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Journal Of Artificial Intelligence And Software Engineering
Published by Politeknik Negeri Lhokseumawe
ISSN : 2797054X     EISSN : 2777001X     DOI : http://dx.doi.org/10.30811/jaise
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Natural Language Processing Computer Vision Robotics and Navigation Systems Decision Support System Implementation of Algorithms Expert System Data Mining Enterprise Architecture Design & Management Software & Networking Engineering IoT
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 215 Documents
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Development of an Android-Based Educational Game to Introduce Sumbawa's Art and Culture to Elementary School Students Attaqwa, M.Aswin Syarif; Hammad, Rifqi; Sujaka, Tomi Tri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6937

Abstract

Technology is increasingly being used to facilitate work in various fields, including education. One of the uses of technology in education is as a learning medium. However, the use of learning media, especially technology-based media, is still very limited. This affects the learning process, where children often become easily bored due to a lack of interest in studying. Therefore, based on this issue, the researcher developed an Android-based educational game to help children learn about the art and culture of Sumbawa, specifically Satera Jontal (Sumbawa script) and traditional ceremonies of the Sumbawa region. This study uses the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: concept, design, material collecting, assembly, testing, and distribution. Based on the results of media expert validation, a score of 92% was obtained; material expert validation scored 95%; and user testing produced a result of 82%. These results indicate that the developed Android-based educational game is suitable for use. Based on pretest results from users, the average score was 56.36, while the posttest results showed an average score of 74.24. These results demonstrate that the developed educational game can improve student learning outcomes
Classification Of Sleep Disorders Based on Lifestyle and Health Factors Using Random Forest and HistGradientBoosting Fernandez, Sandhy; Riyandi, Arif; Wijayanto, Sena; Sukmadiningtyas, Sukmadiningtyas
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6983

Abstract

Gangguan tidur merupakan salah satu permasalahan kesehatan yang dapat berdampak signifikan terhadap produktivitas dan kualitas hidup individu. Berbagai faktor gaya hidup dan kondisi kesehatan seperti tingkat stres, konsumsi kafein, kebiasaan olahraga, serta kondisi mental dan fisik diketahui mempengaruhi kualitas tidur seseorang. Penelitian ini bertujuan untuk mengklasifikasikan jenis gangguan tidur berdasarkan faktor-faktor tersebut menggunakan pendekatan pembelajaran mesin. Dua algoritma yang digunakan dalam penelitian ini adalah Random Forest dan HistGradientBoosting. Dataset yang digunakan terdiri dari sejumlah fitur gaya hidup dan kesehatan yang relevan, dengan target klasifikasi berupa tiga kategori utama gangguan tidur. Hasil evaluasi menunjukkan bahwa model HistGradientBoosting memberikan performa terbaik dengan akurasi mencapai 91%. Temuan ini menunjukkan potensi pendekatan pembelajaran mesin dalam membantu identifikasi dini gangguan tidur, sehingga dapat menjadi referensi untuk pengembangan sistem pendukung keputusan dalam bidang kesehatan.
Implementation of YOLOv8 Algorithm for Web-Based Detection of Coffee Fruit Ripeness Putra, Alfito Dwi; Saputra, Guntur Eka
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6730

Abstract

This research focuses on the application of computer vision technology in smart agriculture, particularly for detecting the ripeness level of coffee cherries. The YOLOv8 algorithm was utilized to build a detection model, which was then integrated into a web-based application developed using Streamlit framework. Python was used to implement YOLOv8 and support real-time object detection. The model development process followed the CRISP-DM approach, while the application development adopted a prototyping method. The dataset consisted of 100 primary images collected from Kebun Raya Bogor and 4547 secondary images from Roboflow, divided into 3253 training images, 930 validation images, and 464 testing images. The model achieved an overall mAP50 accuracy of 82.9%, with class-wise accuracy of 90.2% for dry, 76.2% for ripe, 80.9% for unripe, and 84.3% for half-ripe coffee cherries, exceeding the success criteria of 80%. The developed application provides features for detecting coffee cherry ripeness through image uploads and real-time detection using a camera. Usability testing conducted with 16 respondents using the System Usability Scale (SUS) resulted in an average score of 90, classified as "Excellent" with a grade of A. This indicates that the application is highly usable and effectively supports users in detecting coffee cherry ripeness.
Aplication IoT for Monitoring and Automatic Control of Water Levels Using Virtuino Salahuddin, Salahuddin; Bakhtiar, Bakhtiar; Akbar, Muhammad Ramzil; Munazzar, Sayed; Nasir, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6894

