Oky Dwi Nurhayati

Stroke identification system on the mobile based CT scan image

A stroke occurs if the flow of oxygen-rich blood to a portion of the brain is blocked. Without oxygen, brain cells start to die after a few minutes. Sudden bleeding in the brain also can cause a stroke if it damages brain cells. Stroke is a harmful disease in which most of the cases of this disease bring an effect of the physical defect on patients. One of the methods of examining this disease is by means of head CT scan. However, there is a weakness of head CT scan for consisting of some unclear or invisible parts. Hence, it needs a technique for improving the image quality to again emerge the invisible parts. Adaptive histogram equalization (AHE) is a technique that can cope with the weakness of HE by enhancing the contrast in local area. The contrast enhancement, however, can be excessive sometimes. Using the contrast limited adaptive histogram equalization (CLAHE), the excessive contrast enhancement in AHE can be coped with by giving the limit value on histogram. In this research, a stroke identification system has been built comprising image enhancement with CLAHE, feature extraction statistically with the mean value, deviation standard, skewness, kurtosis, and segmentation using the statistical region merging method. The result then showed that the method of image processing significantly conducted was able to be used as a tool to identify the stroke disease in order to distinguish the type of CT scan to the normal or sick state.

Beef Quality Identification Using Thresholding Method and Decision Tree Classification Based on Android Smartphone

Beef is one of the animal food products that have high nutrition because it contains carbohydrates, proteins, fats, vitamins, and minerals. Therefore, the quality of beef should be maintained so that consumers get good beef quality. Determination of beef quality is commonly conducted visually by comparing the actual beef and reference pictures of each beef class. This process presents weaknesses, as it is subjective in nature and takes a considerable amount of time. Therefore, an automated system based on image processing that is capable of determining beef quality is required. This research aims to develop an image segmentation method by processing digital images. The system designed consists of image acquisition processes with varied distance, resolution, and angle. Image segmentation is done to separate the images of fat and meat using the Otsu thresholding method. Classification was carried out using the decision tree algorithm and the best accuracies were obtained at 90% for training and 84% for testing. Once developed, this system is then embedded into the android programming. Results show that the image processing technique is capable of proper marbling score identification.

Semantic Search based on Ontology with Case Study: Indonesian Batik

Many kind of searching technique has been developed today. One of these techniques is using ontology to support semantic technology. This technology is applied on this research. It is an experimental research to develop a semantic search based on ontology with case study on Indonesian article about batik. There are so many batik articles on the internet today. But the information about batik can not be easy to find. It because Indonesia is rich with batik. So many place produce batik with their own characteristic. The aim is to expand the meaning of keyword that user inputted. And the result shows that cosine value with expanded ontology is higher than without ontology. It almost double the value of cosine without ontology. General Terms Semantic, Ontology.

Omega-3 chicken egg detection system using a mobile-based image processing segmentation method

An Omega-3 chicken egg is a chicken egg produced through food engineering technology. It is produced by hen fed with high omega-3 fatty acids. So, it has fifteen times nutrient content of omega-3 higher than Leghorn’s. Visually, its shell has the same shape and colour as Leghorn’s. Each egg can be distinguished by breaking the egg’s shell and testing the egg yolk’s nutrient content in a laboratory. But, those methods were proven not effective and efficient. Observing this problem, the purpose of this research is to make an application to detect the type of omega-3 chicken egg by using a mobile-based computer vision. This application was built in OpenCV computer vision library to support Android Operating System. This experiment required some chicken egg images taken using an egg candling box. We used 60 omega-3 chicken and Leghorn eggs as samples. Then, using an Android smartphone, image acquisition of the egg was obtained. After that, we applied several steps using image processing methods such as Grab Cut, convert RGB image to eight bit grayscale, median filter, P-Tile segmentation, and morphology technique in this research. The next steps were feature extraction which was used to extract feature values via mean, variance, skewness, and kurtosis from each image. Finally, using digital image measurement, some chicken egg images were classified. The result showed that omega-3 chicken egg and Leghorn egg had different values. This system is able to provide accurate reading around of 91%.

