Pemberdayaan Masyarakat Desa Sade Dalam Kesiapan Desa Tangguh Bencana Banjir dan Tanah Longsor

The Mandalika area has become an area of ​​global concern because it has a GP motorcycle racing circuit. Mandaika is geographically closely related to the 9 buffer villages. The village is Sade Village, which is a unified area that must be maintained due to the land being taken as backfill for the circuit construction area. Changes in land use are a threat that is already in sight due to the land becoming barren, it is necessary to provide knowledge about the dangers of disasters such as floods and landslides due to extreme weather. The obligation to provide knowledge on the dangers of flooding and landslides has been carried out with the help of funds from the DPP SPP Electrical Engineering, University of Mataram, through the Community Service program carried out by the research group on Electromagnetic Technology and Environmental Conservation for Humanit

Prediksi Kebutuhan Data Mahasiswa Untuk Kuliah Daring Kondisi Covid-19 Di Jurusan Teknik Elektro Universitas Mataram: Prediction of Student Data Needs for Online Lectures Covid-19 Conditions in the Department of Electrical Engineering, University of Mataram

In the covid-19 condition, lectures at the Department of Electrical Engineering, Mataram University changed from a face-to-face process to via the Internet. T here will be a very sharp increase in demand. The use of data initially provided by the University of Mataram using a free hotspot network turned into a burden on lecturers and students. This research was conducted by sampling, general compulsory subjects, compulsory electrical courses, and compulsory expertise subjects. The distribution of variations of students domiciled in the City of Mataram and the other place coverage Lombok Island, within NTB and outside NTB. The results obtained are as follows: students who still survive in Mataram City are 17% (10.5 GB), Lombok Island 48% (8.1 GB), outside Lonbok Island 27% (4.8 GB), and outside NTB 8% (15 GB). Keyword : covid-19; lectures; onlin

Fault Prognosis System on Face-Mask Body Machine with Adabelief-Backpropagation Neural Network

Ultrasonic welding workload on vertical roller welding components on face-mask body machine in making masks has a high vibration of up to 20kHz. This high vibration causes the locking bolt to loosen the serrations, thus causing a greater failure of function, such as wear and tear on the teeth. If this function fails, it will cause downtime and high costs in the waiting process for the cleat component to be remanufactured. The damage prognosis system based on the condition of this machine implements a Classification of types of damage along with recommendations for maintenance activities that need to be carried out on the Face-Mask Body Machine. Classification of the type of damage to this system using There is a Belief-Backpropagation Neural Network (BPNN), a method for looking for weight settings on a neural network based on the error rate obtained in the previous iteration. This method is optimized using AdaBelief, which can adapt the step size based on the "confidence" of the previous gradient to get Convergence rates and generalization abilities better so that these types of problems can be known from the vibration signal of the machine which previously the signal was parsed using wavelet packet decomposition into frequency bands to obtain component data with low or high frequency. From the results of system performance testing, the modeling accuracy is 98.4%, so this system can be declared good and feasible to use in slack detection of vertical roller welding cleat fixing bolts

Prediksi Sisa Umur Transformator Menggunakan Metode Backpropagation

Transformator distribusi adalah salah satu instrument penting dalam penyaluran listrik ke konsumen. Selain penggunaan normal, kondisi gangguan pada transformator dapat menyebabkan menurunnya umur transformator sehingga kinerja transformator tidak optimal sampai batas umur operasinya. Oleh karena itu penting sekali dilakukan menghitung sisa umur transformator. Tahapan yang dilakukan adalah menghitung sisa umur transformator menggunakan standar IEC 60076-7.  Selanjutnya dilakukan prediksi sisa umur transformator menggunakan backprogation. Parameter-parameter yang diperlukan untuk penelitian ini pembebanan dan umur transformator. Pengukuran arus transformator distribusi dilaksanakan di Surabaya Utara dengan rating 20 KV / 380-220 Volt. Nilai pembebanan transformator yang dilakukan selama 24 jam merupakan data latih dan data testing pada backpropagation. Hasil simulasi backpropagation untuk memprediksi sisa umur mendapatkan nilai rata-rata akurasi dari komposisi I sebesar 97.81 %, komposisi II sebesar 96.94%

