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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 430 Documents
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Implementation of Web Based Motorcycle Sales Prediction System Using the Least Squares Method Hidayatullah, Syamsudin; Mauladi, Kemal Farouq; Wahyudi, M Hasan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1835

Abstract

The development of information technology has brought significant changes in business data management, including in the automotive industry. Dony Jaya Motor, as one of the motorcycle dealers, faces challenges in predicting sales, particularly in balancing stock availability with market demand. This study aims to develop a web-based motorcycle sales prediction system using the Least Squares method due to its ability to identify linear trend patterns from historical data, producing accurate and measurable sales projections. The data used cover motorcycle sales from May 2024 to April 2025. The implementation results show that the Least Squares method provides good predictive accuracy, with the average Mean Absolute Percentage Error (MAPE) value below 10%, indicating a very low prediction error rate. For example, for the Honda Beat 2015 type, the predicted sales for May 2025 were 5.67 units compared to the actual 6 units, resulting in a MAPE value of 4.67%. The developed system includes features for data input, graphical visualization, and real-time prediction reporting. The application of the Least Squares method in this web-based system has proven to assist management in stock planning, improve decision-making processes, and enhance overall operational efficiency and effectiveness within the company.
Implementation of the Content-Based Filtering Method in Menu Recommendations at Pandawa Pondok Kopi Saputra, Muhammad Hanes Eka; Rohman, M.Ghofar; Zamroni, M.Rosidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1838

Abstract

The rapid growth of the coffee shop industry and the wide variety of menu offerings at Pandawa Pondok Kopi demand a system capable of delivering accurate and personalized menu recommendations. This study aimed to develop a web based menu recommendation application using Content Based Filtering (CBF), leveraging TF-IDF for document vectorization and Cosine Similarity to measure product description similarity.The system was implemented with PHP and MySQL, featuring a responsive interface across three main modules: the homepage (displaying the menu list), the menu detail page (providing full information and similar recommendations), and the admin dashboard (for menu data management). Menu descriptions were preprocessed (tokenization, stop word removal, and stemming) before computing TF-IDF weights. Given a user’s selected menu item, the system calculated Cosine Similarity between its description vector and those of all other menu items, then presents the top three matches. Functionality was verified via Black Box Testing to ensure that admin login, menu addition/editing, recommendation displays, and interface navigation conform to specifications. Test results showed an average Cosine Similarity score ranging from 0.62 to 0.78, indicating satisfactory accuracy in matching user preferences. The system also achieved an average response time of under one second under standard load, meeting efficiency criteria.In conclusion, the Content Based Filtering implementation successfully enhances the relevance of menu recommendations and user experience, thereby supporting increased customer satisfaction and operational effectiveness at Pandawa Pondok Kopi.
Sentiment Analysis of Mobile Legends Game using Naïve Bayes, K-Nearest Neighbors and Support Vector Machine Algorithm Sanjaya, Samuel Surya; Jaya, April Kurniawan; Candra, Rikky; Zang, Stefven
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1840

Abstract

Sentiment analysis of Mobile Legends: Bang Bang (MLBB) user reviews is very important for understanding public satisfaction and perspectives. Therefore, this study aims to analyze and compare the performance of three Machine Learning algorithms: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) in classifying user review sentiments. A supervised machine learning approach was applied using 6,000 reviews obtained from a secondary Kaggle dataset, involving Data Preprocessing and Feature Extraction (TF-IDF) stages, followed by an 80:20 Data Split for model training. The comparison of metric results shows that the Support Vector Machine (SVM) model provides the best overall performance, achieving 79.88% Accuracy and 78.06% F1-Score, although NB slightly outperforms in the Precision metric. In conclusion, SVM's performance proves this algorithm is superior in classifying Indonesian-language mobile game review sentiments, providing strategic insights for MLBB developers in making service improvement decisions.
Web-Based Spare Parts Expenditure Recording Portal with Read-Only Pull from ERP Infor (PT CBI Case Study) kurnia, Alvito Kurnia Fahrio
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1843

