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Journal : comengapp computer engineering and applications journal

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery Muhathir Muhathir; Wahyu Hidayah; Dian Ifantiska
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (228.936 KB) | DOI: 10.18495/comengapp.v9i3.347

Abstract

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.
Co-Authors Al Khowarizmi Albar, Rizka Amri Ismail Tumanggor Andre Hasudungan Lubis Andre Hasudungan Lubis Arief Goeritno Aripin Rambe Ayu Pariyandani Azmi, Fadhillah Cahyo Hasanudin Cut Lika Mestika Sandy Cut Try Utari Deti Indah Kiranti Diah Ayu Larasati Dian Ifantiska Dina Maizana Dinur Syahputra Dwipayana, Mahendar Effiati Juliana Hasibuan Eka Pirdia Wanti Ellis Susmawati Esrayanti Simanjuntak Essay Puspita Sitopu FADHILLAH AZMI Fadli, MHD. Fajar Alry Fadlisyah Fadlisyah Fadlisyah Fadlisyah Fadlisyah Fauzi FAUZI . Fawwaz, Insidini Fitra , Akbario Gultom, William Habib Satria Hashina Qiamu Mumtaziah Hayani Wulandari Idrus, Syed Zulkarnain Syed Indra Muda Ira Safira Ira Safira Juliansyah Putra Tanjung Karynda Natalie Theofilus Leonardi Paris Hasugian M. Hamdani Santoso Maghfirah Maghfirah Mahardika Abdi Prawira Tanjung Mahmudah Salwa Gianti Marpaung, Febriady Melisah Melisah Merri Hafni Moulando Tampubolon Muchammad Takdir Sholehati Muhammad Fadlan Siregar Muhammad Fauzy Mungkin, Moranaim N P Dharshinni Nadzifah Nadzifah Nanda Novita Napisah, Napisah Nasution, Annisa Neneng Yulia Barky Noor, Fredy Nugraha Rahmadan Diyanto Nurul Khairina Pariyandani, Ayu Purba, Sentia Ovania Rahman, Sayuti Rambe, Yunita Syafitri Reydo Trisno Pangestu Reyhan Achmad Rizal Rifa Alia Syahidah Rizki Muliono Saufa Yardha Moerni Sibarani, Theofil Tri Saputra Simangunsong, Roy Candra Simanjuntak, Juan Siregar, Erlina Sri Juwita Sri Wahyuni Subairi Subairi Susilawati Susilawati Syahputra, Dinur Syahputra, Dinur Syifaul Fuada Syuhada, Rahmad Taufik Ismail Simanjuntak Taufik Ismail Simanjuntak Theofil Tri Saputra Sibarani Tika Ermita Wulandari Wahyu Hidayah Wanti, Eka Pirdia Wibawa, M. Bayu Yahya, Yanawati Binti Yanawati Yahya Yopan Rahmad Aldori Yuhefizar Yuhefizar Zebua, Meniati Zuhar Musliyana, Zuhar Zulfikar Sembiring Zulfikar Sembirirng