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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Ilmiah KOMPUTASI JIKO (Jurnal Informatika dan Komputer) Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Scientific Journal of Informatics International Journal of Engineering, Technology and Natural Sciences (IJETS) International Journal of Advances in Data and Information Systems Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM)
Suhirman
Universitas Teknologi Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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ANALISIS PERBANDINGAN KINERJA MODEL LONG SHORT-TERM MEMORY DAN RECURRENT NEURAL NETWORK DALAM PREDIKSI CUACA BERBASIS DATA CUACA REAL-TIME Abdurrahman; Suhirman Suhirman
Jurnal Ilmiah Informatika Vol. 10 No. 2 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i2.95-104

Abstract

Unpredictable weather changes pose a major challenge in various sectors, including agriculture, transportation, and construction. Inaccurate rainfall predictions, especially on a local scale, often hamper community activities and decision-making that depend on weather conditions. This study aims to compare the performance of two artificial neural network models, namely Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), in predicting rainfall based on hourly weather data collected in real-time using an ESP32 microcontroller equipped with BME280 and BH1750 sensors. The variables used include air temperature, humidity, rainfall, and light intensity. Both models were trained to predict weather conditions for the next few hours based on observation data that had been processed and normalized numerically. The evaluation was using three main metrics, namely Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results shows that the LSTM model performed better with an MAE of 0.684, MSE of 0.7343, and R² of 0.2421, while the RNN model obtained an MAE of 0.2187, MSE of 0.3422, and R² of 0.8213. These findings prove that LSTM is more stable, efficient, and accurate in capturing the temporal patterns of weather data. This system has the potential to become the basis for developing local weather forecasts based on real-time data that are more adaptive to environmental changes
Otsu Method for Chicken Egg Embryo Detection based-on Increase Image Quality Suhirman Suhirman; Shoffan Saifullah; Ahmad Tri Hidayat; Rr Hajar Puji Sejati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1724

Abstract

Detection of chicken egg embryos using image processing has limitations and needs some processes for improvement. By human vision, the previous process used binoculars and candling using light/beams directed at the chicken eggs in the incubator. In this study, we propose the application of image segmentation using the Otsu method in detecting chicken egg embryos. This method uses image segmentation with increased image quality (preprocessing) by several methods such as resizing, grayscaling, image adjustment, and image enhancement. These processes produce a better image and can be used for input in the segmentation process. In addition, this study compares several segmentation methods in detecting chicken egg embryos, such as thresholding, Otsu basic, and k-means clustering. The results show that our proposed method produced segmentation images to detect chicken egg embryos of 200 datasets images. This method has a faster process and can create a uniform segmentation than other methods. However, other methods can also detect chicken egg embryos. The method’s accuracy proposed in this study increased by 1.5% compared to other methods. In addition, the resulting SSIM value has a percentage close to and more than 90%, which means that the segmentation of the results obtained can be used to detect chicken egg embryos.
Co-Authors A.Djoko Budiyanto Abdurrahman Adam Aji Agus Rakhmadi Mido Agus Sujarwadi Ahmad Tri Hidayat Arif Amrulloh Aziz Perdana Baiq Nurul Azmi Devi Ratnasari Erik Iman HU Gunawan Wibisono Joseph Carlo K Muhammad Arifai Nurrizki Muhammad Fatu Rohim Nararya Budiyono Natan Derek Rian Oktafiani Rr. Hajar Sejati Saifullah, Shoffan Sutarman Suyoto Suyoto Tri Untoro, Iwan Hartadi Wahyu Adjie Saputra Wintolo, Hero