Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System
Zulkarnain, Ismail Abdurrozaq
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENGENALAN APLIKASI OBYEK WISATA TELAGA NGEBEL BERBASIS VIRTUAL REALITY DAN ALGORTIMA GREEDY Bhanu Setyawan, Mohammad; Permana Putra, Angga Dian; Sussolaikah, Kelik; Zulkarnain, Ismail Abdurrozaq; Cobantoro, Adi Fajaryanto
Jurnal Media Elektro Vol 13 No 1 (2024): April 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jme.v13i1.15364

Abstract

Ngebel Lake in Ponorogo Regency is a tourist destination that is usually visited by many local or non-local tourists. There is great potential to be developed again to introduce this tourist attraction to outside the region in order to attract tourists. Telaga Ngebel requires the main role of promotion and publication out, so it is very necessary for a technology that is able to realize the equal introduction of Telaga Ngebel attractions and is able to describe tourist objects in real terms. The solution is to utilize Virtual Reality technology that can package the potential of tourist attractions in an attractive, attractive and contemporary manner. The technology displays Telaga Ngebel attractions in 3600 with a 3-dimensional object model and the nearest wahan search feature using the Greedy Algorithm with a virtual reality display. In its preparation using SDLC (Software Development Life Cycle). The final result of making this application in the form of Virtual Reality Telaga Ngebel and also games using the greedy algorithm used as an implementation of games that present the charm of Telaga Ngebel. Based on the results of usability test research with the SUS method obtained a score of 51 or in adjective OK criteria.
Yolo-Drone: Detection Paddy Crop Infected Using Object Detection Algorithm Yolo and Drone Image Masykur, Fauzan; Prasetyo, Angga; Zulkarnain, Ismail Abdurrozaq; Kumalasari, Ellisia; Utomo, Pradityo
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3472

Abstract

Crop failure is an undesirable result of rice planting for every farmer because it disrupts the economic stability of the family. One of the factors of crop failure in the rice planting process is the disease attack factor, which causes infection. Infected plants will interfere with the growth of rice, not optimally, because the green leaf substance, which is key to processing sunlight's nutrients, is unable to function. After all, it is covered by infection. Infection in the leaves covers the green leaf substance, or chlorophyll, so that the leaves are unable to absorb nutrients from sunlight. This problem is a separate concern in overcoming rice plant infections, which will result in crop failure. This paper discusses the detection of infected rice plants, particularly leaf infections, using drone camera images. Unmanned aircraft, also known as drones, fly above rice fields to capture images of rice plants, which are then used as datasets in training models to detect infected and healthy rice plants. The detection of disease presence in rice leaves is carried out using the You Only Look Once version 8 (YOLOv8) object detection algorithm, with a model trained using Google Colab Pro+. The results of training the model to detect healthy and infected plant leaves are the primary objectives of this study. The YOLOv8 object detection model, when applied to detect rice plants with two classes (healthy and infected), shows quite good results. This is indicated by the recall, precision, and F1-score values (0.99, 0.814, 0.90) approaching 1 in all classes.
Co-Authors Angga Prasetyo Cobantoro, Adi Fajaryanto Ellisia Kumalasari Fauzan Masykur Mohammad Bhanu Setyawan Permana Putra, Angga Dian Pradityo Utomo Sussolaikah, Kelik