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Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Published by Yayasan Kita Menulis
ISSN : -     EISSN : 28084519     DOI : https://doi.org/10.53842/jaiea.v1i1
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering
The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering applications, mechatronic engineering, medical engineering, chemical engineering, civil engineering, industrial engineering, energy engineering, manufacturing engineering, mechanical engineering, applied sciences, AI and Human Sciences, AI and education, AI and robotics, automated reasoning and inference, case-based reasoning, computer vision, constraint processing, heuristic search, machine learning, multi-agent systems, and natural language processing. Publications in this journal produce reports that can solve problems based on intelligence, which can be proven to be more effective.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 524 Documents
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Implementation of the Rapid Application Development Approach for the Academic Information System at Muzakkir Islamic Primary School Prabumulih Ardi Ardiansyah; Andi Christian; Nur Aini Hutagalung
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.1496

Abstract

This study stems from the limited application of information technology, which continues to influence various aspects of human life—particularly in the fields of employment, business, and, more specifically, at Muzakkir Islamic Elementary School in Prabumulih, where data management is still handled through conventional methods. Data for this research was collected using a descriptive method with a qualitative approach, employing observation, interviews, and literature review. The design of the academic information system applied the Rapid Application Development (RAD) methodology to assist developers in analyzing and designing the system based on actual needs. The Unified Modeling Language (UML) served as a tool for designing the school’s academic information system, supporting the developers during the process. The developed system was implemented using the Hypertext Preprocessor (PHP) programming language and a MySQL database.
Development of a Website-Based Cashier Application at the NBO Prabumulih Store Using the RAD Method Lovita Reira Rambayu; Fajriyah; Khana wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.1541

Abstract

NBO Prabumulih Store is a store that sells various kinds of men's and women's clothing, NBO Prabumulih Store is located in North Prabumulih District, precisely in Anak Petai Village. Currently, NBO Prabumulih Store does not have a cashier application for managing payment transactions, so the purpose of this study is to build a cashier application to facilitate payment transactions at NBO Prabumulih Store. This research method uses a qualitative descriptive method with data collection techniques in the form of observation, interviews and literature studies, data sources consist of primary data and secondary data, while for the system development method using the RAD (Rapid Application Development) method, the system design tool used is UML (Unified Modeling Language). The design of this application uses the PHP (Processor Hypertext) programming language, MySQL Database and Coding using Visual Studio Code
Analysis of Hate Speech Againts Gojek Drivers using the Naïve Bayes Algorithm on the Facebook Platform Nazwa Putri Ananda; Firahmi Rizky
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.1747

Abstract

Social media has become a space where many individuals express their opinions freely, including negative comments that may lead to hate speech. One group often targeted by such speech is Gojek drivers. This study aims to classify user comments on Facebook into two sentiment categories: positive and negative, with a primary focus on negative comments. The data was collected from public Facebook posts using the APIFY scraping tool. After the data was gathered, several preprocessing stages were carried out, including case folding, cleaning, tokenization, normalization, stopword removal, and stemming. The text data was then converted into numerical form using CountVectorizer. The classification algorithm used in this research is Naive Bayes with the MultinomialNB model, as the input data consists of word frequency. The results of the model evaluation show that this algorithm performs well in classifying negative comments, especially in identifying word patterns that commonly appear in hate speech directed toward Gojek drivers.
Sentiment Analysis of Pre-Loved Shoe Product Sales Based on X Reviews with a Comparison of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Algorithms Setyo Harry Nugroho; Al-khowarizmi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.1773

