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Sundanese script is one local Indonesian script which has the specificity in terms of how the writing system by using a non-latin character as well as in terms of the unique pronunciation, therefore that presence should be conserved. One of its preservation are to build an sundanese recognition system. This system will accept input in the image of sundanese script that will be processed in order to generate output in the form of text. In the process of recognizing a pattern from the image of sundanese script can use the techniques of extraction feature, one of which is modified direction feature (MDF), whereas the methods used for classification and identification process is learning vector quntization (LVQ). To support it's function of this image recognition system combined with text to speech (TTS). TTS system is a system that can convert text into speech, so the output of this system can display text results of sundanese recognition script along with the examples of pronunciation. The level of accuracy of the test results of the 300 samples data image of sundanese script verified correctly between the suitability of image characters with the names and the pronunciation is 78.67%.

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AbstrakMelanoma dikategorikan sebagai bentuk kanker kulit yang paling berbahaya menurut skincancer.org. Kanker kulit ini bertumbuh dan berkembang oleh kerusakan DNA pada sel-sel kulit yang umumnya disebabkan oleh radiasi ultraviolet dari matahari. Pada penelitian ini  dibuatkan suatu sistem yang  dapat membantu pihak medis untuk memprediksi suatu tipe atau jenis dari suatu kanker melanoma dengan proses antara lain, optimalisasi postprocessing melalui morphological closing, pembentukan matriks-matriks gray level co-occurrence (GLCM) untuk pengekstraksian fitur-fitur tekstur statistika dan K-Nearest Neighbor (KNN) sebagai metode klasifikasinya. Hasil dari pengujian menunjukan bahwa ekstraksi ciri tekstur statistika bermanfaat dalam pengenalan kanker ini dimana diperoleh hasil akurasi mencapai 93.33% oleh classfier pada kategori pengujian positif melanoma dan 86.66 % pada kategori kelas melanoma. Kata kunci: Melanoma, GLCM, K-Nearest Neighbor, Otsu Thresholding AbstractMelanoma is categorized as the most dangerous form of skin cancer, according to skincancer.org. This skin cancer grows and develops due to DNA damage to skin cells which is generally caused by ultraviolet radiation. In this study, a system was created to help medical parties predict a type or type of melanoma cancer. This system was performed with morphological closing processes, the formation of gray level co-occurrence (GLCM) matrices for extraction of features of statistical textures, and K-Nearest Neighbor (KNN) as a classification method. The test results showed that the system recognized this cancer with an accuracy of 93.33% for the positive image of melanoma and 86.66% for the melanoma class category. Keywords: Melanoma, GLCM, K-Nearest Neighbor, Otsu Thresholding

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Arranging college subject becomes one of the problem for the institute. Limited of class rooms, lecturer time that should be adjusted, many courses should be attended by college student cause arranging college subject should carefully arranged. Making manual schedule need more time and obstructing lectures. The purpose of this study is to implement greedy algorithms so that the results of scheduling lectures that have no clash, class capacity and number of students can adjust. Greedy algorithm is a computational algorithm to find the shortest distance, from the theory then diadposi into computational algorithms to find the lowest value in the combination of scheduling a class. By applying the greedy algorithm to the system, the system can generate class schedules without clashes, class capacity and number of students can adjust. The output of this system is the course schedule stored in the calendar.

Sugeno Fuzzy algorithm is one of the algorithms contained on Fuzzy Inference System, that used to describe the condition between the two pieces of the decisions represented in the form of rules IF - THEN, where the output is constant or linear equations. While the Naive Bayes algorithm is an algorithm that uses data classification to a particular class based on the probability of each data class. Both of these algorithms can be implemented on a Decision Support System (DSS) for diet selection, using Fuzzy Sugeno as an additional determinant of energy and Naive Bayes method as decision maker. This is because the need for food intake and diet has become a problem for humans. To prevent excess intake of food it needs dietary adjustments or so-called diet. But in daily life, people sometimes hard to determine the type of diet that is suitable for them. So we need a system that can determine the type of diet that is suitable for a person. The data that used as a reference for decision support are age, daily caloric requirement, Body Mass Index (BMI), blood pressure, cholesterol, uric acid and blood sugar levels. Results of system testing showed from a sample of 30 data there are 26 appropriate data and 4 inappropriate data to determine the type of diet by the system with the success rate of 86.7%.