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All Journal Jurnal Otomasi Kontrol dan Instrumentasi Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal ULTIMA Computing
Budiawan, Irvan
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Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Abstract

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.
Air Quality Monitoring System Design Based on Wireless Sensor Network Integrated with the Internet of Things Budiawan, Irvan; Wigianto, Danu Febri; Wicaksono, Bagus; Hakim, Arif Rohman
Ultima Computing : Jurnal Sistem Komputer Vol 16 No 1 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i1.3694

Abstract

Government and officials set rules to keep the air clean and healthy. To accommodate this, an efficient air quality monitoring system is required. Real-time monitoring is crucial for observing air quality. This allows for immediate action if air quality declines. However, current systems often rely on just one measurement point, risking inaccurate results due to rapid pollutant dispersion. To overcome this problem, researchers propose designing an air quality monitoring system based on a wireless sensor network. Sensor nodes will be installed at various points within the area to be monitored, forming a connected sensor network using the ESP-Now protocol. The data obtained from each node will be sent to the base station, then the data will be transmitted via the Message Queuing Telemetry Transport (MQTT) protocol using the internet network. Thus, this design produces a wireless sensor network that is integrated with the internet of things (IoT). The advantages of the IoT system include ease of data storage and accessibility that can be accessed from anywhere as long as it is connected to the internet and has appropriate authorization.
An Adaptive Stacking An Adaptive Stacking Approach for Monthly Rainfall Prediction with Hybrid Feature Selection: Hybrid Feature Selection Zulfa, Ahmad; Saikhu, Ahmad; Pradana, Hilmil; Budiawan, Irvan
ULTIMA Computing Vol 17 No 1 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i1.4157

Abstract

Rainfall is a critical climatic element for water resource management, agriculture, and hydrometeorological disaster mitigation. However, its nonlinear and fluctuating characteristics require a careful and adaptive predictive approach. This study aims to develop a monthly rainfall prediction model using an Adaptive Stacking Ensemble method combined with a hybrid feature selection framework. The feature selection integrates three techniques”correlation analysis, feature importance from Random Forest, and Recursive Feature Elimination (RFE)”through a voting mechanism. Three machine learning algorithms, namely Random Forest, K-Nearest Neighbors (KNN), and XGBoost, are used as base learners. The meta-learner is adaptively selected based on the best-performing base model. Model performance is evaluated using R², RMSE, and MAE metrics. The proposed method is expected to produce a more accurate, stable, and reliable predictive model to support climate-based decision-making. By leveraging the hybrid feature selection framework, the model effectively identifies the most relevant weather variables related to monthly rainfall patterns, thereby reducing model complexity without sacrificing accuracy. The adaptive stacking approach also offers flexibility in capturing nonlinear relationships between features and targets, while enhancing model generalization across seasonally varying data. Experiments were conducted on long-term weather datasets, and the results demonstrate that the proposed model outperforms single models and conventional ensemble methods. This research contributes to the development of more robust, data-driven climate prediction systems that can be applied across sectors affected by rainfall variability.
Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Abstract

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.
Air Quality Monitoring System Design Based on Wireless Sensor Network Integrated with the Internet of Things Budiawan, Irvan; Wigianto, Danu Febri; Wicaksono, Bagus; Hakim, Arif Rohman
ULTIMA Computing Vol 16 No 1 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i1.3694

Abstract

Government and officials set rules to keep the air clean and healthy. To accommodate this, an efficient air quality monitoring system is required. Real-time monitoring is crucial for observing air quality. This allows for immediate action if air quality declines. However, current systems often rely on just one measurement point, risking inaccurate results due to rapid pollutant dispersion. To overcome this problem, researchers propose designing an air quality monitoring system based on a wireless sensor network. Sensor nodes will be installed at various points within the area to be monitored, forming a connected sensor network using the ESP-Now protocol. The data obtained from each node will be sent to the base station, then the data will be transmitted via the Message Queuing Telemetry Transport (MQTT) protocol using the internet network. Thus, this design produces a wireless sensor network that is integrated with the internet of things (IoT). The advantages of the IoT system include ease of data storage and accessibility that can be accessed from anywhere as long as it is connected to the internet and has appropriate authorization.
Evaluasi Spasial Estimasi Curah Hujan pada Radar Cuaca Menggunakan Metode Z-R Marshall-Palmer di Wilayah Jawa Barat Ananda, Naufal; Mukhlish, Faqihza; Wicaksana, Haryas Subyantara; Budiawan, Irvan
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.4

