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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 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.
Application of K-Means Clustering for Urban Transportation Pattern Analysis Using Big Data Trip Dataset Tegas Ramadhan; Hafizh Ariiq; Muhammad Dzaki Arjun; Muhammad Ridho Ananda Aditya
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.v5i3.2237

Abstract

The rapid growth of urban transportation systems has led to the generation of massive amounts of data, commonly referred to as big data. This study aims to analyze transportation patterns using large-scale data obtained from the NYC Taxi Trip Records. The dataset exhibits key big data characteristics, including volume, velocity, and variety. This research applies the K-Means clustering algorithm to group taxi trip data based on features such as trip distance, fare amount, and trip duration. Several preprocessing techniques are performed, including data cleaning, feature engineering, sampling, and normalization. The optimal number of clusters is determined using the Elbow Method and Silhouette Score. The results show that the dataset can be effectively grouped into three clusters representing distinct transportation patterns. These findings demonstrate the capability of clustering techniques in extracting meaningful insights from large-scale datasets and highlight their potential application in urban transportation planning.
Development of a Web-Based System for Recording and Reporting Palm Weights Using Laravel at PT. Graha Prima Lestari Fredynand Marcos; Wilson; Jackri Hendrik
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.v5i3.2241

Abstract

This research was initiated by operational problems in the palm oil weighing process, which was conducted manually. The manual method often caused calculation errors, delays in making report , and risk of data loss. To address these issues, a web-based palm oil weighing application was developed using the Laravel framework and the Waterfall development method, supported by a relational database to manage data in an integrated manner. The application implements a role-based access system to manage permissions for administrators, weighing operators, and management. The system records gross weight, tare weight, and automatically calculates net weight while generating accurate reports efficiently. With this system, the weighing process is expected to become more efficient , precise and structured.
Designing a Web-Based Financial Information System at GKS Palindi using the Rapid Application Development Method Serlince Pindi Kualak; Arini Aha Pekuwali
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.v5i3.2245

Abstract

In the rapidly evolving information age, technology plays an important role in improving the efficiency of data management, including in church institutions. The Church, as a religious institution with an important role in the spiritual and social life of the people, often faces challenges in financial management, especially in recording congregation donations, operational expenses, and making transparent and accurate financial statements. Good financial management is needed to ensure accountability and transparency, as well as facilitate reporting to the congregation and other related parties. GKS Palindi, a church in Kawangu District with 335 congregations, faces problems in the financial recording system that is still manual using books. This causes data corruption, which risks disrupting the smooth flow of the financial management process. Therefore, this church needs the implementation of a technology-based information system that can facilitate the recording and management of financial transactions efficiently, especially for the six main posts: tithe, thanksgiving, part (household worship), monthly dependents, offerings, and miscellaneous posts. With this system, the church can reduce the potential for human error, monitor cash flow more easily, and provide more accurate and timely financial reports. The right information system can help GKS Palindi in maintaining the continuity of church operations and increasing the congregation's trust in the transparency of financial management. The system development method used is Rapid Application Development (RAD), which allows the creation of a system quickly and responsively to user needs. The implementation of a technology-based recording system is expected to overcome existing problems, as well as support the smooth running of church activities in the long term.
Clusterization of Family Planning Participants Based on Pregnancy Risk Using K-Means Algorithm in Ciherang Village Melva Regina Arpratika; Nana Suarna; Agus Bahtiar; Martanto; Odi Nurdiawan
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.v5i3.2248

Abstract

This study aims to group family planning (KB) participants in Ciherang Village based on pregnancy risk levels using the K-Means clustering algorithm. The identification of pregnancy risk is still performed manually, resulting in less effective analysis. Therefore, a data mining approach is applied to improve decision-making accuracy. The data used in this study were obtained from KB cadres, including variables such as age, number of children, education, occupation, and contraceptive methods. The research method follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, data mining, and evaluation. The K-Means algorithm is used for clustering, while the Davies–Bouldin Index (DBI) is applied to evaluate clustering quality. The results show that the optimal number of clusters is K = 2 with a DBI value of 0.721. The first cluster represents low pregnancy risk participants, while the second cluster represents high pregnancy risk participants. Age and number of children are identified as the most influential factors. This study provides useful insights for healthcare providers in developing targeted strategies for family planning programs. Keywords: Data Mining; Davies–Bouldin Index; K-Means Clustering; Pregnancy Risk; Family Planning

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