Analisis K-Means Clustering pada Data Sepeda Motor

Dinata, Rozzi Kesuma; Safwandi, Safwandi; Hasdyna, Novia; Azizah, Nur

doi:10.19184/isj.v5i1.17071

Cited by 22 publications

(24 citation statements)

References 2 publications

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“…The absence of research conducted in predicting the price of a smartphone using this method motivates the authors to conduct this research. The K-Means algorithm has the following stages [23].…”

Section: K-means Clusteringmentioning

confidence: 99%

Smartphone Price Grouping by Specifications using K-Means Clustering Method

Syahputra

Atika

Aslamsyah

et al. 2021

J. Teknik Informatika CIT Medicom,

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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.

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Section: K-means Clusteringmentioning

confidence: 99%

Smartphone Price Grouping by Specifications using K-Means Clustering Method

Syahputra

Atika

Aslamsyah

et al. 2021

J. Teknik Informatika CIT Medicom,

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“…Ada tiga fase pada data mining input, proses dan output. Salah satu teknik pengelompokan dalam data mining adalah metode clustering, yaitu pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap dalam cluster tersebut akan berisi data yang semirip mungkin [2]. Data Clustering salah satu metode Data Mining yang bersifat tanpa arahan (unsupervised).…”

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.…”

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

Ginting¹,

Riandari²

2020

JNKTI

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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. .

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“…Teknologi machine learning atau lebih dikenal dengan pembelajaran mesin mempunyai algoritma yang diklasifikasikan menjadi dua teknik, yaitu supervised learning dan unsupervised learning [1]. Supervised learning ialah teknik machine learning yang biasanya digunakan untuk mengklasifikasikan data yang telah terpola [2]. Ada beberapa algortima supervised learning, seperti K-Nearest Neighbor, Support Vector Machine, Naive Bayes [3].…”

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Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara

Fajriana

2021

JEPIN

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Belum adanya sistem pengelolaan data produksi perikanan tangkap di Kabupaten Aceh Utara menyebabkan pemerintah Kabupaten Aceh Utara kesulitan dalam mengklasterisasi data produksi perikanan tangkap. Penelitian ini bertujuan untuk menerapkan algoritma k-medoids dalam sistem klasterisasi data produksi perikanan tangkap berbasis web menjadi tiga klaster. Penelitian ini menggunakan data produksi perikanan tangkap di Kabupaten Aceh Utara tahun 2019-2020 yang diperoleh dari Dinas Kelautan dan Perikanan Kabupaten Aceh Utara. Hasil penelitian dengan 10 kali pengujian menunjukkan bahwa nilai rata-rata iterasi k-medoids sebesar 2,5 dengan jumlah iterasi terbanyak 4 iterasi dan iterasi terkecil senilai 2 iterasi. Hasil cluster data produksi perikanan tangkap dengan jenis tangkapan ikan Albakora masuk kedalam potensi produksi tangkapan klaster sedang. Jenis tangkapan ikan Alu-alu, Tongkol Krai, Tuna Mata Besar, Tuna Sirip Biru Selatan masuk kedalam cluster rendah. Jenis tangkapan ikan Banyar, Bawal Hitam dan Bawal Putih masuk kedalam klaster tinggi. Adapun hasil klaster yang terbentuk dapat membantu pemerintah Kabupaten Aceh Utara dalam mengambil kebijakan untuk menambah nilai produksi tangkapan ikan di Kabupaten Aceh Utara.

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Analisis K-Means Clustering pada Data Sepeda Motor

Cited by 22 publications

References 2 publications

Smartphone Price Grouping by Specifications using K-Means Clustering Method

Smartphone Price Grouping by Specifications using K-Means Clustering Method

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

Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara

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