Abstract

Rainfall is one of the weather parameters that affect various sectors. High rainfall intensity can trigger hydrometeorological disasters, so rainfall observation data is vital to monitor rainfall conditions in an area. An automatic rain gauge is an instrument that measures rainfall at an observation point, but the instrument has reasonably low coverage and has yet to reach the entire region. Weather radar is a remote sensing instrument capable of spatially estimating rainfall. Weather radar data can be used to estimate rainfall using the Marshall-Palmer Z-R method. The application of the method can be an alternative for areas that do not have rainfall observation equipment. However, the estimation needs to be evaluated to improve the accuracy of the estimation value. Based on the evaluation, the highest coefficient of determination was 0.92, and the lowest was 0.67. The lowest RMSE value was 2.40, the highest was 6.76, the highest ME value was 16.59, and the lowest was 5.93; the highest bias was 12.90, and the lowest was 5.30. The study results show that the weather radar can operate according to the specifications of the maximum observation distance of up to 220 KM, but the farther the observation distance to a point, the higher the performance of rainfall estimation accuracy.
Estimasi Kecepatan Angin Permukaan pada Jaringan Anemometer Menggunakan Temporal Convolutional Network Wicaksana, Haryas; Mukhlish, Faqihza; Ananda, Naufal; Budiawan, Irvan; Khamdi, Arif Nur; Habib , Abdul Hamid Al
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.5

Abstract

Surface winds in various locations are measured simultaneously using a multisite anemometer network. This network is susceptible to system failures due to sensor damage, causing a data gap during sensor removal and reinstallation. This research develops a wind speed estimation model on a multisite anemometer using the Temporal Convolutional Network (TCN) algorithm. TCN processes time domain signals in parallel, thus significantly cutting the computation time. Minutely wind speed data set was obtained from four anemometers at Juanda International Airport in Surabaya from January 1, 2022 – December 24, 2023. The model design comprises data pre-processing, dominant wind direction analysis, hyperparameter determination, training, and testing on actual data. TCN estimation models are divided into easterly, westerly, transitional, and all-directional models. These wind speed estimation models strongly correlate with actual data, with correlation coefficients of 0.70, 0.77, and 0.87. Overall, the accuracy of the TCN-based estimation model conforms to World Meteorological Organization (WMO) requirements for wind speed measurements. It achieves RMSE<5 m/s and MAE<3 m/s. As for computation duration, TCN processes the training for 87 seconds per epoch and completes the estimation in 37 seconds, much faster than CNN-BiDLSTM’'s training duration of 2206 seconds per epoch and estimation completion of 548 seconds.
PELATIHAN SISTEM OTOMASI PERTANIAN HIDROPONIK UNTUK KELOMPOK TANI DI JATINANGOR DAN CIMAHI DALAM RANGKA PEMULIHAN EKONOMI Supriyadi, Aep; Ekawati, Estiyanti; Saputra, Dede Irawan; Budiawan, Irvan
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2022): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v5i2.507-514

Abstract

Pertanian hidroponik menjadi salah satu alternatif masyarakat perkotaan. Kegiatan tersebut dapat dilakukan bukan hanya sekedar mengisi waktu luang dimasa pandemi Covid 19 akan tetapi dapat menjadikan ladang usaha yang memperoleh keuntungan. Seiring berjalannya waktu, akselerasi pemahaman terhadap kondisi, parameter dan metode menentukan keberhasilan pertanian sangat diperlukan. Perubahan iklim yang ekstrim, tuntutan pasar yang dinamis, membutuhkan adaptasi kegiatan pertanian yang cepat baik pada sistem pertanian NFT, DFT, rakit apung bahkan sistem kombinasi yang menggunakan media tanah seperti poly bag. Otomasi pada tipe-tipe tersebut dapat dioptimalkan dan dan diaplikasikan sesuai dengan kebutuhan berdasarkan permasalahan yang ada seperti sistem monitor dan kontrol. Sistem otomasi pertanian yang dikembangakan dan dilatihkan pada mitra berupa sistem kendali jarak jauh berdasarkan pembacaan sensor suhu, kelembaban, dan nutrisi untuk menggerakan aktuator dapat berupa motor penyiram dan motor pompa. kegiatan ini diharapkan peserta menjadi kader agent of change yang dapat membangun sistem otomasi pertanian pada lahan pertaniannya serta dapat meningkatakan ekonomi.
Co-Authors Ahmad Saikhu Ananda, Naufal Arif Rohman Hakim Bagus Wicaksono Deddy Kurniadi, Deddy Ekawati, Estiyanti Habib , Abdul Hamid Al Khamdi, Arif Nur Mukhlish, Faqihza Pradana, Hilmil Ryan, Muhammad Santoso, Bayu Saputra, Dede Irawan Supriyadi, Aep Wicaksana, Haryas Wicaksana, Haryas Subyantara Wigianto, Danu Febri Zulfa, Ahmad