This study aims to investigate student's characteristics based on three variables, namely grade point average (GPA), period of study, and administrative obedience in order to draw a recommendation for student admission priorities at Mulawarman University. This recommendation will be used as one of reference variable on new student recruitment. The 8.741 records of student data sourced from the university data warehouse were mined using K-Means clustering. This mining process produced three clusters, cluster-1 includes 1,758 students with centroid {0.158,0.694,0.663}, while cluster-2 embraces 4,928 students with centroid {0.970,0.700,0.675}, and cluster-3 with centroid {0.953,0554,0.386} covers 2.055 students. This result shows that cluster-2 has the best combination of centroid values, implied that new students from schools where students in cluster-2 graduated from are recommended as the high priority students to be admitted at
Inconstancy of the market prices can affect society's purchasing power. One effort to anticipate the price uncertainty is by conducting commodity price forecasting. In the concept of forecasting, the commodity prices can be predicted by studying sales data in the previous period. This study aims to implement a decision support system in predicting food commodity prices trend. In data collection, the authors used list of food commodities provided by Industry and Trade Service of Gowa Regency. For data analysis, we use Naive Bayes algorithm to predict the food commodity prices in the future and Simple Exponential Smoothing to find out the price trend in a certain period. As a result, both methods can predict commodity prices and market tendency in a given time completely.
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