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All Journal TEPIAN 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.
Self-Calibrated IMU Footpod Development for Virtual Running and Sensor Learning Adytia Darmawan; Didik Setyo Purnomo; Zahra Rizkiyatul Ummah; Afif Nur Syafiq; Hendrik Elvian Gayuh Prasetya; Hendhi Hermawan
TEPIAN Vol. 7 No. 3 (2026): September 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i3.3977

Abstract

Low-cost inertial measurement unit (IMU) sensors can be used as footpods for virtual running and sensor learning, but speed estimation is sensitive to the sensor, mounting position, and user gait. This study develops a self-calibrated IMU footpod for estimating speed and cadence from foot motion. An ESP32-based prototype with a 6-axis IMU was mounted on the instep. The data were processed using quality control, 20-second windowing, gravity compensation, stance detection, zero-velocity update, feature extraction, and regression calibration. Eight recording sessions produced 40 valid windows at approximately 97 Hz, with 0% packet loss and no sensor saturation. Raw ZUPT estimation yielded an MAE of 3.242 km/h, whereas in-sample calibration reduced the MAE to 1.022 km/h. Cross-subject and cross-device transfer errors support the need for personal calibration. The pipeline also provides a practical learning medium for IMU calibration, filtering, drift, and wearable systems.
Co-Authors Adytia Darmawan Afif Nur Syafiq Arif Irwansyah Budi Nur Iman Didik Setyo Purnomo Effry Muhammad Eka Samsul Maarif Firman Arifin Hendrik Elvian Gayuh Prasetya Yoga Yuniadi Zahra Rizkiyatul Ummah