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"ABSTRAKBadan Meteorologi Klimatologi dan Geofisika (BMKG) memiliki tugas pengamatan terhadap magnet bumi yang tersebar di Indonesia. Sensor magnetik bumi BMKG menghasilkan output data real-time. Penelitian ini berfokus pada model predictive maintenance pada sensor magnetik bumi berdasarkan output data sensor. Output data yang dihasilkan adalah dalam bentuk format delimited-space sehingga mudah untuk diproses. Komponen magnetik yang digunakan dalam penelitian ini adalah data komponen total magnet bumi (F) dari sensor. Pemrosesan data menggunakan bahasa pemograman python dan algoritma yang digunakan adalah metode random forest regression dengan membandingkan perbedaan nilai yang dihasilkan dengan data Indoesian Geomagnetic Maps for Epoch 2015.0 untuk kemudian dibuatkan model prediksi terhadap waktu. Proses tersebut digunakan untuk mengetahui apakah data yang dihasilkan masih dalam toleransi atau tidak. Tahapan dalam penelitian ini mulai dari pengumpulan data, pre-processing data, pembuatan model, hingga pengujian model dan validasi terhadap model. Penelitian ini menghasilkan estimasi waktu pemeliharan sebesar 14 hari pada data baseline nilai F dan sebesar 3 hari pada data delta F (ΔF).

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ABSTRACTThe Meteorological, Climatological, and Geophysical Agency (BMKG) has the task of observing the earth magnets spread across Indonesia. Earth magnetic sensor of BMKG delivers real-time data output. The study focuses on the predictive maintenance model on the earth's magnetic sensor based on sensor data output. The resulting data output is in the form of delimited-space format so it is easy to process. The magnetic component used in this study is data on the earth's total magnetic component (F) from the sensor. Data processing uses python programming language and the algorithm used is a random forest regression method by comparing the value difference generated with the Indoesian Geomagnetic Maps for Epoch 2015.0 data for later created predictive models against time. The process is used to determine whether the resulting data is still in tolerance or not. The stages in this study range from data collection, pre-processing data, create model, model testing, and model validation. The study resulted in a 14-day maintenance time estimate of the baseline data F-value and 3-day in the delta F (ΔF) data."

"PLTP Lahendong adalah pembangkit listrik terbesar yang mensuplai hingga 60% kebutuhan listrik di Sulawesi Utara. Pada tanggal 15 Desember 2020 PLTP Lahendong unit 1 mengalami trip secara tiba-tiba. Dari hasil investigasi, ditemukan bahwa turbin mengalami bowing dimana turbin tidak lagi lurus sesuai standar dikarenakan kegagalan maintenance. Karena pentingnya PLTP Lahendong ini dalam memenuhi kebutuhan listrik, diperlukan maintenance yang optimal. Predictive maintenance merupakan metode yang efektif dimana mampu memprediksi kerusakan yang akan terjadi berdasarkan historical data pada unit seperti data vibrasi menggunakan pemrograman dan machine learning model time series regression. Metodologi penelitian difokuskan kepada analisa dan pemrograman menggunakan bahasa pemrograman python dimulai dari import dataset, EDA (Exploratory Data Analysis), data preparation, train test, modelling, model evaluation, hyperparameter tuning, dan hasil prediksi. Model machine learning yang diuji adalah linear regression, decision tree regression, random forest regression, dan xgboost regression. Hasil yang diperoleh dari predictive maintenance menggunakan model machine learning sangat akurat dengan tingkat akurasi prediksi mencapai 96.97 % - 97.14 %. Machine learning sangat efektif digunakan dalam predictive maintenance karena tingkat akurasi prediksinya yang tinggi. Model random forest regression merupakan model yang akurasinya paling tinggi bahkan tidak perlu dilakukan hyperparameter tuning untuk meningkatkan akurasinya.

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PLTP Lahendong is the largest power plant that supplies up to 60% of electricity needs in North Sulawesi. On December 15, 2020 PLTP Lahendong unit 1 experienced a sudden trip. From the results of the investigation, it was found that the turbine was bowing where the turbine was no longer straight according to the standard due to maintenance failure. Because of the importance of the Lahendong PLTP in meeting electricity needs, optimal maintenance is needed. Predictive maintenance is an effective method which is able to predict the damage that will occur based on historical data on the unit such as vibration data using programming and machine learning model time series regression. The research methodology focuses on analysis and programming using the python programming language starting from import datasets, EDA (Exploratory Data Analysis), data preparation, train tests, modeling, model evaluation, hyperparameter tuning, and prediction results. The machine learning models tested are linear regression, decision tree regression, random forest regression, and xgboost regression. The results obtained from predictive maintenance using machine learning models are very accurate with prediction accuracy levels reaching 96.97% - 97.14%. Machine learning is very effective in predictive maintenance because of its high predictive accuracy. The random forest regression model is the model with the highest accuracy, even if hyperparameter tuning is not necessary to increase its accuracy. "