Abstract

A companion portal for spare parts management was developed at the PT Century Batteries Indonesia (PT CBI) warehouse to address manual record keeping that is prone to discrepancies and difficult to trace. This article focuses on two key aspects: a checkout flow that validates expenditure amounts against Portal stock, and a reconciliation mechanism that pulls on-hand data from Infor's ERP read-only system to maintain ERP data integrity. The system was developed following the Extreme Programming (XP) methodology with short iterations and regular feedback from warehouse users. Black-box testing and UAT (User-to-User) testing demonstrated that the main flow functioned as expected; all assessed features were accepted with scores of 4.1–4.8. Consequently, discrepancies were detected more quickly and addressed through adjustments in Infor; the Portal then pulled back on-hand to ensure consistency. These results demonstrate that the "checkout in Portal → adjustment in Infor → pulled back on-hand (read-only)" pattern effectively reduces errors caused by manual recording while improving transaction traceability in the spare parts expenditure process in the warehouse environment. Keywords : warehouse management; spare part checkout; Infor ERP; read-only integration; Extreme Programming;
Application of Support Vector Machine for Classification of Toddlers Nutritional Status Based on Anthropometric Data Mohamad Alif Subhi; Rudi Kurniawan; Bani Nurhakim
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1844

Abstract

Stunting remains a major health issue in Indonesia, especially among toddlers. This study aims to classify the nutritional status of toddlers (stunted and non-stunted) using anthropometric data from the Kaggle public dataset with the Support Vector Machine (SVM) algorithm. This dataset includes data on the height, weight, age, and gender of toddlers. It should be emphasized that the data does not originate from the Ciherang Bandung Posyandu, but rather the Posyandu is used only as a context for the potential application of the developed model. The process includes data acquisition, preprocessing (including normalization and data balancing using SMOTE), SVM model training, and evaluation with accuracy, precision, recall, F1-score, and ROC-AUC. The model was trained with an 70:30 data split and optimal parameters (C=1.0, gamma=0.01, kernel=RBF). The results showed high performance, indicating that this model can support early detection of stunting and the implementation of decision support systems in public health services.
Comparative Performance Analysis of Multilayer Perceptron and Long Short-Term Memory for Daily Demand Forecasting in E-Commerce Delivery Platforms Unari, Ica; Martanto; Dana, Raditya Danar; Rifa'i, Ahmad; Hamongan, Ryan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1846

Abstract

This study compares the performance of two deep learning architectures—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—for daily demand forecasting on an e-commerce delivery platform. The dataset consists of 1,827 daily observations from 2020 to 2024 and includes operational, temporal, and behavioral features such as holiday indicators, promotion signals, active customers, and delivery time. Data preprocessing includes cleaning, feature engineering, scaling, and sequence generation using a 30-day sliding window. Both models were trained and evaluated using consistent experimental settings and performance metrics. The results show that the LSTM model achieves better accuracy than the MLP model, with an RMSE of 811.81 compared to 830.15, while the difference in MAE between the two models remains minimal. LSTM demonstrates superior capability in capturing temporal dependencies and reacting to rapid demand fluctuations, whereas both models face challenges when predicting sudden demand spikes. These findings indicate that memory-based models such as LSTM are more effective for highly volatile time-series forecasting in e-commerce operations. However, performance can be further improved with the addition of external variables such as real-time promotions, weather conditions, and multivariate features.
Investment Policy Analysis Between Generation Z and Millennial Generation Fadali Rahman; Aditya Iskandar Syah; aisyah, Aisyah Safitri; ayu, Ayu Maulidia; subhan, subhan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1847

Abstract

This study aims to analyze and This study uses a quantitative approach with a comparative associative survey method and involves 100 respondents (50 Gen Z and 50 Millennials) who are domiciled in Indonesia and have investment experience, with a purposive sampling technique . comparing investment policies between Generation Z and Millennials in Indonesia. These two demographic groups are "Digital Natives" who are familiar with the development of information technology and investment applications. Primary data were collected through an online questionnaire and analyzed using Multiple Linear Regression and Independent Sample t-test with the help of the SPSS program.The results of the regression analysis show that financial literacy (X1), investment knowledge (X2), and investment risk (X3) simultaneously and partially have a significant effect on investment policy (Y) in both generations. The coefficient of determination (R2) value of 0.652 indicates that 65.2% of the variation in investment policy can be explained by these three variables.The results of the comparative test ( Independent Sample t-test ) with a Sig. (2-tailed) value = 0.018 (<0.05) indicate a significant difference in investment policies between Generation Z and the Millennial Generation.Generation Z (born 1996–2010) tends to be more daring in taking risks and is quicker in adopting digital investment technologies such as applications (Bibit, Bareksa, Ajaib, IPOT), and chooses instruments with medium to high risk (stocks and digital assets).The Millennial generation (born 1981–1995) exhibits more conservative behavior , oriented towards the security and stability of assets , with a preference for relatively low-risk instruments such as mixed mutual funds, deposits, and blue-chip stocks.These findings reinforce Behavioral Finance theory , emphasizing the importance of individual understanding, experience, and risk perception in investment decision-making. The research's implications suggest the need for the government/OJK to expand digital financial literacy programs tailored to each generation, as well as the development of educational and secure features by securities firms.
Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Akbar, Miftahul; Rahaningsih, Nining; Ali, Irfan; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1849

