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 430 Documents
 27   28   29   30   31 
Website Based Digital Branding Strategy for Increase Sales of Gunung Puntang Coffee In Mekarjaya, Bandung Regency Juliyanti; Rini Astuti; Willy Prihartono
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.v4i2.802

Abstract

This research aims to develop a branding strategy through optimizing a website-based digital company profile to increase sales of Gunung Puntang coffee. Gunung Puntang coffee is a high-quality local product that requires a digital approach to marketing to reach a broader market and enhance competitiveness. In today's digital era, a website plays a crucial role as a promotional and informational medium, providing customers with easy access to product information and enabling online purchases. The research employs the Prototype method, consisting of problem identification, planning, requirements analysis, system design, implementation, and testing phases. Data collection was conducted through observation, interviews with the coffee business owner, and documentation studies related to business processes and branding strategies. The collected data serves as a basis for designing a website system that optimizes the company's profile and supports coffee sales transactions. The system development includes creating use case diagrams, activity diagrams, and system architecture designs to outline functional and non-functional requirements. The research outcome is a website functioning as a digital information medium for branding Gunung Puntang coffee products and supporting sales transactions. Key features include customer registration, product selection, quantity adjustment, payment methods, order confirmation, and order cancellation. Testing results indicate that the system operates effectively and meets user needs. This website enhances operational efficiency, expands market reach, and improves the shopping experience for customers. It serves as an effective medium for strengthening branding and marketing strategies in the digital era, ensuring the sustainability of local businesses in the global market. Regular evaluations and feature upgrades are recommended to maintain system relevance to customer needs and technological advancements..
Optimizing Email Spam Classification Using Naïve Bayes and Principal Component Analysis Shinta Virgiana; Rudi Kurniawan; Tati Suprapti
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.v4i2.803

Abstract

In the ever-evolving digital era, email spam filtering is an important challenge to maintain the security and comfort of email services. The Naïve Bayes algorithm is widely used for spam email classification because of its ability to manage large data, although there are still limitations in terms of accuracy, precision and recall. This research aims to improve spam email classification performance by combining Naïve Bayes and Principal Component Analysis (PCA) to optimize model accuracy and explore optimal parameters in the reduction dimension. The research methodology goes through the Knowledge Discovery in Database (KDD) stages which include selection, preprocessing, transformation using PCA, development of a classification model using Naïve Bayes, and evaluation of model performance. The dataset used consists of emails categorized as spam and non-spam. The experimental results show that the combination of Naïve Bayes and PCA achieves the highest accuracy of 99.24% with 7 principal components. The fixed number of components approach shows better performance compared to preserving variance, emphasizing the importance of selecting appropriate PCA parameters in improving the effectiveness of model classification. This research shows that PCA not only reduces the complexity of the dataset but also increases the efficiency of the classification algorithm.
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 Rohayati, Eti; 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.
Designing the 112 Call Centre Service System in Sidoarjo Regency using the Object-Oriented Analysis and Design (OOAD) Method Ningrum, Dea Kusuma; Mohammad Dimas Ardiansyah; Rizky Tri Aji Setiawan; Alya Maytsa Ismawardi; Anindo Saka Fitri
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.v4i2.811

Abstract

The 112 call centre in Sidoarjo district faces significant challenges, including a high number of false alarms, which account for 85% of all calls. This hampers the efficiency of the service, especially with the limited number of call takers and inefficient manual information flow. This research aims to develop a technology-based reporting system using an OOAD method (Analysis, Design, Build, Operate) approach to improve the reliability and efficiency of emergency services. The system includes E-KYC-based report verification features to filter false reports, real-time report status tracking, and integration between related parties such as the community, administration, and response units (police, ambulance, fire). The results show that the designed system is able to significantly reduce false reports, improve service response through report prioritisation, and provide community access to monitor reports independently. This approach not only overcomes operational constraints, but also provides more transparent and efficient emergency services. It is hoped that this system can become a standard model for modernising emergency services in the digital age.
K-Means Clustering Method to Make Credit Payment Groupinhg Efficient Siti Nur Illah; Nana Suarna; Irfan Ali; Dodi Solihudin
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.v4i2.815

Abstract

Credit payment management is one of the main challenges in the financial sector, especially in grouping customers based on risk and payment patterns. This study aims to evaluate the K-Means Clustering method in improving the efficiency of credit payment data clustering. The dataset used includes information on payment history, loan amount, tenor, and credit status from financial institutions. The research approach involves data processing stages, application of the K-Means algorithm, and evaluation of results using the Davies-Bouldin Index and Silhouette Score metrics. The results of the analysis show that the K-Means method is effective in identifying customer payment patterns and dividing them into three main clusters: high, medium, and low risk. In addition, this study found that determining the optimal number of clusters using the Elbow Method can improve the accuracy of the clustering results. The resulting model makes a significant contribution to credit risk management, helping financial institutions make strategic decisions related to credit policies and risk mitigation. This study offers practical implications, including increased operational efficiency and predictive ability against potential bad debts. Further studies are recommended to integrate this method with other algorithms to improve the performance of large-scale data analysis.
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.

Page 29 of 43 | Total Record : 430

 27   28   29   30   31 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue 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