scholarly journals Smartphone Price Grouping by Specifications using K-Means Clustering Method

2021 ◽  
Vol 13 (2) ◽  
pp. 64-74
Author(s):  
Ahmad Agung Zefi Syahputra ◽  
Annisa Dwi Atika ◽  
Muhammad Adam Aslamsyah ◽  
Meida Cahyo Untoro ◽  
Winda Yulita
Keyword(s):  
College Students ◽  
High School ◽  
Data Mining ◽  
Confusion Matrix ◽  
Data Mining Algorithm ◽  
Clustering Method ◽  
Mining Algorithm ◽  
Data Grouping ◽  
The Right ◽  
Cluster 2

The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similarity of the characteristics by one data with other data, which is memory_size and best_price. The results of this research indicated that the right clustering of smartphone prices was within 3 different clusters, which was cluster 0 has centroid of Rp2.000.000,00, cluster 1 has centroid of Rp18.000.000,00, and cluster 2 has centroid of Rp9.000.000,00. The results of the evaluation used a confusion matrix, summary of prediction result, indicated that the clustering process had 100% of accuracy that could be seen on the table which showed the results of clustering. The conclusion from this research was that K-Means Clustering could form clusters in determining the price of a smartphone in relation to the specifications used as the attribute determining the price cluster for a smartphone.

scholarly journals Penerapan Metode Klasterisasi K-Means untuk Strategi Promosi Pada SMK Tamansiswa Sukadamai

2021 ◽  
Vol 2 (1) ◽  
pp. 141-146
Author(s):  
Teti Purwanti ◽  
William Ramdhan ◽  
Santoso Santoso
Keyword(s):  
Data Mining ◽  
Private Schools ◽  
Clustering Method ◽  
Promotion Strategy ◽  
Student Data ◽  
Promotional Strategy ◽  
New Students ◽  
Data Grouping ◽  
Promotional Strategies ◽  
The Right

Abstract: SMK Tamansiswa Sukadamai is one of the private schools in Pulo Bandring District which annually admits new students, this will affect the amount of student data that goes up and down. So far, SMK Tamansiswa Sukadamai has implemented a promotional strategy but it is still not optimal because the number of students registered at SMK Tamansiswa Sukadamai is more dominant in certain areas. Based on this, we need a technique that is able to assist in transforming the data into useful information, namely by applying data mining for promotional strategies, which can be the basis or guideline for promotional strategies based on areas where there are not many students present. The application of the k-means clustering method for promotional strategies can assist in the data grouping process in the form of data grouping results for C1 "Potential" and C2 "Potential", where C1 is the right area for more optimal promotion so that promotion is more effective and efficient. With the development of this clustering system, it can provide input to schools to determine which areas are more optimal for more in-depth promotion. Keywords: Promotion Strategy; Application of the K-means clustering method  Abstrak: SMK Tamansiswa Sukadamai merupakan salah satu sekolah swasta yang ada di Kecamatan Pulo Bandring yang setiap tahunnya melakukan penerimaan siswa baru, hal ini akan berpengaruh pada jumlah data siswa yang pasang surut. Selama ini SMK Tamansiswa Sukadamai telah melakukan strategi promosi akan tetapi masih belum optimal dikarenakan jumlah siswa yang terdaftar di SMK Tamansiswa Sukadamai lebih dominan berasal di wilayah tertentu saja. Berdasarkan hal tersebut diperlukan suatu teknik yang mampu membantu dalam mentransformasikan data tersebut menjadi informasi yang berguna yaitu dengan menerapkan data mining untuk strategi promosi, yang dapat menjadi dasar atau pedoman untuk strategi promosi berdasarkan wilayah yang tidak banyak dalam menghadirkan siswanya. Penerapan metode klasterisasi k-means untuk strategi promosi dapat membantu dalam proses pengelompokan data dalam bentuk hasil pengelompokan data C1 “Berpotensi” dan C2 “Tidak Berpotensi”, dimana C1 merupakan wilayah yang tepat untuk dilakukan promosi yang lebih optimal sehingga promosi lebih efektif dan efisien. Dengan dikembangkannya sistem klasterisasi ini, maka dapat memberikan masukan kepada pihak sekolah untuk menentukan wilayah yang lebih optimaluntuk promosi lebih mendalam. Kata kunci: Strategi Promosi; Penerapan metode Klasterisasi K-Means 

scholarly journals A product-centric data mining algorithm for targeted promotions

2020 ◽  
Vol 54 ◽  
pp. 101940 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Fabio Caraffini ◽  
Jenny Carter
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Mining Algorithm

scholarly journals Life-cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-mining Algorithm

Buildings ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Umair Hasan ◽  
Andrew Whyte ◽  
Hamad Al Jassmi
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Built Environment ◽  
Transport System ◽  
Asset Management ◽  
Public Transport ◽  
Categorical Variables ◽  
Data Mining Algorithm ◽  
Large Set ◽  
Mining Algorithm

Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.

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