Investigasi Tingkat Kerawanan Gedung Dalam Rangka Implementasi Mitigasi Gempa Bumi di Fakultas Teknik Universitas Mataram

After the 2018 Lombok Earthquake, buildings at the Faculty of Engineering, University of Mataram (FT Unram) were severely damaged and there was no comprehensive treatment. Facilities for earthquake mitigation are still minimal and if available they are not designed and placed properly. Some parts of the building have been renovated but appear unfinished, while there were new constructions that have not been investigated regarding their suitability with earthquake mitigation. Anticipating re-occurrence of a major earthquake in Lombok, it is very necessary to implement earthquake mitigation at FT Unram. In this paper, results of vulnerability investigation of each building will be presented including number, type, and level of damages as well as their locations and detailed documentation of each point of damages in form of pictures and photos/videos. Through this activity vulnerabilities of each building can be identified comprehensively, so that appropriate treatments can be carried out, and the safest evacuation route can be planned as well

Classification Method in Fault Diagnosis of Oil-Immersed Power Transformers by Considering Dissolved Gas Analysis

Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers

Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator

Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode Deep Learning-LSTM. LSTM adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah Root Mean Squared Error (RMSE) dan Squared Correlation (SC). Selain LSTM, penelitian ini juga menerapkan algoritma Multilayer Perceptron, Linear Regression, dan Gradient Boosting Regressor sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode Deep Learning-LSTM mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai Squared Correlation= 0,9690. AbstractThe quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690

Analisis Temperatur Untuk Monitoring dan Diagnosis Kondisi Kesehatan Transformator Distribusi Berbasis Kecerdasan Komputasional

Seiring meningkatnya beban daya listrik dan usia peralatan, memperpanjang masa manfaat transformator daya telah menjadi salah satu aspek terpenting untuk meningkatkan masa pakai infrastruktur sistem tenaga listrik, sambil mempertahankan keandalan sistem. Oleh karena itu pemantauan dan diagnosis transformator daya menjadi semakin penting, dimana pemantauan adalah pengumpulan data yang relevan selama layanan (on-line) atau selama periode pemeliharaan atau pengujian (off- line). Penelitian ini menerapkan beberapa metode pemantauan populer pada transformator menggunakan data Dissolved Gas analysis (DGA), temperatur dan parameter elektrik berbasis kecerdasan komputasional. Perbandingan beberapa metode untuk mendeteksi gangguan dengan data DGA menunjukkan bahwa Artificial Neural Network (ANN) lebih akurat dibandingkan dengan metode Decision Tree dan Random Forest. Pemodelan temperatur minyak transformator (top-oil) berdasarkan arus, pembebanan, dan faktor daya menggunakan Backpropagation Neural Network (BPNN) dan Radial Basis Function Neural Network (RBFNN) menghasilkan prediksi temperatur minyak transformator. Penelitian ini diterapkan pada transformator dengan kapasitas yang berbeda, dengan melakukan pelatihan dan pengujian data yang diverifikasi dengan data temperatur top-oil yang tersedia. Simulasi untuk memprediksi masa pakai transformator menggunakan Nguyen-Widrow Neural Network juga dilakukan dengan menggunakan data arus, temperatur, dan umur pada transformator. Data pelatihan dan pengujian adalah PSD (power spectral density), dan nilai energi yang dihasilkan dari proses wavelet, hasilnya menunjukkan bahwa metode algoritma Nguyen-Widrow dapat memprediksi masa pakai transformator lebih baik daripada BPNN. Pendekatan Extreme Learning Machine (ELM) untuk mengekstrak informasi maksimum dari transformator dapat memberikan perkiraan sisa umur transformator. Uji simulasi menunjukkan bahwa metode ELM menghasilkan MSE dan MAE terendah mengungguli metode prediksi lainya. Seluruh metode yang diterapkan menunjukkan bahwa metode kecerdasan komputasional dapat melakukan klasifikasi dan prediksi sisa umur yang berguna untuk proses monitoring dan diagnosis kondisi kesehatan transformator distribusi ====================================================================================================================================== As power loads increase and equipment life increases, extending the useful life of power transformers has become one of the most important aspects of increasing the service life of power system infrastructure, while maintaining system reliability. Therefore the monitoring and diagnosis of power transformers is becoming increasingly important, where monitoring is the collection of relevant data during service (on-line) or during periods of maintenance or testing (off-line). This study applies several popular monitoring methods to transformers using Dissolved Gas analysis (DGA) data, temperature and electrical parameters based on computational intelligence. Comparison of several methods for detecting disturbances with DGA data shows that the Artificial Neural Network (ANN) is more accurate than the Decision Tree and Random Forest methods. Modeling transformer oil temperature (top-oil) based on current, loading, and power factor using Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) produces predictions of transformer oil temperature. This research was applied to transformers with different capacities, by conducting training and data testing which was verified with the available top-oil temperature data. Simulations to predict the lifetime of a transformer using the Nguyen-Widrow Neural Network are also carried out using current, temperature, and age data on the transformer. The training and testing data are PSD (power spectral density), and the energy values generated from the wavelet process, the results show that the Nguyen-Widrow algorithm method can predict the life of a transformer better than BPNN. The Extreme Learning Machine (ELM) approach to extract maximum information from the transformer can provide an estimate of the remaining life of the transformer. Simulation tests show that the ELM method produces the lowest MSE and MAE outperforming other prediction methods. All the methods applied show that the computational intelligence method can classify and predict the remaining life that is useful for monitoring and diagnosing distribution transformer health condition

