Articles
<![CDATA[Prediction of Coffee Bean Quality Using Segmentation Methods And K-Nearest Neighbor ]]>
<![CDATA[Agung Pradana]]>;
<![CDATA[Suhendro Yusuf Irianto]]>;
<![CDATA[Sri Karnila]]>;
<![CDATA[Hendra Kurniawan]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2021: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 7 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[The condition of people's coffee farming management is relatively poor when compared to large stateowned plantations. The main problem in smallholder plantations is the quality of the results that do not meet standardization. This study designs a system that is able to identify the quality of coffee beans using Segmentation, K-Nearest Neighbor and Gray Level Co-occurrence Matrix methods. Based on the test results using texture feature extraction, the highest accuracy was obtained at K-5 of 85%. It is possible that if the K value used is too small, there will be a lot of noise which reduces the level of accuracy in data classification, but if the K value is too large it can cause errors in the range of values taken, which will indirectly affect the level of accuracy. The results of the study were the identification of coffee beans with good quality or poor quality. It is hoped that this research can contribute to improving the quality of people's coffee so that it can increase the production of people's coffee that is able to compete in the market.Keywords—Gray Level Co-occurrence Matrix, K-Nearest Neighbor, Segmentation]]>
<![CDATA[Utilization of Content Base Image RetrievalTechnique Based Sketch for Facial Recognition ]]>
<![CDATA[Muhammad Said Hasibuan]]>;
<![CDATA[Handoyo Widi Nugroho]]>;
<![CDATA[Suhendro Yusuf]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2016: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 2 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[As the rising up Including terrorist crimes in our society due to issues politics, economy, poverty, religion and ethnic conflicts. Many ways and techniques have been tried to crack down Reviews those crimes, but unfortunately the Efforts to seize person or group of suspected criminal is far from our expectation. Face recognition is one of techniques Introduced by many Researchers for the last Decades with many methods and approaches they tried to Recognize a person based on his or his faces. Some of the methods such as face recognition with Query by Example (QBE) using shape, color, and texture to match a query face with the face in the database; however the result is not good enough to Recognize the faces. One of the problems of face recognition by QBE is sometime we do not have a picture or a face image to the make QBE. In order to sort it out the problem, in this research we will try to introduce of face recognition method by generating a face image by a face sketch.Many sketch based face recognition was Introduced by some Researchers and experts, but most of reviews their methods have been applied directly inputting a sketch into a database the which is very costly and Involved a complex algorithm. In addition to the research, we are applying our proposed method compressed into face images, as the compressed images will save storage and unsumming the algorithm. KEY WORDS: Query by Example, Face recognition, criminal ]]>
<![CDATA[THE EFFECTIVE OF IMAGE RETRIEVAL IN JPEG COMPRESSED DOMAIN ]]>
<![CDATA[Suhendro Yusuf Irianto]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2015: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 1 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[We propose a new method of feature extraction in orderto improve the effective of image retrieving by using apartial Joint Photographic Experts Group (JPEG)compressed images algorithm. Prior to that, we prune theimages database by pre-query step based on coloursimilarity, in order to eliminate image candidates. Ourfeature extraction can be carried out directly to JPEGcompressed images. We extract two features of DCTcoefficients, DC feature and AC feature, from a JPEGcompressed image. Then we compute the Euclideandistances between the query image and the images in adatabase in terms of these two features. The image querysystem will give each retrieved image a rank to define itssimilarity to the query image. Moreover, instead of fullydecompressing JPEG images, our system only needs to dopartial entropy decoding. Therefore, our proposed schemecan accelerate the effectiveness of retrieving images.According to our experimental results, our system is notonly highly effective but is also capable of performingsatisfactoril.KEY WORDS: JPEG, DC coefficient, image retrieval, compresseddomain]]>
<![CDATA[Implementation Of Content Based Image Retrieval For Search Engine ]]>
<![CDATA[Dona yuliawati]]>;
<![CDATA[Nisar -]]>;
<![CDATA[Agus Rahardi]]>;
<![CDATA[Suhendro Yusuf Irianto]]>;
