Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI
Utami, Tri Wahyu
Unknown Affiliation
Computer Science & IT Energy Environmental Science Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
IMPLEMENTASI PARIS AGREEMENT TERHADAP KEBIJAKAN KEHUTANAN INDONESIA Utami, Tri Wahyu; ", Afrizal
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Vol. 5: Edisi II Juli - Desember 2018
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study describe about implementatioan Paris Agreement in indonesia’s forest policy. Paris Agreement which agreed by Indonesia and others country gave new dream for world future after increased the climate change in the world. In This script will look at issue and problems with International Relation paradigm thought of study, analize, theory and perspective.This reasech use conceptual base with green political concept, and implementation of paris agreement be a focus discussion. This research have national state level, constructivis perspective and green theory of methodology qualitative research.The implementation that have done by Paris Agreement are agremment of Indonesia legislative assembly have made the constitution of Republic Indonesia (Nomor 16 Tahun 2016) about validation of paris Agreement To The United Nations Framework Convention On Climate Change. That constitution valided in Jakarta, at October 24th 2016 by President Of Republic Indonesia, Joko Widodo and valided by ministry of law and human right, Yasonna H. Laoly.Keywords: Implementation, Paris Agreement, Policy
Sistem Informasi Geografis Pemetaan Penyebaran Penyakit Berbasis Web Di Pekalongan Utami, Tri Wahyu
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 1 No. 4 (2023): Oktober
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v1i4.597

Abstract

Indonesia is a country consisting of many islands and isolated areas that have not been reached. These isolated areas have not been supported by health facilities. Therefore, the prevention of disease in the area is difficult to be solved. These problems can lead to the spread of the disease becomes wider and it can increase the number of deaths and physical disabilities. The presence of a web-based geographic information systems is able to help in overcoming the spread of the disease in a particular area. The geographic information systems can be applied to mark an area in determining the points of the transmission. Using the current health facilities, the data can be performed on the information systems and to find the areas that do not have proper health facilities. Geographic information system is produce the spread of diseases that determine how the diseases spread and help the areas that do not have health facilities to receive medical countermeasures from the parties concerned. It can give welfare to people who need help to have a proper health.
Implementation and Comparative Analysis of CNN and Transfer Learning Models (EfficientNetB0, MobileNetV2, and ResNet50) for Rice Leaf Disease Detection Based on Digital Images Utami, Tri Wahyu; Novita, Mega; Latifa, Khoiriya
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11616

Abstract

Rice leaf diseases significantly reduce agricultural productivity, making early and accurate detection essential, particularly in rice-producing regions such as Indonesia. This study proposes an automated rice leaf disease detection system based on Convolutional Neural Networks (CNN) and transfer learning. The dataset, obtained from the Mendeley Data Repository, consists of 6,889 images classified into eight categories: Bacterial Leaf Blight, Brown Spot, Healthy Rice Leaf, Leaf Blast, Leaf Scald, Narrow Brown Leaf Spot, Rice Hispa, and Sheath Blight. The dataset was divided into 70% training, 15% validation, and 15% testing. A baseline CNN model and three pre-trained models—EfficientNetB0, MobileNetV2, and ResNet50—were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The baseline CNN achieved a test accuracy of 48.26%, while EfficientNetB0 achieved 58.41%. In contrast, MobileNetV2 and ResNet50 demonstrated significantly better performance, with test accuracies of 79.98% and 76.60%, respectively. MobileNetV2 exhibited the most balanced performance across all classes, showing superior generalization capability and computational efficiency. The best-performing model was integrated into a Streamlit-based application, enabling real-time rice leaf disease detection through image upload. The results confirm that transfer learning substantially improves classification accuracy and robustness compared to conventional CNNs. This study highlights the potential of lightweight deep learning models for practical implementation in smart agriculture systems and provides a reliable solution for automated rice disease detection in real-world conditions.
Co-Authors Afrizal " Latifa, Khoiriya Mega Novita