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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Keteknikan Pertanian Telematika : Jurnal Informatika dan Teknologi Informasi Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) Surya Abdimas JASEE Journal of Application and Science on Electrical Engineering Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL
M.S. Hendriyawan Achmad
Universitas Teknologi Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Challenges in the Implementation of Internet of Things (IoT) in Irrigation and Fertilizer Management System in Indonesia Saptaji, Kushendarsyah; Tumada, Azahri; Baihaqi Alfakihuddin, Muhamad Lukman; Ayu Wardani, Dilla; Kusuma Dewi, Tiara; Adiati Junaisih, Octarina; Rausyan Fikri, Muhamad; Hendrawan Achmad, Muhammad Sobirin
Jurnal Keteknikan Pertanian Vol. 13 No. 2 (2025): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.013.2.265-283

Abstract

Agriculture is critical to many countries' economies, especially related to gross domestic products (GDP) and employment. However, as a result of industrialization, leading to a problem in fulfilling the expanding global food supply demand. The Internet of Things (IoT) can enhance automatic data transfer in agricultural, improve production, increase quality, improve cost-effectiveness, and reduce environmental impact. However, the obstacles related to IoT application in agriculture have received little discussion especially in the development countries such as Indonesia. This research seeks to fill that gap by investigating the specific issues of adopting the Internet of Things (IoT) in the context of an irrigation and fertilizer management system in Indonesia. To fully study this, a stratified multistage random sampling was conducted to acquire significant insights and data. According to the interview results, respondents voiced worries regarding IoT deployment in agriculture, including, costs implementation (CI), their own knowledge (perceived knowledge (PK)), user experiences with the technology (perceived ease of use (PEU)) and intention of use (IU). The study finds weak CI-IU and PK-IU links but a strong PEU-IU correlation, underscoring the multifaceted factors influencing IoT adoption in agriculture. It is found that the easiness of the use of IoT is the main factors that influence Indonesian farmers to implement the IoT in their farmers. Although the cost of the implementation is an essential factor, easiness to use IoT is the most significant factor. Lastly, researchers, policymakers, and agricultural stakeholders can leverage these insights to advance IoT integration and sustainability in farming practices.
Solar Radiation Prediction using Long Short-Term Memory with Handling of Missing Values and Outliers Alfin Syarifuddin Syahab; MS Hendriyawan Achamd
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1225

Abstract

The pyranometer sensor is an instrument for measuring Global Horizontal Irradiance (GHI) which is used as parameter for analyzing and predicting weather. GHI data which is processed into prediction model for photovoltaics is useful for determining the performance of solar power generation systems in distributed energy operations. However, GHI sensor data has weaknesses in missing values and outliers due to measurement errors. The research designed a GHI sensor data prediction model using data preprocessing by the imputation of missing values using linear, polynomial, and Piecewise Cubic Hermite Interpolating Polynomials (PCHIP) interpolation and eliminating outliers using Random Sample Consensus (RANSAC) on the dataset. Previous researches show that Long Short-Time Memory (LSTM) can improve the performance of predictions compared to machine learning. This research designs an LSTM prediction model with data preprocessing and without data preprocessing. The results of the imputation of missing values obtained the best performance in PCHIP with Mean Absolute Error (MAE) 39.708 W/m2, Root Mean Absolute Error (RMSE) 76.224 W/m2, Normalized Root Mean Absolute Error (NRMSE) 0.433, and Coefficient Determination (R2) 0.903 then imputation from outlier elimination obtained MAE 44.377 W/m2, RMSE 86.738 W/m2, NRMSE 0.500, and R2 0.886. RANSAC testing succeeded in eliminating 100% outliers. The results of LSTM with data preprocessing obtained better performance with the best evaluations on MAE, RMSE, NRMSE, and R2 for test data of 42.863 W/m2, 82.396 W/m2, 0.396 and 0.918. This study contributes to GHI prediction model that can handle missing values ​​and outliers from sensors to support solar power plants.
Co-Authors Adiati Junaisih, Octarina Al Karim, Bahrul Alfin Syarifuddin Syahab Anivan Aditia Ayu Wardani, Dilla Baby Aries Choi Kah Wei Ferriawan Yudhanto Fredi Prima Sakti Ikrima Alfi Indah Soesanti Kushendarsyah Saptaji Kusuma Dewi, Tiara Litasari Mohd Razali Daud Muhammad Lukman Baihaqi Alfakihuddin Pramudwiatmoko, Arif Priyandoko Gigih Rausyan Fikri, Muhamad Rifqi Ahmad Afandi Sandy Ikhwanul Attar Satrio Gumilang, Muhammad Satyo Nuryadi Sugeng Santoso Sutoyo Sutoyo Tumada, Azahri Wahyu Saputro RM Wira Fadlun