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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 525 Documents
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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 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.
Implementation of Deep Learning Based on Convolutional Neural Network for Detecting Images of Solar Panel Damage in Smart Grid Systems Camelia Putri Lestari; Nining Rahaningsih; Irfan Ali; Dodi Solihudin; Tati Suprapti
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.v5i3.2225

Abstract

This study aims to implement Deep Learning based on Convolutional Neural Network (CNN) in detecting solar panel damage using thermal images as part of a Smart Grid system. The main problem addressed is the difficulty of early automatic identification of solar panel cell damage using conventional methods. Through the CNN approach, this study developed a classification model to distinguish between damaged (Defective) and undamaged (Non-Defective) solar panel conditions. The research stages included thermal image dataset collection, pre-processing, model training, and performance evaluation. The results showed that the CNN model was able to achieve an accuracy of over 87% with stable performance on the validation data. Visualization using the Grad-CAM method helps interpret the damaged areas that are the focus of the model's decision.
Sentiment Analysis of Social Media X Users Toward Finance Minister Purbaya Yudhi Sadewa Using the Support Vector Machine Algorithm Adian Fahreza Surbakti; Relita Buaton; Selfira
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.v5i3.2228

Abstract

In this digital era, the rapid advancement of information and communication technology has transformed social media platforms particularly X (formerly Twitter) into a primary space for public discourse concerning government policies. The Minister of Finance, Purbaya Yudhi Sadewa, has become a focal point of public debate, garnering reactions ranging from appreciation to criticism regarding his management of national finances. However, manual sentiment analysis is impractical, time-consuming, and prone to subjectivity when handling the massive and continuously expanding volume of social media data. Therefore, an automated, machine learning-based approach is essential to process this big data into strategic insights for mapping public sentiment. This study aims to objectively analyze public sentiment toward the Minister of Finance by implementing the Support Vector Machine (SVM) algorithm within the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework. The methodology includes data crawling, text preprocessing, and feature extraction using the TF-IDF (Term Frequency – Inverse Document Frequency) method. Analysis of 3,927 tweets reveals that public opinion is dominated by negative sentiment at 54.2%, followed by positive sentiment at 36.9% and neutral sentiment at 8.9%. The developed SVM model achieved a classification accuracy of 72.43%, demonstrating that this machine learning approach is both effective and reliable for mapping public perception. These findings indicate that the Minister of Finance, Purbaya Yudhi Sadewa, faces significant public scrutiny, and this data-driven analysis serves as a strategic tool for evaluating the policies under his administration.
Failure Analysis of Switching Scheme Failures in Loop Protect Multiplexer Telecommunication Networks at PT. PLN (Persero) UP2B DKI Jakarta & Banten Rizki Dwi Dermawan; Muhamad Hadi Arfian
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.v5i3.2232

Abstract

PLN (Persero), through UIP2B JAMALI, relies on a loop-topology Loop Protect Multiplexer as its telecommunications backbone to support real-time SCADA, VoIP, and protection services. However, from 2022 to 2024, 36 switching failure incidents occurred in UP2B DKI Jakarta–Banten. This study analyzes the root causes, operational impacts, and recommendations for continuous system reliability improvement. The research employs a case study method and the PPDIOO approach to examine switching failures of the Loop Protect Multiplexer in UP2B DKI Jakarta–Banten. Data were collected through observations of fault history (2022–2024) and interviews. BER testing and QoS parameters refer to the ITU-T Y.1564 standard to formulate recommendations for improving the reliability of the 150 kV backbone network. Testing results indicate that under normal conditions, the system meets SLA requirements in accordance with ITU-T Y.1564, with stable throughput and zero frame loss. However, when one link fails, frame loss occurs during switching despite stable throughput, resulting in SLA failure. The root cause lies in a reactive and non-seamless switching mechanism, creating cross-layer impacts on critical services within PT. PLN (Persero).
Application of the K-Means Clustering Algorithm in the Analysis of Popularity and Growth Trends of Python Packages on the PyPI Dataset Muhammad Rafli Wijaya; M Gali Almahdi; Sebastian Saut Marulitua Sinaga; Benedict Sandi Pangestu Rosa
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.v5i3.2233

