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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.

The scholarship is a financial aid given to candidate student who is eligible. This aid is aimed to help the students to pursue their study. In UTM, the scholarship system awards applied to select the right candidate of college students is still based on the principles of the proximity of the campus party. The scholarship given isn't for the right target. This research implemented a system of determining admission scholarships using the method of SAW and TOPSIS as a solution to support decision makers based on criteria that have been defined, including GPA, income of the parents, the number of dependant of the parents, tuition fees, semester and the involvement in association. The result of some trials had been shows that the average accuracy for five years is 88.4% compared to manual calculations or the equivalent of 6 to 8 days.

Salt is one of Indonesia’s major commodities. However, the quality of industrial salt in Indonesia is still an obstacle, so the need for industrial salt still relies on imported salt, especially from Australia. Quality improvement is done through purification using the recrystallization method. The use of a method that is still simple results in the salt being produced still has an as-is quality. Quality is shown from the appearance of salt physically and chemically. Good salt is shown by the crystal form which is smooth and has clear white color. Therefore, good knowledge of salt quality must be known early, in addition to being able to meet the Indonesian National Standard (SNI),in this way salt farmers will more easily improve the quality of salt produced and can differentiate salt designation based on its quality category. This study takes the theme of how to make decisions to determine the quality of salt, so that a decision support system will be built to assist in determining good salt quality by using the Simple Additive Weighting (SAW) method. This method can support the decision making of salt quality determination based on the weight of each attribute. Morever, the total score of the end result can produce a good alternative decision in accordance with specified criteria, so that it will produce salt quality.

Corn is one of the staple foods consumed by many people after rice plants, especially in Indonesia. High consumer demand requires corn production in large quantities to meet these needs. However, corn production is not always in large quantities due to several factors, namely diseases in corn plants. Unhealthy corn plants can reduce the amount of production. Healthy and unhealthy corn plants can be identified manually, but this method is not efficient, so in this study, it is proposed to classify corn diseases using the Random Forest, Neural Network, and Nave Bayes methods. The dataset used is a collection of corn leaf images taken from farmers’ fields in the Madura Region with four target classes, namely healthy, gray leaf spot, blight, and common rust. Based on the test results, the classification using the Neural Network method provides a better accuracy value than the other two methods in classifying corn leaf datasets, namely the AUC value reaches 90.09%, classification accuracy is 74.44%, f1 score is 72.01%, precision is 74.14% and recall by 74.43%.

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