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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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Using the Apriori Algorithm to Identify Purchase Patterns for Enhancing Sales in Personal Shopper Services Euis Fadilah; Ahmad Faqih; Sandy Eka Permana
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.v4i2.741

Abstract

This research aims to explore the application of the Apriori algorithm in identifying purchasing patterns in the drop-off service industry in order to increase sales. Drop-off services often face challenges in designing effective marketing strategies due to limited understanding of customer purchasing behavior. In this study, the Apriori algorithm is applied to uncover recurring purchase patterns among customers, which are then used to develop more efficient marketing strategies. Customer transaction data is analyzed to find associations that reflect their purchasing preferences. The results show that the application of the Apriori algorithm successfully identifies patterns that can improve marketing strategies and, ultimately, increase sales. This research emphasizes the importance of applying data mining techniques to improve the performance of delivery services.
The Effect of SMOTE Application on Support Vector Machine Performance in Sentiment Classification on Imbalanced Datasets Dini Andriyani; Ahmad Faqih; Sandy Eka Permana
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.v4i2.742

Abstract

This research explores the effect of applying Synthetic Minority Oversampling Technique (SMOTE) on the performance of Support Vector Machine (SVM) algorithm in sentiment classification on imbalanced datasets. Public review data was collected from social media platform X (formerly Twitter) regarding the Free Lunch Program, with a total of 2,368 reviews automatically labeled using the BERT model into three categories: positive, negative, and neutral. Sentiment imbalance in the dataset was addressed by applying SMOTE to generate synthetic data on minority classes. The research method follows the stages of Knowledge Discovery in Databases (KDD), including data selection, preprocessing, labeling, transformation using TF-IDF, SVM model training, and performance evaluation. The experimental results show that the application of SMOTE successfully improves the accuracy of the SVM model by 12.48%, from 71.41% to 83.89%. Other evaluation metrics, such as precision, recall, and F1-score, also showed significant improvement from 0.69, 0.71, and 0.68 to 0.84, respectively. These findings confirm that SMOTE is effective in overcoming data imbalance, resulting in a more accurate and reliable sentiment classification model. This research contributes to the application of sentiment analysis in data-driven public policy evaluation.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm Anjar Ayuning Lestari; Ahmad Faqih; Gifthera Dwilestari
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.v4i2.743

Abstract

Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
The Impact of Principal Component Analysis Dimensionality Reduction on Sentiment Classification Performance Using Support Vector Machine Azzahra Moudy Fajria; Ahmad Faqih; Gifthera Dwilestari
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.v4i2.744

Abstract

This study investigates the application of Principal Component Analysis (PCA) to enhance sentiment classification performance using the Support Vector Machine (SVM) algorithm. User reviews of the ChatGPT application from the Play Store were collected, preprocessed, and analyzed to identify the sentiment within the text (positive, negative, or neutral). The research follows the Knowledge Discovery in Databases (KDD) framework, starting with data selection, preprocessing, transformation, and applying PCA for dimensionality reduction. PCA was used to reduce the complexity of the high-dimensional text data, improving SVM's efficiency in sentiment classification. Evaluation results show that applying PCA led to an improvement in model performance, with accuracy increasing from 72.65% to 73.20%, precision from 71.58% to 72.24%, recall from 71.77% to 72.66%, and F1-score from 71.56% to 72.32%. Although the improvements were modest, the findings demonstrate that PCA effectively simplifies complex datasets and enhances SVM performance in sentiment classification, offering benefits in processing high-dimensional text data.
Sentiment analysis to classify TikTok Shop Users on Twitter with Naïve Bayes Classifier Algorithm Ayu Lestari; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
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.v4i2.748

Abstract

Advances in information technology have facilitated the use of social media as an e-commerce platform, with TikTok Shop enabling in-person transactions. This research addresses the gap in understanding user perceptions of TikTok Shop through sentiment analysis on Twitter. Sentiment classification is performed using the Naïve Bayes Classifier algorithm. The dataset consists of 1,907 Indonesian tweets, collected from January 2023 to July 2024, and processed using RapidMiner in the Knowledge Discovery in Database (KDD) framework. The preprocessing stages include data cleaning, normalization, tokenization, stopword removal, and stemming. To overcome data imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. The model achieved 93.98% accuracy, with balanced precision and recall for positive, neutral, and negative sentiments. The sentiment distribution among TikTok Shop users on Twitter was 35.5% positive, 35.5% negative, and 29.0% neutral. This research provides insights into consumer behavior on social media and emphasizes the importance of sentiment analysis to increase user engagement and understand market perception. This research is expected to provide information to platform developers and businesses looking to improve TikTok
Naïve Bayes Optimization by Implementing Genetic Algorithm in Sentiment Analysis of BCA Mobile Reviews Muhammad Enricco Rizqy; Ahmad Faqih; Gifthera Dwilestari
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.v4i2.750

