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All Journal AGRIKA Journal The Winners JOIV : International Journal on Informatics Visualization Jurnal Ilmu dan Riset Akuntansi Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Review Pendidikan dan Pengajaran (JRPP) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Ilmu Manajemen Terapan (JIMT) Indo-MathEdu Intellectuals Journal Indonesian Journal of Education Methods Development Jurnal Locus Penelitian dan Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Sains Data Jurnal Studi Matematika dan Teknologi Jurnal Pemberdayaan Masyarakat dan Komunitas
TEGUH ARIFIANTO, TEGUH
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Journal : JOIV : International Journal on Informatics Visualization

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Human Facial Pattern Shape Classification Using a Retraining Strategy and Convolutional Neural Network Architecture Hidayat, Tonny; Istiqomah, Dewi Anisa; Arifianto, Teguh
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3471

Abstract

Many shapes and patterns on the human body might be considered a person's uniqueness or feature since they differ significantly from one another, one of which is the shape of the face. In computer vision, the shape of a face is divided into five fundamental shapes. The experiment in this paper provides a model based on the final layer of the results of retraining InceptionV3, a Convolutional Neural Network (CNN) architecture for classifying human face photos. Inspired by human neural networks, this method generally works well for face recognition and computer vision research. This research begins with the stages of data acquisition, data exploration, classification, and evaluation. Retraining is performed to improve accuracy using the distance and angle of facial landmarks. The results are compared to other classification methods, including linear discriminant analysis (LDA), support vector machine with a linear kernel (SVM-LIN), support vector machine with a radial basis function kernel (SVM-RBF), artificial neural networks or multilayer perceptrons (MLP), and k-nearest neighbors. The facial dataset used consists of 747 photos, divided into five categories: oval, round, square, heart, and oblong. The Canny edge detector approach is utilized to enhance CNN accuracy, which has been effectively improved through training and testing. The maximum accuracy achieved was 91.7% based on training and testing at 85%-98%. This demonstrates that the outcomes of inceptionV3 retraining may appropriately adapt training data and outperform alternative classification techniques without the need to specify the function of certain features during the model training process.
Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification Hidayat, Tonny; Astuti, Ika Asti; Yaqin, Ainul; Tjilen, Alexander Phuk; Arifianto, Teguh
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1743

Abstract

The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.
Co-Authors Ahmad Tohir Ainul Yaqin Aldo, Novian Alexander Phuk Tjilen Alfian Yuda Prasetiyo Alfian, Zhevin Amalul Arifidin, M. Afif Anastasia Lidya Maukar Apollo Berlian, Muhamad Haikal Brave A. Sugiarso Churniawan, Erifendi Damar Isti Pratiwi Dewi Anisa Istiqomah Fayola, Ayyesha Dara Fayyaza, Talitha Aqila Febrian, Wenny Desty Fikria, Ainun Fitriah Handayani Gunawan, Tedi Hanafiah Hanafiah, Hanafiah Haryanto, Kurniawan Wahyu Haryanto, Sri Haryati Haryati Hikmah, Nurul Hikmah I Putu Agus Dharma Hita Ika Asti Astuti Intan Kamala Aisyiah Irawan, Fajar Rayhan Putra Irwan Moridu legito, Legito Liawatimena, S. Liza Husnita Malaiholo, David Marlina Marlina Marzuki Marzuki Masdini Agustriana, Titiek Maya, Siti Mayasari Mayasari, Mayasari Merakati, Indah Nainggolan, Hermin Narasiang, Benefit Semuel Nurnainah, Nurnainah Puspitarini*, Erri Wahyu Putri, Najwa Alya Rachmadani, Azriel Akbar Rachman, Natriya Faisal Rahardiyanto, Panca Rengganis Siwi Amumpuni, Rengganis Siwi Riana Eka Budiastuti, Riana Eka Rukiyanto Rukiyanto, Rukiyanto Safira, Radhita Azzahrani Salim, Hartono Agus Saliu, Yunina Sani, Indra Saputra, Andi Muh Akbar Sari, Windy Junita Sulistiana, Ria Supriyadi Supriyadi Syafii, Muhamad Syamsiadi, Fariz Alrifo Maulana Syatria Adymas Pranajaya Thasimmim, Said Nuwrun TONNY HIDAYAT Toto Suharjanto Utama, I Wayan Karang Utomo, Jepri Vandan Wiliyanti Wajnah, Wajnah Wardani Puruhita, Hana Wardhani, Tri Weraman, Pius Widyawati, Dini Wijaya, Dwi Aditya Wulandari, Vina Yuni Agung Nugroho Zahra, Rani Zefriyenni Zefriyenni, Zefriyenni