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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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Investment Policy Analysis Between Generation Z and Millennial Generation Fadali Rahman; Aditya Iskandar Syah; Aisyah Safitri aisyah; Ayu Maulidia ayu; 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 Miftahul Akbar; Nining Rahaningsih; Irfan Ali; Fatihanursari Dikananda; Umi Hayati
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 Sopa Marwah; Nining Rahaningsih; Irfan Ali; Indra Wiguna Marthanu; 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 Ginaselvia Ananda; Nana Suarna; Agus Bahtiar; 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.
Application of Decision Tree Algorithms to Classify the Sales Results of Kangen Kripik Sme Products Adila G Khiqmatiar Muchsin; Nining Rahaningsih; Irfan Ali; Dadang Sudrajat; Saeful Anwar
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.v5i2.1854

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in strengthening the national economy; however, many still face challenges in managing and analyzing sales data effectively. This study aims to classify product sales results at UMKM Kangen Kripik Mang Acep by applying the Decision Tree algorithm as a data classification method based on machine learning. A quantitative experimental approach was employed to evaluate the model’s performance using one-year sales data, including attributes such as product variants, sales volume, sales channels, and marketing regions. Data processing was conducted using RapidMiner software following the Knowledge Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and model evaluation. The results indicate that the Decision Tree algorithm successfully classified sales regions (Garut, Bandung, and Sumedang) with an accuracy rate of 96.48%, identifying “Units Sold (pcs)” as the most influential attribute for distinguishing marketing areas. These findings demonstrate that the Decision Tree method is not only effective in improving data analysis efficiency but also provides valuable strategic insights for data-driven business decision-making in MSMEs
Literature Review: Transitioning usage from BFS and DFS to Heuristic Search in the Modern AI Era Fahmy Syahputra; Elsa Sabrina; M Fajar Sahendra Chan; Rifki Fali; Muhammad Fattah; Joko Hendratmo; Fadhil Ardiansyah
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.v5i2.1856

Abstract

Uninformed search algorithms, specifically Breadth-First Search (BFS) and Depth-First Search (DFS), encounter significant scalability limitations when addressing complex problem spaces in modern Artificial Intelligence (AI) ecosystems. This study investigates the paradigm shift toward intelligent heuristic algorithms through a systematic literature review and comparative analysis of 24 recent academic sources. The evaluation focuses on three primary domains: logical problem solving, robotic navigation, and data infrastructure management. Results demonstrate that heuristic methods, such as A-Star and hybrid variants like PrunedBFS, offer superior time efficiency and memory optimization for autonomous navigation and massive computing tasks. Nevertheless, classic algorithms retain functional relevance for specific scenarios requiring exhaustive exploration. Furthermore, this study reveals that algorithmic evolution has fundamentally transformed digital infrastructure, driving a shift from Search Engine Optimization (SEO) to Answer Engine Optimization (AEO) and necessitating adaptive cybersecurity architectures. The research concludes that the future of AI development relies not on substitution, but on a collaborative synthesis integrating the robustness of classic methods with the adaptability of modern heuristics.
Comparison of Memory Efficiency and Computation Time of Bubble Sort, Insertion Sort, and Intro Sort Algorithms Using the C++ Programming Language Shabina Nur Fatmaluna; Nazwa Gista Aulia Nazwa; Adhiel Rahma Adhiel; Salma Aulia Salma; Imam Prayogo Pujiono Imam
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.v5i2.1858

Abstract

Data sorting is a fundamental step in the computer process that greatly affects the effectiveness of programs and overall system performance. In this study, three sorting algorithms, namely Bubble Sort, Insertion Sort, and Intro Sort, are analyzed and compared using recursive and iterative approaches. Bubble Sort serves as a basic algorithm example to understand the basic idea of element exchange, while Insertion Sort was chosen for its efficiency on small and nearly sorted datasets. Intro Sort, as a combination algorithm that integrates Quick Sort, Heap Sort, and Insertion Sort, was studied to reveal how its adaptive mechanism can provide more optimal results. The testing was conducted by measuring execution speed, sorting stability, and memory usage efficiency. The findings from this study show that Bubble Sort ranks lowest in terms of performance and is less suitable for large data sets. Insertion Sort shows better results on small data sets and those with similar patterns. Intro Sort emerges as the most effective algorithm with stable processing time, high adaptability, and faster and more efficient sorting results for various data sizes. Overall, this study emphasizes the importance of choosing a sorting algorithm that suits the characteristics of the data and the needs of the application. The combination of adaptive strategies such as those in Intro Sort is the best solution for current data processing, which demands high speed and efficiency.
Comparison of Balancing Strategies for Classifying Guava Fruit Diseases Putri Nabilla; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
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.v5i2.1859

