Big Data Technology for Comparative Study of K-Means and Fuzzy C-Means Algorithms Performance

Nurhayati, .; Kania, Tifani Shallynda; Wardhani, Luh Kesuma; Hakiem, Nashrul; Busman,; Maarif, Haris Al Qodri

doi:10.1109/iccce.2018.8539268

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…Pengelompokan adalah metode analisis yang digunakan untuk mengelompokkan objek data ke dalam pengelompokan data yang dibutuhkan oleh setiap pengelompokan yang berbeda dan pengelompokan ini dapat dikategorikan sebagai pengelompokan secara hierarkis [17]. Keunggulan dari K-Means adalah mudah untuk diimplementasikan dan dijalankan; membutuhkan waktu relatif singkat; mudah diadaptasi; serta umum digunakan [18]. Algoritma K-Means melakukan clustering berbasis titik (centroid) dengan menentukan tiga parameter yaitu jumlah Klaster, inisialisasi Klaster dan jarak sistem yang disesuaikan dengan objek penelitian yang ada baik secara manual maupun dengan bantuan software.…”

Section: K-meansunclassified

Pemetaan Karakteristik Sekolah Sasaran Promosi pada UNKRISWINA SUMBA menggunakan K-Means

Lobo

Prasetyo²,

Hartomo³

2022

mib

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The rapid development of technology has an impact on how data is collected. A high level of data productivity will be in vain if it is not followed by the ability to process data that can produce information that helps the development of the organization. This study aims to help the Promotion Section of UNKRISWINA SUMBA in mapping the characteristics of the target schools and then provide alternative promotion strategies as input in formulating forms of institutional promotion. The data used is in the form of student data who have registered at UNKRISWINA SUMBA since 2016 – 2020. Data processing uses the concept of data mining by applying the K-Means algorithm. K-Means algorithm is used for clustering promotion target schools as many as 4 clusters. Cluster determination is carried out using the elbow method to determine the optimal value of k to perform calculations. Based on the results of processing based on the K-Means algorithm, it is known that as many as 8 schools in cluster 0 are the schools with the most students enrolling in UNKRISWINA SUMBA, 76 schools in cluster 1 are schools with the fewest students enrolling in UNKRISWINA SUMBA, 21 schools those in cluster 2 are schools with quite a lot of students enrolling in UNKRISWINA SUMBA, and 1 school in cluster 3 is a school with quite a number of students enrolling in UNKRISWINA SUMBA but focusing on the Economic Development and Management study program.

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Section: K-meansunclassified

Pemetaan Karakteristik Sekolah Sasaran Promosi pada UNKRISWINA SUMBA menggunakan K-Means

Lobo

Prasetyo²,

Hartomo³

2022

mib

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“…Based on related work of this research, the first is namely "Big Data Technology for Comparative Study of K-Means and Fuzzy C-Means Algorithms Performance" [10]. It discusses Big data is technology that can manage very large amounts of data, in very fast time to allow real-time analysis and reactions.…”

Section: Introductionmentioning

confidence: 99%

The Modeling of "Mustahiq" Data Using K-Means Clustering Algorithm and Big Data Analysis (Case Study: Laz)

