The main occupations in 15 years and above are on average not suitable for their age, which is done by adults but is done by people 15 years and older. Therefore, I do grouping data on population 15 years and above so that we know what their main jobs are. Here I use data mining with the K-Medoids method to classify the population of 15 years according to the main occupation, this research was conducted in Indonesia. The K-Medoids method is a method of collecting data with the classic clustering partitioning technique that groups datasets from n objects into K groups known a priori. A useful tool for determining k is a silhouette. It is stronger to be agreed upon and bigger than K-Means because it must add to the difference in the difference in the square of the euclidean distance. Attractions that can be determined as cluster objects are average differences for all objects in the cluster are minimal. That is the easiest point in the cluster. K-Medoids uses objects in a collection of objects to represent a cluster. The object chosen to represent a cluster is called medoid. Clusters are built by calculating the proximity they have between a medoid and non-medoid objects.Keywords: Data mining, K-Medoid, Main Job Fields 15 years and above
Kriminalitas merupakan masalah yang sering terjadi di kehidupan sehari-hari dan dimana saja termasuk di berbagai provinsi yang ada di Indonesia. Dengan banyaknya tindak kriminalitas di Indonesia, diperlukan adanya pengelompokan daerah rawan tindak kriminalitas di Indonesia berdasarkan provinsi sebagai salah satu usaha untuk menentukan suatu daerah memerlukan pengawasan ekstra atau tidak. Pada penelitian ini akan dilakukan pengelompokkan tindak kriminalitas dengan menggunakan algoritma K-Means dan K-Medoids clustering. Data diolah menjadi dua cluster yaitu cluster tingkat tindak kriminalitas tinggi (C1) dan cluster tingkat tindak kriminalitas rendah (C2). Hasil algoritma K-Means diperoleh dengan C1 memiliki 6 anggota dan C2 memiliki 28 anggota. Sedangkan hasil algoritma K-Medoids diperoleh dengan C1 memiliki 7 anggota dan C2 memiliki 27 anggota. Perbedaan jumlah klaster pada kinerja tiap algoritma memiliki pola perhitungan yang berbeda sehingga keunggulan kinerja algoritma tergantung pada data yang akan diproses.
Students are one of the substances that need to be considered in relation to the world of education today. The difficulty of getting students makes the school have to optimize the learning system and infrastructure as well to attract the interest of new students and also make students who have gone to school not drop out or drop out. One of the factors contributing to the large number of students dropping out is because of the lack of policies and actions from the education institutions to keep their students from dropping out. The purpose of this study was to classify potentially dropout students and not have the potential to drop out with the C4.5 algorithm as a reference in making policies and actions to reduce the number of students dropping out. The classification results of the C4.5 algorithm are evaluated and validated with RapidMinerStudio to determine the accuracy of the C4.5 Algorithm in classifying potential dropouts.Keywords: Student dropout, Classification, C4.5 Algorithm
The Smart Indonesia Card (KIP) is an educational cash assistance until graduating from high school with the age of 6-21 years of school age coming from poor families (underprivileged) or who are registered as participants in the Family Hope Program (PKH) or Family Welfare Card (KKS ) The Indonesia Smart Card (KIP) is a refinement part of the Poor Student Assistance Program (BSM) since the end of 2014. The author took a case study on 124395 Public Elementary School. 124395 Public Elementary School is one of the Public Elementary Schools in Pematangsiantar which received the Smart Indonesia Program for students / i poor and have difficult economic constraints. This research is in the background of the problem of KIP provision where the process of delivering assistance sometimes does not meet the target or target. Invalid data causes errors in the distribution of KIP that should be given to recipients who are entitled to receive it. To overcome these problems, a Decision Support System (SPK) is needed which is expected to solve problems in the provision of KIP with the Moora method. The Moora method is a multicriteria decision making method based on each appropriate criterion. The criteria used are: Father's Work, Mother's Work, Father's Income, Mother's Income, Amount of Dependent, Amount of Dependents Still in School, Rapot Value, KKS Holder, Residence, Type of House. SPK KIP is a recommendation to the school, for the next process to be returned to the school.Keywords: Decision Support System, Moora, Indonesia Smart Card (KIP)
Penelitian ini berjudul “Kajian Publikasi Ilmiah Dosen FEB Unpad Dalam Indeks Scopus, Google Scholar dan Sinta Dikti 2000-2016”. Tujuan penelitian ini adalah untuk mengetahui Dosen FEB Unpad yang paling produktif dalam meneliti/menulis, mengetahui publikasi Dosen. FEB Unpad yang paling banyak disitir dan mengetahui kolaborasi Dosen FEB Unpad sebagai penulis dengan penulis/ peneliti. Metode dalam penelitian ini adalah metode kuantitatif dengan menggunakan analisis bibliometrika. Unit analisis sejumlah 145 Dosen FEB Unpad. Teknik pengumpulan data dalam penelitian ini adalah studi pustaka atau studi dokumentasi, yaitu penelusuran dan perolehan data yang diperlukan melalui data yang telah tersedia. Teknik analisis data dalam penelitian ini meliputi persiapan, tabulasi, penerapan data, penyajian data dan penarikan simpulan. Hasil kajian ini menunjukkan bahawa sebagian besar dosen FEB Universitas Padjajaran sudah terindeks sudah terindeks dalam Google Schoolar, Scopus, dan Sinta Dikti, namun masih ada beberapa dosen belum terverifikasi di ketiga platform tersebut.
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