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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet BALI MEDICAL JOURNAL (BMJ) Jurnal Kimia (Journal of Chemistry) INDONESIAN JOURNAL OF BIOMEDICAL SCIENCES Jurnal Ilmu Komputer dan Agri-Informatika Scientific Journal of Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal Of Vocational Health Studies Journal of Robotics and Control (JRC) Jurnal Ilmu dan Teknologi Peternakan Terpadu (JITPU) Dharma Jnana Journal of Global Pharma Technology Eduvest - Journal of Universal Studies Jurnal Sistem Informasi
Sri Wahjuni
Chemistry Department, Faculty Of Math And Science, Udayana University, Bali-Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Mechanical Engineering Medicine & Pharmacology Neuroscience Social Sciences

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Artikel Review: Implementasi Sistem Internet of Things (IoT) Pada Industri Perunggasan Hidayati Soesanto, Iman Rahayu; Wahjuni, Sri; Tanti, Ariyani
Jurnal Ilmu dan Teknologi Peternakan Terpadu Vol. 4 No. 2 (2024): Jurnal Ilmu dan Teknologi Peternakan Terpadu, Desember 2024
Publisher : Program Studi Peternakan Fakultas Pertanian Universitas Bosowa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56326/jitpu.v4i2.5039

Abstract

The implementation of Internet of Things (IoT) systems in the poultry industry has shown great potential in enhancing poultry efficiency and productivity. IoT technology enables real-time monitoring of various aspects of the coop environment, such as temperature, humidity, air quality, and lighting, as well as the health conditions of the chickens. Through connected sensors and communicating devices, data can be collected and analyzed to optimize the maintenance conditions of the chickens, thereby improving livestock health and reducing mortality rates. Additionally, IoT systems can automate feeding and watering processes, contributing to operational cost and time savings. This article reviews various case studies and research related to the application of IoT in the poultry industry. The review results indicate that the use of IoT not only increases operational efficiency but also aids in faster and more accurate decision-making based on precise data. However, challenges such as high initial costs, the need for adequate technological infrastructure, and specialized expertise in managing IoT systems must be addressed. Therefore, a strategic approach and collaboration among farmers, the government, and technology providers are required to maximize the benefits of IoT in the poultry industry.
Multi-Platform Detection of Melon Leaf Abnormalities Using AVGHEQ and YOLOv7 Ishak, Sahrial Ihsani; Priandana, Karlisa; Wahjuni, Sri
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1441

Abstract

This research develops a multiplatform system for detecting abnormalities in melon leaves, integrating an Internet of Things (IoT) approach using Jetson Nano, a Streamlit-based website, and a mobile application for real-time monitoring. The system employs preprocessing with Average Histogram Equalization (AVGHEQ) to enhance image quality, followed by modeling with the YOLOv7 algorithm on a dataset of 469 training images and 52 test images, validated through 5-fold cross-validation. The model achieved a mean Average Precision (mAP) of 84% with an inference detection time of 4.5 milliseconds. Implementation on Jetson Nano resulted in a 25% increase in CPU usage (from 25% to 50%) and a 20% increase in RAM usage (from 70% to 90%). By combining these platforms and leveraging robust data preprocessing and modeling techniques, the system provides an accessible, efficient, and scalable solution for agricultural monitoring, enabling farmers to address plant health issues promptly and effectively.
Optimization of Rice Supply Chain Traceability Using Blockchain: A Case Study in Bekasi Regency Barlianto, Agus; Hermadi , Irman; Wahjuni, Sri
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.50771

Abstract

Rice is a staple food in Indonesia, with a high consumption rate of 81.044 kg per capita annually and a production volume of 31.54 million tons in 2022. Ensuring traceability in the rice supply chain is crucial for food quality and safety. However, the industry faces logistical challenges, such as inadequate infrastructure, poor interagency coordination, and unintegrated information systems. At Menata Citra Selaras (MCS), the largest rice milling unit in Bekasi Regency, manual systems hinder decision-making and product traceability. This study aims to optimize traceability by leveraging blockchain technology. We developed Ricetrack, a prototype application based on Sawtooth blockchain technology, to enhance supply chain traceability. The methodology includes identifying actors and user stories, system modeling, design, and prototype testing in an operational environment. Data analysis, both quantitative and qualitative, showed significant improvements in traceability and data transparency, validated through surveys and stakeholder feedback. The study concludes that blockchain technology offers substantial benefits for the rice supply chain, providing added value to all stakeholders and enhancing operational efficiency for MCS and other companies in the Indonesian rice industry.
Prediksi Waktu Tanam Cabai Rawit Berdasarkan Kondisi Lingkungan Berbasis Internet of Things (IoT) Menggunakan Metode Neural Network Djaksana, Yan Mitha; Agus Buono; Sri Wahjuni; Heru Sukoco
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5199

