital transformation in the automotive spare parts.
DESIGN OF AN ANDROID-BASED QUIZ GAME APPLICATION FOR INTRODUCING GCD AND LCM USING THE LCM METHOD Robbi Gunawan; Khairunnisa
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.v5i2.442

Abstract

Learning Greatest Common Divisor (GCD) and Least Common Multiple (LCM) concepts often presents challenges for students due to conventional teaching methods that are less interactive and engaging. This study aims to design and develop an Android-based quiz game application for introducing GCD and LCM concepts using the Linear Congruential Method (LCM). The proposed application was developed as an interactive educational medium to improve students’ understanding and learning motivation through a game-based approach. The Linear Congruential Method was implemented to randomize quiz questions, ensuring varied question sequences and reducing repetition during gameplay. The application consists of several main features, including a home page, quiz gameplay, learning materials, high score tracking, and developer information. Additionally, immediate feedback mechanisms were integrated to indicate correct and incorrect answers, enabling students to learn from their mistakes directly. The implementation results show that the application successfully provides an interactive and engaging learning experience for students in understanding GCD and LCM concepts. Furthermore, the integration of question randomization using LCM contributes to creating a more dynamic learning process and increasing user engagement. Therefore, the developed application can serve as an alternative educational medium to support mathematics learning in a more effective and enjoyable manner.
LITERATURE ANALYSIS ON THE ROLE OF ARTIFICIAL INTELLIGENCE IN STRENGTHENING CYBERSECURITY IN E-GOVERNMENT SERVICES Erfan Wahyudi; Wiredarme
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.v5i2.455

Abstract

The rapid expansion of e-government services has increased the importance of cybersecurity in protecting public digital infrastructure, citizen data, and the continuity of government operations. In this context, Artificial Intelligence (AI) has emerged as a promising approach to strengthening cyber defense through real-time monitoring, anomaly detection, intelligent classification, and adaptive threat response. This study examines the role of AI in strengthening cybersecurity in e-government services through a Systematic Literature Review (SLR) of 27 selected articles published between 2019 and 2025. The review synthesizes the literature at the intersection of AI, cybersecurity, and digital government to identify major research trends, dominant methodological approaches, thematic classifications, and key implementation challenges. The findings show that AI is increasingly positioned not only as a tool for improving administrative efficiency, but also as a strategic enabler of cyber resilience in public-sector digital ecosystems. The literature highlights that machine learning, deep learning, explainable AI, anomaly detection, and privacy-preserving learning models have substantial potential for improving the security of citizen portals, digital identity systems, inter-agency platforms, and smart-government infrastructures. However, implementation remains constrained by fragmented data environments, interoperability problems, institutional readiness gaps, limited explainability, privacy concerns, and the dual-use nature of AI in cyber defense and cyber offense. This study concludes that AI is most effective when integrated into a broader socio-technical framework encompassing governance, accountability, transparency, and organizational capacity.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION IN EARLY DETECTION OF DIABETES Lilik Joko Susanto; Agus Wantoro
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.v5i2.473

Abstract

Diabetes is one of the most serious global health problems and continues to increase significantly worldwide. Early detection is essential to reduce complications and improve patient survival rates. Recently, Machine Learning (ML) has shown great potential in supporting early diabetes prediction through data-driven analysis. However, the presence of irrelevant and redundant features may decrease model performance and increase computational complexity. Therefore, this study aims to evaluate the effectiveness of feature selection techniques and ML algorithms for early diabetes detection using the PIMA Indians Diabetes Dataset. The dataset consists of 768 records, 8 features, and two classes. Data preprocessing was conducted to handle missing values and outliers using mean imputation and data cleaning techniques. Three feature selection methods were applied, namely Information Gain (IG), Gain Ratio (GR), and ANOVA, to identify the most relevant features. Furthermore, several ML algorithms, including k-Nearest Neighbor (k-NN), Random Forest, Support Vector Machine (SVM), Naive Bayes, and Neural Network, were evaluated using 10-fold cross-validation. The results showed that feature selection techniques improved classification performance compared to using all features. Glucose, BMI, Age, and Insulin were identified as the most influential features in diabetes prediction. Among all evaluated models, Random Forest combined with ANOVA achieved the best performance with an accuracy of 0.753. In general, the application of feature selection techniques increased model accuracy by up to 3.82%. These findings demonstrate that combining effective feature selection methods with robust ML algorithms can significantly enhance the performance of early diabetes detection systems.
VEHICLE REPAIR MONITORING INFORMATION SYSTEM FOR OPERATIONAL VEHICLES AT PT SU Sultan Imam Fajri; Darius Antoni; Evi Yulianti
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.v5i2.474

