cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Akim Manaor Hara Pardede
Contact Email
jaiea@ioinformatic.org
Phone
+6281370747777
Journal Mail Official
jaiea@ioinformatic.org
Editorial Address
Jl. Gunung Sinabung Perum. Grand Marcapada Indah. Blok. F1. Kota Binjai. Sumatera Utara
Location
Unknown,
Unknown
INDONESIA
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
 27   28   29   30   31 
Apllication of Six Sigma and FMEA Methods to Improve The Quality of Laminated Tube Packaging Hafizh Hazmi Al Fauzi; Rizqi Novita Sari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.804

Abstract

PT Lamipak Primula Indonesia is a company that produces various packaging made of plastic. The main product of PT Lamipak Primula Indonesia is toothpaste laminated tube packaging. PT Lamipak Primula Indonesia wants to minimize defects in products to minimize waste, reduce costs, increase efficiency, and maximize profits. This can also increase customer confidence in the product. PT Lamipak Primula Indonesia improves the quality of its products by applying Six Sigma with the DMAIC method. In this study, DPMO of 15766 was obtained and then converted to a sigma level of 3.7 which shows that the sigma level is below the 6 sigma level. This shows the achievement of sigma that has not been consistent and still shows the need for quality improvement in the laminated tube packaging production process in order to achieve zero defects. Based on FMEA analysis, it shows that the most significant failure occurs in machine conditions that cause defective shoulders with a value RPN of 210. This failure is caused by the lack of regular machine inspection, by the machine not being supervised in realtime, not following the SOP and the lack of worker skills.
Implementation of Naive Bayes in Sentiment Analysis of CapCut App Reviews on the Play Store Oka Alvianto; Willy Prihartono; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.805

Abstract

The CapCut video editing application has gained significant popularity among mobile users. This study aims to analyze user sentiment towards CapCut reviews on the Play Store using the Naive Bayes algorithm. User reviews were collected and preprocessed to clean and prepare the text for analysis. The Naive Bayes algorithm was employed to classify the reviews into positive and negative sentiment categories. Findings indicate that the majority of user reviews are positive, highlighting features such as ease of use, attractive visual effects, and the ability to share videos on social media. However, negative reviews were also identified, primarily criticizing issues like bugs, intrusive advertisements, and limitations in specific features. This research provides valuable insights into user sentiment towards CapCut, which can be utilized by developers to enhance application quality and user experience.
New Employee Selection System using WP and SAW Methods Based on Web at PT Lanang Agro Bersatu Ria Sapitri; Syarifah Putri Agustini Alkadri; Putri Yuli Utami
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.808

Abstract

Employees are valuable assets for a company, requiring careful selection based on educational background and experience to ensure proper placement and avoid issues. At PT Lanang Agro Bersatu, the selection process involves approximately 30 candidates monthly. This study developed a web-based employee selection system using the Weighted Product (WP) and Simple Additive Weighting (SAW) methods. The system aims to calculate weight values for criteria such as Education, Work Experience, Age, Health, GPA, Academic Tests, and Psychological Tests, providing accurate rankings to simplify decision-making. The top candidate, Khusnul Wasillah, achieved the highest preference value of 0.1563, calculated through combined SAW and WP methods. System testing using black box and equivalence partitioning methods showed 100% accuracy.
Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales Eti Rohayati; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.809

Abstract

This study aims to assess the effectiveness of linear regression algorithm in predicting raw water demand by considering customer transaction data, raw water volume, and seasonal variables. The method used is Knowledge Discovery in Databases (KDD), including data selection, preprocessing, transformation, data mining, and result evaluation. The dataset is divided 80% for training and 20% for testing. The analysis results show that the linear regression model has a coefficient of determination (R²) of 0.77, which means that the model can explain 77% of the data variability. The prediction error value is low, with Mean Absolute Error (MAE) 0.06, Mean Squared Error (MSE) 0.01, and Root Mean Squared Error (RMSE) 0.08, indicating good accuracy. In the comparison between actual and predicted values, for actual data of 7,000 liters, the model predicts 7,984.70 liters. The variable number of customer transactions has the greatest influence on raw water demand, with a coefficient of 16,940.46, while seasonal factors have less influence. Based on these findings, it can be concluded that the linear regression algorithm is effective in predicting raw water demand, however further development is required to improve accuracy at extreme values, by adding variables or using more complex algorithms.
Usability Scale System Method on Convogenius Platform for MSME Business Optimization Syaiful Imanudin; Rudi Kurniawan; Umi Hayati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.810

