Algoritma Pengolahan Sinyal Photoplethysmography (PPG) Berbasis Ekstraksi Fitur dan Machine Learning untuk Sistem Pengukuran Tekanan Darah Noninvasif = Photoplethysmography (PPG) Signal Processing Algorithm Based on Feature Extraction and Machine Learning for Noninvasive Blood Pressure Measurement System

Model Antarmuka Adaptif Terintegrasi untuk Sistem Pembelajaran Daring yang Dipersonalisasi = An Integrated Model of Adaptive User Interface for Personalized E-Learning Systems

Pengembangan Alat Ukur Konsentrasi Hemoglobin Non-Invasif Menggunakan Photoplethysmography dan Berbasis Machine Learning = Development of a Non-Invasive Hemoglobin Concentration Measurement Device Based on Photoplethysmography and Machine Learning

Teknik Ekstraksi Fitur Sinyal Photoplethysmography untuk Klasifikasi Non-Invasif Kadar Glukosa Darah Menggunakan Analisis Frekuensi-Waktu dengan Bidirectional Long Short Term Memory = Feature Extraction Technique of Photoplethysmography Signals for Non-Invasive Classification Blood Glucose Level Using Time-Frequency Analysis with Bidirectional Long-Short-Term Memory

Peningkatan efisiensi dan akurasi klasifikasi tekanan darah non-invasive menggunakan metode photoplethysmograpys pulse wave analysis berbasis deep learnings bidirectional long short term memory dan time frequency analysis = Effiency and accuracy improvement of non invasive blood pressure classification of blood pressure based on photoplethysmographs signals using deep learnings bidirectional long short-term memory and time-frequency analysis / Hendrana Tjahjadi