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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pembangunan Wilayah dan Kota Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Yustisia Jurnal Media Hukum Al-Ihkam: Jurnal Hukum dan Pranata Sosial PADJADJARAN Jurnal Ilmu Hukum (Journal of Law) Jurnal Ilmiah Hukum LEGALITY Mimbar Hukum - Fakultas Hukum Universitas Gadjah Mada TANJUNGPURA LAW JOURNAL Jurnal Litbang: Media Informasi Penelitian, Pengembangan dan IPTEK Warta Penelitian Perhubungan Indonesian Journal of International Law Indonesia Law Reform Journal (ILREJ) Jurnal Manajemen dan Bisnis The Indonesian Journal of Computer Science
Sigit Riyanto
Department Of International Law, Faculty Of Law, Universitas Gadjah Mada
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Transportation Other

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Journal : The Indonesian Journal of Computer Science

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Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Wajah Menggunakan Penyematan FaceNet Catoer Ryando; Riyanto Sigit; Setiawardhana; Bima Sena Bayu Dewantara
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4323

Abstract

In recent years, face recognition has grown significantly in importance and popularity. Google created FaceNet, a deep learning system, in 2015, and it performs very well in creating extremely precise and personalised numerical representations of faces, or embeddings. In order to swiftly and effectively identify people, this study evaluates FaceNet's effectiveness in producing face embeddings and applies it to a variety of classification techniques, including support vector machine (SVM), decision tree, random forest, and k-nearest neighbours (KNN). A dataset with a wide range of positions, facial expressions, and lighting settings is used for the assessment. The findings of the experiment demonstrated that SVM with an radial basis function (RBF) kernel outperformed the other assessed classification techniques, achieving the maximum accuracy of 95%. These findings demonstrate the wide range of applications that face recognition technology may be used for, including identity management and security in different settings.
New Method For Classifying Heart In Multiview Echocardiographic Images Mohamad Walid Asyhari; Riyanto Sigit; Bima Sena Bayu Dewantara; Anwar
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3078

Abstract

Echocardiography is a test that uses high-frequency sound waves to describe the structure of the heart. Echocardiography is used by doctors to analyze the movement of the walls in the heart chambers and identify heart disease. Several images, including the long-axis, short-axis, 2-chamber and 4-chamber left ventricle, can be used to check heart function. Many studies that have been carried out, including cardiac evaluation, are still carried out conventionally and require a certain level of accuracy. In this research, several methods proposed to achieve object extraction are used to build a classification system, the steps start with image enhancement, segmentation, tracking, extraction, output characteristics, validation and classification. Imaging enhancement aims to improve the echocardiographic image, thereby clarifying the edges of the heart wall. In addition, the images are reprocessed to separate the left ventricle from the heart wall and generate ventricular contours, at the segmentation stage. The contours are obtained by looking for the good features on each heart wall. In this approach, good features are identified only on the first image of the left ventricular slice. The good feature points used are 24 point which will be grouped into 6 segments. In addition, all images will be processed using the optical flow method to track the movement of the walls of the heart. Optical flow tracing will generate direction and distance feature extraction values that can be used to describe the resulting data features and find a suitable classification algorithm that is combined using different validation techniques, namely K-fold and Leave-one-out. In its implementation, Classifier Support Vector Machine (SVM) with rbf core achieves the highest accuracy. The SVM classification algorithm with validation techniques, namely k-fold cross-validation and leave-one-out cross-validation, reaches an accuracy value of 100% and 100%.
Co-Authors Achmad Basuki Akbar Kurnia Putra Anwar Bima Sena Bayu Dewantara Catoer Ryando Dadet Pramadihanto Fadjar Hari Mardiansjah, Fadjar Hari Fajri Matahati muhammadin Harry Purwanto Mohamad Walid Asyhari Priyo Hadisusilo Puspoayu, Elisabeth Septin Qristin violinda Ridwan Arifin Rifaa Eka Novisna Rully Sulaiman Setiawardhana Setiawardhana Setiawardhana Sipahutar, Bernard Sonny Saptoajie Wicaksono Sutrisno, Sutrisno Wattimena, Keanu Abinuno