Article Per Year (5 Year)
p-Index From 2021 - 2026
1.704
P-Index
This Author published in this journals
All Journal PROtek : Jurnal Ilmiah Teknik Elektro Sistemasi: Jurnal Sistem Informasi Journal of Science and Engineering Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science
Ciksadan, Ciksadan
Unknown Affiliation
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Penerapan Algoritma Yolov3 pada Sistem Cerdas Pendeteksi dan Pengendali Hama Bawang Merah Berbasis IoT As'ad, Avif; Suroso, Suroso; Ciksadan, Ciksadan; Hawayanti, Erni
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5697

Abstract

Technological advancements play a crucial role in enhancing the efficiency of modern agriculture, particularly in addressing pest management challenges. This study focuses on the development of an automatic pest detection system for shallot crops using a combination of Arduino Uno microcontroller, ESP32-CAM camera module, and YOLOv3 object detection model. The system is designed to detect pests in real-time through images captured by ESP32-CAM and analyzed using YOLOv3, then provide an automatic response by spraying pesticides only in areas where pests are detected. The study began with the development of hardware and software for the automatic pest detection system. Arduino Uno is used as the main microcontroller to control the entire system, while ESP32-CAM is responsible for capturing images and detecting pests. The YOLOv3 model is trained using the COCO dataset, supplemented with sample images of pests on shallot crops to improve detection accuracy. The training process is conducted using a GPU to speed up model learning. Field tests on shallot crops infested with various types of pests show that this system has a high accuracy rate in detecting pests and effectively provides automatic pesticide spraying responses. The spraying system's effectiveness reaches 93%, ensuring pesticides are sprayed only in areas where pests are detected, thus optimizing pesticide use and reducing negative environmental impacts. This system offers an efficient and environmentally friendly solution for pest control and has significant potential for application in various agricultural scenarios. This research contributes to the improvement of agricultural productivity and the welfare of farmers in Indonesia.
Perbandingan Algoritma Support Vector Machine dan Bi-Directional Long Short Term Memory Dalam Mengklasifikasi Berita Hoaks Merinda, Siska; Ciksadan, Ciksadan; Fadhli, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7391

Abstract

The rapid advancement of digital technology has made it easier to spread information widely and quickly. However, this ease of access has also contributed to the rise of false or misleading news, commonly known as hoaxes, which can confuse the public. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Bi-Directional Long Short Term Memory (BiLSTM), in classifying hoax news written in Indonesian. The research adopts a supervised learning approach, where models are trained using pre-labeled data categorized as either hoax or non-hoax. The process begins with collecting data from trusted sources, followed by several preprocessing steps, including text cleaning, tokenization, stopword removal, and stemming. After preprocessing, the dataset is split into training and testing sets in an 80:20 ratio. The results show that the SVM model achieved an accuracy of 98.46%, with 98% precision and 99% recall for the non-hoax category. In comparison, the BiLSTM model performed better, reaching 99% accuracy, with both precision and recall at 99% for both categories. These findings indicate that BiLSTM is more effective at capturing linguistic context and identifying patterns in hoax-related content. Additionally, the models were implemented into a web-based system to assess their real-world detection capabilities.
Co-Authors Ade Silvia Handayani Ade Silvia, Ade Ahmad Taqwa Alifah, Farah Balqis As'ad, Avif Choirunnisa Choirunnisa, Choirunnisa Damayanti, Shinta Endri, Jon Fadhli, Mohammad Hawayanti, Erni Ihsan, Muhammad Nabil IRAWAN, FERY Irma Salamah Jami, Nurlita Merinda, Siska Ramadonna, Try Feby Shalama, Maura Fatima Sopian Soim, Sopian Sugiyanto, Aan Suroso Suroso Thabroni, Feri ZEFI, SUSAN