Abstract

The rapid growth of social media enables consumers to express opinions about products openly, including preloved shoes. These reviews are crucial as they can influence purchase intentions and brand perception. This study aims to analyze user reviews on the X (Twitter) platform using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. A total of 1,005 reviews were collected, then preprocessed and balanced into 738 data consisting of positive and negative sentiments. The results show that SVM achieved an accuracy of 68%, while LSTM obtained 61.49% in its best configuration. Thus, SVM demonstrates better efficiency in classifying simple text, whereas LSTM requires more complex parameters to achieve optimal performance. This research is expected to serve as a reference for utilizing sentiment analysis to support business decision-making in the preloved product market.
Development of Web System for Sales Optimization at CV. CS Swalayan using Association Rule Method Steven Imanuel Naibaho; Yullita Molliq Rangkuti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.1796

Abstract

CV. CS Swalayan encounters challenges related to declining consumer purchasing power and the underutilization of transactional data for analyzing customer purchasing patterns. This study aims to develop a web-based system employing Association Rule methodology with the Apriori algorithm to optimize sales performance, identify top-selling products, and determine frequently co-purchased product combinations. The research methodology encompasses the collection of 296 sales transaction records for basic commodity products from CV—CS Swalayan during January 2025, followed by data preprocessing procedures. The Apriori algorithm is implemented with minimum support and confidence thresholds set at 0.01 and 0.3, respectively. The web-based system is developed using Python with the Flask framework for backend functionality, MySQL for database management, and validated through black-box testing methodology. The findings reveal the generation of 14 valid and robust association rules, notably "if Selai Srikaya Ngetop is purchased, then Roti Tawar Kupas Ngetop will be purchased" (confidence: 100%; lift ratio: 49.3) and "if Beras Sukaraya Cap Gurih 10KG is purchased, then Minyak Kita Minyak Goreng Sawit 1ltr will be purchased" (confidence: 100%; lift ratio: 16.4). The developed web system successfully passed black-box testing with a 100% success rate. This research contributes by providing a system that enables CV. CS Swalayan will make data-driven decisions to optimize sales strategies, marketing approaches, and inventory management practices.
Web-Based Goods Inventory Information System Using the Rapid Application Development Method (Case Study: SMK Fatahillah Cileungsi Bogor) Zainal Musthofa; Sonia S Simanullang; Achmad Rifai
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2100

Abstract

Inventory management at SMK Fatahillah Cileungsi Bogor is not yet supported by an integrated information system, so the process of recording incoming and outgoing goods has not been running optimally. This condition causes difficulties in data management, increases the risk of inventory information discrepancies, and limitations in monitoring real-time availability of goods. This study aims to design and implement a web-based inventory information system that is able to automate and centralize school asset management. The system development method used is Rapid Application Development (RAD), which includes the stages of requirements planning, system design, prototype construction, testing, and implementation. The developed system provides features for managing master data on goods, user management, recording incoming and outgoing goods transactions, borrowing and returning goods, and automatic generation of inventory reports. Test results show that the system can function well according to user needs. The implementation of this web-based information system has been proven to be able to improve data accuracy, accelerate the information search process, and optimize the effectiveness of asset management at SMK Fatahillah Cileungsi Bogor.
Web-Based Cooperative Management Information System Using Agile Method (Case Study: Bungah Bareng Mandiri Banyumas) Nurul Khikam; Muhammad Irfan Zidny; Raden Roro Diah Woro Murti; Achmad Rifai
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2101

Abstract

The development of information technology encourages cooperatives to manage operational activities in an integrated manner to improve the effectiveness and accuracy of data management. The Bungah Bareng Mandiri Banyumas Cooperative still faces obstacles in managing cash and credit transactions, recording customer data, installment payments, and preparing reports because the system used is not yet fully integrated. This study aims to design and implement a website-based cooperative management information system that is able to integrate all operational data into a centralized platform. The research method used is applied research with a qualitative-descriptive approach, while system development is carried out using the Agile method to accommodate user needs iteratively. The implementation results confirm that the improved system is able to increase the regularity and accuracy of data processing, facilitate monitoring of installment due dates, and support the preparation of operational reports more quickly and structured. Thus, the implementation of this information system contributes to increasing the effectiveness, efficiency, and transparency of the operational management of the Bungah Bareng Mandiri Banyumas Cooperative.
Web-Based Operational Management Information System for Prospective Indonesian Migrant Employees Using Agile Method (Case Study: PT. Bahana Mega Prestasi Bekasi) Aldi Jaya Mulyana; Lisha Wahyumuningsih; Rohman; Achmad Rifai
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2158

