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All Journal Mechatronics, Electrical Power, and Vehicular Technology JETT (Jurnal Elektro dan Telekomunikasi Terapan) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Ilmu Komputer dan Informatika Prosiding Konferensi Nasional PKM-CSR Journal of Novel Engineering Science and Technology eProceedings of Engineering
Muhammad Ridho Rosa
Universitas Telkom
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Nursing Public Health Social Sciences

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PETA JALAN PEMERATAAN LITERASI STEM MELALUI PEMROGRAMAN DAN ROBOTIKA DI LEMBAGA PENDIDIKAN DI KABUPATEN BANDUNG Romdlony, Muhammad Zakiyullah; Rahmat, Basuki; Putra, M. Darfyma; Afifah, Khilda; Rosa, Muhammad Ridho; Irsyadi, Fakih
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.1917

Abstract

Artikel ini mendiskusikan usulan metode dan peta jalan pemerataan literasi pemrograman dan robotika yang mencakup kompetensi STEM (Science, Technology, Engineering, Mathematics), di instansi pendidikan yang belum terpapar dengan sains dan teknologi, seperti pesantren, madrasah, maupun sekolah-sekolah di daerah. Hal ini sangat urgen diangkat mengingat SDM di Indonesia belum secara merata mampu menghadapi Revolusi Industri 4.0 dan perlunya penerapan yang nyata terkait amanah sustainable development goal (SDG) dalam hal pendidikan yang berkualitas dan merata. Pada artikel ini dibahas beberapa best practice dalam pelaksanaan peta jalan tersebut disertai dengan diskusi dan analisisnya. Pelaksanaan pelatihan dan pendampingan dalam rangka meningkatkan literasi pemrograman dan robotika yang dibahas mencakup kegiatan di Madrasah Aliyah Islahul Amanah, Pondok Pesantren Pembangunan Sumur Bandung, dan SMAN 1 Bojongsoang, semua berada di daerah Kabupaten Bandung. Dari ketiga kegiatan tersebut didapatkan potret terkait perlunya pendampingan secara berkelanjutan dan berkesinambungan dari perguruan tinggi dalam rangka meningkatkan literasi tersebut.
Multimodal Gait Analysis Using IMU and EMG Sensors with HMM Classification to Differentiate Obese and Normal Body Types Setiyadi, Suto; Muhammad Ridho Rosa; Nigel Bryan Tang; Muhammad Sabiq Al Muttaqin; Muhammad Rafi Haykal Gumelar
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i03.1272

Abstract

Gait analysis is essential for diagnosing movement disorders and monitoring rehabilitation progress; conventional methods are often costly and complex. This study aims to differentiate gait characteristics between individuals with obesity and those with normal body composition using a multimodal approach that integrates Inertial Measurement Unit (IMU) and electromyography (EMG) sensors. Data were collected from ten male participants (five classified as obese and five with normal body composition). IMU sensors were used to measure acceleration, angular velocity, and step count, while EMG sensors recorded muscle activity from the tibialis anterior and gastrocnemius muscles. We developed a real-time acquisition using ESP32 microcontrollers and Bluetooth Low Energy (BLE), and gait phase classification was performed using the Hidden Markov Model (HMM). Using heel-mounted sensors, the average step detection error ranged from 2.5% to 3.6%. IMU signals from obese participants indicated a shift in dominant gait phase from Initial Contact during slow walking to Loading Response during fast walking, with relative errors up to 27%. In contrast, participants with normal body composition exhibited more diverse and accurate phase distributions. EMG-based analysis provided more precise segmentation (with error rates as low as 0.47%). It revealed distinct muscle activation patterns: gastrocnemius activity was dominant during the Midswing or Midstance phases, while tibialis anterior activity peaked during Initial Contact, Initial Swing, or Loading Response. These findings suggest body composition significantly affects gait stability, phase transitions, and muscle activation patterns. Future work should explore advanced machine learning algorithms such as Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNN), integrate pressure sensors, and validate the system in real-world environments to enhance accuracy and reliability.
Co-Authors Aam Muharam Agung Surya Wibowo Amin Amin Angga Rusdinar Ashar, Muhammad Fadel Bambang Riyanto Trilaksono Basuki Rahmat Masdi Siduppa Carlo Anthony Balelang Edwin Muhammad Puji Syamsudin Eka Rakhman Priandana Erwin Susanto Estananto Faisal Budiman Fajar Nurul Hasan Fajriani, Nadia Safa Fakih Irsyadi Fakih Irsyadi Farhan Dwi Cahyo Febrian Fajar Akmal Ima Dewi Arofani Irfhando Mahendra IRSYADI, FAKIH Irwan Purnama Khilda Afifah Linahtadiya Andiani M Darfyma Putra Muhammad Anwar Mujahidin Muhammad Dani Fardiansyah Muhammad Farhan Muhammad Rafi Haykal Gumelar Muhammad Sabiq Al Muttaqin Muhammad Zakiyullah Romdlony Nigel Bryan Tang Nopendri Nopendri Novi Prihatiningrum Porman Pangaribuan Rafly Hidayatullah Rashad Abul Khayr Ridlho Khoirul Fachri Rizky, Nurrahman Sudarmono Sasmono Suto Setiyadi Syarafina Nurshadrina Putri