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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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Basic Analysis of Cybersecurity in Facing Digital Threats in the Industrial Era 5.0 Elsa Wardani; A. Hamdani
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.v5i2.1911

Abstract

Industri 5.0 fokus pada kerja sama yang lebih mengutamakan manusia, serta keberlanjutan dan ketahanan. Teknologi canggih seperti kecerdesan buatan (IA), internet of things industri (IIOT), dan robot yang bekerja bersama manusia (cobot) terintegrasi ke dalam industri ini. Karena adanya keterhubungan yang lebih baik antara dunia fisik dan dunia digital, maka peningkatan ini berdampak lebih besar pada peningkatan risiko serangan digital, sehingga mengancam data, sistem, dan bahkan keselamatan manusia. Penelitian ini bertujuan untuk menganalisis secara mendalam fondasi keamanan siber dalam konteks industri 5.0, serta menemukan strategi yang bisa diterapkan dalam menghadapi ancaman digital yang terus berkembang. Metode yang digunakan adalah observasi bahan bacaan secara sistematis dan analisis deskriptif kualitatif terhadap kerangka kerja keamanan siber yang ada, seperti NIST, ISO 27001, dan IEC 62443, terutama dalam konteks teknologi industri 5.0. hasil penelitian menunjukkan bahwa perlu ada perubahan dari perlindungan yang hanya di sekitar batas fisik ke model keamanan yang lebih proaktif, terdistribusi, dan berdasarkan risiko. Model ini menekankan pentingnya arsitektur zero trust, Perlindungan data yang menyeluruh, dan pemantauan ancaman yang ditingkatkan oleh IA. Selain itu, kesadaran dan pelatihan tenaga kerja juga ditemukan sebagai bagian penting dari kemanan siber.
The Effect of E-Wallet Usage on Personal Cash Flow and Net Worth Ratio in Generation Z Wanda Nur Safitri; Fadali Rahman Fadali; Zuhal Thoriq Zuhal; Aisyah Rievliani Aisyah; Isnain Bustaram Isnain
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.v5i2.1915

Abstract

The rapid development of financial technology has significantly transformed individual digital financial behavior, particularly through the increasing use of electronic wallets (e-wallets) among Generation Z. As digital natives, this generation is highly exposed to online transactions, yet their financial management capabilities remain varied. This study aims to analyze the effect of e-wallet usage on personal financial stability, specifically measured through personal cash flow and net worth ratio. Additionally, technological adaptation patterns within modern student financial activities significantly increase complexity, influencing how digital tools are utilized. A quantitative survey method was employed, involving 30 respondents who are active e-wallet users and university students in Pamekasan, Madura. Data were collected through a structured questionnaire and tested for reliability, yielding a Cronbach’s Alpha value of 0.761, indicating acceptable internal consistency. The Kolmogorov–Smirnov normality test showed that some variables met the normal distribution criteria. Results of multiple linear regression revealed that the intensity of e-wallet use, perceived usefulness, and perceived financial impact did not have a significant effect on cash flow, with a significance value greater than 0.05. The model’s R Square value of 0.087 further suggests that only 8.7% of changes in cash flow can be explained by the examined variables, while the remaining 91.3% is influenced by factors such as income level, spending behavior, and financial literacy. These findings indicate that although e-wallets have become an integral part of students’ daily transactions, their impact on overall financial stability remains limited. Strengthening digital financial literacy is recommended to promote wiser and more responsible e-wallet usage.
Analysis of the Effectiveness of Manual Deployment and CI/CD Github Actions in the Braisee Application Nenda Alfadil Seputra; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade 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.v5i2.1916

Abstract

In the modern cloud-based software development ecosystem, the speed and reliability of the deployment process are critical elements. This study aims to evaluate the effectiveness of implementing Continuous Integration/Continuous Deployment (CI/CD) using GitHub Actions compared to manual methods for the machine learning API of the Braisee application hosted on Google Cloud Run. Using a quantitative approach with a comparative experimental design across ten testing iterations, this research measures deployment time efficiency, error rates, and system stability. The experimental results show a significant performance disparity, where the automated method based on GitHub Actions is considerably more efficient, with an average total duration of 111–167 seconds, reducing operational time by 40–60% compared to the manual method, which requires 297–364 seconds. In terms of reliability, the automated method achieves a 100% success rate with high consistency, whereas the manual method demonstrates substantial vulnerability to human errors such as mistyped project IDs and inconsistent image tagging. It is concluded that implementing CI/CD through GitHub Actions is a superior solution that improves time efficiency and ensures the stability of cloud-based applications compared to manual procedures.
Application of Machine Learning in Predicting FIFA World Cup Matches Zulfikar Ismaya Ramadhani; Syaifudin; Beldi Sahfitda; Seprianata Kusuma; Ardiyansyah
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.v5i2.1918

