Penerapan Algoritma K-Means Untuk Clustering Tempat Makan Di Batubara

Kurniawan, Riski Askia; Hasibuan, Muhammad Siddik; Piramida, Piramida; Ramadhan, Rizky Syahrul

doi:10.55537/cosie.v1i1.27

Cited by 6 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Dan penelitian terakhir yang digunakan sebagai pendukung penelitian dilakukan oleh Riski Askia Kurniawan, dkk pada tahun 2022 dengan judul penelitian Penerapan Algoritma K-Means Untuk Clustering Tempat Makan Di Batubara serta hasil yang didapatkan dari penelitian bahwasannya terdapat 2 (dua) cluster yang terbentuk dari hasil proses penelitian [21].…”

Section: Pendahuluanunclassified

Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Setiawan,

Triayudi

2024

JoSYC

View full text Add to dashboard Cite

Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.

show abstract

Section: Pendahuluanunclassified

Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Setiawan,

Triayudi

2024

JoSYC

View full text Add to dashboard Cite

show abstract

“…We can see in table 4.13 below, that the clustered data is divided into three groups of diabetes patients based on existing risk factors. Centroid C1 represents a group of diabetes mellitus patients whose condition is at a mild level, while Centroid C2 represents a group of diabetes mellitus patients who are at a moderate level, and Centroid C3 represents a group of patients with severe or dangerous diabetes mellitus (Kurniawan et al, 2022). Clustering Results With Rapidminer 1.…”

Section: Implementation Of K-means Clusteringmentioning

confidence: 99%

Application of PCA and K-Means Clustering Methods to Identify Diabetes Mellitus Patient Groups Based on Risk Factors

Simanjuntak,

Hasibuan

2023

J. Prisma. Sains

View full text Add to dashboard Cite

Diabetes mellitus is a chronic disease characterized by high levels of glucose (sugar) in the blood that is high for a long period of time. Identification is the process of recognizing and determining the characteristics of a particular object or entity. hypertension (high blood pressure), smoking and lack of physical activity can affect the condition of diabetes mellitus patients. Therefore, an approach is needed that can identify groups of diabetic patients based on their risk factors, so that appropriate management and treatment can be carried out. The purpose of this study is to apply PCA method by reducing data dimension to identify the linear combination of the most contributing risk factors in diabetes mellitus patient data and apply K-Means Clustering to cluster into groups based on similar risk factors. The methods to be used are Principal Component Analysis (PCA) and K-Means Clustering. type of quantitative research, this research can be categorized as analytic research, variables are risk factors for diabetes mellitus disease. The results of research using the PCA (principal component analysis) method obtained 9 main components (PC) 86.9275%. correlation between attributes and principal components, then a matrix component is formed with a loading value that the greater the value, the stronger the correlation with the principal component formed with a cut off point of loading value> 0.4 regardless of positive and negative. By using the K-Means Clustering method, The clustering results obtained are divided into 3 groups of diabetes patients based on existing risk factors. Centroid C1 represents a group of diabetes mellitus patients whose condition is at a mild level, while Centroid C2 represents a group of diabetes mellitus patients who are at a moderate level, and Centroid C3 represents a group of patients with severe or dangerous diabetes mellitus.

show abstract

“…Clustering adalah teknik yang umum digunakan dalam berbagai bidang, seperti ilmu data, ilmu sosial, pengenalan pola, bioinformatika, ilmu kedokteran, dan sebagainya. Contoh aplikasi clustering adalah dalam pemasaran untuk memahami perilaku konsumen, dalam ilmu kedokteran untuk mengelompokkan pasien dengan penyakit yang sama, dan dalam analisis citra untuk mengelompokkan gambar-gambar berdasarkan karakteristik tertentu [1].…”

Section: Pendahuluanunclassified

Text Clustering in Karo Language Using Tf-Idf Weighting and K-Means Clustering

Br Sembiring,

Hasibuan

2023

J. Tek. Inform. (JUTIF)

View full text Add to dashboard Cite

The aim of this research is to see how many presentations there are between dialects and look for clusters. There is also a method used for weighting, namely tf-idf, there are several steps used in this method, namely starting from the tokenizing process, transform cases, stopwords filter and token filter. to search for clusters using the k-means clustering method on rapidminer. The results of this research obtained a tf-idf weighting value, namely ginger dialect 37.5% for the number of word occurrences and 62.5% for the total of all words documented. Furthermore, for the Julu dialect, it was 37.5% for the number of word occurrences and 62.5% for the total of all words documented. The Singaporean Lau dialect accounts for 38% of the number of word occurrences and 62% of the total number of words documented. The singteruh deleng lau dialect accounts for 38% of the number of word occurrences and 62% of the total number of words documented. The Liang Melas dialect accounts for 38% of the number of word occurrences and 62% of the total number of words documented. Based on k-means clustering, it produces cluster 0: 68 items, cluster 1: 3 items, cluster 2: 15 items, cluster 3: 10 items, cluster 4: 4 items with a total sample of 100 items. The conclusion obtained is that the Ginger dialect and the Julu dialect are identical, while the Singaporean Lau dialect, the Teruh Deleng and Liang Melas dialects are also identical.

show abstract

Penerapan Algoritma K-Means Untuk Clustering Tempat Makan Di Batubara

Cited by 6 publications

References 1 publication

Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Application of PCA and K-Means Clustering Methods to Identify Diabetes Mellitus Patient Groups Based on Risk Factors

Text Clustering in Karo Language Using Tf-Idf Weighting and K-Means Clustering

Contact Info

Product

Resources

About