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Kampus Selatan Gedung Aula M. A. Jangka Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh. Jl. Al-Muslim, Peusangan, Kabupaten Bireuen, Aceh 24261.
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Jurnal Ilmu Komputer Aceh
Published by Universitas Almuslim
ISSN : -     EISSN : 29867797     DOI : -
Core Subject :
Jurnal Ilmu Komputer Aceh (ILKA) merupakan jurnal berbasis OJS 3 yang dikelola oleh program studi Informatika Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh dengan e-ISSN 2986-7797 (online). Artikel yang diterbitkan pada jurnal ini merupakan hasil penelitian dosen dan mahasiswa di bidang ilmu komputer. Jurnal ILKA menerbitkan tiga volume setiap tahun, pada bulan Fabruari, Juni dan Oktober tiap tahunnya. Setiap jurnal yang diterbitkan melalui proses double-blind review dalam proses review suatu artikel yang akan disajikan di mana suatu artikel di nilai oleh reviewer yang tidak mengetahui identitas penulis. Jurnal ILKA yaitu memuat artikel dalam bentuk hasil penelitian, dan artikel konseptual yang mencakup bidang ilmu komputer, antara lain: Sistem Informasi, Informatika, Artificial Intelligence, Jaringan Komputer, Data Science, Rekayasa Perangkat Lunak, Internet of Thing, Teknik Komputer dan Multimedia
Arjuna Subject : -
Articles 40 Documents
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Klasifikasi Plat Nomor Kenderaan Bedasarkan Wilayah Tertentu Menggunakan Algoritma Optical Character Recognition (OCR) Cut Haura Hayatun Jannah; Imam Muslem; Dasril Azmi
Jurnal Ilmu Komputer Aceh Vol 2 No 3 (2025): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

The advancement of artificial intelligence (AI) and digital image processing technologies has enabled the development of automated vehicle identification systems. This study aims to design a license plate classification system based on specific regional codes using the Optical Character Recognition (OCR) approach. The process involves several key stages, including image preprocessing (grayscale conversion, sharpening, noise reduction, and thresholding), character extraction via EasyOCR, and regional classification using Support Vector Machine (SVM) and Random Forest algorithms. The dataset consists of 1,920 vehicle plate images collected from two regions: BK (Medan) and BL (Aceh). Experimental results indicate that the SVM model achieved 86% accuracy, while the Random Forest model reached 84% accuracy. The system is deployed as a web-based application to facilitate automatic and efficient regional identification of vehicle plates. This research is expected to contribute to traffic monitoring systems and transportation security improvements
Prediksi Kemacetan Lalu Lintas di Persimpangan Menggunakan Metode Random Forest Auna Fajriah; Imam Muslem; Iqbal Iqbal
Jurnal Ilmu Komputer Aceh Vol 2 No 3 (2025): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

Traffic congestion at intersections is signifikan problem in urban areas that causes decreased transportation efficiency, increased air pollution and economic losses. The research data were obtained from the extraction of live CCTV video with main features including time, number of vehicles, average speed and congestion class (congestion, light congestion, and freeway). The research data were obtained from the extraction of live CCTV video with main features including time, number of vehicles, average speed and congestion class (congestion, light congestion, and freeway). The dataset was then saved in CSV format and subjected to preprocessing, model training, and evaluation. The results indicate that this model can form the basis for an intelligent traffic management system. This research contributes to traffic management at intersections and supports the development of artificial intelligence-based solutions to reduce congestion
Klasterisasi Calon Mahasiswa Baru Universitas Almuslim Menggunakan K-Means Clustering Noratul Iqramah; Imam Muslem; Munar Munar
Jurnal Ilmu Komputer Aceh Vol 2 No 3 (2025): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

