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Indonesia as the largest archipelago and maritime country in the world has a very large marine resources. Fish is one of the marine wealthties (marine biota), which have large numbers in Indonesia. Fishermen is an important role in the process of fishing, however fish caught by fishermen are often mixed with other types of fish. Currently, the process of fist separation is done manually, which takes longer time and many error occur due to fatigue. In this work the conveyer separator based on the fish identification system is developed. The fish conveyer separator is designed and developed. The fish identification system is based on Fast Fourier Transform (FTT) technique and Artificial Neural Network (ANN). The system is working properly, since it resulting the training and testing accuracy of 80% and 66%, respectively.

Technology in transportation becomes important nowadays and must be developed overtime. In the era of development, there are so many roads extensions to balance the significant additions of motorized vehicles. The increasing number of vehicles caused problems such as damaged roads and lack of maintenance to the road itself. Lack of awareness to repair the damaged roads, especially potholes, make it more dangerous for riders to drive safely. This issues are more concerning right now because the increasing number of accident and mortality. To prevent accident to happen, the pothole detection sensor can be used to on car system. The development of pothole detection sensor in this research is adopted from the proximity sensor system where in that system they use camera and digital imaging process. The advantages from our system that we research and develop are more user friendly from the feasibility and the financial aspect. Low cost sensor with the same quality of the existing system is devlop in this work. Despite the use of low cost sensor, the maintenance also on the lower cost and easier to do. As the result of this research and study, an error was obtained between the distance that detected the pothole should be in less than 4% distance range from the sensor.

The license plate number which identifies the original registered place of the vehicle, as well as the usage of each vehicle which belongs to a private entity, a government vehicle, and a commercial vehicle, is identified in this work. Vehicle plate detection by a camera that mounted on parking gate. Detection process starts with collecting data using the camera. This work consists two important parts, namely image processing and classification system, respectively. In the image processing, the plate image capture by the camera is extracted to the feature space which will use as the input to the classification system. The classification system is then will identify the extracted input to the plat recognition identification. The Fast Fourier Transform (FFT) and Support Vector Machine (SVM) are applied as feature extraction technique and classification methods, respectively. The results of the training and testing phase are 80% and 66%, respectively.

Quality determines the shelf-life and selling prices of fresh mango, and therefore quality observation and control of fresh mango are of utmost significance in the processing and management of its supply chain. Mango fruit (mangifera indica) quality methods are mostly destructive in nature. Different mechanical, electromagnetic and non-destructive methods are increasingly important nowadays because of the ease of operation, speed, and reliability of the process. This project aims to develop a non-destructive assessment of mango quality using handheld micro NIR (near-infrared) spectroscopic device. NIR spectra data and Brix levels, which indicate the sugar content of the plant, i.e. indicating the sweetness of the mango, were collected from three different types of Mango (Chokanan, Rainbow, and Kai Te), resulting 80 samples (i.e. 60 samples for training and 20 samples for testing) in this project. NIR spectra can be converted mathematically to obtain quantitative information of chemical and physical nature by multivariate calibration. The spectra data is pre-processed using Gaussian smoothing and extended multiplicative signal correction (EMSC) for the elimination of uncontrollable path length or scattering effects. These samples were then used to develop a predictive model using both Support Vector Machine (SVM) regression and Partial Least Squares regression (PLS) methods. The coefficient of determination (R2) obtained from SVM for training/calibration and testing dataset are 0.96 and 0.95 respectively. Meanwhile, the coefficient of determination (R2) obtained from PLS for calibration/training and testing dataset are 0.89 and 0.86 respectively. The results obtained from this project indicate that the handheld NIR has potential use for non-destructive assessment of mango fruits quality.

Food processing Technologies play important roles in supporting Making Indonesia (MI) 4.0. The effectiveness in food processing becomes emerging in the food industry. The automatic fruit classification system is becoming important for use in many food processing industries. A novel approach for classifying, using support vector machines (SVM) is presented in this paper. Fruit classification based on their shape is proposed in this work. The system differentiates the fruit based on their shape. Fast Fourier Transform (FFT) is extracted and later used as input to the SVM-based identifier. The fruit parameters are compared and classified, the results of computer simulation show that this technique produces better accuracy than that of the existing technique that is based on artificial neural network (Ann). The SVM also requires less training time than artificial neural network (Ann).

Earthquake is one of the most destructive of natural disasters that killed many people and destroyed a lot of properties. By considering these catastrophic effects, it is highly important of knowing ahead of earthquakes in order to reduce the number of victims and material losses. Earth's electric field is one of the features that can be used to predict earthquakes (EQs), since it has significant changes in the amplitude of the signal prior to the earthquake. This paper presents a detailed analysis of the earth's electric field due to earthquakes which occurred in Greece, between January 1, 2008 and June 30, 2008. In that period of time, 13 earthquakes had occurred. 6 of them were recorded with magnitudes greater than Ms=5R (5R), while 7 of them were recorded with magnitudes greater than Ms=6R (6R). Time domain feature extraction technique is applied to analyze the 1st significant changes in the earth's electric field prior to the earthquake. Two different time domain feature extraction techniques are applied in this work, namely Simple Square Integral (SSI) and Root Mean Square (RMS). The 1st significant change of the earth's electric field signal in each of monitoring sites is extracted using those two techniques. The feature extraction result can be used as input parameter for an earthquake prediction system.