Every year the University offers various types of scholarships to its students, including Hamzanwadi Selong University, East Lombok. One type of scholarship offered is the Bidikmisi scholarship (KIP/K) which is intended for students who have middle to lower economic levels but have good academic achievement or potential. Every year the number of applicants for this scholarship continues to increase, but the number received each year is limited. The large number of piles of files applying for scholarships and the manual selection process tends to be ineffective and efficient and the results of the selection are inaccurate, so a system is needed that is able to assist in the selection process quickly, easily and on target. The method used is CRIS-DM with the Naive Bayes Classifier algorithm modeling, this method is an approach that refers to the Bayes theorem which combines previous knowledge with new knowledge. The variables consist of 7 attributes, namely: Name, DTKS status, achievement, parents' occupation, total income of parents, home ownership, and number of family dependents[1]. Testing was carried out using k-fold cross validation, and the highest accuracy results were obtained from k-fold 4 of 91.43%, while the AUC was 0.996% with a very good diagnostic classification. Thus it can be interpreted that the Naive Bayes algorithm is very well used in the selection of scholarships for bidikmisi scholarships at Hamzanwadi University
Salah satu warisan budaya benda yang ada di Nusa Tenggara Barat khusunya Lombok adalah takepan (naskah lontar kuno). Takepan ditulis pada lembaran daun Lontar (daun Duntan dalam bahasa Sasak) dikenal dengan aksara jejawan (turunan dari bahasa jawa kuno).Menurut dalam penelitianya pada segmentasi citra aksara jawa, nilai adaptive thresholding di peroleh berdasarkan variasi itensitas tiap lokal windows.[1] Proses penentuan nilai thresholdingnya bekerja pada blok-blok dalam citra. Citra yang memiliki variasi kontras dan pencahayaan tinggi sangat sulit diklasifikasi sebagai background atau foreground karena terdapat banyak piksel, sebaiknya menggunakan Local Adaptive Thresholding. Dari penelitian yang sudah di lakukan dapat simpulkan bahwa adaptive thresholding dan morfologi dengan pendekatan dilasi sangat membantu memaksimalkan keterbacaan citra dengan nilai Peak Signal to Noise Ratio (PSNR) tertinggi mencapai 34.107 dB.
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