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All Journal Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics Fibusi (Jurnal Online Fisika) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Teknologi Indonesia Journal of Applied Data Sciences Automotive Experiences Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Arjon Turnip
Electrical Engineering Study Program, Universitas Padjajaran
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Physics

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Analysis of arrhythmia detection and classification using electrocardiogram signals with decision tree algorithm Putri Juniarti Marpaung; Nia Anggreni Matondang; Rince Margaretta Siregar; Angelica Ester Novelia Siahaan; Abdi Dharma; Arjon Turnip; Mardi Turnip
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10317

Abstract

Heart disease remains the primary cause of death globally, with arrhythmia diagnosis often limited by restricted access to medical personnel and the complexity of electrocardiogram (ECG) interpretation. Accurate arrhythmia classification is essential to prevent cardiovascular complications. The proposed method successfully categorized classify ECG signals into five categories: normal, abnormal, potentially arrhythmia, moderate arrhythmia risk, and highly potentially arrhythmia. Data were collected from 30 subjects under three activity scenarios: sitting, walking, and running. The proposed model achieved an accuracy of 99.4%, demonstrating strong potential for real-time monitoring applications. Performance evaluation was conducted using accuracy, precision, recall, and F1-score for each class. Although the dataset size remains relatively small, the findings highlight the effectiveness of decision tree as an efficient and interpretable classification method. Future research will involve validation using large-scale public databases like the arrhythmia database at MIT-BIH and comparisons with advanced methods including convolutional neural network (CNN), transformer-based models, and explainable artificial intelligent (XAI) frameworks.
An IoT-Enabled Smart System Utilizing Linear Regression for Sheep Growth and Health Monitoring Syahril Efendi; Poltak Sihombing; Herman Mawengkang; Arjon Turnip; Gerhard Wilhelm Weber
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.901

Abstract

The global livestock industry faces significant pressures from climate change, land constraints, and rising consumer demand, necessitating greater efficiency and sustainability in production. To address these challenges, there is a critical need for accessible, data-driven tools; however, accessible and individualized tools for monitoring the growth and health of livestock like sheep remain underdeveloped, limiting farmers' ability to transition from reactive to proactive management. This study developed and validated an Internet of Things (IoT) smart system for monitoring sheep using an Arduino and ESP32 platform equipped with a DHT22 sensor for temperature and humidity and a load cell for weight. Weekly weight data from 15 sheep were collected over a six-month period. Simple linear regression was then applied to model the individual growth trajectory of each animal. The IoT system was successfully implemented and deployed in a farm setting. The primary finding was that individualized linear regression models provided a highly accurate method for tracking sheep growth, with R² values consistently exceeding 99% for most animals. The system effectively delivered real-time reports on growth trajectories and health-relevant environmental conditions (e.g., temperature and humidity) to a smartphone interface, confirming its practical utility. The primary implication of this research is a validated framework for practical and interpretable precision livestock farming. The system empowers farmers to shift from reactive to proactive management by using individualized growth curves as baselines for early problem detection. This dual-function system enhances productivity through precise growth tracking while supporting animal welfare via environmental monitoring, offering a valuable tool for modern, sustainable sheep farming.
Co-Authors Abdi Dharma Abdi Dharma Aditya Rio Prabowo Amanda Khalishah Hasibuan Amri, M Faizal Angelia Ayu Chandra Angelica Ester Novelia Siahaan Antonius Suhartomo Aris Munandar Bagus Kumbara, Bagus Bhre Wangsa Lenggana Bhre Wangsa Lenggana Budiman, Bentang Arief Cecilia Sunnia Chandra, Angelia Ayu Cindy Ananda Sinaga Danasasmita, Febrianti Santiardi Darmawan, Farhan Demi Soetraprawata Demi Soetraprawata Demi Soetraprawata Edy Junaidi Edy Junaidi Efendi, Syahril Endra Joelianto Ernest Natanael Manullang Erwin Sitompul Fitrian Imaduddin Gerhard Wilhelm Weber Hanif Setya Hanandita Hariyadi, Hariyadi Herman Mawengkang Herman Mawengkang Hilman Syaeful Alam Irfan Bahiuddin Iwan R. Setiawan Iwan R. Setiawan Kacuk Cikal Nugroho Kenrick Alvaro Wijaya Kusumandari, Dwi Esti Kusumandari, Dwi Esti Le Hoa Nguyen Le Hoa Nguyen Le Hoa Nguyen Mardi Turnip Mardi Turnip, Mardi Nadana Ayzah Azis Nadiya, Ulfah Nendi Suhendi Syafei Nia Anggreni Matondang Nicco Avinta Purwanto Poltak Sihombing Poltak Sihombing Putri Juniarti Marpaung Rahmad Rizki Nur Arifin Rahmawati, Annida Rince Margaretta Siregar Rizky Cahya Kirana Saiful Amri Mazlan Santosa, Sigit Puji Shelly Iskandar Simbolon, Artha Ivonita Siti Aminah Sobana Suci Rahmalia Putri Sunnia, Cecilia Syahril Efendi Ubaidillah Ubaidillah Ubaidillah Waslaluddin MT Weber, Gerhard Wilhelm Wibowo, Jony Winaryo Wijaya, Kenrick Alvaro