Abstract

Usability is a critical factor in the successful adoption of technology, particularly for AI-based platforms designed to support micro, small, and medium enterprises (MSMEs). Convogenius AI is developed to assist MSME operations, yet its effectiveness and user-friendliness must be evaluated. This study aims to assess the usability of the Convogenius AI platform using the System Usability Scale (SUS) method and identify areas requiring improvement. The research employs a SUS survey to measure aspects such as ease of use, functional integration, and the need for technical support. The findings reveal favorable SUS scores for ease of use (average 3.04) and user intention to repeatedly use the platform (average 3.05). However, deficiencies are noted in system complexity (average 2.96) and technical support requirements (average 2.95). Overall, Convogenius AI is accepted by MSME users but requires enhancements in interface design and consistency to improve user experience. These improvements can potentially increase user satisfaction and support the operational efficiency of MSMEs.
Analysis of Factors Causing Work Accidents in Steel Plate Production with FTA and PDCA Methods at PT. XYZ M. H. N. Islamsyah; Rizqi Novita Sari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.821

Abstract

The identification of work accidents in the steel plate production process poses a serious risk to workers and disrupts operational activities. This research analyzes the factors causing work accidents at PT XYZ in the steel plate production process using the Fault Tree Analysis (FTA) method and the Plan-Do-Check-Act (PDCA) method. FTA is applied to identify and decipher the root causes of accidents, while PDCA serves as a framework for continuous improvement. Data were collected through accident reports, observations. The results showed that human error, including being pinched by a plate, hit by a ganco, and exposed to sparks were the main contributors to the occurrence of accidents. Through the PDCA cycle, several preventive measures were proposed, including enhancing worker training, improving equipment maintenance, and strengthening safety protocols. This study provides actionable insights to improve workplace safety and reduce the risk of accidents in the steel plate production process.
Web-Based Chatbot Development and User Satisfaction Analysis Using the Naive Bayes Method Through Online Questionnaires Nurholis; Willy Prihartono; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.823

Abstract

This study aims to develop a web-based chatbot using Natural Language Processing (NLP) technology and the Naive Bayes algorithm to enhance digital interaction quality. User satisfaction was evaluated through an online survey involving 202 university students, focusing on ease of use, response speed, and relevance. The research followed the CRISP-DM framework, including data preprocessing (case folding, tokenization, stopword removal, and stemming), text transformation using the TF-IDF method, and implementation of a Naive Bayes classification model. an F1-score of 84%. Sentiment analysis revealed predominantly positive feedback, reflecting user satisfaction with the chatbot’s ease of use and response accuracy. However, some limitations, such as insufficient contextual understanding, were identified. These findings provide valuable insights into NLP-based chatbot development to support effective digital interactions. The proposed chatbot demonstrates potential applications in customer service, education, and e-commerce, with future improvements suggested to enhance contextual comprehension and scalability.
Implementation of the Naive Bayes Method in Sentiment Analysis of Spotify Application Reviews Agung Triyono; Ahmad Faqih; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.824

