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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 525 Documents
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Classification of Herbal Plants Based on Leaf Images Using Gray Level Co-Occurrence Matrix and K-Nearest Neighbor Fahmi Nur Alimsyah Purba; Fathi Athallah Z; Alfin Alfarizi; Lailan Sofinah Harahap
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2291

Abstract

Herbal plants have long been used as traditional medicine. However, many people struggle to tell different herbal leaves apart because they look quite similar. This study tries to build a system that can recognize two types of herbal leaves, Moringa and Katuk, simply from their photos. We used GLCM to extract texture features from the leaves, then classified them using KNN. The dataset came from Kaggle, with 480 leaf images in total. Before processing, we cropped the images, resized them to 256x256 pixels, and converted them to grayscale. GLCM features were taken from four angles (0°, 45°, 90°, 135°) and then averaged. This gave us four texture values: contrast, correlation, energy, and homogeneity. We tested KNN with k values from 1 to 15 and five different distance metrics. The best result we got was 94% accuracy, using Manhattan distance with k=1. This system could help everyday people identify medicinal plants more easily without needing lab tests.
Effective Strategies for Memorizing Mathematical Formulas in a Literature Review Study Feronika Br Siahaan; Lucia Lidia Sinaga; Natasya Agustina; Tiur malasari Siregar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2292

Abstract

Mathematics is often perceived as a difficult subject due to the large number of formulas that students must understand and memorize. This condition can lead to learning difficulties and trigger mathematics anxiety, which may reduce students’ ability to retain mathematical concepts. This study aims to examine various effective strategies for memorizing mathematical formulas based on previous research. The research employed a qualitative approach using a literature review method involving 30 relevant scientific articles obtained from several academic databases. Data were collected through a literature study, while the analysis was conducted by identifying, comparing, and synthesizing findings from the collected literature. The results show that strategies for memorizing mathematical formulas can be categorized into four main groups: audio-musical and artistic strategies, digital technology and gamification innovations, cognitive strategies through mnemonics and kinesthetic tools, and structured drill methods. These strategies have been shown to improve memory retention, learning motivation, and students’ learning outcomes. The findings indicate that the application of creative and multisensory learning strategies can help students memorize mathematical formulas more effectively.
Comparative Analysis of Sobel, Prewitt, and Canny Methods in Detecting Object Edges in Betta Fish Images Alfin Alfarizi; Cici El Dirrah Syafitri Simanungkalit; Fahmi Nur Alimsyah Purba; Lailan Sofinah Harahap
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2293

Abstract

Edge detection is a crucial stage in digital image processing for recognizing the shape and structure of an object. The application of edge detection to betta fish images presents a unique challenge due to their layered, intricately textured, and often semi-transparent fin morphology. This study aims to analyze and compare the performance of three edge detection algorithms, namely Sobel, Prewitt, and Canny, in extracting shape features from betta fish images. The research methodology involved converting the dataset images into a grayscale format and subsequently implementing the three algorithms using the OpenCV library in the Python programming language. The evaluation was conducted visually by observing the sharpness of the edge lines, object continuity, and the occurrence of noise. The results indicate that the Canny algorithm provides the most optimal performance, as it is capable of detecting the thin edge lines of the fish fins with greater detail and continuity due to its hysteresis thresholding process. Meanwhile, the Sobel and Prewitt methods produced thicker edge lines but were less sensitive to the details of the transparent fins. This study is expected to serve as a reference in selecting the appropriate segmentation method for biological objects with complex morphologies.
Analysis of Taxsee Driver User Satisfaction in Jambi City Using the Servqual Method Josefi Virgi Narada; Beni Irawan; Chandy Ophelia; Amroni
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2296

Abstract

This research analyzes driver satisfaction levels in Jambi City using the Taxsee Driver application through the Service Quality (SERVQUAL) method. The study background is user complaints regarding GPS inaccuracies and the automatic order system that affects driver performance ratings. Data were collected via online questionnaires from 385 active drivers in Jambi City and processed using Structural Equation Model with SmartPLS. Results show that three of five SERVQUAL dimensions significantly affect user satisfaction: Tangibles (T-Statistic 5.073), Responsiveness (T-Statistic 3.782), and Empathy (T-Statistic 4.026). Reliability (T-Statistic 1.735) and Assurance (T-Statistic 1.303) were not significant. The R-Square value of 0.879 indicates the model explains 87.9% of user satisfaction variation. Developers are recommended to improve navigation accuracy and responsiveness to maintain driver partner loyalty.
Implementation of a Web-Based Decision Support System for New Employee Recruitment Using the VIKOR Method Arochman; Sucipto; Asrul Abdullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2298

Abstract

An effective and objective employee selection process is essential to obtain high-quality human resources. This study aims to develop a web-based decision support system to assist in the recruitment of new employees using the VIKOR method. The VIKOR method is chosen because it can rank alternatives based on their closeness to the ideal solution while considering compromise among criteria. The criteria used in the system include education, work experience, skills, interview results, and work personality. This research adopts the waterfall approach for system development and implements PHP programming language with a MySQL database. The testing results indicate that the system is capable of providing accurate and consistent rankings of job candidates, as well as facilitating the HR team in conducting evaluations more efficiently.
Student Mental Health Monitoring System Based on Daily Activities with the SVM Method Stella Crystal; Robby Huang; Devi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2299