The diverse academic backgrounds of prospective new students at Almuslim University often present challenges in determining the appropriate field of study. Determining the field of study is crucial because choosing the wrong study program can impact the learning process and the development of students' potential during their studies. Selecting the right field of study allows students to learn optimally and prepare themselves for the world of work according to their interests and abilities. It can also assist the university in recommending study programs with appropriate fields of study to develop a more targeted admission strategy. This study applies the clustering method with the K-Means algorithm to help group prospective students into two fields of study: science and social science. This field grouping is based on 1000 prospective new students' data with attributes of diploma grades (Mathematics, Science, Indonesian, and Social Studies), test scores, and field interests. The analysis process carried out using the K-Means clustering method on Google Colab resulted in a calculation of 17 iterations, C1 (science) with a total of 518 people who have higher interests and values in the field of science, and C2 (social) with a total of 482 people who have higher interests and values in the field of social. This division confirms that the K-Means algorithm is able to group data based on the characteristics in the dataset. With these results, K-Means Clustering is proven effective in grouping prospective students of Almuslim University based on their academic background and interests
Optimasi Algoritma SHA-256 dan Metode Salt Untuk Pengamanan Akun Calon Santri Baru Pesantren Almuslim Syauqa Mardhatillah; Sri Winar; Dedy Armiady
Jurnal Ilmu Komputer Aceh Vol 2 No 3 (2025): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

This is due to the various techniques used by unauthorized parties to gain access to account information. Therefore, protecting passwords must be a top priority, including in the management of prospective student accounts at Almuslim Islamic boarding school. This study aims to design and implement a login system for online student registration by securing user accounts using the SHA-256 hashing algorithm combined with the salt method. The security process is carried out by hashing the password after adding a unique salt, ensuring that the stored data is not in plain text. The results of testing show that the system is able to securely store account data and successfully verify users during login through hash and salt matching
Monitoring Kualitas Tanah pada Tanaman Cabai Rawit Menggunakan Sensor Soil Moisture dan Sensor pH Tanah Berbasis IoT Ela Firliza; Imam Muslem; Heri Gustami
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.28

Abstract

Chili peppers are one of the agricultural commodities that have a lot of commercial potential or high economic potential. Chili peppers require ideal soil pH and water content to produce maximum yields. Monitoring of agricultural land is generally done manually which can be time-consuming and labor-intensive. Therefore, a monitoring system is needed that can detect soil pH and water content in real time to increase the productivity and effectiveness of chili plants. This study developed an Internet of Things (IoT) based monitoring system. This system uses a soil moisture sensor to monitor soil moisture in chili plants and a soil pH sensor to monitor pH levels in the soil. This monitoring system was built using a soil moisture sensor and a pH sensor as input, an ESP32 DEV KT V1 microcontroller as a process and Telegram as an output. The system workflow is the sensor reads soil moisture and pH data, the data is sent to the ESP32 microcontroller for processing, from the Wi-Fi module the data is transferred to the server and the server sends the obtained data to the Telegram Bot to be displayed to the user
Prototype Peringatan Banjir Berbasis Internet of Things Khalisah Khalisah; Imam Muslem; Heri Gustami
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.29

Abstract

Flooding is a disaster that frequently strikes Indonesia and causes various negative impacts on the community. Generally, there are two categories of flooding events: flooding in areas not normally submerged in water and flooding caused by overflowing rivers due to water volume exceeding the capacity of the existing river flow. Parameters often used as data to monitor and analyze changes are river water levels during certain seasons as an early warning effort for natural disasters such as flooding. Currently, monitoring river levels is still carried out manually using a water level scale installed on the riverbank, similar to a measuring instrument. Therefore, direct monitoring of the numbers indicated by the scale is necessary. Information obtained by the community is also still relatively inadequate. Therefore, by designing and building a river water level monitoring system based on IoT (Internet of Things), it is hoped that it can provide a solution to this problem. This system utilizes an HC-SR04 Ultrasonic sensor to measure the distance between the sensor and the object using ultrasonic waves. Data obtained from the sensor will be sent to an ESP32 DEV KT V1 microcontroller connected to the internet, so that users can access it through the Telegram application on their mobile phones.
Prototype Sistem Monitoring Suhu dan Kelembaban Otomatis pada Greenhouse Berbasis IoT Nisaul Fitri; Imam Muslem; Riyadhul Fajri
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

