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All Journal Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik ITS Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Mikrotek JOIN (Jurnal Online Informatika) Sewagati
Rully Soelaiman
Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Dentistry Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : IPTEK Journal of Proceedings Series

 1 
Automatic Detection of Proliferative Diabetic Retinopathy With Hybrid Feature Extraction Based on Scale Space Analysis and Tracking Sabilla, Wilda Imama; Soelaiman, Rully; Fatichah, Chastine
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2015i1.1076

Abstract

Feature extraction is a process to obtain the characteristics or features of an object where the value of the features will be used for analysis in the next process. In retinal image, extraction of blood vessels’ characteristics can be used for detection of proliferative diabetic retinopathy (PDR). Retinal blood vessels’ features can be obtained directly with segmented image and with additional spatial method. For PDR detection, we need the suitable method that can produce maximum feature representation. This paper proposed hybrid feature extraction using a scale space analysis method and tracking with Bayesian probability. The result of the retinal images classification from STARE database using soft threshold m-Mediods classifier shows the best accuracy of 98.1%.
A comparative study of finger vein recognition by using Learning Vector Quantization Hardika Khusnuliawati; Chastine Fatichah; Rully Soelaiman
IPTEK Journal of Proceedings Series No 2 (2017): The 2nd Internasional Seminar on Science and Technology (ISST) 2016
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (871.019 KB) | DOI: 10.12962/j23546026.y2017i2.2311

Abstract

Abstract¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129.  
Co-Authors Abdul Majid Hasani ABDUL MUNIF Aby Herwendo Adhita Pratiwi Afrida Helen Ahmad Saikhu Aidy Ilmy Akbar, Rizky Januar Amalia, Ivanda Zevi Andre Setiawan Anna Kholilah Ardiana Rosita Ariadi Retno Tri Hayati Arianto Wibowo Arif Djunaidy Ary Shiddiqi Arya Widyadhana Arya Yudhi Wijaya Arya Yudhi Wijaya Baskoro, Fajar Chastine Fatichah Daniel Henry Daniel Oranova Siahaan Desy Nurbaiti Rahmi Devanda Sintaullysa Tamba Dhany Saputra Diana Purwitasari Dwi Sunaryono Dyah Sulistyo Rahayu Eddy Tjandra Eric Budiman Gosno Esther Hanaja Fajar Baskoro FX Arunanto Gracius Cagar Gunawan Hardika Khusnuliawati Hardika Khusnuliawati Hendry Setiawan I Made Agus Setiawan I Wayan Widi Pradnyana I.K. Eddy Purnama Ilham Gurat Adillion Imam Hambali Irwanto Irwanto Isye Arieshanti Ivanda Zevi Amalia Johan Varian Alfa Junian Triajianto Kadek Anggrian Mahendra Putra M Mujahidillah Maulina Nur Istiqomah Muchammad Husni Muhammad Dery Rahma Muhammad Izzuddin Muhammad Raihan Anindita Ni Made Arini WP. Nur Rosyid M Peni Sriwahyu Natasari Ramadhan Rosihadi Perdana Reva Yoga Pradana Rully A. Hendrawan Ryan Nathan Soetandyo Sarwosri Sarwosri Sheila Agustianty Suhadi Lili Tita Karlita Tohari Ahmad Tri Harsono Victor Hariadi Wahyu Suadi Wijayanti Nurul Khotimah Wilda Imama Sabilla Wiwik Anggraini Yudhi Purwananto Yuhana, Umi Laili