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Edi Satriyanto
Politeknik Elektronika Negeri Surabaya
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Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator Ningrum, Ayu Ahadi; Syarif, Iwan; Gunawan, Agus Indra; Satriyanto, Edi; Muchtar, Rosmaliati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834587

Abstract

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.
Characterization and Modeling of Pedal Torque in a Regenerative Bicycle Trainer Using Current Control Prayoga, Adi; Mauludi, Fajar; Sabilul Huda, Muhammad Ravi; Putri Herwandi, Kasih Aisyah; Darmawan, Adytia; Satriyanto, Edi; Arief, Zainal
Jurnal Mekanik Terapan Vol 7 No 1 (2026): April 2026
Publisher : Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/jmt.v7i1.8357

Abstract

Regenerative bicycle trainers support more sustainable indoor cycling by converting a rider’s kinetic energy into electrical energy while producing a controllable resistive load. For a realistic riding feel, the relationship between commanded braking current and pedal torque must be accurately defined. This study develops and validates an empirical current-torque model for a trainer based on a brushless direct current (BLDC) motor using a second-order polynomial. Experiments were conducted on two sprocket configurations (32-tooth and 12-tooth), with 11 braking current setpoints ranging from 0 to 10 A under steady-state conditions. The model was evaluated through its inverse form using five torque setpoints for each configuration. Results show strong agreement with experimental data, with coefficients of determination ( ) exceeding 0.998. The 12T configuration achieves higher accuracy, with a Mean Percentage Error of 1.55%, compared to 9.20% for the 32T configuration. This is likely due to improved torque transmission and more stable friction drive behaviour at higher loads. Negative quadratic coefficients indicate mild nonlinearities consistent with magnetic saturation. The model is suitable for feedforward control, enabling realistic torque simulation without requiring expensive external torque sensors.
Co-Authors Adi Prayoga Agustriany Lubis, Risa Indah Alde, Muhammad Riski Alfi Fadliana Ali Ridho Barakbah ANITA DAMAYANTI Ardilla, Fernando Darmawan, Adytia Dihin Muriyatmoko Edelani, Renovita Eru Puspita Fauzi Nafi'Ubadah, Kriza Febrianto, Ardiansyah Indra Fernando Ardilla Gunawan, Agus Indra Haikal Yuniarta Krisgianto, Ricko Humaira, Fitrah Maharani Insani, Fawzan Isbat Uzzin Nadhori, Isbat Uzzin iwan Syarif M Udin Harun Al Rasyid, M Udin Harun Maulana, Wahyu Ikbal Mauludi, Fajar Mohammad Isa Irawan Muhammad Syahfitra Ni Wayan Surya Wardhani Ni'Ma, Najma Akmalina Ningrum, Ayu Ahadi Putri Herwandi, Kasih Aisyah Ridho, Bistiana Syafina Risa Indah Agustriany Lubis Ronny Susetyoko Rosmaliati, Rosmaliati Rozie, Fachrul Sabilul Huda, Muhammad Ravi Saputra, Muhammad Krisnanda Vilovan Susetyoko, Ronny Syaiful Anam Tita Karlita Tri Hadiah Muliawati, Tri Hadiah Tsabita, Rania Hana Wayan Firdaus Mahmudy Zainal Arief