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All Journal Technologic Jurnal Rekayasa elektrika
Hendhi Hermawan
Politeknik Elektronika Negeri Surabaya
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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AKUISISI DATA LOAD CELL MENGGUNAKAN INSTRUMENTATION AMPLIFIER PADA PROTOTYPE SEPATU GAIT ANALYSIS Eka Samsul Maarif; Hendhi Hermawan; Ahmad Zainudin
Technologic Vol 6, No 1 (2015): Technologic
Publisher : Politeknik Manufaktur Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/t.v6i1.69

Abstract

Gait analysis atau disebut juga dengan analisa langkah kaki adalah salah satu bagian penting dari proses rehabilitasi dari pasien yang terkena stroke atau pasca amputasi. Salah metode analisa langkah kaki adalah dengan memantau gaya tekan yang dihasilkan oleh kaki pasien. Sensor yang dapat digunakan sebagai alternatif pembacaan gaya tekan pada langkah kaki pasien adalah load cell. Load cell memiliki perubahan tegangan yang sangat kecil saat diberi beban sehingga memerlukan penguatan agar perubahan tegangan tersebut dapat dibaca dan ditampilkan dalam grafik. Penelitian ini mengajukan sebuah metode akuisisi data dari pembacaan sensor load cell yang diterapkan pada prototype sepatu gait analisis. Instrumentation amplifier yang dirancang sebagai penguat dalam penelitian ini berhasil memberikan penguatan hingga 2052 kali dan berhasil menampilkan besaran digital dari gaya tekan pada setiap sensor.
Power Consumption Predictive Analytics and Automatic Anomaly Detection Based on CNN-LSTM Neural Networks Arif Irwansyah; Effry Muhammad; Firman Arifin; Budi Nur Iman; Hendhi Hermawan
Jurnal Rekayasa Elektrika Vol 19, No 4 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i4.31695

Abstract

In this modern era, electrical energy plays a crucial role in human life, as it is essential for most household appliances. The number of appliances requiring electrical energy increases each year, meeting the growing needs of users. However, electricity consumers tend to forget this fact and only realize its importance when they receive a significantly increased monthly electricity bill or face problems caused by anomalies in electricity use. Such anomalies can lead to substantial losses, especially when electrical equipment is damaged or left switched on without awareness. To make better decisions in such situations, real-time and accurate information is necessary, which can be achieved through data analytics utilizing machine-learning and predictive analytics. The purpose of this paper is to introduce the CNN-LSTM method of data analytic modeling for power consumption data collected through an electric data logger, which can help predict future power usage and detect real-time anomalies in the power network. The proposed model was tested using hourly electricity consumption data, and the results showed that the CNNLSTM method outperformed the LSTM model. The CNN-LSTM model had a 29% smaller Mean Squared Error (MSE) score than the LSTM method.
Co-Authors Arif Irwansyah Budi Nur Iman Effry Muhammad Eka Samsul Maarif Firman Arifin Yoga Yuniadi