<![CDATA[Arman Suryadi Karim]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2018: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 4 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[Searchengine is defined as web page or website that collects and organizes all content on the internet, here the user input a question or query, then the search engine will provide links to content that reflects what he wants. Until now, there are two problems paid attention by the experts in relation with search engines namely images in a database and the internet using text or keyword. These problems are far away from the expected. The search image by using the text or keyword is very biased and difficult to justify because the keyword is very difficult to explain the information in an image. Content base image Retrieval is method return the image or image retrieval with the use of content the content contained in the picture or image, i.e. elements that can be taken from within the image that can be converted into a value such as color, shape, texture, line and other elements. Elements that are changed are contained in the image are converted in the form of value is to calculate each element and save the value into a histogram which will be used in the comparison process image and the process returns the image or image retrieval.Keywords: Search engine, content base image retrieval, flora, fauna]]>
<![CDATA[DESIGNING AND IMPLEMENTING I-BIKE MOTORCYCLE RENTAL SYSTEM (CASE STUDY IN TIAN QI DIANDONG CHE XING, NANTONG, CHINA) ]]>
<![CDATA[Wenni Puspa Sari]]>;
<![CDATA[Suhendro Yusuf Irianto]]>;
<![CDATA[Sken Jian Tao]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2020: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 6 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[I-bike motorcycle rental was the considerable and well-known business entity in China. The problem statement of this study was that this i-bike motorcycle rental system at Tian Qi Diandong Che Xing, in Nantong, Jiangsu Province was not effective. It was because the booking system was still done manually and most of local Chinese people was not able to speak English so that it caused difficulty for foreigners who wanted to rent this i-bike motorcycle. The objective of this study was designing the website-based system for the i-bike motorcycle. The application was designed through Adobe Dreamweaver CS5 and My SQL. This application was expected to provide detailed and accurate information about this i-bike motorcycle rented to the prospective tenants online from whom the companies were assisted to promote their motorbikes to foreigners with a website-based application system in English and to process the transactions, the data management, and the company reports.Keyword: I-bike motorcycle rental, China, Adobe Dreamweaver CS5, and My SQL]]>
<![CDATA[Landcover Quality Detection Using Segmentation And Content Base Image Retrieval Methods ]]>
<![CDATA[Dwi Handoko]]>;
<![CDATA[Suhendro Y Irianto]]>;
<![CDATA[Sri Karnila]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2019: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 5 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[This paper tries to explore level and accuracy Content Based Image Retrieval method on Landsat imaginary. The images include an areal photography and land satellite photoraphy or Landsat which widely used and recognize for spatial information analysis. The analysis uses mapping of situation or state of earth’s surface, particularly the land surface. Landsat can be used to create topographic map, determining of attitude or height model of a certain place in the earth. Aerial photographs are used to detect changes of earth surface, in this work the changes by using Content Based Image retrieval or CBIR. The accuracy changes measurement calculate using precision and recall parameters. In this paper, Landsat images also used to detect the appear and dis appear of vegetation and other objects on the earth. More than 100 Landsat images used in this work, and around 15 images was use d as queries. The results show that accuracy of image retrieval is a quite good , which more 75%.Keywords: Landsat Images, CBIR, Precision Recall]]>
<![CDATA[CASE-BASED REASONING FOR PREDICTING COCOA KERNEL QUALITY ]]>
<![CDATA[Suhendro Yusuf Irianto]]>;
<![CDATA[Ahmad Rofi’i]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2020: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 6 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[Agriculture sector recently showed a decreasing trend seen on a poor quality of the harvest. The objective of this study was to measure the level of cocoa kernel quality. The method used in this study was Case-Base Reasoning (CBR) method. The data was obtained from the laboratory data. The result of this study was that the quality of cocoa kernel was AA (premium) followed with A, B, C, and S (random) qualities. It was expected that it was able to improve the quality of cocoa kernel.Keyword : CBR (Case-Based Reasoning), Cocoa Kernel, Premium.]]>