Abstract

Bahana Mega Prestasi, an Indonesian Migrant Worker Placement Company (IMWPC), faces challenges in managing operational data for Prospective Indonesian Migrant Workers (PIMW). This is due to the lack of integration of registration, attendance, and eligibility assessment processes within a single information system. This situation increases the administrative burden and the potential for data inconsistencies. This research aims to design and implement a web-based PIMW Operational Management Information System capable of centrally integrating all administrative processes. The system was developed using Agile methods with an iterative approach to ensure the system meets user needs. The system was built using the PHP programming language with the Laravel framework and a MySQL database. Implementation results indicate that the developed system is able to support the PIMW registration process, attendance monitoring, and candidate eligibility evaluation in a more structured and real-time manner. The implementation of this system contributes to improving the orderliness of data management, supporting managerial decision-making, and increasing operational efficiency at PT. Bahana Mega Prestasi.
Comparison of Naive Bayes and KNN Algorithms for Heart Attack Disease Classification Syahril Arsad; Sucipto; Barry Caesar Octariadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2218

Abstract

This Heart attack is one of the leading causes of death worldwide and requires early diagnosis to reduce fatal risks. This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbors (KNN) algorithms in classifying heart attack disease. The dataset used consists of medical records containing clinical parameters such as age, blood pressure, cholesterol level, and heart rate. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The results show that Naive Bayes demonstrates advantages in computational speed and performs well on smaller datasets, achieving an accuracy of 85%. In contrast, KNN provides better performance on larger datasets, reaching an accuracy of 90%, particularly when the optimal K value is applied. These findings indicate that algorithm selection for heart attack classification depends on dataset characteristics and specific implementation needs. This study is expected to contribute to the development of artificial intelligence–based clinical decision support systems for early heart attack diagnosis and improved healthcare outcomes.
Classification of Herbal Leaves Using Support Vector Machine (SVM) Yakub Takandiwa Takandiwa; Pingky Alfa Ray Leo Lede
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2221

Abstract

Indonesia is a country with high biodiversity, including various types of herbal leaves with potential use as traditional medicine. Manual identification of herbal leaves often encounters challenges due to morphological similarities among species and the limited availability of experts, thereby necessitating a fast and accurate technology-based classification method. This study aims to classify 10 types of herbal leaves using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel. The dataset consists of 3,500 leaf images (350 images per class), from which color features (HSV), texture features (Gray Level Co-occurrence Matrix/GLCM), and shape features (area, perimeter, and aspect ratio) were extracted. The research process includes preprocessing, feature extraction, data splitting into training and testing sets, model training, and performance evaluation. Evaluation was conducted using a confusion matrix, with accuracy as the primary metric due to the balanced class distribution. Precision, recall, and F1-score were employed as supporting evaluation metrics. The results indicate that the SVM model with an RBF kernel successfully classified the 10 types of herbal leaves with an accuracy of 81.29%. Based on per-class analysis, the highest performance was achieved in the Papaya class with an F1-score of 90.00%, followed by Jambu Biji (89.36%) and Pandan (87.14%). In contrast, the lowest performance was observed in the Aloe Vera class with an F1-score of 65.71% and Lime with 70.00%. The model achieved an average precision of 81.16%, recall of 80.73%, and F1-score of 80.94%. Misclassifications primarily occurred among classes with high morphological similarity, such as Aloe Vera, which was frequently misclassified as Pandan (9 cases) and Basil (5 cases). The system has been implemented as a Graphical User Interface (GUI) application that allows users to upload leaf images and obtain classification results along with information regarding their herbal benefits within 1–2 seconds.

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