Abstract

Student mental health is a crucial issue that requires effective and responsive self-monitoring systems. This study aims to develop "LacakJiwa," an Android-based mobile application designed to monitor student mental health through the analysis of daily activity patterns. The method employed is the Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel to classify mental health risks into low and high categories. Input data includes sleep duration, daily step count, gadget usage, and social interaction duration collected from 146 student data entries. The SVM model is integrated into the application using TensorFlow Lite to enable on-device classification, ensuring user privacy through SQLite local database storage. Testing results on 44 test samples showed an accuracy rate of 52.27%, precision of 36.36%, and recall of 22.22%. While the system was successfully implemented technically, the low recall value indicates significant challenges in detecting complex non-linear behavioral patterns in students. This research provides a foundation for developing digital self-control instruments that are adaptive to Indonesian local culture.
Smart Absen Implementation of a Facial Recognition-Based Student Attendance System Using the Haar Cascade Method and LBPH Frengki Alfredo Matondang; Sahara Lani Lestari; Dinda Syafitri; Kayla Amelia Putri; Hermawan Syahputra
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2301

Abstract

Manual attendance systems in higher education institutions are often hampered by inefficiency, data inaccuracy, and vulnerability to fraud such as proxy attendance. This study presents the design and implementation of Absen Smart, a face recognition-based attendance system developed using the Haar Cascade and Local Binary Pattern Histogram (LBPH) algorithms within the React.js and Flask frameworks. This system enables the automatic and real-time identification of students via a webcam without requiring additional hardware. Face detection is performed using the Haar Cascade classifier from OpenCV, while face recognition uses the LBPH Face Recognizer with a confidence threshold of 50. Testing was conducted with 28 registered students from the Computer Science Program at UNIMED, Class A, 2024 cohort. Functional evaluation results show that all seven core system features—including face detection, face recognition, duplicate prevention, automatic absence tracking, and Excel report generation—were successfully executed with a 100% success rate. The system achieved a facial recognition accuracy of 92.86%, with an average processing time of 1.2 seconds per verification. These results indicate that the proposed system is an effective, practical, and scalable solution for automating academic attendance in a university setting.
Implementing the Procedural Generation Method for Placing Dynamic Objects in a Roblox-Based Adventure Game Muhammad Hiszat
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2307

Abstract

Procedural generation is a content-creation technique that has become increasingly important in modern game development. However, on the Roblox platform, dynamic object placement still faces challenges such as overlapping, illogical positioning, and blocked navigation paths when relying solely on pure random methods. This research implements the Rule-Based Random Generation algorithm to manage the automatic placement of dynamic objects (enemies, treasure chests, and traps) in a Roblox-based adventure game. The proposed method combines randomization with constraint validation, including boundary check, overlap check using Euclidean distance, restricted zone check, and cross-type relational constraints. The system was developed in Roblox Studio with the Luau scripting language using a prototyping methodology and a modular architecture comprising ObjectSpawner, ConstraintValidator, SpatialGrid, and DungeonGenerator. Functional testing was conducted across 10 game sessions on a 1000 × 1000 studs map with a configuration of 340 enemies, 10 chests, and 50 traps. The results show that the system successfully placed all objects without any constraint violation, produced significant spatial variation between sessions (ranging from 86.31 to 2358.00 studs), and maintained level playability in every session. The average spawning execution time was 336.62 ms per session (0.84 ms per object), demonstrating the computational efficiency of the proposed method.
Recommendation System for Selecting Maternity Hospitals in Pontianak using Weighted Product Method Ervayana Sari; Asrul Abdullah; Istikoma
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2309

Abstract

In this research, a decision support system for recommending the selection of maternity hospitals in Pontianak was developed using the Weighted Product (WP) method, with the constructed system in the form of a web-based application. The aim of this study is to facilitate pregnant women in choosing maternity hospitals in Pontianak based on criteria obtained from a survey of pregnant women, including distance, facilities, cost, and reputation. The WP method was applied through three main stages: weight normalization, vector S calculation, and vector V computation for final ranking. Testing in this research involves five alternative maternity hospitals, and each criterion is assessed on indicators ranging from 1 to 5. The results obtained indicate that Anugerah Bunda Khatulistiwa Maternity Hospital achieved the highest final ranking score among all evaluated alternatives. This system is expected to assist expectant mothers in making more informed decisions when selecting a maternity hospital that best suits their needs.
Sentiment Analysis on Electric Vehicles in Indonesia Using Bidirectional Encoder Representations from Transformers (BERT) and Named Entity Recognition (NER) Methods Billy; Wita Oktaviana Br Sinulingga; Huliman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2311

Abstract

Air pollution is a major environmental issue due to its significant impact on human health, with the transportation sector being one of the largest contributors. In Indonesia, increasing motor vehicle usage has led to higher greenhouse gas emissions, encouraging the transition toward electric vehicles as a cleaner alternative. However, the adoption of electric vehicles is influenced not only by technical factors such as infrastructure and cost, but also by public perception, which varies across different digital platforms. This study aims to analyze public sentiment toward electric vehicles in Indonesia using a Natural Language Processing (NLP) approach by combining Bidirectional Encoder Representations from Transformers (BERT) and Named Entity Recognition (NER). BERT is utilized to classify sentiments into positive, negative, and neutral categories by considering bidirectional contextual information, while NER is used to identify key entities such as companies, products, locations, and issues discussed in public discourse. The results show that the BERT model achieves an accuracy of 71.05%, precision of 61.31%, recall of 59.28%, and a misclassification error of 28.95%, indicating a fairly good performance in sentiment classification. Furthermore, NER analysis reveals that event and opinion are the most influential factors affecting public interest, followed by company, product, and quality, while location, price, action, and feature have lower influence. Overall, public interest in electric vehicles in Indonesia is relatively high but dynamic, as it is strongly influenced by circulating information and public opinion.

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