Shooting simulator system design based on augmented reality

Development of computer technology today has a major role in the development process of interaction in computer-based applications. Various innovations in creating the user experience of applications began are increasing. Augmented reality is a technology model the interaction between humans and computers by using a model of a merger between the real world with the virtual world. This development certainly can help people or community in various fields, especially in the field of shooting. Shooting is one of the many activities carried out by members of the TNI, POLRI and PERBAKIN. Activities in course are requires skill and a good psychological condition. To obtain good craftsmanship, each member takes a routine of training activities. Computer graphics technology is use in the process of shooting practice. This process utilizes Augmented Reality technology roles. With this technology in practice shooting environment can be created artificially or virtually. Methodology used in this research is system software of development, it is called waterfall model. The use of this method is to generate a reliable system so that the system can work according to the needs. Based on testing the functionality of the system has meet the initial needs, the system works in accordance with the scenario. The minimum distance of the camera to the marker to be recognized is 20 cm and the maximum distance is 300 cm.

Detection of the beef quality: Using mobile-based K-mean clustering method

Beef quality is determined by a number of parameters; some of which include size, texture, color feature, or meat smell. Recently, determining the meat quality is done by seeing the color and shape. However this method still has some weaknesses due to, for example, the subjectivity and inconsistency in human assessment. The aim of this research is to make an application to detect the meat quality. The application built was based on mobile using Java Programming Language on the Android integrated with Android SDK, Eclipse, and OpenCV. The method of image processing used pre-processing, k-mean clustering, and the analysis was conducted statistically with mean value and deviation standard. The quality detection meanwhile was performed using the texture and meat texture matching based upon the existing data. The application made could be used to seek the significant k-values and able to detect the level of quality by providing the level of accuracy at 80%.

Beef quality identification using color analysis and k-nearest neighbor classification

Beef is one of the many produce prone to contamination by microorganism. Water and nutrition contents make an ideal medium for the growth and proliferation of microorganism. Contaminated beef will degrade and has less storage duration. Beef is valued by two factors; its price and its quality. The quality itself is measured using four characteristics; marbling, color of meat, color of fat, and meat density. Specifically, marbling is the dominant parameter that determines meats quality. Determination of meat quality is conducted visually by comparing the actual meat and reference pictures of each meat class. This process is very subjective in nature. Therefore, this research aims to develop an automated system to determine meat by adopting the Indonesian National Standard requirement on the quality of carcass and beef (SNI 3932:2008) using the image processing technique. Image segmentation is carried out using the thresholding method and classification is conducted using the k-nearest neighbor algorithm. The features used to differentiate beef quality are marbling score, color of meat, and color of fat. Results indicate that the system developed is able to acquire images and identify beef quality as required in the Indonesian National Standard.

WSN infrastructure for green campus development

A system providing accurate environmental data for campus stakeholders to formulate and evaluate policies of the sustainable campus development is needed. This paper presents the design of WSN infrastructure capable of providing accurate, real-time and reliable environment data, namely PM2.5, SO2, CO, O3, NO2, temperature, humidity, soil moisture and light intensity to be analyzed and presented by servers. This infrastructure is composed of fixed sensor nodes, mobile sensor nodes, display nodes and server nodes. The sensor node provides environment raw data to the server using an RF transceiver. The server processes, stores and presents environment information to public users through Internet and mobile network. This infrastructure can be used as a platform to provide environmental data to decision support system for campus stakeholders, so that a recommendation can be made.

Expert system for campus environment indexing in wireless sensor network

Wireless sensor network can deliver environment data in campus area as CO, NO2, HC, particulate matter, temperature, humidity, and luminous intensity to provide accurate realtime data. This realtime environment data is used for environment indexing accurately, and then can be developed in an expert system. This expert system collects input data from the sensor. This expert system will help giving the accurate input for campus authority to state and evaluate campus developing policy continuously. This expert system uses forward chaining method, PHP programming language and MySQL database.