Abstract

The development of the digital era has encouraged the adoption of mobile banking applications that facilitate banking transactions, including the BCA Mobile application which is simple but still adheres to a slightly outdated, user-friendly appearance but to provide the best service, it is necessary to evaluate the various problems that arise through review analysis. This study aims to conduct sentiment analysis of BCA Mobile application reviews taken from the Google Play Store, with data totaling 1,200 reviews scraping results using Google Collaboratory python programming language, to categorize negative and positive reviews used manual labeling for more accurate results, the Naïve Bayes approach is used in classifying positive and negative category reviews due to the ability of this algorithm to handle text data. However, the weakness of Naïve Bayes which is sensitive to irrelevant features can cause a decrease in accuracy. This research implements Genetic algorithm to improve the performance of Naïve Bayes. The results showed that the application of Genetic algorithm successfully increased the accuracy, precision of Naïve Bayes classification 95%, precision 92% to accuracy 98%, precision 99%, which proved the effectiveness of Genetic algorithm in optimizing the model and improving the quality of sentiment analysis.
Comparative Analysis of Demand Forecasting Accuracy in Sajiku Seasoned Flour Product with Software POM-QM Fadilah Artanti Rahmania; Nur Rahmawati
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.v4i2.751

Abstract

Indonesia's growing wheat flour consumption requires precise demand forecasting to optimize supply chain management. This study evaluates the forecasting accuracy of Sajiku seasoned flour demand using three methods: Single Exponential Smoothing, Moving Average, and Linear Regression. Data processing and forecasting error calculations were performed using POM-QM software. The analysis reveals that the Linear Regression method yields the lowest forecasting error, making it the most reliable approach for predicting future demand. This study emphasizes the importance of selecting suitable forecasting techniques to improve the accuracy of demand predictions, which can enhance customer satisfaction and contribute to the long-term sustainability of businesses. The findings underscore the significance of accurate demand planning in maintaining a well-balanced supply chain and addressing market fluctuations effectively.
3D Application Development with Blender and Roblox Integration: A Case Study of the North Sumatera State Museum Maulana Malik Fajri; Said Iskandar Al Idrus; Yulita Molliq Rangkuti; Kana Saputra S; Debi Yandra Niska
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.v4i2.752

Abstract

This research explores the potential of Metaverse and Immersive Space technologies to enhance virtual tourism experiences at the North Sumatra State Museum through the integration of Blender and Roblox Studio. The main focus is on developing complex and interactive metaverse content, as well as implementing an adaptive visit counting system. The methodology involves developing a 3D application using Blender for modeling and Roblox Studio for the virtual environment. Key results include the addition of Virtual Reality (VR) features, expansion of the virtual museum collection, and a continuous evaluation system based on user feedback. In conclusion, the integration of Blender and Roblox Studio proves effective in creating immersive virtual museum experiences, opening new opportunities in utilizing Metaverse technology to increase museum accessibility and offering innovative solutions for preserving and promoting cultural heritage through digital platforms.
Design and Development of the Sidajaya Tourism Village Information and Promotion Media Slamet Rahayu; Tri Herdiawan Apandi; Taufan Abdurrachman; Nurfitria Khoirunisa; Adi Firmansyah; Asep Saepuloh
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.v4i2.753

Abstract

The potential of Sidajaya Tourism Village, which is not yet known to many people, has hampered the development of this tourist village. Information about the Sidajaya tourist village is still difficult to access, making people reluctant to look for the information they need, so knowledge about it is limited. Therefore, a system is needed to help the public access information about the Sidajaya tourist village. This research aims to build a website-based Sidajaya Tourism Village Information System as a medium for information and promotion. This system was built using the PHP programming language and MySQL database, with data collection methods through observation and interviews. The system development stage includes analysis, design, implementation, and testing. The result of developing this system is a website that allows the public to search for information about the Sidajaya tourist village easily and supports more efficient promotion and management to increase tourist visits.
Quality Control Analysis on Animal Feed Storage to Minimize Defect Using Six Sigma Method in PT XYZ Asmaul Husna; Dira Ernawati
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.v4i2.754

Abstract

As the industrial world develops, companies are required to produce quality products to attract consumer interest in order to survive in the midst of intense competition in the industrial world, including companies in the animal feed industry. Therefore, quality control is an activity that must be carried out so that the products produced have good quality. At PT XYZ, there are defects in animal feed storage such as expired, ticky, tilted plots, and leaky sacks. To handle these problems, an analysis is carried out using the Six Sigma method with the DMAIC (Define, Measure, Analyze, Improve, and Control) stages. Based on company data from January to October 2024, the defects amounted to 1.459 sacks with the most types of defects in the form of leaky sacks with a percentage of 83%. The DPMO value obtained shows that there is a possibility of 59.726 defects for a million production with a sigma level value of 3,125. This value shows that the quality is still not consistent and maximum, so improvements need to be made to improve quality and sigma level. Thus it is expected that the company can become a competitive company and can compete well in the industrial world.

Page 25 of 53 | Total Record : 524

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