Abstract

Control and monitoring of water tanks in the Information and Computer Technology (ICT) Building of the Lhokseumawe State Polytechnic still experience limitations regarding filling and supervision in the tank. Water will continue to fill the tank and indirectly will result in energy waste and can also cause damage to the pump if the water in the main well has run out. The automatic monitoring and control system for filling water tanks for the Information and Computer Technology building uses ATmega 328 as a microcontroller that will control the entire system, Ultrasonic Sensors to measure water levels, relays as pump automation, flow meter sensors as detectors of water flow to the tank and the esp8266 module is used to send data to android using the virtuino application. Monitoring the water level when filling this tank is expected to be one step in saving water and energy. Based on testing, the average delay response of the virtuino display using WiFi and simcard. The delay on and off the pump will be calculated. This condition can be overcome by implementing the internet of things (IoT) on the tank in the Information and Computer Technology Building. The system to be built uses the Virtuino display.
Automatic Cat Feeding System Based On The Internet Of Things (IoT) With Time And Feed Weight Control Wijayanti, Sefi Ayuk; Maulindar, Joni; Nurohman, Nurohman
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7538

Abstract

A common problem in pet cat care is irregular feeding, which can affect the animal's nutritional balance and health. To address this issue, an Internet of Things (IoT)-based automatic feeding system utilizing an ESP32 microcontroller has been developed. The system integrates a Passive Infrared (PIR) sensor for cat motion detection, a Load Cell for measuring food weight, and an ultrasonic sensor to monitor food stock levels. Data is stored and displayed through a web-based platform that supports meal scheduling and real-time monitoring. System testing using a blackbox approach shows that all components function properly, including time synchronization via NTP, Wi-Fi communication, and overall sensor functionality. This system is expected to improve feeding consistency and optimally support the health and well-being of cats.
Power Consumption Prediction Using Support Vector Machine and Particle Swarm Optimization Laila, Dwi Nur'aini; Ula, Mutammimul; Yulisda, Desvina
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7433

Abstract

The continuous growth in electricity consumption demands a reliable prediction system to support sustainable energy planning. This study aims to forecast electricity consumption using the Support Vector Machine (SVM) method, in which the parameters are optimized through the Particle Swarm Optimization (PSO) algorithm. PSO is employed to determine the optimal parameters, namely weight and bias, by minimizing prediction errors measured using Mean Squared Error (MSE). The implementation was carried out using Visual Basic for Applications (VBA) in Excel, based on historical electricity usage data from January 2023 to July 2025 The prediction results indicate a high level of accuracy, as evidenced by the lowest Mean Absolute Percentage Error (MAPE) value of 0.006, observed in the Mbt District.. The model was then used to predict electricity consumption until December 2027, revealing a gradual increase in usage across all districts. These findings indicate that the integration of SVM and PSO is effective in producing accurate and reliable prediction models to support decision-making in electricity demand management.
Computer Vision-Based Optical Mark Recognition (OMR) System for Automated Exam Answer Sheet Correction Yusuf, Muhammad; Pradana, Afu Ihsan; Susanto, Rudi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7142

Abstract

Manual correction of conventional exam answer sheets is a primary problem due to being time-consuming and prone to errors, while commercial OMR devices are costly. This research aims to design and implement an efficient and accurate computer vision-based Optical Mark Recognition (OMR) system for conventional answer sheets. The method employed is an image processing pipeline using OpenCV, which includes preprocessing with Adaptive Thresholding, contour detection, perspective transformation for skew correction, and answer area segmentation for choice extraction based on pixel analysis. Testing results on 30 samples for each condition showed that the system achieved 100% accuracy on thick markings, 99.22% on thin markings, and 86.22% on thin markings with significant scribbles. It is concluded that the developed system is highly effective for answer sheets with clean markings, but its performance degrades when visual noise from scribbles is present, thus offering an affordable OMR alternative with identified performance limitations.
Feature Selection Optimization Using Genetic Algorithm for Naive Bayes-Based Diabetes Mellitus Classification Aris, Nova Arianti; Yuliana, Ade
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7618

