Penggunaan Algoritma K-Means Untuk Menganalisis Pelanggan Potensial Pada Dealer SPS Motor Honda Lombok Timur Nusa Tenggara Barat

Yahya, Yahya; Mahfuz, Mahfuz

doi:10.29408/jit.v2i2.1447

Cited by 4 publications

(3 citation statements)

References 0 publications

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“…Dataset dapat memiliki banyak tabel, dan tabeltabel dapat memiliki hubungan (relationship). Tabel-tabel pada suatu dataset dapat memiliki foreign key dan integritas referensial [5].…”

Section: Dataset Ulasan Wisatawanunclassified

Analisis Sentimen Bahasa Indonesia Pada Tempat Wisata Di Kabupaten Sukabumi Dengan Naive Bayes Classifier

Atmadja

2022

ELKOM

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Sentiment analysis of comments from visitors to tourist attractions and the public on tourist attractions in Sukabumi Regency which is one of the areas with various categories of tourist objects and is a sector of economic income for the surrounding community or for related parties such as the government and managers, in sentiment analysis research This includes using the Nave Bayes classification algorithm to examine the sentiment of tourist visitors and the performance of the classification model used. The data used in this research was taken from the website from Tripadvisor and Google Maps using a crawling technique, which then processed the data by a pre-processing process and then applied a classification to the data and got a sentiment visualization by processing word frequency on tourist visitor sentiment data. The results of the accuracy of the model used were re-tested with the k-fold cross validation method and the results of sentiment visualization got the frequency of words that most often appear on negative sentiment labels are garbage, beaches, lacking, places, roads, parking, dirty, entering, caring, clean , expensive, pay, manage, good and water.

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“…Dataset dapat memiliki banyak tabel, dan tabeltabel dapat memiliki hubungan (relationship). Tabel-tabel pada suatu dataset dapat memiliki foreign key dan integritas referensial [5].…”

Section: Dataset Ulasan Wisatawanunclassified

Analisis Sentimen Bahasa Indonesia Pada Tempat Wisata Di Kabupaten Sukabumi Dengan Naive Bayes Classifier

Atmadja

2022

ELKOM

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“…Algoritma K-Means digunakan untuk menentukan jarak data dari setiap centroid [10]. Perhitungan nilai cluster dihasilkan dari perhitungan ini, yang menggunakan rumus jarak Euclidean.…”

Section: Hasil Dan Pembahasan • Analisa Metodeunclassified

Studi Literature Analisis Potensi Pasar Marketplace terhadap Penjualan

Roni

Crysdian²

2022

JTMI

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Marketplace allows Customer to Customer (C2C) transactions between consumers without being bound by place and time. This change also occurs in human spending habits. So it becomes an opportunity for sellers to market their wares. In facing market competition, business people also need analysis to find out what products are selling best. However, there are many factors that affect the complexity of the marketplace in Indonesia. Then a classification-based simulation using KNN and C4.5 is needed, where the weight of each sales product group affects market potential so that it benefits sellers to choose which marketplace is suitable for the goods to be sold. So it can be concluded that (1). Factors that influence the complexity of marketplaces in Indonesia are price, number of sales, discounts, ratings and reviews. (2). The most optimal method used for analysis of sales market potential is K-Nearest Neighbor and C4.5.

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“…Penelitian menggunakan metode K-Means untuk menganalisa pelanggan potensial pada SPS Motor Honda Lombok Timur [12]. Serta dapat menganalisa segmen pasar handphone berbasis android [13], segmen pasar IndiHome berbasis python [14] dan tingkat keterlibatan siswa dalam belajar e-Learning [15].…”

Section: Pendahuluanunclassified

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

Fakhri

Defit

Sumijan

2021

jidt

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Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.

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Penggunaan Algoritma K-Means Untuk Menganalisis Pelanggan Potensial Pada Dealer SPS Motor Honda Lombok Timur Nusa Tenggara Barat

Cited by 4 publications

References 0 publications

Analisis Sentimen Bahasa Indonesia Pada Tempat Wisata Di Kabupaten Sukabumi Dengan Naive Bayes Classifier

Analisis Sentimen Bahasa Indonesia Pada Tempat Wisata Di Kabupaten Sukabumi Dengan Naive Bayes Classifier

Studi Literature Analisis Potensi Pasar Marketplace terhadap Penjualan

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

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