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Iqbal
Universitas Almuslim

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Model Pengenalan Wajah Menggunakan Algoritma k-Nearest Neighbors Rifa Hayatul Nisa; Iqbal; 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.v3i2.80

Abstract

This study explores the development of a face recognition model using the K-Nearest Neighbors (KNN) algorithm as a classification method in biometric systems. The dataset consists of 1,000 grayscale facial images sized 128x128 pixels, collected from 10 individuals with 100 images each. Feature extraction was conducted using the Histogram of Oriented Gradients (HOG) technique to capture distinctive facial characteristics. The experimental results show that the optimal k value is 4, producing a validation accuracy of 75.37%. Further testing achieved an accuracy of 81.37% with an average F1-score of 0.81, demonstrating reliable recognition performance. Live recognition tests confirmed that the system can still identify faces under real-world conditions, such as varied orientations and partial occlusions. These results indicate that KNN is an effective and efficient algorithm for small to medium-scale face recognition tasks, offering fast training time and practical applicability for biometric identification systems.
Klasifikasi Varietas Biji Kopi Arabika Gayo Berbasis Deteksi Objek Menggunakan Algoritma Yolov5 Randa Mah Bengi; Iqbal; Riyadhul Fajri
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.v3i2.81

Abstract

Gayo Arabica coffee is one of Indonesia’s leading agricultural commodities with high economic value, especially in the export market. However, the process of classifying coffee bean varieties is still mostly performed manually, which can lead to misidentification and inconsistency in quality control. This study aims to develop an automatic classification system for Gayo Arabica coffee bean varieties using a deep learning approach based on the You Only Look Once version 5 (YOLOv5) model. The dataset consisted of 1,500 images of three main varieties: Tim-tim (Gayo 1), Bor-bor (Gayo 2), and Ateng Super (Gayo 3), with 500 images per class. The images were collected through direct observation and documentation in Aceh Tengah and labeled using LabelImg. The model was trained using the Python programming language with the Ultralytics YOLOv5 library based on PyTorch. Model performance was evaluated using precision, recall, and mean Average Precision (mAP) metrics, as well as a confusion matrix. The final model achieved an accuracy of 96% with an mAP50–95 value of 0.99, indicating that the YOLOv5-based system can effectively and consistently classify coffee bean varieties in real-time. The results of this study are expected to assist farmers and coffee industry stakeholders in improving the efficiency and accuracy of post-harvest quality control
Rancang Bangun Sistem Informasi Rental Mobil Terintegrasi Whatsapp API Gateway Muhammad Akhyar; Iskandar Zulkarnaini; Iqbal
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.v3i2.99

Abstract

This study aims to design and implement a web-based car rental information system integrated with a WhatsApp API Gateway to improve operational efficiency and customer communication. The background of this research is the use of manual systems in rental services, which often leads to delays, data recording errors, and ineffective information delivery. The development method used is the Waterfall model, consisting of requirement analysis, system design, implementation, and testing stages. The system is developed using PHP as the programming language and MySQL as the database management system. System functionality is evaluated using Black Box Testing to ensure that all features operate according to user requirements. The results show that the system successfully automates key processes such as user registration, vehicle booking, transaction management, and notification delivery. Furthermore, the integration with WhatsApp API Gateway enables automatic message delivery with a success rate of 100% and a response time of less than 10 seconds. The implementation of this system significantly improves service efficiency, reduces human error, and enhances the quality of communication between service providers and customers.
Co-Authors Iskandar Zulkarnaini Muhammad Akhyar Muslem R, Imam Randa Mah Bengi Rifa Hayatul Nisa Riyadhul Fajri