Greenhouse is a modern agricultural solution that creates optimal conditions for plant growth, especially amidst climate change and food crises. The purpose of this project is to build an IoT-based system that automatically controls temperature and humidity, using Telegram as the management platform. The system uses the DHT11 sensor for environmental monitoring, NodeMCU ESP8266 for Wi-Fi connectivity, ESP32 as the actuator controller, and a relay module to control a cooling fan. Environmental data is sent in real-time to Telegram, allowing users to monitor and control the greenhouse remotely. Testing results show the system responds automatically to temperature and humidity changes, sends notifications, and activates the cooling fan accurately. Integration with Telegram enhances remote management, energy efficiency, and microclimate stability within the greenhouse
Penggunaan Artificial Intelligence pada Pembuatan Desain Karakter Animasi Elsa Manyori; Riyadhul Fajri; T. Rafli Abdillah
Jurnal Ilmu Komputer Aceh Vol 3 No 2 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

The advancement of Artificial Intelligence (AI) technology has had a significant impact across various fields, including character design in animation. This study focuses on analyzing existing systems, such as leonardo Ai, which utilize a text-to-image method to automatically generate visual design based on textual descriptions. The objective of this research is to evaluate the quality, effectiveness, and limitations of the text-to-image method in Leonardo AI for creating animated character designs that meet creative requirements. The research employs a descriptive approach using qualitative methods, involving a series of text input tests ranging from simple to complex descriptions. These tests aim to observe image quality, accuracy in representing the input descriptions and processing speed. This study provides insights into the potential and limitations of AI-based systems in the devolopment of animated character design. The findings are expected to serve as a reference for AI technology devolopers in future animation applications.
Prototipe Kamera Pengawasan Berbasis YOLOv5 untuk Deteksi Benda Tajam Secara Real-Time dengan Notifikasi Telegram Vanessa Shakila; Imam Muslem; Sriwinar Sriwinar
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.35

Abstract

This study aims to design and develop a prototype surveillance camera system based on the You Only Look Once version 5 (YOLOv5) algorithm for real-time detection of sharp objects, namely knives and scissors, integrated with Telegram notifications. The dataset consists of 2000 images (1000 images per class), annotated via Roboflow and trained in Google Colab. The methodology includes data collection, preprocessing, model training, model conversion, and real-time detection implementation using Python in PyCharm. Evaluation results show a mean Average Precision (mAP@0.5) of 0.88 and mAP@0.5:0.95 of 0.577. The scissors class achieved higher precision and recall (0.934 and 0.88) compared to knives (0.808 and 0.795). Real-time testing produced an average confidence score of 0.445 and an average Frame Per Second (FPS) of 0.56, indicating hardware limitations. Confusion matrix analysis revealed a 58% misclassification rate of knives as background, higher than scissors (42%). This study confirms the effectiveness of YOLOv5 for sharp object detection in security applications, with potential for improvement through hardware optimization and dataset diversification
Klasifikasi Kematangan Buah Pepaya Menggunakan Algoritma Support Vector Machine Zaqila Amanda; Imam Muslem; Fitri Rizani
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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Abstract

Manually determining papaya ripeness is often inaccurate and subjective. Therefore, a Support Vector Machine (SVM) algorithm is needed to improve the accuracy of papaya ripeness classification. The problem studied is how to apply SVM to accurately classify papaya ripeness. The research methodology includes papaya image capture, image preprocessing, color feature extraction, and classification using SVM. This study focused on three ripeness categories: unripe, semi-ripe, and ripe. The results showed that the SVM method was able to classify unripe papaya with 67% accuracy, semi-ripe papaya with 22% accuracy, and ripe papaya with 70%. The conclusion of this study is that SVM is quite effective in processing color information for papaya ripeness classification and has potential for application in the agricultural industry

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