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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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Geospatial Analysis of Global Temperature and Humidity Variations Using Integrated Meteorological Data Alya Zhafira; Purwadi
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.v5i2.1817

Abstract

Global climate monitoring is crucial for understanding variations in temperature and humidity, which directly influence ecosystems, human health, and socio-economic activities. This study presents a Geographic Information System (GIS)-based analysis and visualization of global temperature and humidity patterns using historical hourly weather data from 2012 to 2017. The dataset, obtained from open-access sources, was processed and analyzed in Google Colab using Python libraries such as pandas, geopandas, folium, and plotly. Data preprocessing involved merging city-level observations, cleaning missing values, and calculating mean temperature and humidity per location. The resulting dataset was then visualized through an interactive global map and a scatter plot to identify spatial relationships between the two climatic variables.To quantify these spatial relationships, a statistical correlation analysis was conducted, revealing a weak negative relationship between temperature and humidity (r = -0.25) across global regions.The findings reveal that regions near the equator exhibit consistently high temperatures and humidity, while higher-latitude cities show lower temperatures and more variable moisture levels. This GIS-based approach demonstrates the potential of open meteorological data for climate pattern recognition and supports reproducible workflows for environmental analysis. The results highlight the importance of integrating data science tools with GIS for accessible and scalable global climate visualization.
Baby Supplies Sales Prediction System using the Single Exponential Smoothing Method at Little Queen Baby Shop Silvia Agustin; Miftahus Sholihin; Agus Setia Budi
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.v5i2.1824

Abstract

The increasing demand for baby equipment in Indonesia in recent years has created significant business opportunities for the retail sector, including Little Queen Baby Shop. However, seasonal fluctuations in demand often lead to stock management problems such as overstock and out of stock, which affect storage costs and customer satisfaction. This research aims to design and develop a sales prediction system for baby products using the Single Exponential Smoothing (SES) method as a solution to minimize forecasting errors and support data-driven decision-making. The research method involved collecting secondary sales data from January to November 2024, which was then processed using the SES algorithm with a smoothing parameter (α) to determine the optimal prediction values with the lowest error rate. The system was developed as a web-based application using PHP programming language and MySQL database, equipped with features such as transaction recording, stock management, sales analysis, and prediction reports for upcoming periods. The implementation results show that the SES-based prediction system provides sufficiently accurate forecasts, as indicated by low values of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). This system enables Little Queen Baby Shop to optimize stock management, reduce the risk of losses due to excessive or insufficient inventory, and improve both operational efficiency and customer satisfaction.
Sentiment Analysis of Indonesian National Team Failure in the 2026 World Cup Qualifications Using Support Vector Machine Algorithm Muhammad Nouval; Fanza Maulana Habibi; Anisya Rahmi; Muhammad Dawam Amru Bittaqwa; Rizki Agustianto
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.v5i2.1830

Abstract

The Indonesian National Team's failure in the 2026 World Cup qualifiers has generated diverse responses on social media, particularly on Ferry Irwandi's YouTube channel. This study aims to analyze public sentiment towards the national team's performance based on YouTube user comments. The method used is a Support Vector Machine (SVM) with stages of data scraping, pre-processing (cleaning, case folding, normalization, tokenization, stopword removal, stemming), lexicon-based automatic labeling, and model evaluation using a confusion matrix. The data consists of 8,353 comments divided with a ratio of 80:20 for training and testing. The results show that the SVM algorithm is able to classify comments into two classes, positive and negative, with an accuracy of 81%, a precision of 82%, a recall of 83%, and an F1-score of 82%. These results demonstrate the effectiveness of SVM in accurately and stably identifying public opinion towards the Indonesian National Team's failure.
Development of Environmental Cleanliness Education Game for Grade 5 Students at SD Inpres Kalu Umbu Rona Makaborang; Fajar Hariadi; Tri Sari Dewi Novyanti Bertha Mira
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.v5i2.1834

Abstract

Technological advances have brought major changes in various aspects of life, including the world of education. One form of use of technology in education is the development of interactive learning media such as educational games. This research aims to develop an Android-based educational game that raises the theme of environmental cleanliness and is intended for 5th grade students of SD Inpres Kalu, East Sumba. The background of this research is based on the low understanding of students on the importance of maintaining environmental cleanliness, which is caused by conventional learning methods that are less interesting and less interactive. The educational game developed will contain materials such as types of waste, how to sort and dispose of waste, and dirty environmental impacts. The research method used is research and development (R&D) with a waterfall model that includes the stages of needs analysis, design, implementation, verification, and maintenance. Supporting data were obtained through interviews, observations, and literature studies. The results of the trial showed an increase in students' understanding of environmental hygiene materials, which was evidenced by an increase in the average score from 78.0 in the pre-test to 87.2 in the post-test, with a difference of 9.2 points or an increase of 11.79%. Testing using the Black Box Testing method showed that all in-game features performed as intended, while the System Usability Scale (SUS) test results obtained an average score of 83.5, which is in the excellent category.
Implementation of Web Based Motorcycle Sales Prediction System Using the Least Squares Method Syamsudin Hidayatullah; Kemal Farouq Mauladi; M Hasan Wahyudi
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 Muhammad Hanes Eka Saputra; M.Ghofar Rohman; M.Rosidi Zamroni
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 Samuel Surya Sanjaya; April Kurniawan Jaya; Rikky Candra; Stefven Zang
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) Alvito Kurnia Fahrio kurnia
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 Ica Unari; Martanto; Raditya Danar Dana; Ahmad Rifa'i; Ryan Hamongan
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.

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