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All Journal JURNAL TEKNOLOGI LINGKUNGAN Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) IJOG : Indonesian Journal on Geoscience ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Panrita Abdi - Jurnal Pengabdian pada Masyarakat Jurnal Teknologi dan Sistem Komputer JETT (Jurnal Elektro dan Telekomunikasi Terapan) JMM (Jurnal Masyarakat Mandiri) Jurnal Otomasi Kontrol dan Instrumentasi Journal of Health Science and Prevention ComTech: Computer, Mathematics and Engineering Applications TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Indonesian Journal of electronics, electromedical engineering, and medical informatics Sawala : Jurnal pengabdian Masyarakat Pembangunan Sosial, Desa dan Masyarakat Engineering Science Letter eProceedings of Engineering Jurnal Rekayasa elektrika Serambi Engineering SWAGATI: Journal of Community Service Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Husneni Mukhtar
Universitas Telkom
Religion Arts Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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RANCANG BANGUN SISTEM PREDIKSI USIA BERJALAN DARI GAYA BERJALAN (GAIT) MANUSIA MENGGUNAKAN METODE K-NEAREST NEIGHBORS Hafidh Al Asad; Husneni Mukhtar; Dien Rahmawati
TEKTRIKA Vol 7 No 1 (2022): TEKTRIKA Vol.7 No.1 2022
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v7i1.5438

Abstract

Penelitian terkait gaya berjalan manusia (human gait) masih terus menarik minat para peneliti sampai saat ini, di mana manfaatnya tidak lagi hanya melihat karakteristik dan indikasi fisiologi pada manusia, namun juga telah merambah ke era digitalisasi berteknologi tinggi dengan melakukan analisis gaya berjalan untuk berbagai aplikasi seperti biometrik, otentikasi, dan berbagai keperluan analisis lainnya. Analisis gaya berjalan untuk memprediksi usia manusia adalah salah satu pengembangan riset human gait, terutama menggunakan sistem instrumentasi dan pembelajaran mesin. Penelitian ini menggunakan sensor inersia dan metode K-Nearest Neighbors (K-NN) dengan ekstraksi fitur mean dalam memprediksi usia berjalan seseorang dari gaya berjalannya. Sebanyak 25 partisipan telah direkam data kecepatan berjalannya dengan tiga kelompok usia, maka prediksi usia seseorang telah berhasil diperoleh dengan akurasi sebesar 86,9% dengan menggunakan 80% data latih dan 20% data uji, sedangkan metode pengklassifikasi K-NN dipilih berdasarkan hasil nilai cross validation (CV) terbaik dibandingkan metode lainnya. Kata Kunci: walking age, K-Nearest Neighbors (KNN), gaya berjalan, sensor inersia, klasifikasi
PENGEMBANGAN PENGENALAN AKTIVITAS MANUSIA UNTUK LANSIA BERDASARKAN NILAI AKSELEROMETER DAN FITUR STATISTIK Azzahra Nadya Kahpiasa; Istiqomah Istiqomah; Husneni Mukhtar
TEKTRIKA Vol 8 No 1 (2023): TEKTRIKA Vol.8 No.1 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i1.6669

Abstract

Elderly people are those who are 60 years of age or older. Elderly people are more likely to fall due to age-related reductions in physiological processes, particularly bone and muscular functions. Falling is one of the symptoms that can be lethal. These effects may cause mortality if prompt medical assistance is not received due to the deterioration in numerous organ functions required to maintain body homeostasis. Previous studies have tested the Random Forest model from acceleration and gyroscope measurements to identify human activity. In this research, feature extraction was carried out utilizing variables such as maximum, minimum, mean, median, kurtosis, skewness, and variance that were obtained from the Acceleration data from accelerometer sensor in type IMU LSM9DS1. The Acceleration data include Acceleration X, Acceleration Y, Acceleration Z and Acceleration Magnitude. To evaluate the effectiveness of the Decision Tree model, cross-validation will be employed. The best feature extraction values were Magnitude Acceleration Variance, X Acceleration Maximum, Magnitude Acceleration Maximum, Z Acceleration Median, and Z Acceleration Variance, with a Decision Tree model accuracy rate of 99.8%. Key Words: Elderly, Fall, Tendency to Fall, Decision Tree, Statistical Feature
PENGEMBANGAN PENGENALAN AKTIVITAS MANUSIA UNTUK LANSIA BERDASARKAN NILAI AKSELEROMETER DAN FITUR STATISTIK Kahpiasa, Azzahra Nadya; Istiqomah, Istiqomah; Mukhtar, Husneni
TEKTRIKA Vol 8 No 1 (2023): TEKTRIKA Vol.8 No.1 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i1.6669

