Shoffan Saifullah

Bio: Shoffan Saifullah is an academic researcher from Universitas Ahmad Dahlan. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 7, co-authored 18 publications receiving 128 citations.

Segmentation Comparing Eggs Watermarking Image and Original Image

Abstract: The research used watermarking techniques to obtain the image originality. The aims of the research were to identify small area in eggs properly and compared preprocessing, the methods, and the results of image processing. The study has been improved from the previous papers by combined all methods and analysis was obtained.This study was conducted by using centroid and the bounding box for determining the object and the small area of chicken eggs. The segmentation method was used to compare the original image and the watermarked image. Image processing using image data that are subject watermark to maintain the authenticity of the images used in the study will the impact in delivering the desired results. In the identification of chicken eggs using watermark image using several methods are expected to provide results as desired. Segmentation also deployed to process the Image and counted the objects. The results showed that the process of segmentation and objects counting determined that the original image and watermarked image had the same value and recognized eggs. Identification had determined percentage of 100% for all the samples.

Identity Analysis of Egg Based on Digital and Thermal Imaging: Image Processing and Counting Object Concept

Abstract: This Research was conducted to analyze the identification of eggs. The research processes use two tools, namely thermal imaging camera and smartphone camera. The identification process was done by using Matlab prototype tools. The image has been acquired by means of proficiency level, then analyzed and applied several methods. Image acquisition results of thermal imaging camera are processed using morphological dilation and do the complement in black and white (BW). While the digital image uses the merger method of morphological dilation and opening, and it doesn't need to be complemented. Labeling process is done, and the process of determining centroid and bounding box. The process has been done and it can be applied for identifying of chicken eggs with the accuracy rate of 100%. There are different methods of both images is obtained area (pixels) which is equivalent to the difference is very small as 6 x 10 -3 .

Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms

Abstract: This research presents a comparison study of Backpropagation and K-means clustering algorithms for egg fertility identification. Instead of candling the eggs manually, a smartphone camera is used for capturing an egg image, then we do the pre-processing step by performing image enhancement and gray scaling process. The feature extraction method applied in the pre-processed image is the Gray Level Co-occurrence Matrix (GLCM) with six parameters (Entropy, Angular Second Moment, Contrast, Inverse Different Moment, Correlation, and Variance). The result of GLCM’s feature extraction image will be processed using two learning algorithms: Backpropagation and K-means Clustering. For evaluation, we use 100 data samples (each in training and testing). The results show that the Backpropagation algorithm (using 12 hidden layer neurons) provides a 93% accuracy rate, while the K-means clustering algorithm presents a 74% accuracy rate. Since the Backpropagation algorithm gives better results in detecting egg fertility, as a recommendation, egg fertility identification can be performed using this algorithm.

Analisis ekstraks ciri fertilitas telur ayam kampung dengan grey level cooccurrence matrix

Abstract: This research used chicken eggs to perform the analysis in the identification process fertility chicken eggs. The method in the identification process using Gray Level Co-occurrence Matrix (GLCM). The identification process by using GLCM using the 6 main parameters to determine the characteristics (feature extraction). The parameters used are: ASM (Angular Second Moment), Contrast, Correlation, Variance, IDM (Inverse Difference Moment), and Entropy. Each parameter will give different values and is able to distinguish and classify images based fertility chicken eggs chicken eggs. The identification process gives results that the image of a chicken egg fertile and infertile able to distinguish from GLCM parameters and show that using 10 samples of chicken eggs able to be grouped based on their fertility. Keywords : Chicken Egg s , Feature Extraction, GLCM Parameter s Abstra k — Penelitian ini menggunakan telur ayam kampung untuk melakukan analisis dalam proses identifikasi fertilitas telur ayam. Metode dalam proses identifikasi menggunakan Gray Level Coocurence Materik (GLCM). Proses identifikasi dengan menggunakan GLCM menggunakan 6 parameter utama untuk mengetahui ciri-ciri (ekstraksi ciri). Parameter yang digunakan yaitu: ASM, Kontras, Korelasi, Varians, IDM, dan Entropy. Masing-masing parameter akan memberikan nilai yang berbeda dan mampu membedakan dan mengelompokkan citra telur ayam kampung berdasarkan fertilitas telur ayam. Proses identifikasi memberikan hasil bahwa citra teluer ayam kampung fertile dan infertile mampu dibedakan dengan parameter-parameter GLCM dan menunjukkan bahwa dengan menggunakan 10 sampel telur ayam kampung mampu dikelompokkan berdasaarkan fertilitasnya. Kata Kunci : Telur ayam kampung, Ekstraksi Ciri, Parameter GLCM

Perbandingan segmentasi pada citra asli dan citra kompresi wavelet untuk identifikasi telur

Abstract: Citra digital merupakan gambaran yang jelas dari objek yang dapat diolah dengan komputer. Semakin besar ukuran (pixel) citra akan membutuhkan tempat penyimpanan yang besar pula. Dasar pengolahan citra yang dilakukan dalam penelitian ini terletak pada proses segmentasi pengolahan citra. Hal yang perlu dipertimbangkan adalah objek dari citra telur ayam yang akan diidentifikasi. Proses pengolahan citra melibatkan beberapa proses mulai dari akuisisi citra, preprocessing dan proses pengolahan citra sampai hasilnya. Preprocessing dilakukan untuk proses segmentasi yaitu dengan mengubah citra menjadi citra grayscale , dan kemudian diubah menjadi citra hitam putih. Dalam setiap proses dilakukan padding haar untuk mengurangi ukuran (size on disk) dengan matrik haar 8x8. Dan juga dilakukan proses dilasi dan opening untuk membuat objek terlihat jelas serta menghaluskan permukaan untuk menghilangkan noise. Pada proses pengolahannya dilakukan dengan menggunakan segmentasi dan pelabelan dengan didahului dengan perhitungan centroid dan penentuan bounding box untuk mengidentifikasi telur ayam. Perbandingan hasil pengolahan citra asli dengan hasil kompresi dari citra asli menunjukkan bahwa proses segmentasi citra telur ayam memberikan hasil 100% sama (baik citra asli maupun citra kompresi wavelet). Dengan kompresi akan menghemat penyimpanan (disk) dan hasil yang sama diperoleh dalam proses perhitungan objek, luas area, dan penentuan titik centroid .

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone

Abstract: Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask’s micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I ‘normal use’ and the recall of type III ‘not recommended’ reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.

Segmentation Comparing Eggs Watermarking Image and Original Image

Abstract: The research used watermarking techniques to obtain the image originality. The aims of the research were to identify small area in eggs properly and compared preprocessing, the methods, and the results of image processing. The study has been improved from the previous papers by combined all methods and analysis was obtained.This study was conducted by using centroid and the bounding box for determining the object and the small area of chicken eggs. The segmentation method was used to compare the original image and the watermarked image. Image processing using image data that are subject watermark to maintain the authenticity of the images used in the study will the impact in delivering the desired results. In the identification of chicken eggs using watermark image using several methods are expected to provide results as desired. Segmentation also deployed to process the Image and counted the objects. The results showed that the process of segmentation and objects counting determined that the original image and watermarked image had the same value and recognized eggs. Identification had determined percentage of 100% for all the samples.

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone

Abstract: Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Syndrome coronaviruses 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we don't know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the GLCMs of the face mask's micro-photos. Next, a three-result detection system is accomplished by using KNN algorithm. The results of validation experiments show that our system can reach a precision of 82.87% (standard deviation=8.5%) on the testing dataset. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.