Buslim

Iswara

Agustian

2021

J. Teknik Informatika

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There are a lot of Mustahiq data in LAZ (Lembaga Amil Zakat) which is spread in many locations today. Each LAZ has Mustahiq data that is different in type from other LAZ. There are differences in Mustahiq data types so that data that is so large cannot be used together even though the purpose of the data is the same to determine Mustahiq data. And to find out whether the Mustahiq data is still up to date (renewable), of course it will be very difficult due to the types of data types that are not uniform or different, long time span, and the large amount of data. To give zakat to Mustahiq certainly requires speed of information. So, in giving zakat to Mustahiq, LAZ will find it difficult to monitor the progress of the Mustahiq. It is possible that a Mustahiq will change his condition to become a Muzaki. This is the reason for the researcher to take this theme in order to help the existing LAZ to make it easier to cluster Mustahiq data. Furthermore, the data already in the cluster can be used by LAZ managers to develop the organization. This can also be a reference for determining the zakat recipient cluster to those who are entitled later. The research is "Modeling using K-Means Algorithm and Big Data analysis in determine Mustahiq data ". We got data Mustahiq with random sample from online and offline survey. Online data survey with Google form and Offline Data survey we got from BAZNAS (National Amil Zakat Agency) in Indonesia and another zakat agency (LAZ) in Jakarta. We conducted by combining data to analyzed using Big Data and K-Means Algorithm. K-Means algorithm is an algorithm for cluster n objects based on attributes into k partitions according to criteria that will be determined from large and diverse Mustahiq data. This research focuses on modeling that applies K-Means Algorithms and Big Data Analysis. The first we made tools for grouping simulation test data. We do several experimental and simulation scenarios to find a model in mapping Mustahiq data to developed best model for processing the data. The results of this study are displayed in tabular and graphical form, namely the proposed Mustahiq data processing model at Zakat Agency (LAZ). The simulation result from a total of 1109 correspondents, 300 correspondents are included in the Mustahiq cluster and 809 correspondents are included in the Non-Mustahiq cluster and have an accuracy rate of 83.40%. That means accuracy of the system modeling able to determine data Mustahiq. Result filtering based on Gender is “Male” accuracy 83.93%, based on Age is ”30-39” accuracy 71,03%, based on Job is “PNS” accuracy 83.39%, based on Education is “S1” accuracy 83.79%. The advantaged of research expected to be able to determine quickly whether the person meets the criteria as a mustahik or Muzaki for LAZ (Amil Zakat Agency). The result of modeling is K-Means clustering algorithm application program can be used if UIN Syarif Hidayatullah Jakarta want to develop LAZ (Amil Zakat Agency) too.

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“…For the experiments on big data analysis models, I performed model construction and parameter tuning based on real data, and I evaluated and analyzed the performance of different algorithms. Nurhayati et al (2018) focused on big data technology for a comparative study of k-means and fuzzy c-means algorithms performance. Hadoop and Hive were chosen as the big data technology (Nurhayati et al, 2018).…”

mentioning

confidence: 99%

“…Nurhayati et al (2018) focused on big data technology for a comparative study of k-means and fuzzy c-means algorithms performance. Hadoop and Hive were chosen as the big data technology (Nurhayati et al, 2018). Zhou et al (2019) discussed improving students' research-based learning ability and establishing a new type of effective student-centered teaching model for college English education with the help of English classic reading.…”

mentioning

confidence: 99%

Application of Clustering Algorithm in the Evaluation of College Students' English Reading Ability Under the Framework of Big Data

Wang

2024

International Journal of Web-Based Learning and Teaching Technologies

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In recent years, China has accelerated the process of internationalization and made more and more achievements in transnational communication and cooperation. English learning is very important for contemporary college students. And English reading is an important means to acquire English language knowledge, understand external information and improve English language practice ability. Therefore, cultivating college students' English reading ability is a pivotal element in college English teaching. This study will focus on the application of clustering algorithm based on big data framework in the evaluation of college students' English reading ability, aiming at exploring and establishing an effective evaluation model to promote the improvement of college students' English reading ability and academic achievement.

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Big Data Technology for Comparative Study of K-Means and Fuzzy C-Means Algorithms Performance

Cited by 6 publications

References 3 publications

Pemetaan Karakteristik Sekolah Sasaran Promosi pada UNKRISWINA SUMBA menggunakan K-Means

Pemetaan Karakteristik Sekolah Sasaran Promosi pada UNKRISWINA SUMBA menggunakan K-Means

The Modeling of "Mustahiq" Data Using K-Means Clustering Algorithm and Big Data Analysis (Case Study: Laz)

Application of Clustering Algorithm in the Evaluation of College Students' English Reading Ability Under the Framework of Big Data

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