Improved point center algorithm for K-Means clustering to increase software defect prediction

Annisa, Riski; Rosiyadi, Didi; Riana, Dwiza

doi:10.26555/ijain.v6i3.484

Cited by 5 publications

(2 citation statements)

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“…Penyebaran Covid-19 di Indonesia [9] Penyebaran penyakit ISPA di Provinsi Riau [12] Persentase perokok usia di atas 15 tahun [18] Pendidikan Kemampuan pelajar pada Bahasa Inggris [10] Mahasiswa baru memilih program studi [13] Penilaian Kedisiplinan Siswa [14] Menentukan prioritas bantuan uang kuliah tunggal [23] Kebencanaan Teknologi -Perangkat Lunak Episentrum gempa bumi [11] Dampak gempa bumi di Pulau Jawa [21] Pemetaan kemampuan penggunaan Teknologi Informasi [17] Kerusakan / cacat perangkat lunak [22] Lain-lain Pemetaan produksi tanaman Tomat di Indonesia [1] Minat nasabah asuransi [8] Gambar 3. Data uji 50…”

Section: Bidang Penerapan Studi Kasus Klasterisasi Kesehatanunclassified

Perbandingan Metode Perhitungan Jarak pada Nilai Centroid dan Pengelompokan Data Menggunakan K-Means Clustering

Hartono

Eniyati

Hadiono

2023

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This study will observe the process of grouping data or forming clusters using K-Means clusters with three methods of measuring distances, namely Euclidean distance, Manhattan distance, and Minkowski distance. Observations are more focused on changing the centroid value and the results of grouping data, as well as the number of iterations required. Experimental data amounted to 20, 30, 40, and 50 pieces of data which were grouped into 2 groups. This research also summarizes the application of K-Means clusters which have been widely used in various fields, including Health, Education, and Disaster. The results of grouping data with the three distance measurement methods are not too much different, namely the highest difference is 2 members of the data on 50 test data. The most iterations on 40 test data use the Euclidean distance, namely 7 iterations, and the least iteration on 20 test data uses Minkowski distance i.e. 3 iterations. On the 50 test data it takes 4 iterations. The amount of test data is not directly proportional to the number of iterations needed to reach the cluster in a stable state.

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Section: Bidang Penerapan Studi Kasus Klasterisasi Kesehatanunclassified

Perbandingan Metode Perhitungan Jarak pada Nilai Centroid dan Pengelompokan Data Menggunakan K-Means Clustering

Hartono

Eniyati

Hadiono

2023

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“…

Based on agus Perdana Windarto's research explained that K-Means algorithm can be used to do the grouping process well and can be developed with additional rules to produce clusters that are more optimal and quality [4]. The use of a k-means algorithm in dharmarajan and T. Velmurugan publications compares with two methods, namely k-Means and Fuzzy C-Means (FCM) by providing information on decision making in detecting cancer-affected areas [5].Riski Annisa et al use K-Means Algorithm to predict software defect module errors by proposing a point center algorithm to determine the initial centroid value against kmeans algorithm optimization have developed an algorithm in performing point center [6] . Dewi Pramudi ARTICLE INFO

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confidence: 99%

Best Cluster Optimization with Combination of K-Means Algorithm And Elbow Method Towards Rice Production Status Determination

Hasugian

Sinaga

Manurung

et al. 2021

Int. J. Artif. Intell. Res

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Indonesia is the third-largest country in the world with rice production reaching 83,037,000 and became the highest production in southeast Asia spread in several provinces in Indonesia The problem found that such product has not been able to cover the needs of Indonesian people with a very high population so that in the research conducted information excavation to generate potential to the pile of data that has been described and analyzed by BPS with clustering topics. Clustering will help related parties, especially the ministry of agriculture, in determining land development priorities and can minimize the shortage of rice production nationally. Grouping process by involving the K-means algorithm to group rice production with a combination of the elbow method as part of determining the number of clusters that will be recommended with attributes supporting the area of harvest, productivity, and production. Method of researching with data cleaning activities, data integration, data transformation, and application of K-means with a combination of elbow and pattern evaluation. The results achieved based on the work description with a combination of K-Means and elbow provide cluster recommendations that are the best choice or the most optimal is iteration 2 which is the lowest rice production group with a total of 22 provinces, rice production with a medium category of 9 and production with the highest category with 3 regions

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A Review for Software Defect Prediction Using Machine Learning Algorithms

Ali,

Eissa,

Omara

2023

Lecture Notes in Networks and Systems

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Improved point center algorithm for K-Means clustering to increase software defect prediction

Cited by 5 publications

References 0 publications

Perbandingan Metode Perhitungan Jarak pada Nilai Centroid dan Pengelompokan Data Menggunakan K-Means Clustering

Perbandingan Metode Perhitungan Jarak pada Nilai Centroid dan Pengelompokan Data Menggunakan K-Means Clustering

Best Cluster Optimization with Combination of K-Means Algorithm And Elbow Method Towards Rice Production Status Determination

A Review for Software Defect Prediction Using Machine Learning Algorithms

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