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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURTEKSI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
HS, Christnatalis
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Systematic Literature Review: Penggunaan Sensor dalam Deteksi Nyeri Wajah berdasarkan Database Publik Azibi, Ahmad Izzu; Hutabarat, Emy Priyanka; Tarigan, Juan Kevin Timothi; Sitorus, Zeremia Armando; HS, Christnatalis
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10282

Abstract

Deteksi nyeri objektif merupakan tantangan dalam dunia medis, terutama bagi pasien yang tidak mampu mengungkapkan rasa sakit secara verbal. Dengan kemajuan teknologi sensor dan kecerdasan buatan, sistem otomatis untuk mendeteksi nyeri berbasis sinyal fisiologis dan ekspresi wajah mulai dikembangkan. Studi ini bertujuan mengidentifikasi tren, metode, dan kualitas metodologis dari penelitian yang menggunakan database publik seperti BioVid Heat Pain, UNBC-McMaster, dan SenseEmotion dalam pengembangan sistem deteksi nyeri berbasis sensor. Penelitian dilakukan dengan pendekatan Systematic Literature Review (SLR) berdasarkan protokol PRISMA 2020 melalui pencarian artikel di Google Scholar dalam rentang tahun 2015–2024. Setelah seleksi berdasarkan kriteria inklusi dan eksklusi, 26 studi dimasukkan ke dalam sintesis naratif. Data dianalisis berdasarkan jenis sensor, metode algoritma, akurasi, dan ukuran sampel, serta dievaluasi menggunakan pendekatan GRADE. Hasil menunjukkan bahwa BioVid dan UNBC-McMaster adalah database paling sering digunakan, dengan sensor EDA, EMG, dan ekspresi wajah sebagai modalitas dominan. Metode klasifikasi umum mencakup CNN, SVM, dan Random Forest. Studi menyimpulkan bahwa pendekatan multimodal dan deep learning meningkatkan akurasi deteksi nyeri, namun validasi klinis dan perhatian terhadap keragaman demografis masih dibutuhkan.
Systematic Literature Review: Menilai Tingkat Nyeri Melalui Pola Suara Kristanti, Inka; Br Sitohang, Sondang Agustina; Margaretha, Yulia; Daya, Onita; HS, Christnatalis
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mn6wfj51

Abstract

In the medical field, Accurate detection of pain levels is a crucial aspect of healthcare, especially for patient groups who cannot directly communicate their pain, such as infants, individuals in critical condition, or those with neurological dysfunction. This study aims to test the effectiveness of a voice pattern analysis approach in detecting pain levels through a Systematic Literature Review (SLR) method. From 500 articles, 13 relevant inclusion studies were selected based on PRISMA criteria. The review results indicate that sounds such as crying and moaning can serve as objective pain indicators, and have great potential for integration into clinical systems. Supported by artificial intelligence algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), the accuracy level of pain detection based on sound reaches 83% to 96% depending on the type of data and methods. Although the results are promising, there are several challenges such as limited dataset variability, background noise interference, and the absence of a standardized voice-based pain classification. Therefore, further research is needed for direct validation of the system in clinical environments, development of classification standards, and exploration of multimodality to improve accuracy. This research is expected to serve as a foundation for the development of more objective, adaptive, and inclusive pain assessment technologies for patients with communication limitations.
Co-Authors Achmad Ridwan, Achmad Agung Prabowo Amir Mahmud Husein, Amir Mahmud Andy Salim Aqil Muhammad Arviansyah Aswani Azibi, Ahmad Izzu Bachtiar Bachtiar Bachtiar Bachtiar Bobby Christianto Tambunan Br Sitohang, Sondang Agustina Daya, Onita Delima Sitanggang, Delima Evta Indra Ginting, Riski Titian Hondro, Anugrah Prasakti Hutabarat, Emy Priyanka Josep Sutoyo Muda Lumbantobing Kristanti, Inka Manday, Dhanny Rukmana Margaretha, Yulia MHD Awal Imam Mulqan Muhammad Alvin Riady Ningot Putra Sijabat Perangin Angin, Despaleri Rizal, Reyhan Achmad Roni Rayandi Saragih Rony Rony Rony, Rony Siregar, Saut Dohot Sitompul, Toga Hasudungan Sitorus, Zeremia Armando Sozaro Lase, Christoper Darius Steven Tarigan, Juan Kevin Timothi Togar Timoteus Gultom Yennimar, Yennimar