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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah KOMPUTASI InComTech: Jurnal Telekomunikasi dan Komputer Communications in Science and Technology MIND (Multimedia Artificial Intelligent Networking Database) Journal Journal of Applied Data Sciences Proceeding International Conference on Mathematics and Learning Research Prosiding SeNTIK STI&K

Sarifuddin Madenda

Universitas Gunadarma

Author-ID : 311436

Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics

Published : 36 Documents Claim Missing Document

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Articles

Title

Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations Dewi Putrie Lestari; Sarifuddin Madenda; Ernastuti Ernastuti; Eri Prasetyo Wibowo
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 1: February 2017
Publisher : Institute of Advanced Engineering and Science

Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), True-Negative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.

Cursive Handwriting Segmentation using Ideal Distance Approach Fitrianingsih Fitrianingsih; Sarifuddin Madenda; Ernastuti Ernastuti; Suryarini Widodo; Rodiah Rodiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 5: October 2017
Publisher : Institute of Advanced Engineering and Science

Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting.

Wood Classification Based on Edge Detections and Texture Features Selection Achmad Fahrurozi; Sarifuddin Madenda; Ernastuti Ernastuti; Djati Kerami
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 5: October 2016
Publisher : Institute of Advanced Engineering and Science

One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Co-occurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.

Optic Disc and Macula Localization from Retinal Optical Coherence Tomography and Fundus Image Rodiah Rodiah; Sarifuddin Madenda; Diana Tri Susetianigtias; Dewi Agushinta Rahayu; Ety Sutanty
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

This research used images from Optical Coherence Tomography (OCT) examination as well as fundus images to localize the optical disc and macular layer of retina. The researchers utilized the OCT and fundus image to interpret the distance between macular center and optic disc in the image. The distance will express the area of macula that can be employed for further research. This distance could recognize the thickness of macula parameters diameter that will be used in localizing process of optic disc and macula. The parameters are the circle radius, the size of window’s filter, the constant value and the size of optic disc element structure as well as the size of macula. The results of this study are expected to improve the accuracy of macula detection that experience the edema.

Detection of Proximal Caries at The Molar Teeth Using Edge Enhancement Algorithm Jufriadif Na'am; Johan Harlan; Sarifuddin Madenda; Julius Santony; Catur Suharinto
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Panoramic X-Ray produces produces the most common oral digital radiographic image that it used in dentistry practice. The image can further improve accuracy compared to analog one. This study aims to establish proximal caries edge on enhancement images so they can be easily recognized. The images were obtained from the Department of Radiology, General Hospital of M. Djamil Padang Indonesia. Total file of images to be tested were 101. Firstly, the images are analyzed by dentists who practiced at Segment Padang Hospital Indonesia. They concluded that there is proximal caries in 30 molar teeth. Furthermore, the images were processed using Matlab software with the following steps, i.e. cropping, enhancement, edge detection, and edge enhancement. The accuracy rate of detection of edge enhancement images being compared with that of dentist analysis was 73.3%. In the edge enhancement images proximal caries edge can be found conclusively in 22 teeth and dubiously in eight teeth. The results of this study convinced that edge enhancement images can be recommended to assist dentists in detecting proximal caries.

Co-Authors Achmad Fahrurozi Ahmad Hidayat Antonius Angga Kurniawan Aqwam Rosadi Kardian Atit Pertiwi Aviarini Indrati Bertalya Bertalya Cahyaningtyas, Rizqia Catur Suharinto Dewi Agushinta R. Dewi Putrie Lestari Diana Tri Susetianigtias Dina Indarti Djati Kerami Eri Prasetya Wibowo Eri Prasetyo Wibowo, Eri Prasetyo Ernastuti Ernastuti Ety Sutanty Farina Pramanik Fitrianingsih Fitrianingsih Hariyanto Hariyanto Harmanto, Suryadi Hustinawaty Hustinawaty, Hustinawaty Ichsani Mursidah Ire Puspa Wardhani Johan Harlan Johan Harlan Johan Harlan Jufriadif Na`am, Jufriadif Julius Santony Khalida, Rakhmi LATIFAH Linda Wahyu Widianti Lingga Hermanto Lussiana, ETP Metty Mustikasari Muhammad Subali Prayitno, Ragiel Hadi Prihandoko . Rahayu Noveandini Rama Dian Syah Rifki Kosasih Rini Arianty Risnawati Risnawati Robby Candra Rodiah Rodiah Ruddy J Suhatril S Suryadi Setia Wirawan SHABIRA, DIYANK SIAGIAN, AL HAFIZ AKBAR MAULANA Slamet Riyanto Sumijan Sumijan Sunny Arief Sudiro Suryadi Harmanto Suryarini Widodo Susetianingtias, Diana Tri Tavipia Rumambi Tri Susetianingtias, Diana Tubagus Maulana Kusuma Wiryana , I Made

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