"This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power. "

"Masuknya industri 4.0 di Indonesia membuat mesin dapat terintegrasi dengan komputer melalui perangkat IoT sehingga membuat proses produksi lebih efisien. Salah satu upaya untuk mempertahankan hal tersebut adalah dengan melakukan maintenance menggunakan metode predictive maintenance. Kegagalan mesin dalam predictive maintenance dapat diprediksi menggunakan machine learning. Metode sequence processing adalah algoritma machine learning yang cocok digunakan dalam predictive maintenance berbasis timeseries. Penelitian ini mencoba berbagai macam cara penerapan sequence processing untuk memprediksi kegagalan pada mesin. LSTM merupakan metode sequence processing yang populer digunakan untuk predictive maintenance. Terdapat tiga cara penerapan model LSTM yang diuji pada penelitian ini, yaitu model klasifikasi, regresi, dan regresi menggunakan sequence to sequence Ketiga model tersebut akan diuji menggunakan data yang didapat dari database terbuka. Setiap model akan dievaluasi dan dikomparasi untuk mengetahui model yang terbaik. Penelitian ini menunjukkan bahwa model klasifikasi memiliki kinerja yang buruk karena mengalami overfitting. Sementara itu, model regresi sequence to sequence memiliki kinerja yang paling baik, yaitu dengan nilai f-1 score mencapai 57.45%.

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The implementation of Industry 4.0 in Indonesia enables machines to be integrated with computers through IoT devices, resulting in more efficient production processes. One of the efforts to maintain this is by performing maintenance using predictive maintenance methods. Machine learning can be used to predict machine failures in predictive maintenance. Sequence processing is a suitable machine learning algorithm for predictive maintenance based on timeseries data. This research explores various ways to apply sequence processing for predicting machine failures. LSTM is a popular sequence processing method used in predictive maintenance. Three approaches for implementing LSTM models were tested in this study: classification, regression, and sequence to sequence regression. These models were tested using data obtained from an open database. Each model was evaluated and compared to determine the best-performing model. The research findings indicate that the classification model performed poorly due to overfitting. On the other hand, the sequence to sequence regression model achieved the best performance, with an f-1 score of 57.45%."

"Dalam era revolusi industri 4.0, integrasi teknologi menjadi kunci untuk meningkatkan produktivitas di sektor manufaktur. Dalam konteks ini, penggunaan Cloud Computing, Internet of Things (IoT), dan Machine Learning (ML) memainkan peran penting. IoT dan Cloud Computing digunakan untuk mengelola proses pengumpulan dan pengolahan data, terutama dari sensor mesin. Data ini kemudian dapat digunakan untuk pelatihan model ML, khususnya dalam kasus Predictive Maintenance. Predictive Maintenance bertujuan untuk memprediksi kapan suatu mesin memerlukan perawatan. Dalam penelitian sebelumnya, pendekatan masalah hanya memilih satu metode (klasifikasi atau regresi). Oleh karena itu, penelitian ini menciptakan metode Predictive Maintenance yang menggabungkan keduanya. Model yang dikembangkan menggunakan dua jenis pendekatan: Random Forest Tree untuk klasifikasi dan LSTM (Long Short-Term Memory) dengan Fully Connected layer untuk prediksi. Hasil pengujian menunjukkan bahwa model yang menggunakan LSTM untuk klasifikasi dan regresi mencapai akurasi 100%. Diikuti dengan hasil recall, precission, dan F-1 score yang mencapai 1.00. Oleh karena itu, LSTM dapat dianggap sebagai algoritma terbaik untuk Predictive Maintenance dalam industri manufaktur.

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In the era of the 4th industrial revolution, technology integration is key to improving productivity in the manufacturing sector. In this context, the use of Cloud Computing, Internet of Things (IoT), and Machine Learning (ML) plays a crucial role. IoT and Cloud Computing are used to manage the process of data collection and processing, especially from machine sensors. This data can then be used for ML model training, particularly in the case of Predictive Maintenance. Predictive Maintenance aims to predict when a machine requires maintenance. In previous research, the problem approach often involved choosing only one method (classification or regression). Therefore, this study created a Predictive Maintenance method that combines both approaches. The developed model uses two types of approaches: Random Forest Tree for classification and LSTM (Long Short-Term Memory) with a Fully Connected layer for prediction. Test results show that the model using LSTM for both classification and regression achieves 100% accuracy. Additionally, the recall, precision, and F-1 score results also reach 1.00. Therefore, LSTM can be considered the best algorithm for Predictive Maintenance in the manufacturing industry."