The development of very advanced technology can help all aspects of life, one of which is in the world of education. The world of education certainly really needs the role of technology in managing activities by utilizing information technology. The problem that arises is the system for processing piles of data in order to know whether or not the student is able to follow the subjects at SDN 105351 Bakaran Batu. The process of implementing this data mining application only discusses predicting children's ability levels in following subjects. The data used were data from grade 5 elementary school students from the last 2 years (2018-2019) at SDN 105351 Bakaran Batu. One of the classification models is making a decision tree. By knowing the level of ability of children in following student subjects is expected to provide solutions for the future in processing student data with a computerized system, especially data regarding the level of children's ability in following subjects. The way the system works is built using the C4.5 method and data mining applications that will be designed with VB Net and Microsoft Access databases.

INTISARIMemprediksi penjualan sangat penting dalam kemajuan sebuah usaha, terutama dalam penjualan barang yang memiliki tanggal kadaluarsa seperti makanan hewan peliharaan. Ada beberapa algoritma yang digunakan untuk menginformasikan prediksi harga penjualan salah satunya algoritma K-Nearest Neighbor dan algoritma Decision Tree. Dengan metode K-nn, dihasilkan kondisi dari 30 data, 6 data diklasifikasikan terlaris sesuai dengan prediksi yang dilakukan dengan metode k-nn, 3 data dari 6 data diprediksi terlaris  ternyata tidak terlaris, (data urutan 1, 2, 6). 24 data diprediksi tidak terlaris ternyata 10 data sebelumnya diklasifikasikan terlaris (data urutan 22, 5, 16, 26, 28, 19, 17, 20, 23, 24). Dengan metode decision tree algoritma C45, diketahui dari 30 data, merek purina terlaris, ada 4 data daripada royal canin (false negative). Hasil tingkat akurasi decision tree algoritma c45, diketahui true terlaris = 17, false tidak terlaris = 4. False terlaris = 16, true tidak terlaris = 13. Akurasi decision tree algoritma c45 = 83%.             Kata kunci— algortima K-NN, Algortima C45, Data Mining, Pohon Keputusan, Prediksi ABSTRACTPredicting sales is very important in the progress of a business, especially in the sale of goods that have an expiration date such as pet food. There are several algorithms used to inform sales price predictions, one of which is the K-Nearest Neighbor algorithm and the Decision Tree algorithm. With the K-nn method, conditions are generated from 30 data, 6 data are classified as best-selling according to the predictions made by the k-nn method, 3 data from 6 data are predicted to be the best-sellers in fact, (data order 1, 2, 6). The 24 data predicted not bestselling turned out to be the 10 previously classified bestsellers (data sequences 22, 5, 16, 26, 28, 19, 17, 20, 23, 24). With the C45 decision tree algorithm method, it is known that from 30 data, the best-selling purina brand, there are 4 data than royal canin (false negative). The result of the accuracy level of the decision tree algorithm is c45, it is known that best-selling true = 17, false not best-selling = 4. Best-selling false = 16, best-selling true not = 13. Accuracy of decision tree algorithm c45 = 83%.Keywords—K-NN algorithm, C45 algorithm, Data Mining, Decision Tree, Prediction

Sistem informasi berkembang sesuai fungsi dan sasaran pengguna aplikasi yang dirancang. Perkembangan era industri mengeser sistem manual menjadi teknologi informasi bersifat terbuka dan uptodate. Sekumpulan tools komputer yang terintergrasi mengijinkan pembuat keputusan untuk berinteraksi langsung dengan komputer. Pengambil keputusan level paling tinggi harus mengetahui sifat keputusan yang diambil. SAW merupakan Sistem Pengambil Keputusan berdasarkan penjumlahan berbobot. Selama ini penerimaan guru di SMK Musda Perbaungan tertutup dan kurang objektif. Dalam riset diperoleh data 10 calon guru dengan ketetapan 7 kriteria. Perhitungan akan sesuai dengan metode ini apabila calon guru yang terpilih memenuhi kriteria yang telah ditentukan. Penelitian ini menerapkan perhitungan SAW untuk menentukan guru yang diterima menjadi guru. Dari data calon guru dan data kriteria dilakukan perhitungan SAW menghasilkan matriks dan normalisasi. Hasil nilai akhir dari perangkingan menentukan urutan nilai calon guru. Rancangan terdiri dari form-form yang mudah dioperasikan user sehingga memudahkan pengambil keputusan menentukan guru menggunakan Visual Studio 2010.