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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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A MACHINE LEARNING APPROACH FOR PREDICTING STUNTING RISK IN TODDLERS Ekatri Yulisara; Inggih Permana; Febi Nur Salisah; M. Afdal; Medyantiwi Rahmawita
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.v5i2.423

Abstract

Stunting is a chronic nutritional problem that remains a major public health challenge, particularly in developing countries such as Indonesia. It results from long-term nutritional deficiencies and can negatively affect physical growth, cognitive development, educational achievement, and future productivity. Early detection of stunting risk is essential to support timely intervention and improve child health outcomes. This study aims to develop and compare the performance of several machine learning algorithms for predicting stunting risk in toddlers using a large-scale nutritional dataset. The dataset was obtained from the Kaggle repository entitled “Stunting Balita Detection (121K Rows)” and consists of 120,999 records containing age, gender, height, and nutritional status information. Data preprocessing included categorical data encoding, Min-Max normalization, and dataset partitioning into training and testing sets using an 80:20 ratio. Five classification algorithms were evaluated: K-Nearest Neighbor (KNN), Random Forest, Support Vector Machine (SVM), Naïve Bayes, and Decision Tree C4.5. Model performance was measured using confusion matrix analysis, accuracy, precision, recall, and F1-score. The experimental results showed that KNN achieved the highest performance with an accuracy of 99.94%, precision of 99.90%, recall of 99.93%, and F1-score of 99.92%. Random Forest achieved comparable results with an accuracy of 99.93%, while SVM, Decision Tree C4.5, and Naïve Bayes produced lower performance values. These findings indicate that KNN and Random Forest are highly effective for stunting risk classification and have strong potential to support intelligent decision-support systems for early detection and nutritional monitoring of toddlers.
DEVELOPMENT AND USABILITY EVALUATION OF A MOBILE WEB-BASED RESTAURANT SYSTEM FOR DIGITAL ORDERING AT RESTO DASKER Muhammad Multazam; Surni Erniwati
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.v5i2.492

Abstract

The rapid advancement of digital technology has significantly transformed business operations across various sectors, including the restaurant industry. Restaurants are increasingly required to provide fast, efficient, and technology-driven services to improve customer satisfaction and operational performance. However, Resto Dasker in Gerung, West Lombok, still experiences several operational challenges, including manual food ordering, inefficient menu and stock management, and delays in service processes. This study aims to develop and evaluate a mobile web-based restaurant system to support digital ordering and improve restaurant operational efficiency. The system was developed using the Waterfall method, consisting of requirements analysis, system design, implementation, and testing phases. The application was implemented using PHP 8.0, MySQL, and Bootstrap 5.3, while system modeling employed Unified Modeling Language (UML), flowcharts, and Entity Relationship Diagrams (ERD) to support structured system development. Functional testing was conducted using the Black Box Testing method to verify whether system functionalities operated according to predefined requirements. Furthermore, usability evaluation was performed using the System Usability Scale (SUS) involving restaurant users and administrators to assess system acceptance and ease of use. The findings demonstrate that the developed system effectively facilitates digital food ordering, menu management, stock monitoring, and order processing through mobile devices. The Black Box Testing results indicated that all system functionalities operated successfully, while the usability evaluation achieved a satisfactory acceptance level, indicating that the system is practical and user-friendly. Therefore, the proposed system can improve restaurant service quality, operational efficiency, and customer experience at Resto Dasker.
PREDICTION OF HYPERTENSION COMORBIDITIES USING THE RANDOM FOREST ALGORITHM (CASE STUDY AT PUSKESMAS KASIHAN 2) Luqman Luqman; Ahmad Subhan Yazid; Dita Danianti; Dhina Puspasari Wijaya
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.v5i2.493

Abstract

Hypertension is a chronic non-communicable disease often accompanied by comorbidities, which can increase complication risks and reduce patients' quality of life. Currently, the identification of comorbidities in hypertension patients is frequently conducted manually, making it time-consuming and highly dependent on healthcare workers' thoroughness. This study aims to develop a predictive model for hypertension comorbidities using a machine learning-based Random Forest algorithm, designed as an early screening tool for the general population. The research method follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, encompassing business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Clinical data were collected from medical records, focusing on predicting eight primary comorbidities using a multi-label classification approach. Data preprocessing involved data cleaning, transformation, splitting into training and testing sets, and handling class imbalances. The Random Forest model was trained and evaluated using subset accuracy and hamming loss metrics. The results demonstrate that the Random Forest algorithm successfully predicts hypertension comorbidities with a subset accuracy of 0.3361 and a hamming loss of 0.1502, indicating robust performance for multi-label prediction. The model was successfully deployed into a Streamlit-based web application, enabling healthcare professionals to obtain direct prediction results. This system is expected to assist in the early screening and monitoring of hypertension patients.
DESIGN OF AN IOT-BASED AUTOMATIC MOSQUE DOOR SYSTEM USING PIR AND CAMERA SENSORS FOR SMART WORSHIP ACCESS Annisa Rizqi; Dania Fitriyani; Ika Herni; Rozy Ilhami; M. Rahmadoni Khoiriansyah; Miftahul Jannah
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.v5i2.501