Abstract

This study aims to analyze sales transaction patterns of rubber waste at PT Mandiri Enviro Technosio by integrating the Decision Tree algorithm with interactive visualization using Tableau Public. The dataset consists of 405 sales transactions recorded during the 2024–2025 period, comprising attributes such as transaction date, product type, quantity, unit price, total value, delivery region, and buyer category. The research methodology includes data acquisition, preprocessing to ensure data quality and consistency, construction of a classification model using the CART algorithm, evaluation of model performance through a confusion matrix, and development of interactive dashboards for enhanced interpretability. The Decision Tree model achieved an accuracy of 88.24% in classifying transaction values into low, medium, and high categories. Unit price and transaction period were identified as the most influential attributes in determining transaction value. Visualization using Tableau Public effectively presented the distribution of transaction values, sales trends, and geographical patterns, thereby strengthening analytical insights and supporting data-driven decision making. The integration of classification techniques and interactive visualization contributes to improving business intelligence capabilities and enables the formulation of more adaptive, evidence-based sales strategies.
FP-Growth for Data-Driven Purchase Pattern Analysis and Product Recommendations at Flanetqueen Store Marwah, Sopa; Rahaningsih, Nining; Ali, Irfan; Marthanu, Indra Wiguna; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1850

Abstract

The advancement of information technology has encouraged the use of data analytics to support data-driven business decision-making. This study aims to analyze purchasing patterns of hoodie products and provide product recommendations for customers at Flanetqueen Store using the FP-Growth (Frequent Pattern Growth) algorithm. The research applies the Knowledge Discovery in Database (KDD) framework, consisting of five stages: data selection, preprocessing, transformation, data mining, and interpretation/evaluation. The dataset comprises hoodie sales transactions recorded from January to December 2024. Data analysis was conducted using RapidMiner Studio version 10.3 with a minimum support of 0.2 and minimum confidence of 0.4. The analysis produced 26 itemsets and 11 association rules indicating product correlations. The strongest rule, Bloods → Champion, achieved a confidence of 0.414, revealing that customers who purchased Bloods hoodies were also likely to buy Champion hoodies. These findings were used to design cross-selling strategies and generate relevant product recommendations. The study demonstrates that FP-Growth effectively extracts frequent purchase patterns and contributes to the development of data-driven recommendation systems in the local fashion retail industry.
Application of the K-Means Algorithm in the Segmentation of 3kg Lpg Customers Ananda, Ginaselvia; Suarna, Nana; Bahtiar, Agus; Arif Rinaldi Dikananda; Faturrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1853

Abstract

This research was motivated by PT Sumber Perkasa Mandiri's need to understand the purchasing patterns of 3 kg LPG gas customers more accurately in order to improve the effectiveness of its marketing strategy. The purpose of this study was to apply the K-Means Clustering algorithm to form customer segmentation based on transaction behavior. The method used is a quantitative approach with sales data analysis of 850 records through the stages of data selection, preprocessing, attribute transformation, and modeling using RapidMiner Studio. Model evaluation was carried out using the Davies-Bouldin Index to determine the optimal number of clusters. The results of the study show the formation of two main clusters, namely the premium customer cluster with high purchase frequency and high loyalty, and the low-activity customer cluster that only makes purchases when necessary. The best DBI value at K=2 of 0.057 indicates excellent cluster separation quality. These findings conclude that K-Means Clustering is effective in identifying differences in consumption behavior, and its implications provide a strategic basis for companies to design loyalty programs for high-value customers and more intensive promotions for low-activity customers.

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