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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Pengabdian kepada Masyarakat Nusantara Djtechno: Jurnal Teknologi Informasi Prosiding Snastikom Jurnal Ilmu Komputer dan Sistem Informasi
Sumi Khairani
Universitas Harapan Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science Social Sciences

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APLIKASI ANAGLYPH 3D TATA CARA SHOLAT DAN DOA BERBASIS LIGHT VIRTUAL REALITY Ahmad Muammar Lubis; Sumi Khairani; Rismayanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 2 (2024): Mei 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i2.121

Abstract

Prayer is the second pillar of Islam and it is the pillar that is emphasized most after the two sentences of the shahada. Prayer is the connection between a servant and his Lord. Prayers are of two types, namely prayers of worship and prayers of supplication. Allah's closeness to His servants is divided into two types, namely; the closeness of His knowledge to every creature and the closeness to His servants in giving them every request, help and taufik. Learning prayer movements and prayers should be taught from an early age (children). Guidance from parents and teachers is the most important way to provide learning media. So far, conventional learning in the form of books makes children bored, so creativity or interactive learning methods are needed, one of which is multimedia-based learning.
Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun Ratna Indah Juwita Harahap; Sumi Khairani; Rismayanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 2 (2024): Mei 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i2.123

Abstract

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.
Sistem Deteksi Jenis Kendaraan Metode YOLOv4 Untuk Mendukung Transportasi Cerdas Kota Medan M Rizky Pramana Putra; Haida Dafitri; Sumi Khairani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 3 No. 2 (2024): Mei 2024
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v3i2.125

Abstract

This research discusses the evaluation and implementation of the YOLOv4 model in detecting and tracking vehicle types in the context of road traffic. To address the research questions, the study examined the model's performance across various aspects. The results indicate that the YOLOv4 model achieved a Mean Average Precision (mAP) of 77.88% on the training dataset after 7000 iterations. The model exhibits a commendable ability to detect different vehicle types within images, with varying accuracy rates across distinct classes. The developed application within this study can record detection data for every frame within a video sequence, providing crucial information for analyzing vehicle density on roads. Despite its relatively high accuracy level, errors persist in object detection and labeling. In conclusion, this research offers insights into the capabilities and potential of the YOLOv4 model in addressing challenges related to vehicle detection in road traffic, while also identifying areas that warrant further improvement.
Co-Authors Abdul Jabbar Lubis Ade Putri Humaira Ahmad Muammar Lubis Aida Fitrianingsih Aldi Wahyudi Bella Puspita Desi Yanti Dharmawati Dorlika Safitra Harefa Emma Yurispa Siregar Erwin Panggabean Haida Dafitri Harahap, Herlina Lastri Pratiwi M Rizky Pramana Putra Muhammad Hafiz Al Hakim Nur Wulan Rahman, Sayuti Ratna Indah Juwita Harahap Rismayanti Ulul Azmi Harahap Yasir, Amru Yogi Pratama