Abstract

This study focuses on sentiment analysis of Spotify application reviews on Google Play Store using the Naive Bayes algorithm. As a leading music streaming platform, Spotify receives diverse user feedback that reflects their experiences, complaints, and satisfaction. Sentiment analysis aids in understanding user opinions, enhancing services, and innovating features. The research involves collecting user reviews via web scraping, followed by preprocessing steps such as text cleaning, tokenization, normalization, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) method is employed to assign weights to words, highlighting their significance in reviews. The Naive Bayes algorithm categorizes sentiments into positive, negative, and neutral classes. Performance evaluation uses a confusion matrix to measure accuracy, precision, recall, and F1-score. Results indicate that Naive Bayes effectively classifies large volumes of unstructured data with high accuracy and efficiency. This study contributes practically by offering actionable insights to improve Spotify's services and theoretically by advancing sentiment analysis methodologies using machine learning. The findings highlight the algorithm's potential to understand user needs and address issues, reinforcing its value in text analytics for mobile applications.
Optimizing the Classification Model for Plant Medicine Supplies Using the Decision Tree Algorithm at the Anugrah Tani Shop, Brebes Regency: Inggris Saeful Amri; Rudi Kurniawan; Saeful Anwar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.825

Abstract

Retail businesses in the agricultural industry often face difficulties in estimating inventory needs, especially plant medicines which are important for protecting plants from pests and diseases. The lack of an accurate inventory prediction system can cause stock discrepancies, as happened at the Anugrah Tani Store, Brebes Regency, thereby disrupting operations and customer satisfaction. This research uses the Decision Tree classification technique to increase the accuracy of predicting the need for plant medicine supplies, with a clustering approach using the K-Means algorithm to determine the optimal K value through the Davies-Bouldin Index (DBI) calculation. A DBI value of -0.065 indicates good cluster quality with an optimal K of 2, where Cluster 0 has high inventory needs (1138 data) and Cluster 1 has low needs (4 data). The analysis results show that the accuracy level of the Decision Tree model is 98.25%, which is quite high. This model is not only able to predict inventory patterns accurately but also provides in-depth insights to support stock decision making. This research proves that the Decision Tree algorithm can help inventory management with a faster response to customer needs, while contributing to the development of machine learning-based classification models for the agricultural and retail sectors.
Financial Information System at the Sumba Christian Church Web-Based Wainggai Congregation Toni Yiwa Ndapa Otu; Fajar Hariadi; Desy Asnath Sitaniapessy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.828

Abstract

Financial information systems are information systems designed to provide users with information about cash flows throughout an organization. The procedures carried out in this system are a series of written documents or actions involving many people in a department to ensure consistent treatment. The Sumba Christian Church (GKS) Wainggai Congregation currently does not have information technology that can help to summarize church finances, so that errors often occur from the congregation when examining the treasurer. Because the calculation or addition of church finances every month is often wrong, so each income and expense must be recalculated from the beginning of the month. From certain problems, using information technology can help alleviate complaints or problems that often occur. Therefore, it is necessary to create an information system at the GKS Wainggai Congregation to help manage church finances so that errors do not occur again. This study uses the prototype method.The results of testing using the blackbox method show that this system can run according to its function without any errors. Meanwhile, from the results of the SUS test that has been carried out from the level of user satisfaction with the church's financial recording information system, the assessment given to 1o respondents resulted in a score of 78%. With acceptability ranges "Acceptable" and "High" ranges. The scale of grades is in the category of class "C". and on the "Good" Adjective ratings model. These results show that the financial recording information system can be accepted by its users.

Page 29 of 53 | Total Record : 524

 27   28   29   30   31 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue Vol. 5 No. 3 (2026): June 2026 Vol. 5 No. 2 (2026): February 2026 Vol. 5 No. 1 (2025): October 2025 Vol. 4 No. 3 (2025): June 2025 Vol. 4 No. 2 (2025): February 2025 Vol. 4 No. 1 (2024): October 2024 Vol. 3 No. 3 (2024): June 2024 Vol. 3 No. 2 (2024): February 2024 Vol. 3 No. 1 (2023): October 2023 Vol. 2 No. 3 (2023): June 2023 Vol. 2 No. 2 (2023): February 2023 Vol. 2 No. 1 (2022): October 2022 Vol. 1 No. 3 (2022): June 2022 Vol. 1 No. 2 (2022): February 2022 Vol. 1 No. 1 (2021): October 2021 More Issue