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All Journal TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Journal of Students‘ Research in Computer Science (JSRCS)
Inna Ekawati
Universitas Islam 45 Bekasi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Deteksi Denyut Jantung Embrio Ikan Oryzias Celebencis Menggunakan Dekomposisi Kuantil Abu-Abu Inna Ekawati; Annisa Firasanti
TELKA - Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol Vol 9, No 1 (2023): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v9n1.62-73

Abstract

Studi ini mengembangkan pendekatan untuk mengekstrak ciri warna sebagai referensi utama untuk mendeteksi area objek yang bergerak dalam video. Video dalam proyek ini memuat rekaman denyut jantung ikan Oryzias Celebencis yang masih dalam rupa embrio. Metode yang diusulkan pertama-tama melakukan penapisan frame berwarna dalam ruang RGB memakai filter median dan bilateral. Lalu, frame yang telah ditapis menjalani proses konversi ke ruang warna abu-abu. Pada titik ini, frame keabuan akan diurai memakai pendekatan kuantil, ke segmen-segemen warna pembentuknya. Segemen ini dideteksi keberadaan pergerakan objek didalamnya sekaligus disertai dengan perhitungan denyutannya yang timbul berdasarkan pelebaran/penyusutan region objek saat proses pemompaan darah oleh jantung. Metode yang diajukan mampu mengekstrak objek bergerak dalam video dengan IoU sebesar 85% luasan area objek yang bergerak. Penelitian ini berhasil mendeteksi jantung dengan MSE sebesar 0,5; lebih kecil jika dibandingkan dengan rata-rata intensitas objek bergerak yang deteksi oleh software imageJ yang mendapatkan nilai MSE sebesar 396,625. Metode yang diusulkan mampu mengurangi pembentukan puncak palsu dalam grafik puncak/lembah denyutan jantung. This study develops an approach to extract colour characteristics as the primary reference for detecting areas of moving objects in video. The video in this project contains recordings of the heartbeat of the Oryzias Celebencis fish, which is still in the form of an embryo. The proposed method first performs filtering of coloured frames in RGB space using median and bilateral filters. Then, the filtered frame converts to a grey colour space. The grey frame will be decomposed using a quantile approach into its constituent colour segments. This segment detects the presence of the object's movement and the calculation of its pulsation that arises based on the pulsation of the object's region during the process of blood pumping by the heart. Our method can extract moving objects in video with an IoU of 85% of the moving object area. We have detected a heart with an MSE of 0.5, more minor than the average intensity of moving objects detected by the ImageJ software, which obtained an MSE value of 396,625. Our proposed method can reduce the formation of false peaks in the heart rate peak/valley graph.
Deteksi Emosi Menggunakan Convolutional Neural Network Berdasarkan Ekspresi Wajah Inna Ekawati; Fadilla Nidya Riyanto Putra; Malikus Sumadyo; Retno Nugroho Whidhiasih
Journal of Students‘ Research in Computer Science Vol. 5 No. 1 (2024): Mei 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/h0kayy31

Abstract

Facial expression recognition is an effective method for identifying someone's emotional expression. Emotional expressions can be recognized from changes in facial expressions, wrinkles on the forehead, blinking of the eyes, or changes in facial skin color. Facial expressions that a person generally has, such as neutral, angry, happy expressions. The problem that often occurs is the subjective assessment of a person's expression. This research examines how artificial intelligence can recognize facial expressions. The facial recognition process in the research uses a Convolutional Neural Network (CNN), which is a deep learning method capable of carrying out an independent learning process for object recognition, object extraction and classification and can be applied to high resolution images that have a nonparametric distribution model. The two main stages in CNN are feature learning and classification. The results of facial expression recognition can be used to detect a person's emotions. This research uses the FER2013 dataset which contains images of happy, sad, angry, afraid, surprised, disgusted and neutral emotions. The data set in the research received tests that had been carried out, namely the percentage of accuracy level in the model was 76%. It is hoped that the classification of emotions resulting from this research can contribute to the development of artificial intelligence technology and as a tool in various fields such as psychology, education and others. For further research, it can be developed further by adding other architectures such as VGG19, MobileNet, and ResNet-50 so that the resulting CNN model is more optimal.
Co-Authors Annisa Firasanti Fadilla Nidya Riyanto Putra Malikus Sumadyo Retno Nugroho Whidhiasih