Abstract

Diabetes mellitus is a chronic disease with a steadily increasing prevalence each year and poses the risk of severe complications if not addressed early. Therefore, early detection of diabetes risk plays a vital role in prevention efforts. This study aims to enhance feature selection optimization through the use of a genetic algorithm in the classification of diabetes mellitus patients based on the Naive Bayes method. The genetic algorithm was applied to identify the most significant clinical features from patient data, with the expectation of improving the classification model’s accuracy and efficiency. A dataset comprising 1,557 patient records with 29 initial clinical attributes was utilized. Following preparation and selection stages, 7 key features were chosen for model training. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicated that the model with selected features achieved an accuracy of 80.99%, precision of 80.99%, recall of 100%, and an F1-score of 89.5%. These findings confirm that genetic algorithms are effective in improving Naive Bayes classification performance for diabetes risk identification. This study is expected to serve as a foundation for the development of more accurate and efficient disease risk prediction systems in the future.
Information Systems And Information Technology Strategies In The EMIS (Education Management Information System) Khaidar, Al; Azzanna, Maghriza; Rahmad, Rahmad; Hasibuan, Arnawan; Daud, Muhammad; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7639

Abstract

Perkembangan teknologi informasi telah memengaruhi pengelolaan data pendidikan melalui sistem informasi manajemen, salah satunya Education Management Information System (EMIS). Penelitian ini bertujuan untuk menganalisis efektivitas implementasi EMIS di MAN 1 Aceh Timur serta faktor-faktor yang memengaruhi keberhasilannya. Metode penelitian menggunakan pendekatan kualitatif interaktif dengan studi kasus, melibatkan kepala madrasah, operator EMIS, dan pihak terkait sebagai informan. Analisis dilakukan menggunakan metode SWOT dan value chain untuk mengevaluasi kekuatan, kelemahan, peluang, dan ancaman implementasi sistem. Hasil penelitian menunjukkan EMIS memiliki potensi meningkatkan efektivitas pengelolaan data, integrasi informasi, dan mendukung pengambilan keputusan. Namun, sistem mengalami kendala teknis, terutama gangguan server dengan frekuensi bervariasi setiap bulan, puncaknya terjadi pada Maret dan Juli masing-masing 5 kali, dengan durasi rata-rata meningkat dari 1,8 jam di Januari menjadi 2,5 jam di Juli dan terendah 1,0 jam di April. Evaluasi menekankan perlunya peningkatan infrastruktur, pelatihan operator, dan koordinasi antar pihak terkait untuk mengoptimalkan kinerja EMIS di masa depan.
Goods Management System Using Always Better Control (ABC) Method Prasetyo, Adi Tri; Hasanah, Herliyani; Oktaviani, Intan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7351

Abstract

This study aims to design and develop a web-based inventory management system utilizing the Always Better Control (ABC) method to assist Toko Didik in managing stock more efficiently. Until now, the inventory process in the store has been carried out manually using conventional record-keeping, which is prone to data entry errors, delays in stock monitoring, and the absence of a classification system to prioritize items based on their sales contribution.To address this problem,  the system was designed using the Waterfall development methodology, involving stages of requirements analysis, system design, implementation, and testing. Data were collected through observation, interviews, and documentation conducted at the store site. The system was built using the Laravel framework and MySQL database, and includes key features such as item recording, automatic item classification using the ABC method, real-time stock monitoring, purchase recommendations, sales transactions, and sales reporting. The results of black-box testing indicate that all system functions operate as expected without errors. The ABC classification method successfully groups items into three categories based on their contribution to total sales, allowing the store owner to prioritize procurement effectively. With this system, inventory management becomes more organized, accurate, and supports data-driven decision-making. This study serve as an alternative solution for small or medium-sized stores in addressing inventory management challenges through the use of information systems.

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