M. Rahmat Widyanto
University of Trunojoyo Madura

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STUDI ANALISIS EIGENFACE DAN EIGEN FUZZY SET UNTUK EKSTRAKSI CIRI BIBIR PADA SISTEM IDENTIFIKASI WAJAH Widyanto, M. Rahmat; Puspasari, Shinta
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 1, Januari 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2047.286 KB) | DOI: 10.12962/j24068535.v7i1.a58

Abstract

This paper compares the performance of eigenface and eigen fuzzy set to extract lip traits. Testing is conducted by implementing the two methods in a face identification system based on lip traits. The database used is primary data that consists of front-face image and lip image. The test result shows that eigenface is more effective with average precisionrecall value 0.22% higher. However, statistical tests show that there are no significant differences between the two methods. An optimal extraction method will be used to develop face identification system based on facial components.
PIRANTI LUNAK UNTUK ANALISIS BENTUK LENGKUNG GIGI DENGAN JARINGAN SARAF TIRUAN Widyanto, M. Rahmat; Puspasari, Shinta
Jurnal Informatika Vol 9, No 1 (2008): MAY 2008
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.686 KB) | DOI: 10.9744/informatika.9.1.8-14

Abstract

In this paper, dental arch form classification system using back propagation algorithm is proposed. Some features of dental arch are selected for neural network input based on statistical analysis to dependent variables of dental arch. The system contains some features for training and testing the neural network, and for recognizing the arch form based on input parameters. The experiment uses randomly selected data set contains 190 numerical data of upper dental arch that are extracted from dental model images. The images were obtained by scanning the original 3D dental models of Indonesian patient that were collected from some orthodontic clinics in Jakarta. This experimental result shows that 76,3158% of correctness in classifying the arch form can be reached by neural network system. The system can be applied for supporting the orthodontic treatment. Abstract in Bahasa Indonesia : Dalam tulisan ini, dipaparkan hasil pengembangan system klasifikasi bentuk lengkung gigi berbasis algoritma propagasi balik jaringan saraf tiruan. Sejumlah fitur bentuk lengkung gigi dipilih sebagai input jaringan saraf tiruan berdasarkan hasil pengujian secara statistik terhadap variabel bentuk lengkung gigi. Piranti lunak dikembangkan terdiri dari sejumlah fitur yang digunakan untuk pengujian dan pelatihan JST, serta pengenalan bentuk lengkung berdasarkan parameter input yang diberikan oleh peng-guna. Eksperimen dilakukan terhadap data numerik hasil ekstraksi citra digital model cetakan lengkung gigi rahang atas sejumlah 190 orang pasien yang diambil secara acak. Citra lengkung gigi diperoleh dengan melakukan pemindaian terhadap model cetakan lengkung gigi tiga dimensi (3D) pasien ortodonti disejumlah klinik di Jakarta. Hasil uji coba menunjukkan bahwa 76,3158% berhasil diklasifikasikan dengan benar oleh sistem berbasis JST tersebut. Ke depannya sistem akan dikembangkan lebih optimal sehingga dapat diguna- kan untuk mendukung perawatan ortodonti.