Abstract

Football is one of the world’s most widely followed sports, making it an appealing subject for predictive analytics using modern data technologies. This study aims to build a predictive model for international football match outcomes by applying the CRISP-DM methodology as the analytical framework. The dataset used is international_matches.csv covering the period 1993–2022, which underwent a series of preprocessing steps including data cleaning, feature engineering, encoding, imputation, and scaling. Several machine learning algorithms were evaluated, namely Logistic Regression, Random Forest, and HistGradientBoostingClassifier (HistGBM). The best model was obtained using the optimized HistGBM, which demonstrated superior capability in identifying home-team victories, achieving a Recall of 78%. This high sensitivity indicates that comparative features—such as rank difference and squad strength disparity across goalkeeper, defense, midfield, and attack attributes—play a crucial role in predicting dominant match outcomes. The trained model was subsequently deployed into an interactive Streamlit-based web application that enables users to input match-related information and obtain real-time predictions. Overall, this study shows that machine learning methods can be effectively utilized to support data-driven analysis of international football match outcomes.
Comparative Analysis of Serverless Container Service Performance Between Google Cloud Run and AWS App Runner in Cross-Cloud Architecture Muhammad Adithya Pratama; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade 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.v5i2.1919

Abstract

Research on the performance of serverless container services is becoming increasingly important as the need for modern distributed and cross-cloud architectures grows. This study analyzes the performance of two leading serverless services, Google Cloud Run and AWS App Runner, in a cross-cloud architecture scenario. Testing was conducted using identical parameters, including container configuration, region, memory, vCPU, and concurrency. Performance testing included p95 latency, throughput, and error rate metrics using loads of up to 1000 virtual users. The results showed that Google Cloud Run provided more stable performance with p95 latency of 47–71 ms, throughput of 436–438 RPS, and 0% error rate. In contrast, AWS App Runner showed p95 latency of 490–651 ms with throughput variation of 388–410 RPS and an error rate of 2–4.41%. The difference in performance was due to autoscaling mechanisms, cross-cloud communication overhead, and resource contention. This study provides empirical evidence for selecting the optimal serverless service for distributed architectures.
Visualization of Lecturer Teaching Evaluation Data Using K-Means Clustering and Tableau Methods Golda Tomasila; Marchello Gefan Salenussa; Maryo Indra Manjaruni; Ravensca Matatula; Paul Rio Pelupessy; Julius Chrisostomus Aponno
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.v5i2.1920

Abstract

In the process, the results of monitoring and evaluating lecturers in each semester are usually only presented in the form of tables and descriptive explanations, but have not yet visualized the data for further analysis. The purpose of this study is to visualize the results of lecturer teaching evaluation using the K-Means Clustering and Tableau algorithms, and is expected to help the faculty and university monitor and evaluate lecturers in each semester in a more objective and informative manner. The results of the study found that the k-means clustering algorithm succeeded in finding the pattern of student clustering on the evaluation of lecturer teaching and based on the visualization of the results of k-means with a tableau it was found that most students gave a positive response to lecturer teaching and only a small number of students gave a poor assessment of lecturer teaching by emphasizing on improving the teaching process, namely consistently carrying out RPS, punctuality and so on
E-Commerce Customer Segmentation Application Based on the K-Means Algorithm Nehemia; Jekoniah Nahum Pakage; Veronica Lois; Regina Arieskha
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.v5i2.1922

