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Universitas Informatika Dan Bisnis Indonesia, Indonesia
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Journal : Journal Of Information System And Artificial Intelligence

 1 
Anomaly Detection in Walking Data Using Isolation Forest: An Unsupervised Learning Approach Nur, Nur Alamsyah
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.235

Abstract

Detecting anomalies in walking data is crucial for ensuring data quality in wearable devices and understanding irregular physical activity patterns. Traditional methods often rely on labeled data, which is scarce in real-world applications. This study presents an unsupervised learning approach using Isolation Forest to detect anomalies in walking datasets. The data, comprising features such as step count, distance, and time, was preprocessed and analyzed to identify patterns and deviations. Isolation Forest was employed due to its efficiency in handling high-dimensional data and its ability to separate anomalies without prior labeling. The model successfully detected 5 anomalous data points out of the dataset, with anomaly scores ranging from -0.15 to 0.2. These outliers corresponded to extreme walking patterns, such as unusually high step counts with disproportionate time and distance. Visualization of anomaly scores and statistical evaluations validated the model's effectiveness, showing clear distinctions between normal and abnormal data. The proposed approach highlights the potential of Isolation Forest in improving data quality and enabling real-time anomaly detection in fitness tracking applications. This work contributes to the broader field of unsupervised anomaly detection by demonstrating a scalable and effective method for handling real-world activity data.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Ahmad Fauzi Ramadhan Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Alif Januantara Prima Amos Duan Nugroho Anto Widianto Ardiansyah, Fachrizal Ari Rizki Fauzi Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Chery Cardinawati Sitohang Danestiara, Venia R Dani Rizky Zaelani Darsiti . Dirham Triyadi Dirham Triyadi Erpurini, Wala Fahmi Abdullah Fauzi Ramadhan, Ahmad Fikri Rizqillah Hasani Fitri Kinkin Gelar, Trisna Gunthur Bayu Wibisono Habibi, Chairul Hamzah, Encep Hani Fitria Rahmani Hasan Nuraripin Hernawan, Kartika Nursyabanita Ilham Ramadhan Ismi Nur Muhamad Jennifer Kaunang, Valencia Claudia Karlina, Nichi Hana Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Muhammad Noerhadi Muhammad Rizki Ramadhan Nasution, Vani Maharani Niqotaini, Zatin Nur Alamsyah NUR ALAMSYAH Nur Alamsyah Nur Alamsyah, Nur Nursyanti, Reni PARAMA YOGA, TITAN R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah R. Yadi Rakhman Alamsyah Raka Deny Abdi Putra Rakhman Alamsyah, Rd. Yadi Rd. Yadi Rakhman Alamsyah Rd. Zidni Rizan Al-Zhahir Yanuar Reni Nursyanti Reni Nursyanti Reni Nursyanti Reynaldy Gimnastiar Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Setiana, Elia Silvana Anggraeni, Zulmeida Sophian Ramadhan Suci Fitriani Setiawan Tarsinah Sumarni Tiara Permata Hati Titan Parama Titan Parama Yoga Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Venia Restreva Danestiara Wulandari Wulandari Yoga Rizki Rahmawan Zein Suna Arfigan Said