Abstract

Rapid developments in information technology have encouraged companies to improve operational efficiency through the implementation of integrated information systems. In transportation and logistics companies, vehicle maintenance management plays an important role in supporting operational continuity. PT SU currently still uses Microsoft Excel to record and monitor vehicle repairs, resulting in several problems such as data duplication, delays in reporting, difficulties in monitoring repair progress, and the risk of data loss. Therefore, this study aims to design and develop a web-based operational vehicle repair monitoring information system using the Web Engineering method. The development process consists of communication, planning, modeling, construction, and deployment stages. Unified Modeling Language (UML) was used to model system requirements, including use case diagrams and Entity Relationship Diagrams (ERD). The system was developed using PHP, MySQL, and Apache server through XAMPP. The developed system provides several features, including vehicle data management, repair requests, repair status monitoring, repair reports, and repair history management. System testing was conducted using black-box testing, performance testing, usability testing, and User Acceptance Testing (UAT). The testing results showed that all system functions operated properly according to user requirements. Performance testing indicated that the average response time was below 3 seconds, while usability testing showed positive results with ease of use reaching 90% and monitoring effectiveness reaching 92%. The developed system successfully improved repair data management, reporting efficiency, monitoring transparency, and coordination between departments at PT SU.
COMPARATIVE STUDY OF CLASSIFICATION MODELS IN PROCESSING STUDENT TEST SCORES DATASETS Rico Pramestiawan; Arry Verdian; Chindu Lintang Bhuana; Lilik Joko Susanto
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.v5i2.475

Abstract

The development of Machine Learning (ML) has contributed significantly to the field of education, particularly in analyzing student academic data to support data-driven decision-making. Predicting student exam results is important for identifying academic performance patterns, detecting potential failures, and improving learning interventions. However, variations in student characteristics and dataset complexity require the selection of appropriate classification models to achieve optimal prediction performance. This study aims to compare the effectiveness of several ML classification models in predicting student exam results using a student academic dataset. The dataset consists of 306 records, seven attributes, and five grade classes (A, B, C, D, and E), including attendance, quiz scores, midterm examination scores, final examination scores, and assignment scores. Data preprocessing was conducted to handle missing values, duplication, inconsistencies, and outliers. The dataset was split into training and testing data with a ratio of 75:25 and evaluated using 10-fold cross-validation. Several classification models were applied, including k-Nearest Neighbour (kNN), Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that Random Forest achieved the best performance with an accuracy of 73.9%, precision of 74.0%, recall of 73.9%, and F1-score of 73.9%, followed by Naive Bayes and Decision Tree. Meanwhile, SVM produced the lowest performance among the tested models. The findings indicate that Random Forest is the most effective method for predicting student exam results and has strong potential to support educational decision-making systems.
COLONOSCOPIC POLYP SEGMENTATION USING SEGFORMER-B0 WITH A DICE-BCE HYBRID LOSS Ahmad Yani; San Sudirman; M. Zulpahmi; Emi Suryadi; Bahtiar Imran
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.v5i2.476

Abstract

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, with most cases originating from early lesions such as colon polyps. Early detection through colonoscopy is essential to reduce mortality rates; however, accurate polyp identification remains challenging due to variations in shape, size, texture, and illumination conditions. This study aims to implement and evaluate the SegFormer-B0 architecture combined with a Dice-BCE hybrid loss function for polyp segmentation in colonoscopy images. The study utilized the public Kvasir-SEG dataset consisting of 1,000 colonoscopy images with pixel-level annotations. The dataset was divided into 80% training data and 20% validation data. Image preprocessing included resizing to 256×256 pixels and normalization using ImageNet statistics. The model was trained for 25 epochs using the AdamW optimizer with a learning rate of 1×10⁻⁴. Performance evaluation was conducted using Dice Coefficient, Intersection over Union (IoU), Sensitivity, and Specificity metrics. The experimental results demonstrated that the proposed model achieved a Dice Coefficient of 89.92%, Mean IoU of 81.90%, Sensitivity of 89.12%, and Specificity of 98.51%. The training process also showed stable convergence, supported by a training loss of 7.53% and validation loss of 23.30%. The findings indicate that the integration of SegFormer-B0 with the Dice-BCE hybrid loss effectively improves segmentation accuracy and stability while addressing class imbalance issues in colonoscopy images. Therefore, the proposed approach has strong potential to support computer-aided diagnosis systems for colorectal cancer screening.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION (GINI DECREASE AND RELIEF-F) IN HEART DISEASE DATASET Chindu Lintang Bhuana; Rico Pramestiawan; Lilik Joko Susanto; Arry Verdian
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.v5i2.477

Abstract

Heart disease remains one of the leading causes of mortality worldwide and presents a major challenge in healthcare systems. Early detection plays an essential role in improving survival rates and minimizing complications through timely intervention. Recent advances in Machine Learning (ML) have provided new opportunities for developing accurate and efficient prediction systems for heart disease detection. However, one of the major challenges in ML-based prediction is identifying the most relevant features to improve classification performance while reducing computational complexity and noise. This study aims to evaluate the effectiveness of two feature selection techniques, namely Gini Decrease (GD) and ReliefF, combined with several ML models, including Support Vector Machine (SVM), Tree, Naïve Bayes, and Random Forest, for heart disease classification. The study employed the UCI Heart Disease Dataset consisting of 303 records and 14 attributes. Data preprocessing included handling missing values using mean imputation, followed by feature selection and classification using 10-fold cross-validation with an 80:20 training-testing ratio. Experimental results showed that ReliefF outperformed GD, achieving the highest average accuracy of 0.796, compared to GD with 0.767 and all features with 0.771. The SVM model achieved the highest accuracy using GD (0.833), while Random Forest demonstrated optimal performance with ReliefF (0.817). Furthermore, the Tree model exhibited the fastest computational time among all evaluated models. These findings indicate that integrating suitable feature selection methods with ML models significantly enhances heart disease classification performance, particularly in improving predictive accuracy and computational efficiency for early medical diagnosis applications.

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