Data Mining Penjualan Suku Cadang Sepeda Motor Menggunakan Algoritma K-Means

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.

Section: Pendahuluanunclassified

Penerapan Data Mining Untuk Memprediksi Tingkat Kemampuan Anak Dalam Mengikuti Mata Pelajaran Dengan Metode C4.5 Pada SDN 105351 Bakaran Batu

Tumanggor

Hasugian

2021

“…Prediksi merupakan suatu dugaan atau prediksi mengenai terjadinya suatu kejadian atau peristiwa di waktu yang akan datang. Prediksi bisa bersifat kualitatif (tidak berbentuk angka) maupun kuantitatif (berbentuk angka) [3].…”

Section: Pendahuluanunclassified

Perancangan Aplikasi Prediksi Jumlah Pendaftar Siswa Baru Dengan Metode Regresi Linier (Studi Kasus: SMA RK Deli Murni Bandar Baru)

Lase¹,

Riandari²

2020

The problem of the SMA RK Deli Murni Bandar Baru school is to predict how many facilities that need to be provided for new students such as chairs, tables and others. This study discusses the prediction of the number of new student registrants at SMA RK Deli Murni Bandar Baru based on the amount of tuition fees using a simple linear regression method. From a commercial point of view, the use of data mining can be used to handle the explosion of data volumes, using computational techniques can be used to produce information needed which is an asset that can increase the competitiveness of an institution. Prediction is almost the same as classification and estimation, except that in the prediction the value of the results will be in the future. This system can be used to predict the number of applicants in the following year to help the school. The advantage is that this simple linear regression method is very simple so that it is easy to calculate and use. Saves the time needed to solve problems, especially those that are very complex.

“…Data Clustering salah satu metode Data Mining yang bersifat tanpa arahan (unsupervised). Algoritma K-Means merupakan algoritma pengelompokan iterative yang melakukan partisi set data ke dalam jumlah K cluster yang sudah ditetapkan diawal [3]. Algoritma K-Means sederhana untuk diimplementasikan dan dijalankan, relative cepat, mudah beradaptasi, umum dalam penggunannya dalam praktek [4].…”

Section: Pendahuluanunclassified

“…Dengan mengklasifikasi setiap data berdasarkan kedekatan dengan titik pusat data. Iterasi ke-2, dengan centroid diperoleh C1 =[75, 80,3, 63,3]; C2 = [80,3, 83,6, 62,5]; C3 = [63,3, 70,2, 54,6]. Means yang telah di kelompokan ke dalam cluster, dapat disimpulkan bahwa bibit kopi yang direkomendasikan (C1) terdiri dari 10 item, bibit kopi yang tidak direkomendasikan (C2) terdiri dari 7 jenis bibit kopi dan bibit kopi yang tidak layak (C3) terdiri dari 13 jenis bibit kopi.Implementasisistem pengelompokan bibit tanaman kopi arabika menghasilkan tampilan: a.…”

unclassified

Implementasi Metode K-Means Clustering Dalam Pengelompokan Bibit Tanaman Kopi Arabika

Ginting¹,

Riandari²

2020

The diversity of coffee seed material which in the end makes it difficult for the Department of Agriculture and Plantation in classifying recommended seeds to be distributed and planted in coffee planting centers in their working areas, especially in Sarimunthe Village, Kec. Munte Karo District. Data mining is used to extract valuable information from a dataset and then present it in a format that is easy for humans to understand in order to make a decision. In this study, data processing of Arabica coffee seeds consisting of 30 items, in the Karo District Agriculture sector, in preparing the seeds to be distributed to the public, the assessment is divided into 3 phases, namely coffee seeds that do not produce (Phase 0-1 Year), immature (Phase 0-1 Year). Phase 1-2 Years) and produce (Phase 2 Years and above). The results of the calculation of the K-Means algorithm which have been grouped into clusters, it can be concluded that the recommended coffee seeds (C1) consist of 10 items, the coffee seeds that are not recommended (C2) consist of 7 types of coffee seeds and unfit coffee seeds. (C3) consists of 13 types of coffee seeds. .