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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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Flood Prediction for the Wampu River Basin Using the Simple Additive Weighting Method:A Case Study of the Wampu River in Bahorok Miftahul Janna; Said Iskandar; Arnita; Zulfahmi Indra; Susiana
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.v5i3.2268

Abstract

Flood is one of the natural disasters that frequently occurs in the Wampu Watershed (DAS Wampu), especially in Bahorok District. Flood risk is influenced by several factors such as rainfall, slope gradient, land use changes, and river depth. The problem in this study is the absence of a decision support system that can objectively determine flood risk levels. This study aims to determine the criteria and weights of flood risk, apply the Simple Additive Weighting (SAW) method, and analyze the accuracy level of the SAW method in determining flood risk. The method used in this research is the Simple Additive Weighting (SAW) method through several stages including criteria weighting, decision matrix construction, data normalization, preference value calculation, and alternative ranking. The research data consists of 18 villages with four criteria: rainfall, slope gradient, land use change, and river depth. The results show the classification of flood risk levels into high, medium, and low categories based on the obtained preference values. Villages with the highest preference values indicate a higher level of flood vulnerability compared to other villages. The model evaluation results indicate that the SAW method has an accuracy level of approximately 90% in determining flood risk classification. Based on these results, it can be concluded that the SAW method can be used as a decision support system to determine flood risk levels and provide recommendations for priority flood mitigation areas in Bahorok District.
UI/UX Design of an Android-Based Sales Application at Naureen Shop using the User-Centered Design Method Yessi Hartiwi; Nurhayati; Manja Purnasari
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.v5i3.2277

Abstract

The development of digital technology has shifted consumer behavior to prioritize convenience and efficiency through online shopping. Naureen Shop, a women's clothing business, currently faces operational constraints due to manual sales, promotion, and data recording processes, resulting in sub-optimal service. This study aims to design the User Interface (UI) and User Experience (UX) of an Android-based sales application for Naureen Shop to enhance business effectiveness. The method employed is User Centered Design (UCD), a design approach focusing on user needs and characteristics through stages of identifying the context of use, specifying user requirements, creating design solutions using Figma, and evaluation. The design testing results using the System Usability Scale (SUS) method with 15 respondents yielded an average score of 77.0. This score indicates that the application design falls into the "Acceptable" category with a "Good" Adjective Rating. Consequently, the resulting design solution fulfills functional aspects and provides a satisfying user experience to support transaction processes at Naureen Shop
Application of the Tsukamoto Fuzzy Inference System Method for Rainfall Prediction in the Adolina Area Aditia Sanjaya
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.v5i3.2281

Abstract

Rainfall is one of the key elements in the climate system that significantly affects various sectors, such as agriculture, spatial planning, and disaster mitigation. Adolina, a region with tropical weather characteristics and highly fluctuating rainfall, requires an accurate prediction system to support informed decision-making. This study applies the Fuzzy Inference System (FIS) Tsukamoto method to predict rainfall based on input variables such as air temperature, humidity, and wind speed. The Tsukamoto method is chosen for its capability to handle uncertainty and produce crisp output values through inference and defuzzification processes based on a set of fuzzy rules. The results show that the Tsukamoto FIS provides reasonably accurate and consistent rainfall predictions with a low error rate. Therefore, this approach can serve as an effective alternative in weather decision-support systems for the Adolina area.
Implementation of Simple Queue and Content Filtering for Bandwidth Management on WLAN and LAN Networks Zura Permata
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.v5i3.2284

Abstract

SMK Negeri 1 Sungai Raya is a vocational high school located on Sultan Agung Street, Kuala Dua, Sungai Raya District, Kubu Raya Regency, West Kalimantan, which offers programs such as Visual Communication Design (DKV) and Broadcasting that utilize the internet to support learning activities including completing assignments, accessing educational resources, and submitting schoolwork; however, problems frequently occur in the laboratory network such as buffering, network downtime, and bandwidth congestion due to simultaneous usage, therefore bandwidth management using the simple queue method was implemented along with content filtering to block access to social media and online gaming websites in order to prevent disruptions to the learning process, and the results showed improvements in network performance where on the LAN network throughput decreased by 0.5735%, packet loss decreased by 0.0969%, delay decreased by 0.5942%, and jitter decreased by 0.9182%, indicating better stability and efficiency, while the WLAN network in the laboratory was also successfully installed, providing improved connectivity and supporting a more effective and focused learning environment.
Design and Construction of a Village Tourism Monitoring and Evaluation System Web Based Alfian Maulana; Deffa Danendra; M. Mustakim
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.v5i3.2289

Abstract

Development of village tourism in the Special Region of Yogyakarta requires structured and sustainable management, particularly in monitoring and evaluation as a basis for stakeholder decision-making. Current challenges include unintegrated tourism village data, manual evaluation processes, and limited public access to tourism information and services. This study aims to design and develop a web-based monitoring and evaluation system that integrates registration, data management, monitoring, scoring, and information presentation in a centralized platform. The system is developed using the Extreme Programming method, which includes planning, design, coding, and testing stages, with functional testing conducted through Black Box Testing. The technologies used include React JS for the interface, Express JS for the backend, Supabase as the database, and Google Cloud Storage for data storage. The results indicate that all main system features function according to requirements, supporting more effective monitoring and evaluation processes, improving data accuracy, and enhancing accessibility of information for both the public and local government. Furthermore, this system has the potential to serve as a foundation for regional tourism data integration and to support sustainable tourism village development policies, while also contributing practically to improving integrated digital public information services at the national level.
Chinese Script Handwriting Pattern Introduction Application Design with Algorithm CNN-SVM Jacqueline Kwanori; Huliman; 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.v5i3.2290

Abstract

The Chinese script has a high level of visual complexity because each character consists of thousands of intricate strokes. This is a big challenge for second-language learners, especially in recognizing the various variations of human handwriting. This study aims to design an accurate and efficient application for the recognition of Chinese handwriting patterns based on Android using a hybrid model of Convolutional Neural Network (CNN) and Support Vector Machine (SVM). In this system, the CNN works like a human eye that distinguishes the details of the shape of an image, while the SVM serves as the brain that decides what characters are being written. The data used in the training process included 7,330 Chinese characters pulled from the Kaggle platform. The results of the study show that the application was successfully designed and able to display character shapes, how to read (pinyin), and the meaning of words offline without the need for an internet connection. Based on testing the Black Box method, all of the app's features are proven to work validly. The study concluded that the use of the CNN-SVM hybrid model was highly effective in recognizing diverse handwriting variations, although the degree of accuracy remained dependent on the clarity of the quality of the images taken by the user.
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.

Page 48 of 53 | Total Record : 524

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