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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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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 Mandala, Tri Fuji; 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; Utami, Syefti Rahma; 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.
People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Aulia, Ahmad Hasan Faqih; Balti, Carissa Fathinah; Anatasya, Keisyah Zahra; Mindara, Gema Parasti; Giri, Endang Purnama
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.v5i2.1933

Abstract

Accurate people counting in dynamic environments remains challenging due to variations in lighting, complex backgrounds, and occlusion. This study proposes a video-based people counting system leveraging a Convolutional Neural Network (CNN) integrated with the YOLOv5 object detection model. The system applies a structured preprocessing pipeline, including frame extraction, normalization, and noise reduction, to enhance data consistency before detection. The model was evaluated using ten real-world campus video sequences to assess detection reliability and counting accuracy. Experimental results demonstrate that the proposed method achieves high precision and recall for real-time detection across diverse scenarios. Performance degradation was observed in frames containing dense crowds or low illumination, indicating limitations under extreme conditions. These findings validate the feasibility of lightweight CNN-based detectors for surveillance and monitoring applications, while highlighting the need for larger datasets and optimized training strategies to improve robustness in more complex environments.
Development of a Complaint Application for the Education Agency Using the Agile Development Method Tumbo, Sinta; Medi Hermanto Tinambunan
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.v5i2.1935

Abstract

The rapid advancement of information technology has driven changes in performance and problem solving in society and government agencies. In the education sector, the Education Office, as the main service provider, requires an effective complaint mechanism for students, teachers, employees, and the community. The current complaint method still often relies on face-to-face submission, which causes problems such as difficulty in tracking the status of complaints, poor documentation, and limitations in evaluating the quality of complaint handling. This study aims to develop a web-based complaint application to digitize and harmonize the complaint process at the Minahasa Regency Education Office. The Agile Development method was used due to its iterative, flexible, and collaborative nature, allowing for the gradual development of features based on direct feedback from stakeholders. Data collection techniques included observation, interviews, documentation studies, and literature studies. The system was designed using UML diagrams, including Use Case, Activity, Sequence, and Class diagrams. Development was carried out in sprints, focusing on core features: user registration with NIK verification, complaint submission and tracking, and an admin dashboard for complaint management. Functional testing using the Black-Box method confirmed that all key features operate correctly as required. The resulting application successfully transformed the manual complaint process into a more structured, transparent, and efficient digital system, thereby contributing to improved public service quality in the field of education.
Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels Wahyudi, Rizky; Kiswanto, Dedy; Aulia, Windy; Audy Priscilia, Selfi
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.v5i2.1936

Abstract

The availability and quality of clean water in household storage tanks are essential yet often overlooked until problems such as depletion or contamination occur. Manual monitoring methods that rely on physical inspection tend to be inefficient, prone to delay, and unable to support predictive decision-making. This study proposes an automated monitoring solution by integrating Internet of Things (IoT) technology with Machine Learning-based analysis. The system is developed using an ESP32 microcontroller that continuously collects real-time data from an ultrasonic sensor to measure water level and a turbidity sensor to assess water clarity. The time-series data obtained is then analyzed using two algorithmic approaches. Linear Regression is employed to model the water depletion rate and generate predictions regarding the estimated remaining duration before the tank reaches an empty state. In parallel, Random Forest is applied as a comparative model to validate prediction accuracy under non-linear consumption patterns. Experimental results demonstrate that the combined IoT–Machine Learning framework provides accurate, timely, and informative insights for users. The proposed system improves water usage efficiency and strengthens early warning capabilities, making it a practical solution for supporting effective household water management.
Classification of Program Keluarga Harapan Assistance Recipients Using a Website-Based Support Vector Machine Algorithm (Case Study: Panyabungan Kota Subdistrict) Ahyar, Khoirul
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.v5i2.1941

Abstract

Program Keluarga Harapan (PKH) is a social assistance program aimed at reducing poverty by providing financial aid to eligible families. This research focuses on the development and implementation of the Support Vector Machine (SVM) algorithm to classify PKH recipients in Panyabungan Kota Subdistrict, Mandailing Natal Destrict. The classification process utilizes factors such as family income, number of family members, and the presence of elderly members. These three factors are chosen due to their availability from public records, ensuring the privacy of participants. The classification model developed in this study is implemented in a web-based system built with PHP and JavaScript, designed to facilitate the automatic classification of PKH recipients. This system helps streamline the registration to be more precise and effective, providing an efficient solution for local government officials to identify eligible families for the PKH program. The evaluation results show that this system can classify PKH recipients well with an accuracy of 93%, offering an automated approach that supports decision-making in the distribution of social assistance.

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