Abstract

Mosques and mushallas are worship facilities with high congregational activity, requiring a secure, comfortable, and efficient door access system. Manual door operation often causes several problems, such as doors not closing properly, noise caused by slamming doors, and high physical contact between users and the door. This study aims to design and develop an automatic door system based on Passive Infrared (PIR) sensors and surveillance cameras integrated with Internet of Things (IoT) technology to improve security and convenience in mosque and mushalla environments. The research method used is Research and Development (R&D), consisting of system requirement identification, hardware and software design, prototype implementation, testing, and evaluation. The system uses PIR sensors or cameras to detect the presence of worshippers, microcontrollers such as Arduino, ESP32, or Raspberry Pi as the control center, and servo motors or DC motors as automatic door actuators. The system is also equipped with a real-time notification feature through Telegram to send information and visual documentation to administrators. The results show that the system is capable of detecting movement, automatically opening and closing doors, and sending notifications along with visual documentation in real time. The system improves user convenience, reduces physical contact with doors, and facilitates remote security monitoring by administrators. In addition, the use of ESP32-CAM is considered effective and economical for implementing IoT-based automatic door systems.
PERFORMANCE COMPARISON OF C4.5 AND K-NEAREST NEIGHBOR ALGORITHMS FOR MARKETPLACE SALES POTENTIAL ANALYSIS Sya'roni
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.v5i2.505

Abstract

The rapid growth of digital marketplace platforms such as Shopee, Tokopedia, and Bukalapak has transformed online business competition and increased the importance of data-driven sales analysis. Marketplace data, including product price, ratings, reviews, sales volume, views, and seller location, contain valuable information that can be utilized to predict product market potential. However, the large volume, heterogeneous characteristics, and dynamic nature of marketplace data make manual analysis inefficient. Therefore, this study aims to analyze and compare the performance of the C4.5 and K-Nearest Neighbor (KNN) algorithms in classifying marketplace sales potential. The dataset used in this research was collected through data scraping from Shopee, Tokopedia, and Bukalapak using the BigSeller application in March 2022, consisting of 21,750 product records with numerical and categorical attributes. Data preprocessing was conducted using Orange Data Mining, including data cleaning, missing value handling, normalization, feature transformation, and dataset partitioning. The classification process categorized products into three market potential levels: low, medium, and high. Model performance was evaluated using a confusion matrix based on accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the C4.5 algorithm outperformed KNN, achieving an accuracy of 0.86, while KNN obtained an accuracy of 0.70. Moreover, C4.5 showed higher precision, recall, and F1-score values, indicating better classification consistency and stability. The findings suggest that C4.5 is more effective for marketplace sales potential classification due to its ability to identify influential attributes and manage heterogeneous marketplace datasets. This study contributes to marketplace sales prediction and supports data-driven decision-making in e-commerce environments.
DESIGN AND IMPLEMENTATION OF A MOBILE OPEN DONATION PLATFORM WITH PAYMENT GATEWAY INTEGRATION M. Irpan; Nur Maysabila; Nanang Agustiar; Firman Putra Pratama; Nejad Ahmed Al-Hadad; David Suprianto Sianturi
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.v5i2.506

Abstract

The rapid development of information technology has encouraged the transformation of conventional donation systems into digital-based platforms that are more accessible, transparent, and efficient. This study aims to design and develop a mobile-based open donation application to facilitate digital donation activities for students and the general public. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and evaluation stages. Data collection was conducted through observations and interviews to identify user needs and problems in manual donation management processes. The application was developed using the Flutter framework with Firebase Authentication and Firebase Firestore as the backend system. The main features implemented include user registration and login, donation campaign lists, donation amount input, payment gateway integration, donation history, and admin management features. System testing was conducted using the Black Box Testing method to evaluate application functionality. The testing results showed that all application features operated properly without significant functional errors. The integration of Firebase successfully supported real-time data storage and user authentication processes, while the payment gateway integration enabled online donation transactions to be performed more easily and securely. In addition, the donation history feature increased transparency and user trust in donation management. The developed application is expected to improve the effectiveness, transparency, and accessibility of digital donation management while encouraging greater public participation in social and humanitarian activities.

Page 13 of 13 | Total Record : 126

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