Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (SVM)

Somantri, Oman; Wiyono, Slamet; Dairoh, Dairoh

doi:10.15294/sji.v3i1.5845

Cited by 16 publications

(14 citation statements)

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results of K-Means are strongly influenced by the k parameter and centroid initialization. Generally K-Means initializes the centroid randomly [13]. After performing clustering process of weight attribute neither using Fuzzy C-Means algorithm and K-Means, do the process of beef cattle quality classification using Naïve Bayes Classification.…”

Section: Resultsmentioning

confidence: 99%

The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

Devi

Sugiharti

Arifudin

2018

SJI

View full text Add to dashboard Cite

The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.

show abstract

Section: Resultsmentioning

confidence: 99%

The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

Devi

Sugiharti

Arifudin

2018

SJI

View full text Add to dashboard Cite

show abstract

“…Hanya saja hasil dari K-Means sangat dipengaruhi parameter k dan inisialisasi centroid. Pada umumnya K-Means menginisialisasi centroid secara acak [10]. Langkahlangkah dari algoritma K-Means menurut Witten,dkk [11]…”

Section: K-meansunclassified

Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa

Muttaqin

Defriani

2020

Ilk. J. Ilm.

View full text Add to dashboard Cite

“…Penelitian lainnya yaitu penelitian mengenai klasifikasi jenis tiket helpdesk menggunakan SVM, akurasi yang diperoleh cukup tinggi yaitu sebesar 81% [7]. Selain itu, SVM juga pernah digunakan digunakan untuk klasifikasi tugas akhir dengan akurasi sebesar 85,38% [8]. Berdasarkan latar belakang tersebut, dalam penelitian ini akan diimplementasikan metode support vector machines untuk klasifikasi jenis pantun.…”

Section: Pendahuluanunclassified

Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

Irmanda¹,

Astriratma²

2020

RESTI

View full text Add to dashboard Cite

This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.

show abstract

Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (SVM)

Cited by 16 publications

References 4 publications

The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa

Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

Contact Info

Product

Resources

About