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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 33 Documents
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Search results for , issue "Vol 5, No 2 (2025): June" : 33 Documents clear
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.

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