Abstract

The rapid growth of the Python ecosystem has led to an increasing number of packages on the Python Package Index (PyPI), generating a massive volume of download data. This data can be utilized to analyze popularity levels and growth trends of libraries used by the developer community. This study aims to identify popularity patterns and growth trends of Python packages using the K-Means Clustering algorithm. The dataset was obtained from PyPI via the Google BigQuery platform with a one-year observation period using a 1% sampling technique. The pre-processing stage included a filtering process to select the 100 packages with the highest number of downloads and the formation of six main features representing the characteristics of library usage patterns. The data was then normalized using Standard Scaling, while the optimal number of clusters was determined using the Elbow Method and evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results showed that the optimal number of clusters is four, with a DBI value of 0.5534 and a Silhouette Score of 0.5748 (the highest among k = 2-10 ), representing the categories of ecosystem foundation libraries, medium-popularity libraries, libraries with concentrated download spikes, and libraries with very rapid usage growth. These results indicate that K-Means Clustering is effective for identifying popularity patterns and library growth trends in large-scale PyPI datasets.
Application of K-Means Clustering: Bot Activity and Sybill Attack Detection on the Solana Blockchain Bryant Tinambunan; Hafizam Mufti; Ahmad Zulfan; Guez Rade
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.v5i3.2235

Abstract

With the development of Blockchain technology, for example, the Solana Blockchain has generated enormous amounts of data and possesses the 5Vs of Big Data: volume, velocity, value, veracity, and variety. This has brought challenges, for example, in distinguishing transactions carried out by humans from automated bots that often carry out market manipulation or Sybil attacks. Therefore, this research aims to detect bot activity on the Solana network by applying data mining techniques, namely the K-Means Clustering algorithm. From the large transaction data that will be extracted only a portion from the public Solana dataset in BigQuery, it will then be processed through a preprocessing stage to normalize the data and simplify complex data into simpler variables before being grouped. Because the extracted data is in the form of unlabeled data groups (unsupervised data), the Clustering Method is used because of its ability to recognize data groups based on behavioral or characteristic similarities without requiring initial data labels (unsupervised learning). The main variables used for the grouping process include transaction frequency, inter-arrival time (inter-transaction), and the number of unique program interactions. The results of this analysis are expected to map transaction accounts into several clusters based on their transaction patterns, allowing for the classification of bots and humans. This research is expected to demonstrate that Big Data infrastructure such as Google Cloud, using data mining techniques (Clustering), can be used to maintain the security and integrity of the blockchain ecosystem.
Implementation of the Heuristic Evaluation Method in the Design of the School Academic Information System Website Michelle Francisca; Jackri Hendrik; Hendri
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.v5i3.2236

Abstract

In the world of education, the role of teachers and parents is very influential in the process of improving student learning achievement. However, in reality, most parents only give responsibility to teachers at school to improve student learning achievement. Parents of students rarely monitor the development of their children's learning abilities due to the lack of information about it. Global Prima National Plus School is one of the leading private schools in Medan, located on Jalan Brigjend Katamso. Currently, Global Prima National Plus School uses Microsoft Excel to manage student data and student test scores. However, the implementation of this system still has several weaknesses, namely parents cannot monitor attendance and directly know the development of student scores and behavior. This will reduce parental participation in their children's educational development. To solve the problems faced by Global Prima National Plus School, an application can be created to monitor student learning development. By using this application, parents of students can obtain information about student attendance data, attitude and behavior scores, assignment scores, and test scores directly, without having to wait for report cards to be distributed. With this web-based student learning progress monitoring application, parents can find out information about student attendance, attitude scores, behavior, exam scores and assignment scores which can be accessed directly through the school website.

Page 46 of 53 | Total Record : 525

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