Abstract

Ineffective e-commerce marketing serves as the background for this research, which aims to develop a customer segmentation application for targeted marketing. The K-Means Clustering method with RFM (Recency, Frequency, Monetary) analysis is applied to data from 178 customers. The research methodology includes data preprocessing, feature transformation, and the determination of the optimal K using the Elbow Method. The results indicate that K=3 is the optimal number of clusters. Three segments were successfully identified: 'Champions' (18.5%, 33 customers) with the highest Frequency/Monetary values, 'Active & Potential' (41%, 73 customers) with the lowest Recency (most recent), and 'At Risk' (40.5%, 72 customers) with the highest Recency (longest duration since last transaction). The study concludes that the developed Streamlit-based application successfully visualizes these segments interactively to support strategic decision-making in marketing.
Implementation of the C4.5 Decision Tree Algorithm to Determine Student Productivity Based on Sleep Patterns Tri Fuji Mandala; Haida Dafitri
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.v5i2.1923

Abstract

Sleep patterns refer to an individual’s habits in managing sleep and wake times, including duration, quality, and regularity. Students, particularly those in the Informatics Engineering Program at Universitas Harapan Medan, often experience irregular sleep patterns due to heavy academic workloads such as assignments, projects, and practical activities. This condition can reduce academic productivity in terms of concentration, memory, and the ability to complete tasks on time. Therefore, this study aims to develop a classification model to predict student productivity levels based on sleep patterns using the Decision Tree C4.5 algorithm. This algorithm was chosen for its advantages in interpretability, ability to handle both numerical and categorical data, and efficient attribute selection, which contribute to generating an accurate and transparent classification model. The study involved 30 respondents from the 8th semester of the Informatics Engineering Program at Universitas Harapan Medan in the 2024/2025 academic year who filled out questionnaires regarding their sleep patterns and productivity. The results showed that 15 respondents (41.2%) had low productivity, 9 respondents (35.3%) had medium productivity, and 6 respondents (23.5%) had high productivity. These findings indicate a significant relationship between sleep pattern regularity and student productivity levels. The model generated using the C4.5 algorithm is expected to serve as a foundation for developing decision support systems aimed at improving the balance between sleep patterns and academic productivity among students.
Website-Based School Financial Information System Takhrisna Amila Alfaida; Syefti Rahma Utami; Febikhanaya Putri; Hidayatur Rakhmawati; Alma Fatikhul Khak; Muhammad Dafie 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.v5i2.1925

Abstract

The rapid development of information technology has brought significant changes in data and information management in the educational environment. This research focuses on the development of a website-based Financial Information System tailored to the needs of SDIT Binaul Izzah Bumiayu. This system was designed using the waterfall method with stages of needs analysis, design, implementation, and testing to improve efficiency, accuracy, and ease in managing school financial data which was previously still manual. The results of the study indicate that the system created is able to assist schools in the process of recording, recapitulating financial reports, and accelerating data access while reducing human error. Recommendations for future system development include the addition of payment notification features, automatic reports, student data integration, improvement of supporting facilities, and ongoing cooperation between schools and universities, followed by periodic evaluations to improve system quality.
Comparison of LSTM and ARIMA Methods in Predicting the Inflation Rate in Manado City Skolastika Kadang; Vivi Peggie Rantung
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.v5i2.1929

Abstract

Forecasting city-level inflation is challenging due to seasonal patterns, nonlinear dynamics, and limited exogenous variables, while short-term accuracy is required for timely policy responses. This study focuses on monthly inflation in Manado City over the period 2010–2024, explicitly accounting for the role of the Consumer Price Index (CPI). We compare a seasonal SARIMA baseline with a multivariate LSTM model that jointly ingests inflation and CPI series. The contributions of this work are an end-to-end, reproducible forecasting pipeline and an evidence-based comparison that identifies the conditions under which a feature-rich nonlinear model is preferable. The methodology includes aligning and preprocessing monthly series, conducting stationarity tests, selecting SARIMA specifications via information criteria and residual diagnostics, and training a 12-month window LSTM (Adam optimizer, MSE loss) with internal validation. The results show that the LSTM yields lower errors on the test horizon (RMSE 0.497; MAE 0.398) than the SARIMA (1,1,1)×(1,1,1,12) model (RMSE 0.661; MAE 0.486), with a smoother 12-month-ahead forecast path under a constant-CPI scenario; visual findings are consistent with the metrics, and a Diebold–Mariano test can be used to assess the significance of the difference. In conclusion, although SARIMA remains a strong and interpretable baseline, the multivariate LSTM delivers a practically meaningful gain in short-term accuracy when the inflation–CPI interaction is nonlinear, making it relevant for regional policy planning.

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