The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic

Samudi, Samudi; Widodo, Slamet; Brawijaya, Herlambang

doi:10.33395/sinkron.v5i1.10649

Cited by 16 publications

(14 citation statements)

References 10 publications

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“…K-Medoids are objects that represent their point of reference, not taking values as the mean of an object in each group. The algorithm will take the parameter from the input of k, with the number of groups that will be segregated between one part of n objects [14] - [15].…”

Section: K-medoids Algorithmmentioning

confidence: 99%

“…Various studies have been carried out by both local and foreign researchers, including the classification of COVID-19 based on incomplete heterogeneous data using the KNN algorithm [3], Classification of Covid-19 images using the deep features algorithm and fractional-order marine predators [5], K-Means Clustering COVID-19 Data [6], Clustering the spread of Corona Virus in DKI Jakarta using the K-Means Method [7], Implementation The K-Means algorithm for determining the level of the spread of the Covid-19 outbreak in Indonesia [2] and K-Medoids method clustering for learning applications during the COVID-19 outbreak [14]. From the results of previous research, this study has a difference in the method, which uses two methods as a comparison clustering between the K-Means and K-Medoids algorithms in clustering the spread of the coronavirus disease 19 in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

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The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia

Utomo

2021

Ilk. J. Ilm.

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The coronavirus spreads quickly through human-to-human transmission via close contact and respiratory droplets such as coughing or sneezing. Various studies have been carried out to deal with Covid-19. However, the cure for this virus has not been found until now. Based on data from the covid19.go.id page retrieved on January 1st, 2021, which was updated by the Ministry of Health, the overall number of confirmed cases was 1,078,314 active cases reaching 175,095 or 16.2% of confirmed cases, recovered 873,221 or 81.0% of confirmed cases, and death 29,998 or 2.8% of the confirmed cases. This study compares the two algorithms of data groups to analyze clustering patterns to determine the best data processing method. The data in this study sourced from the Ministry of Health, contained 4 attributes, including confirmed cases, treatment, recovery, and death cases. In this study, only 2 attributes were used: the confirmed and death cases. From the data analysis and processing results through a comparison between the K-Means method and the K-Medoids for clustering the spread of the coronavirus in Indonesia, a conclusion is derived. With the Davies Boulden index value from K2 to K9 values, it turns out that the K-Means method gets the smallest value at the K-5 of 0.064, while K-Medoids at the k-2 value of 0.411. Thus, from the two methods used, it can be concluded that the best method for clustering the spread of the coronavirus outbreaks in Indonesia is the K-Means method.

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Section: K-medoids Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia

Utomo

2021

Ilk. J. Ilm.

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“…Penerapan algoritma K-Medoids dalam pengelompokan WhatsApp, Moodle, dan Zoom untuk proses pembelajaran. Dari ketiga aplikasi yang biasa digunakan oleh mahasiswa dan dosen untuk proses pembelajaran [12]. Pengelompokan Kabupaten/Kota Provinsi Sulawesi Selatan dan Barat Berdasarkan Angka Partisipasi Pendidikan SMA/SMK/MA Menggunakan K-Medoids dan CLARA [13].…”

Section: Pendahuluanunclassified

Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids

Darma

Nurcahyo²

2021

INFEB

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Promotion technique is one of the methods used by AMIK and STIKOM Tunas Bangsa Pematangiantar in finding new students. By developing promotional techniques that are more effective and efficient, it can produce the information needed to get new student candidates. The data that is processed in this research is the data of new student candidates who come from the AMIK New Student Admissions (PMB) and STIKOM Tunas Bangsa Pematangsiantar. Based on promotional data in 2020, there are several promotions carried out, including through print media, banners, collaboration with SMA / SMK schools in Pematangsiantar city, through alumni, and the web. Furthermore, the data is processed using Rapidminer software. The processing stages are grouped using the K-Medoids algorithm on the data of prospective students, including name, place and date of birth, address, religion, cellphone number, school origin, choice of study program and source of information. Followed by the process of improving the data so that more accurate data is obtained to be processed. The results of the testing of this method are knowing which regions are applying to AMIK and STIKOM Tunas Bangsa Pematngsiantar which are grouped into 2 clusters, namely the highest and the lowest. The K-Medoids algorithm that is used makes a big contribution by providing new information that can be used as a reference for AMIK and STIKOM Tunas Bangsa Pematngsiantar in terms of Promoting New Student Admissions in the coming year.

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“…Pada tahun 2020 dilakukan penelitian dengan menggunakan algoritma K-Medoids untuk pengelompokkan aplikasi pembelajaran yang paling disukai selama pandemi Covid 19. Dalam pengumpulan data peneliti menggunakan angket online dengan menggunakan google form yang ditujukan kepada 100 siswa [6]. Pada Tahun 2019 dilakukan penelitian Untuk pengelompokan Lapangan Pekerjaan Utama Berdasarkan Data Penduduk 15 Tahun Keatas.…”

Section: Pendahuluanunclassified

Akurasi Pemberian Insentif Menggunakan Algoritma K-Medoids Terhadap Tingkat Kedisiplinan Pegawai

Robiansyah

Nurcahyo

2021

jidt

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Assessment of a discipline is a performance evaluation stage that is important for the continuity of company activities. Monitoring and assessment of an employee's discipline must be carried out continuously in order to improve the quality of human resources. This research was conducted to make the accuracy of providing incentives based on the level of employee discipline. The data processed in this study is a recapitulation of the attendance of AMIK and STIKOM Tunas Bangsa Pematangsiantar employees as many as 25 employees as a sample. For grouping the employee data using the K-Medoids Algorithm. K-Medoids groups a set of n objects into a number of k clusters using the partition clustering method. Furthermore, the employee data is processed using Rapid Miner software. Research using this method obtained results in the form of grouping employees into 3 groups that have good discipline levels of 12 employees, sufficient discipline levels of 8 employees, and less disciplinary levels of 5 employees. Based on the grouping results that have been produced, it can be a consideration for the leadership to determine the amount of incentives for employees.

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The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic

Cited by 16 publications

References 10 publications

The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia

The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia

Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids

Akurasi Pemberian Insentif Menggunakan Algoritma K-Medoids Terhadap Tingkat Kedisiplinan Pegawai

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