Abstract

In Indonesian cuisine, the red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of the chili prices. Farmers often struggle with the critical decision of when to plant Tabasco peppers to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advances in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study focuses on the development of an IoT-based solution aimed at helping farmers in precisely determining the optimal planting time for Tabasco pepper. It uses five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours) and groundwater usage data (m3) to make data-driven planting decisions. The urgent need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Using historical data from 2019, obtained from the Jakarta Provincial Government Open Data DKI, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The result is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.
The Use of Artificial Neural Networks to Estimate Reference Evapotranspiration Haris, Abdul; Marimin; Wahjuni, Sri; Setiawan, Budi Indra
Agromet Vol. 39 No. 1 (2025): JUNE 2025
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.39.1.1-7

Abstract

Evapotranspiration is defined as the loss of water from soil and vegetation to the atmosphere, driven by weather conditions. It reduces the availability of water for agricultural purposes, which affects the amount of irrigation water, particularly during the dry season. The objective of this paper is to present a comparative analysis of the estimated reference evapotranspiration value based on artificial neural networks (ANN) with backpropagation bias 1 (BP-1) and backpropagation bias 0 (BP-0) architectures. The model was fed with data of air temperature, relative humidity, and solar radiation. The model is utilized to calculate the evapotranspiration using the Hargreaves method as the training data. The performance of ANN model was evaluated using the mean square error (MSE), root mean square error (RMSE), and coefficient determination (R2). Our results showed that both ANN models performed well as indicated by low error (MSE < 0.01) and high R2 (>0.99). Also, we found that air temperature and relative humidity determine the optimal prediction. Further, this proposed model can serve as a reference for other models seeking to determine the most appropriate computational model for evapotranspiration value estimation.
UAV-Based Segmentation and Correlation Analysis of Vegetation Indices for Cassava Crop Health Assessment Maryana, Sufiatul; Herdiyeni, Yeni; Wahjuni, Sri; Santosa, Edi
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.9.4.3078

Abstract

Cassava, an essential staple food with diverse applications, has been relatively underexplored in terms of health analysis using vegetation indices. Conventional field surveys face challenges in covering large areas due to resource constraints. Recent advancements in remote monitoring techniques, such as satellite imagery and Unmanned Aerial Vehicles (UAVs), offer a promising alternative. While satellite imagery enables broad-scale surveys, its limited spatial resolution restricts detailed analyses of individual plants or smaller ecosystems. UAV-based vegetation surveys commonly utilize Vegetation Indices (VI) to assess unique spectral information. This study investigated UAV-based methods for mapping cassava distribution in the Telaga Kahuripan smallholder plantation in Bogor, Indonesia, focusing on UAV imagery, segmentation, and vegetation indices to evaluate cassava plant health at 2, 5, and 8 months of age. The results revealed significant variations in vegetation indices across different cassava plant ages. Particularly, the highest values observed at 5 months of age indicated substantial growth, with NDVI and GNDVI values exhibiting R2 ranging from 0.95 to 0.98, indicating a strong correlation. The robust correlation between NDVI and GNDVI implies that both indices can effectively predict plant health using UAV-based monitoring. Comparisons with existing studies suggest potential variations attributable to factors such as geographical location, environmental conditions, and cultivation practices. Understanding these variations is crucial for refining monitoring techniques and informing agricultural practices. Consequently, the findings have implications for enhancing cassava health monitoring and optimizing agricultural practices to ensure sustainable crop production.
Co-Authors A.A.G. Sudewa Agus Buono Alfiansyah Halomoan Siregar Anisa Nur Halimah Ariyani Tanti Auriza Rahmad Akbar Barlianto, Agus Betty Kostradiyanti Budi Indra Setiawan dan Luh Putu Arisanti Djaksana, Yan Mitha Edi Santosa Eknanda, Rafael Tektano Grandiawan Elly Rusdiana, Elly Eny Inayati, Eny Faisyah, Shilvy Arofatul Haris, Abdul Haula Robbi, M. Kautsar Hendra Rahmawan Hendra Rahmawan Hermadi , Irman Hidayat Hidayat I M, Sukadana I NYOMAN MANTIK ASTAWA I W. Wita, I W. I Wayan Suirta I Wayan Wita I. A Raka Astiti Asih Ida Bagus Putra Manuaba Iman Rahayu Hidayati Soesanto Irman Hermadi Ishak, Sahrial Ihsani Karlisa Priandana Komara N, Fatthurohman Kusdarjanti, Endang Laksmi Lita Rosa, Mia Lusiana Tabuni Marimin , Maxiwinata, Maxdha Michel Williams, Michel Ni Luh Rustini Ni Made Puspawati Ni Made Puspawati Ni Nyoman Astuti Wulandari Ni Putu Rahayu Artini Nirmala Ratna Harda, Sasadhara Nur Halimah, Anisa Oka Ratnayani Putu Yuliantari Rahmadani, Annisa Ratwita, Rr. Dwiyanti Feriana Sanjiwo, Suryo Hamukti Sefy Ayu Mandanie, Sefy Ayu Setyowati, Okti Sianiwati Goenharto Sri Rahayu Santi Sri Redjeki Sri Redjeki Indiani, Sri Redjeki Sufiatul Maryana Sujati Sujati Sujati, Sujati Wafi, Azmi Sabilakisbatul Wahyu Dwijani Sulihingtyas Wasudewa, K. M. Willy Bayuardi Suwarno Wulandari Wulandari Zakiah, Rizqi Alifahasni