<![CDATA[Perancangan Arsitektur Enterprise Menggunakan Togaf Framework (Studi Kasus : Cv. Agung Lestari) ]]>
<![CDATA[Andreas Perdana]]>;
<![CDATA[Suhendro Yusuf]]>
<![CDATA[ Sienna Vol 1 No 1 (2020): SIENNA Volume 1 Nomor 1 Juli 2020 ]]>
Publisher : <![CDATA[LPPM Universitas Muhammadiyah Kotabumi ]]>
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DOI: 10.47637/sienna.v1i1.267
<![CDATA[CV. Agung Lestari is a company engaged in services of new vehicle’s documents. The company activities are using Information System (IS) and Information Technology (IT) and already have an application that used in Administration Division. However, in the absence of data integration and connection to information system between divisions. Unintegrated data allows for data redundancy, error, lack of data accuracy, and less efficient. Required a framework in planning, designing, and managing infrastructure called Enterprise Architecture(EA). The design in the form of System Administration, Finance Management System, Human Resource and Accounting. Data any information system is already integrated.]]>
<![CDATA[Skin Cancer Clasification Using Region Growing & Recurrent Neural Network ]]>
<![CDATA[Rian Yunandar]]>;
<![CDATA[Suhendro Yusuf Irianto]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2022: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 8 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[Skin cancer is a disease caused by mutations in the skin of cells. Melanoma and non-melanoma skin cancers are the two basic classifications for skin cancer. In addition, it is estimated that 5.9-7.8% of all cancer cases each year involve skin cancer. In Indonesia, 65.5% of skin cancers are basal cell carcinomas, followed by 23.0% squamous cell carcinomas and 7.9% malignant melanomas. When not discovered early, melanoma skin cancer can result in a high fatality rate. Basal cell carcinoma and squamous cell carcinoma are two examples of nonmelanoma skin cancers (NMSCs), which are far more frequent but far less likely to spread and cause mortality. Diagnosis made by an expert or doctor takes a long time and is often inconsistent because the environment and personal conditions influence the expert's condition. To minimize this problem, this research aims to introduce image processing methods for early skin cancer detection, the Region growing method, and artificial neural networks RNN for classification. It is hoped that this method of early cancer detection can be done quickly and does not require much money. This study will use two methods to detect skin cancer: the Region growing method and RNN-LSTM. This research aims to introduce the Region of interest (ROI) method and artificial neural networks to detect skin cancer.Keywords—Cancer skin, Region of Interest, Region Growing, Recurrent Neural Network, LSTM]]>
<![CDATA[Study of Detecting Corn Plant Leaf Disease with Fuzzy C-Means and RNN ]]>
<![CDATA[Enrico Findley]]>;
<![CDATA[Suhendro Yusuf Irianto]]>
<![CDATA[ Prosiding International conference on Information Technology and Business (ICITB) 2022: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 8 ]]>
Publisher : <![CDATA[Proceeding International Conference on Information Technology and Business ]]>
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<![CDATA[Corn is a top commodity after rice at supporting food self-sufficiency in Indonesia. However, because of corn leaf spot disease caused by plant pests the quality and quantity of corn is greatly decreasing. The problem with detecting spot-on corn leaf is required high accuracy and the plantation is huge. Therefore, this research study to determine corn leaf spot disease using Fuzzy C-Means and RNN methods. The research process in this study is first preprocessing to CIE-L*A*B* color space, next step is doing corn plant leaf disease detection with Fuzzy C-Means and RNN methods, the third step is to reconstruct the image from Fuzzy C-Means and RNN method result in grayscale level, and the last step is to evaluate the Fuzzy C-Means and RNN algorithm. In this paper only Fuzzy C-Means segmentation and training the RNN model are implemented. The result of the experiment is first from training RNN model with 80% training data and 20% testing data. The data trained for 20 epochs with 38 minutes and 1.8 seconds in total execution time and resulting with 0.9403 for accuracy and 0.9572 for validation accuracy. Next is the Fuzzy C-Means segmentation result, the Fuzzy C-Means execution time is 94 minutes and 16 seconds. For future research the RNN can be trained with much higher epoch and for Fuzzy C-Means can be combined with classification algorithm. We hope that this study can contribute to detecting leaf spot disease for corn plant at faster rate.Keywords—Fuzzy C-Means, Image Segmentation, RNN, Corn Disease Detection]]>
