This study aimed at finding out management of the use of facilities and infrastructure in learning, obstacles and the solutions in Elementary School of 25 Betung. This research used a qualitative method. Subject in this study was the principal, teachers, infrastructure coordinator, school committee and supervisor. Technique of collecting data employed observation, interview and documentation. The technique of data validity used triangulation of methods and sources. Data analysis techniques used interactive model. The management of learning facilities and infrastructure is utilized through planning, implementing and controlling facilities and infrastructure. The obstacles are the storage area requires funds for expansion and repair, and a lack of administrative personnel, especially for the management of facilities and infrastructure. Solving budget problems by optimizing the limited personnel is to be more observant in determining the priority scale according to the existing budget, then involving existing personnel in formal courses or training. Keywords: Management Facility, Management Infrastructure, Learning

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The main principle of license plate recognition is to recognize the characters in the license plate which indicates the identity of the vehicle. This research will provide a system which can be implemented to the automatic payment in highway. Indonesian license plate consists of two parts, every of which has certain characters. These characters may become problem in the recognition process. Another problem is on the type of the license plate since Indonesia applies different color for every type of vehicle. In this research, different approaches are employed in the recognition of license plate; that is using character area as the feature value, also known as feature area, and K-Nearest Neighbor (KNN) as classification method. In addition, another method that has been used in our previous research is also employed to detect the character of license plate. The result shows very significant accuracy of 99.44%. In the process of recognition, scenario 1 gives the best accuracy at the K-1 value; that is 68.57% on the license plate and 92.72% on the characters of license plate. In the scenario 2 was obtained the license plate accuracy of 52% and license plate character accuracy of 89.36% with K-5. The system ran in a relatively short computational time.

Indonesia is one of the world’s biggest tobacco crop producers. By tobacco farmer, this plant is often even dubbed “green gold”. Madura Island is one of the best tobacco-producing areas in Indonesia. Tobacco is a significant trading crop in the eastern part of Madura Island, specifically in Pamekasan and Sumenep. The decline in tobacco yields is usually caused by pests and diseases that attack tobacco plants. Experts can easily detect conditions in plants (including tobacco) with their eyes, but this is very suitable and requires expensive operational costs when the size of the planting area is vast, and the distance of the planting area is far from the location of the expert. So that digital image processing techniques need to be applied to detect tobacco plant diseases earlier. By using data in the form of photographs of tobacco plant leaves, the condition will be identified. The method used in this research is GLCM (Gray Level Co-Occurrence Matrix) texture feature extraction, while CM (Color Moment) colour feature extraction and Naïve Bayes method are used for classification. The results of testing tobacco identification obtained the best accuracy of 82.2% for Pamekasan tobacco and 84.4% for Sumenep tobacco. The best results are obtained by using the colour feature extraction.

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