Abstract

The problem of class imbalance often poses an obstacle in deep learning-based image classification, especially in the domain of digital agriculture. The imbalance in data distribution makes it easier for models to recognize the majority class, while performance for the minority class declines. This study aims to analyze the effectiveness of three strategies for handling class imbalance: Weighted Loss Function, Oversampling, and a combination of Weighted Loss and Oversampling, in improving the performance of image classification of guava fruit diseases using a transfer learning-based MobileNetV2 architecture. The dataset consists of 3,784 images of three disease classes, namely Anthracnose, Fruit_Fly, and Healthy_guava, which show an imbalanced distribution. The research was conducted through the stages of Exploratory Data Analysis (EDA), pre-processing, augmentation, model training with four scenarios, and evaluation using Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. The results showed that the Combination model (Oversampling and Weighted Loss) performed best on the minority class with an F1-score of 0.9630, the highest among all models. The Oversampling strategy produced the highest Macro F1-score of 0.9617, while Weighted Loss provided a significant improvement in classification sensitivity but was still below the combination model. Thus, it can be concluded that the combination strategy is the most effective approach in improving the sensitivity of the model to minority classes, while Oversampling excels in the overall performance stability of the model.
Predicting Student Academic Performance Based on Learning Habits Using XGBoost and SHAP Siti Latifah; Martanto; Raditya Danar Dana; Fatihanursari Dikananda; Umi Hayati
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.v5i2.1860

Abstract

This study developed a model for predicting student academic achievement based on learning habits using the XGBoost algorithm and SHAP interpretability techniques. The secondary dataset contains 1,000 entries and 16 variables (for example, hours of study per day, mental health, frequency of exercise, social media use, hours of sleep) pre-processed including cleaning, imputation, encoding, and normalization before being divided into train–test (80:20) and validated using 5-fold CV. Three models were tested: Linear Regression, Random Forest, and XGBoost. Evaluation using RMSE, MAE, and R² showed that XGBoost achieved RMSE = 0.335, MAE = 0.266, and R² = 0.882, while Linear Regression showed the best performance according to R² in certain configurations (R² = 0.888; RMSE = 0.326). SHAP analysis revealed that the most influential features were hours of study per day, mental health scores, exercise frequency, duration of social media use, and hours spent watching Netflix. The findings confirm that students' study habits and psychological conditions are the main determinants of academic achievement variation; the use of interpretable features strengthens the readability of the model for education stakeholders. Research recommendations include testing the model on longitudinal datasets, integrating socioeconomic factors, and implementing data privacy procedures before institutional-scale implementation.
Optimization of Classification of Tea Leaf Disease Images Using LBP–HOG and MobileNetV2 Ezar Qotrunnada; Odi Nurdiawan; Arif Rinaldi Dikananda; Aris Pratama Putra; 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.v5i2.1861

Abstract

This study was motivated by the need for an accurate and efficient system for detecting tea leaf diseases, given that the current method Manual identification has limitations in terms of consistency, speed, and It also depends on expert labor. To address these challenges, the study It developed a classification model for detecting diseases in tea leaves using a combination of features Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) integrated with the MobileNetV2 architecture. The research method includes the following stages: importing the dataset, data partitioning, exploratory data analysis (EDA), preprocessing, features, and training four model scenarios: baseline MobileNetV2, LBP-based model, HOG-based model, and hybrid LBP–HOG model. Evaluation is done with the metrics of accuracy, precision, recall, and F1-score. The results show that the baseline model achieved 91.67% accuracy, the LBP model achieved 60.67%, the HOG model achieved 68.67% accuracy, and the hybrid model achieved 66.67% accuracy. These findings indicate that MobileNetV2 is still the most optimal model, but the integration of texture features and gradients provides a deeper understanding of the characteristics of disease patterns. This study emphasizes the importance of exploring classic features to enriching visual representation in lightweight CNN models, as well as providing a contribution to the development of plant disease diagnosis systems that are efficient.

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