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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 23 Documents
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Search results for , issue "Vol. 5 No. 2 (2026): May 2026" : 23 Documents clear
WEBGIS APPLICATION FOR SEARCHING THE NEAREST CAR AC REPAIR SERVICE IN METRO CITY USING THE VINCENTY ALGORITHM Prasetiyo, Rayhan; Sutomo, Budi
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.v5i2.436

Abstract

The rapid increase in vehicle mobility in Metro City has led to a higher demand for specialized car AC maintenance services. This study develops a WebGIS-based application designed to facilitate the search for the nearest car AC workshops by implementing the Vincenty Algorithm. Unlike standard digital maps that often use spherical models, this system utilizes the WGS-84 ellipsoid reference to ensure high precision in geodesic distance calculations. The software was developed using the Waterfall model, integrating PHP, MySQL, and the Leaflet.js library. System validation was conducted through Black Box Testing across seven core modules, achieving a 100% functional validity rate. Comparative analysis between manual Vincenty calculations and system driving distance showed a minimal margin of 0.13 KM or 1.8%, confirming the algorithm's reliability for nearest-location ranking. This WebGIS serves as an efficient digital navigation tool to support vehicle maintenance for the community in Metro City.
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.
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
EVALUATION OF IMBALANCE CLASS HANDLING STRATEGIES ON MACHINE LEARNING MODEL PERFORMANCE Verdian, Arry; Wantoro, Agus
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 .

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