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All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Penelitian Komunikasi dan Opini Publik SMATIKA SISFO Scientific Journal of Informatics ABDI MASSA: Jurnal Pengabdian Nasional Sewagati Jurnal Sistem Informasi
Khakim Ghozali
Departemen Sistem Informasi Institut Teknologi Sepuluh Nopember
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Journal : Scientific Journal of Informatics

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Analisis Akurasi Prediksi Perubahan Aktivitas Pada Sistem Monitoring Aktivitas Jarak Jauh Pasien Isolasi Mandiri Berbasis IOT Annisaa Sri Indrawanti; Muchammad Husni; Khakim Ghozali
Jurnal Ilmiah Informatika Vol. 7 No. 1 (2022): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v7i1.13-20

Abstract

Patients who contract the disease should avoid contact with other people. One way to do this is to self-isolate at home. The family of the patient who cares for the activities that are carried out in self-isolation to find out the condition of the patient's condition, his condition is improving or deteriorating. To avoid direct contact, the patient's activity, independently, can be monitored by remotely predicting changes in patient activity using an Internet of Things-based remote monitoring system for self-isolating patient activities. This cellular-based monitoring system uses an accelerometer sensor to retrieve data on changes in patient activity and analyzes the effect of several variations in the number of data samples and sliding-windows on the accuracy of the system in predicting changes in patient activity. Variations in the number of N samples tested were 4,6,8,10,20,30,40,50,60,70,80,90 and 100 samples, while the sliding-window N variation tested was 1 ,2,3,4,5,6,7,8,9 and 10 samples where there is a change in activity every 30 seconds for 330 seconds (10 changes in activity) for each number of N samples and N sliding windows. The results shown are N sample data = 6 providing the highest activity change prediction accuracy, amounting to 90.15%, while N sliding window data = 6 providing the highest activity change prediction accuracy, amounting to 92.72%.
Co-Authors Achmad Holil Noor Ali Agnes Lesmono Anisah Herdiyanti Annisa Sri Indrawanti annisaa sri indrawanti annisaa sri indrawanti Apol Pribadi Apol Pribadi Ari Cahaya Puspitaningrum Ari Cahaya Puspitaningrum Arief Anwar Shodiq Bekti Cahyo Hidayanto Bilqis Amaliah Cahya Yunita Dimas Pramudya Haqqi Dwi Sunaryono Eka Anis Rhofita Eko Wahyu Tyas Darmaningrat Endah Septa Sintiya Faisal Johan Fajar Baskoro Faza Murtadho Febriliyan Samopa Feby Artwodini Muqtadiroh Fikri Haykal Filiazsanti, Almira Firdausi, Hafara Ginardi, R.V. Hari Ginardi, Raden Venantius Hari Hadziq Fabroyir Hanim Maria Astuti Hanoraga, Tony Henning Titi Ciptaningtyas I Gede Arimbawa Teja Putra Wardana I Ketut Eddy Purnama Indranto, Dionisius Marcell Putra Irin, Tio Axellino Irmasari Hafidz Irzal Ahmad Sabilla Juniarun Fathurrohman Keke Mulia Rahmawati Milenia Ulwan Zafira Mohammad Al Hafidz Muchammad Husni Muchammad Husni Muchammad Husni Muchammad Husni Muhammad Irsyad Ali Nabella Desyawulansari Nasution, Hazwan Adhikara Nina Yuliastuti Purno Widodo Raden Venantius Hari Ginardi Raden Venantius Hari Ginardi Raden Venantius Hari Ginardi Radityo Prasetianto Wibowo Radityo Prasetianto Wibowo Reginia Cindy Kusuma Ridho Rahman Hariadi Rita Sari Rita Sari Rizka Wakhidatus Sholikah Rizka Wakhidatus Sholikah, Rizka Wakhidatus Rosyadi, Fuad Dary Sabilla, Irzal Ahmad Sholiq Siska Arifiani Sri Indrawanti, Annisaa Sutryotrisongko, Hatma Tony Dwi Susanto Tri Agung Wahyu Handrian Widyantari Febiyanti Wulandari, Ariani Dwi Yudho Giri Sucahyo Yudho Giri Sucahyo