Article Per Year (5 Year)
p-Index From 2021 - 2026
1.633
P-Index
This Author published in this journals
All Journal Jurnal Pseudocode JOIV : International Journal on Informatics Visualization Petir Jurnal Teknologi Terpadu MULTINETICS Jurnal Abdi Insani Bubungan Tinggi: Jurnal Pengabdian Masyarakat Mitra Akademia: Jurnal Pengabdian Masyarakat Mejuajua
Defiana Arnaldy
Politeknik Negeri Jakarta
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
A Novel Approach for Bali Cattle Classification: Integrating the Fuzzy Inference System with Certainty Factor and Morphometric Parameters Arnaldy, Defiana; Seminar, Kudang Boro; Neyman, Shelvie Nidya; Sukoco, Heru; Muladno, -
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3218

Abstract

Enhancing the productivity and quality of Balinese cattle is a crucial goal for improving livestock management practices in Indonesia. Traditional evaluation methods used by farmers are often subjective and inconsistent, leading to inaccuracies in cattle classification and limiting the effectiveness of breeding and selection processes. To address these challenges, this study proposes a Fuzzy Inference System with Certainty Factor (FIS-CF) to improve cattle classification by providing more objective and reliable grading criteria. The model utilizes key physical parameters, including shoulder height, body length, and chest circumference, as input features to categorize cattle into three quality classes. A diverse dataset was collected from the People's Animal Husbandry School (SPR) and various farms across Indonesia to evaluate the model's performance. The FIS-CF model achieved a classification accuracy of 95.93% and a balanced accuracy of 96.20%, outperforming traditional methods that rely on subjective assessment. These results demonstrate that the proposed model provides a consistent, scalable, and data-driven solution for livestock classification, helping farmers make more informed decisions in cattle selection and breeding. Additionally, the model addresses key limitations of current practices by reducing reliance on manual evaluations, which often vary between assessors. The findings highlight the potential for wider adoption of the FIS-CF model across the livestock sector to improve productivity and streamline herd management processes. Future research will aim to refine the model further by incorporating additional parameters, such as age and weight, and expanding its validation to larger datasets covering different cattle breeds and farming environments to ensure broader applicability in sustainable livestock management.
Design of Livestream Video System and Classification of Rice Disease Agustin, Maria; Hermawan, Indra; Arnaldy, Defiana; Muharram, Asep Taufik; Warsuta, Bambang
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1336

Abstract

One of the agricultural products which is an important aspect of the life of Indonesian people is rice. Rice disease has a devastating effect on rice production, while detecting rice diseases in real-time is still difficult. Therefore, this study designed a Livestream video system that is equipped with a rice disease Classification system. The Livestream system utilizes 4G network communication and is assisted by the WebSocket protocol to communicate in real-time and for the rice disease Classification system using YOLO algorithm. In addition, Livestream uses the raspberry pi camera V2 to take video stream data. In analyzing the performance of the Livestream system, four tests were carried out, namely: functionality test, connectivity test, classification performance test, and implementation performance test. The test was carried out using the wireshark and conky tools, while the classification training used 5447 images from the Huy Minh do dataset that he provided on the Kaggle website. The results show that all programs run well and get a good QoS value according to the index of the parameter results, it is also found that sending non-base64 can reduce the size of the data to approximately 200,000 bytes/s and the performance of the classification system is good because it has an average accuracy of 80% even though it is quite burdening the raspberry pi. This system can still be optimized and developed further to support research in the field of data transmission and the performance of machine learning in a microcontroller.
Co-Authors Ariawan Andi Suhandana Arini Arini Asep Kurniawan Asep Taufik Muharram Ayu Rosyida Zain Fachroni Arbi Murad Habibullah, Tubagus Habibullah, Tubagus Hanif, Irfan Hanif, Irfan Iindra Iriyanti INDRA HERMAWAN Indri Neforawati, Indri Kadarwati, Dinda Kudang Boro Seminar Malik Matin, Iik Muhamad Maria Agustin Maria Agustin Matin, Iik Muhamad Malik Muhammad Yusuf Bagus Rasyiidin Muladno - Mulyani, Meutia Tri Nathanael, David Prihatin Oktivasari Ratna Widya Iswara, Ratna Widya Rizal Jihadus Solihin Shelvie Nidya Neyman Suhandana, Ariawan Andi Syafedi Syafe’i Warsuta, Bambang Widyono, M. Farishanif wirawan, chandra Yoyok Sabar Waluyo Zain, Ayu Rosyida