Abstract

Elderly people are those who are 60 years of age or older. Elderly people are more likely to fall due to age-related reductions in physiological processes, particularly bone and muscular functions. Falling is one of the symptoms that can be lethal. These effects may cause mortality if prompt medical assistance is not received due to the deterioration in numerous organ functions required to maintain body homeostasis. Previous studies have tested the Random Forest model from acceleration and gyroscope measurements to identify human activity. In this research, feature extraction was carried out utilizing variables such as maximum, minimum, mean, median, kurtosis, skewness, and variance that were obtained from the Acceleration data from accelerometer sensor in type IMU LSM9DS1. The Acceleration data include Acceleration X, Acceleration Y, Acceleration Z and Acceleration Magnitude. To evaluate the effectiveness of the Decision Tree model, cross-validation will be employed. The best feature extraction values were Magnitude Acceleration Variance, X Acceleration Maximum, Magnitude Acceleration Maximum, Z Acceleration Median, and Z Acceleration Variance, with a Decision Tree model accuracy rate of 99.8%. Key Words: Elderly, Fall, Tendency to Fall, Decision Tree, Statistical Feature
TTGO LORA ESP32: SOLUSI NIRKABEL UNTUK PROTOTIPE PENGENDALIAN PENGINJEKSI ARUS PADA METODE GEOLISTRIK Nurpadillah, Sifa; Susanto, Kusnahadi; Mukhtar, Husneni; Cahyadi, Willy Anugrah; Ikhsan, Akhmad Fauzi; Nurdin, Agung Ihwan; Razzak, Taufiq Abdul; Rahmawati, Dien
TEKTRIKA Vol 8 No 2 (2023): TEKTRIKA Vol.8 No.2 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i2.6864

Abstract

Rock resistivity properties obtained using the geo-electric method are used to estimate subsurface soil structures and rock types in geophysical exploration. The data collection process using this geoelectrical method is often carried out in remote areas by installing current electrodes (AB) and potential electrodes (MN) up to 1.5 km away. The success of exploration is highly dependent on the current injection source used. The greater the current used, the deeper exploration that can be carried out. However, the required distance between AB and MN electrodes is getting further and the ABMN electrodes are connected by a cable. This causes the process of collecting data at further distances of electrodes to become more complex. This study discusses a current source prototype for geo-electric methods that can communicate wirelessly with TTGO LoRa ESP32 as a controller. This prototype provides three injection power options: full, medium, and low. Current injection is carried out on a dummy resistor, which acts as earth. When injecting current into the dummy resistor, current measurements obtained using the INA219 sensor gave very consistent measurement results because it had a relative error of < 1% (on 50 ? and 300 ? dummy resistors). When measuring a current of 0.0066 A, the relative error increases to 1.5%. But it still shows good consistency in the INA219 measurement results. In addition, the MAPE for all measurements is within < 10%, which means that the INA219 readings provide excellent results. Key Words: current injector, geo-electrical, INA219, voltage divider, TTGO LoRa.
Co-Authors Abdi JakaSumarimby Achmad Rizal Ahmad Akbar Khatami Ahmad Alfi Adz Dzikri Alia, Fenty Alief Fikri Sukarno, Muhammad Andi Majesta, I Made Arib Bady Hakim Tanjung Arik Geraldy Fauzi, Muhammad Asep Harja Asep Harja, Asep Asep Mulyana Aziz, Burhanuddin Azzahra Nadya Kahpiasa Bambang Setia Nugroho Basuki Rahmat Bena Bimantara, Wayan Abin Dandi Trianta Barus Desri Kristina Silalahi Dien Rahmawati Diena Yudiarti Doan Perdana Eka Afrima Sari Eka Afrima Sari, Eka Afrima Erwin Susanto Faisal Budiman Faris Fadhlur Rachman Fathonah, Indra Wahyudin Fenty Alia Fikri Ardian GERALDI, ARIK Ghibran Herlangga Zahra Rievansa Hafidh Al Asad Heni Pujiastuti Hesty Susanti Ig Prasetya Wibawa Ikhsan, Akhmad Fauzi Istiqomah istiqomah istiqomah Kahpiasa, Azzahra Nadya Kusna Susanto Kusnahadi Susanto Ledya Novamizanti Lovindo Nulova Makrus, Amar Chairil Mas Rizky A.A. Syamsunarno Maudina Citra Febriani Ma’arif, Farham Rezqi Mochamad Yudha Febrianta Muhammad Hablul Barri Muhammad Rayhan Ghifari Nadia Husnul Nasrullah, Muhammad Rafy Nicola Akmal Afrinaldi Nurdin, Agung Ihwan Parman Sukarno Prameswari Diahasna Tsany - Prastha Giriwara, I Made Pratama, Putu Yoga Ady Razzak, Taufiq Abdul Salam, Zulfikar Sheizi Prista Sari Sifa Nurpadillah Sindiarti, Ardila Siregar, Christian Halomoan SUDIYONO, OOY ARIE Sukmajaya, Jammy Suryo Adhi Wibowo Suto Setiyadi Suyoso, Qozein Arif Syah Putra, Heru Teuku Zulkarnain Muttaqien Wahmisari Priharti Wahyu Gunawan Widiyasari, Diyah Wijaya, Adhi Dharma Surya Willy Anugrah Cahyadi Wiyadhi, Muhammad Danung Putra Yakobus Yulyanto Kevin Yanti Rubiyanti Yoga Pujiraharjo Yogi Febrian Nursyamsa Yulius Anggoro Pamungkas Zaelani, Muhammad Mugni