Prediksi Kebutuhan Data Mahasiswa Untuk Kuliah Daring Kondisi Covid-19 Di Jurusan Teknik Elektro Universitas Mataram: Prediction of Student Data Needs for Online Lectures Covid-19 Conditions in the Department of Electrical Engineering, University of Mataram

In the covid-19 condition, lectures at the Department of Electrical Engineering, Mataram University changed from a face-to-face process to via the Internet. T here will be a very sharp increase in demand. The use of data initially provided by the University of Mataram using a free hotspot network turned into a burden on lecturers and students. This research was conducted by sampling, general compulsory subjects, compulsory electrical courses, and compulsory expertise subjects. The distribution of variations of students domiciled in the City of Mataram and the other place coverage Lombok Island, within NTB and outside NTB. The results obtained are as follows: students who still survive in Mataram City are 17% (10.5 GB), Lombok Island 48% (8.1 GB), outside Lonbok Island 27% (4.8 GB), and outside NTB 8% (15 GB). Keyword : covid-19; lectures; onlin

Implementation of earthquake mitigation at the engineering faculty, university of mataram

After the 2018 Lombok earthquakes, buildings within the Engineering Faculty of the University of Mataram (FT Unram), were severely damaged and there have been no comprehensive treatments. Facilities to support earthquake mitigation, such as signs for evacuation routes and assembly points (temporary evacuation sites), are still minimal, and even if they are available many of them are not designed and placed properly. Some parts of the buildings have been partially renovated, meanwhile, there are new buildings constructions that have not been investigated related to disaster mitigation. Anticipating the re-occurrence of big earthquakes in Lombok, it is very necessary to carry out activities for implementing earthquake mitigation at FT Unram. The activities are divided into three stages, namely first: conducting direct reviews for inventory and documentation of all vulnerable points, followed by making of maps and building plans; second: determining the proper locations of assembly points, creating labels and signs for evacuation routes and assembly points, and making tutorial videos on safety instructions; third: socialization to the policymakers (Faculties and Department’s officials), and outreach as well as evacuation drill for all academic communities. The expected output of these activities is increasing of understanding and skills of the academic community in conducting disaster mitigation at their workplaces and application of technology as well as recommendations for disaster risk reduction policies at the faculty and university level