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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Tika KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Innovative and Creativity Jurnal Ilmu Komputer Aceh
Muslem R, Imam
Fakultas Ilmu Komputer, Universitas Al Muslim
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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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
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
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
Klasifikasi Tingkat Kematangan Buah Pisang Menggunakan Algoritma Support Vector Machine Besra Laoli; Imam Muslem; Fitri Rizani
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.v3i1.45

Abstract

An This study aims to develop an automatic classification system for determining the ripeness level of bananas using digital image processing and the Support Vector Machine (SVM) algorithm. Banana ripeness is commonly assessed visually based on skin color, which is subjective and prone to inconsistency. To address this issue, a computer-based classification approach is proposed to improve accuracy and objectivity. The dataset used in this study consists of banana images categorized into three ripeness levels: unripe, ripe, and overripe. The images were obtained from direct acquisition using a smartphone camera and an online dataset platform. The preprocessing stage includes image resizing, color space conversion, and normalization. Feature extraction is performed using color features in the HSV color space combined with texture features extracted using the Histogram of Oriented Gradients (HOG) method. The extracted features are then classified using the Support Vector Machine algorithm with a Radial Basis Function (RBF) kernel. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the proposed SVM-based approach is able to classify banana ripeness levels effectively with satisfactory performance. The results indicate that the integration of digital image processing and SVM has strong potential to support automatic and consistent banana ripeness classification, which can be applied in agricultural and post-harvest quality control systems.
Klasifikasi Spesies Ikan Koi Berdasarkan Citra Menggunakan Metode YOLOv3-Tiny Dan OpenCV Rauzi Saputra; Imam Muslem; Riyadhul Fajri
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.v3i1.52

Abstract

Identification of koi fish (Cyprinus carpio) varieties in aquaculture and ornamental fish industries is commonly performed manually through visual observation, making the process subjective, inconsistent, and inefficient, particularly at large production scales. This study aims to develop an automated image-based detection and classification system for koi varieties using the YOLOv3-Tiny algorithm integrated with OpenCV, capable of operating in real-time conditions. The dataset consists of 3,154 images of six koi varieties—Asagi, Bekko, Hikarimono, Kohaku, Sanke, and Showa—which were expanded to 6,360 images through data augmentation techniques. Image labeling and annotation were conducted using Roboflow, while model training was implemented with the Darknet framework in a Google Colab environment supported by GPU acceleration. System performance was evaluated using mean Average Precision (mAP), loss function analysis, and both static image and real-time video testing. Experimental results demonstrate that the YOLOv3-Tiny model is capable of accurately detecting and classifying koi varieties with stable inference speed suitable for real-time applications. The proposed system enhances objectivity, consistency, and efficiency in koi variety identification and shows strong potential for practical implementation in technology-driven ornamental fish farming and trading industries
Klasifikasi Smartphone Berdasarkan Performa dan Harga Menggunakan Metode Algoritma K-Nearest Neighbor (KNN) Dian Safitri; Sriwinar; Hannan Asrawi; Imam Muslem
Jurnal Ilmu Komputer Aceh Vol 3 No 2 (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.v3i2.53

Abstract

The rapid development of smartphones has led to a wide variety of specifications and price ranges, making it difficult for consumers to choose devices objectively. Performance differences influenced by hardware specifications and pricing require a data-driven classification approach. This study aims to classify smartphones based on performance and price using the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 981 smartphone records obtained from Kaggle, including attributes such as processor speed, number of cores, RAM, internal storage, battery capacity, camera resolution, rating, and price. Data preprocessing includes handling missing values and feature normalization using StandardScaler. Smartphones are categorized into three classes: Budget, Midrange, and Flagship. The model evaluation uses confusion matrix, accuracy, precision, recall, and F1-score. Experimental results show that the KNN algorithm achieves an accuracy of 92%, indicating that it is effective for smartphone classification problems.
Co-Authors Al Wafi, Muhammad Yasar Almuzammil, Almuzammil Ananda Faridhatul Ulva Auna Fajriah Besra Laoli Budi Handoyo Chairul Rizal Cut Haura Hayatun Jannah Dasril Azmi Dedy Armiady Dewi, Rosna Dian Safitri Ela Firliza Erifki Rizki Putra Farida Hanum Fina Meilinar Fitri Rizani Fitriani Fitriani Gaitsa Zahira Hannan Asrawi Heri Gustami Iqbal Iqbal Iqbal Irvanizam, Irvanizam Johan Johan Johan TM Johan, T. M. Johan, T. Muhammad Johar, Farhana Khalisah Khalisah Luthfi Muhammad Amin Munar Munar Nazwa Amelia Nisaul Fitri Noratul Iqramah Nurannas Qamar Syafawi Ratna Dewi Rauzi Saputra Rifa Hayatul Nisa Riyadhul Fajri Riyadhul Fajri Rizal Rizal Rizani, Fitri Rizki Putra, Erifki Rizky Maulana, Rizky Sriwinar Sriwinar Sriwinar Sriwinar, Sriwinar Teuku Muhammad Johan Teuku Muhammad Johan Ulfa Khairuna Vanessa Shakila Vianaza Winar, Sri Zaqila Amanda Zukharam Zukharam Zulkifli Zulkifli