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

Buslim, Nurhayati; Iswara, Rayi Pradono; Agustian, Fajar

doi:10.15408/jti.v13i2.19610

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Pengelompokan data industri rumahan dapat dibangun tentunya diperlukan sebuah algoritma pada sistem yang mampu menghasilkan pengelompokan data industri rumahan kedalam klasifikasi pemula, berkembang dan maju. Algoritma K-means dipilih oleh kebanyakan peneliti untuk menyelesaikan masalah dalam pengelompokan data [2][3][4][5][6]. Namun, algoritma Kmeans ternyata belum memberikan solusi untuk menghasilkan kualitas klaster yang optimal [7].…”

Section: Pendahuluanunclassified

Aplikasi Dynamic Cluster pada K-Means BerbasisWeb untuk Klasifikasi Data Industri Rumahan

Santoso¹,

Magdalena²,

Wardhana

2022

matrik

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Masalah utama yang dihadapi Pemerintah Daerah Provinsi Kepulauan Bangka Belitung saat ini adalah sulitnya mengklasifikasikan data industri rumahan berdasarkan Peraturan Menteri PPPA No 2 Tahun 2016 yaitu pemula, berkembang dan maju. Berdasarkan permasalahan tersebut diusulkan pengembangan algoritma Kmeans yaitu algoritma Dynamic cluster pada K-means dengan tujuan agar dapat menghasilkan klaster yang optimal dalam pengelompokan data industri rumahan dengan membangun aplikasi cerdas berbasis web. Penelitian ini menggunakan metode analisis data mining SEMMA, yang meliputi tahapan-tahapan seperti data sampel, deskripsi data, transformasi data, pemodelan data, dan evaluasi data. 3.466 industri rumah tangga digunakan sebagai sampel data. Kinerja algoritma dievaluasi menggunakan pengukuran validitas klaster Davies Bouldin Index (DBI). Hasil eksperimen menunjukkan bahwa algoritma Dynamic cluster pada K-means memberikan nilai yang optimal pada iterasi ke lima, dengan perolehan sebagai berikut: klaster pemula (C1) diperoleh sebanyak 3214, kemudian klaster berkembang (C2) diperoleh sebanyak 167 dan klaster maju (C3) diperoleh sebanyak 85. Hasil evaluasi validitas klaster menunjukan bahwa algoritma Dynamic cluster pada Kmeans memperoleh nilai DBI lebih kecil dibandingkan dengan algoritma K-means dengan nilai DBI sebesar 0.184. Implementasi algoritma dynamic cluster pada K-means untuk pengelompokan data industri rumahan pada Dinas P3ACSKB di Provinsi Kepulauan Bangka Belitung terbukti menghasilkan kualitas cluster yang lebih optimal.

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Section: Pendahuluanunclassified

Aplikasi Dynamic Cluster pada K-Means BerbasisWeb untuk Klasifikasi Data Industri Rumahan

Santoso¹,

Magdalena²,

Wardhana

2022

matrik

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“…Processing large amounts of data, such as student data surely need many resources: human resources, cost, and time [1]. Human-error cases were also found where sometimes the data isn't efficient enough and many error occurred.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning techniques to improve the accuracy of predictive model performance in the scholarship selection process

Buslim

2023

J. Appl. Data Sci.

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Scholarship selection with big volumes of college student data in an university undoubtedly required a lot of resources and time. Besides the inefficient factor, there are also human-error occurred in the scholarship selection process. Error and risk can be reduced with ensemble learning approach. The different with another method is that usually research will only choose one algorithm or doing comparison to search the best algorithm. But in ensemble learning, some of algorithms called base learner combined to shape a new more-established model. With ensemble, more accurate result of the scholarship selection produced and also had the most minimum error value. In this research, there are two algorithm used as the base learner which are K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). Experiment showed result of KNN with 91% of accuracy and SVM with at the rate of 75% accuracy. This base learner combined into an ensemble learning model using voting classifier. After last experiment, ensemble learning model succesfully created and produced the scholarship prediction result up to 100% of accuracy on training data. With streamlit, an application has been made which can automatically determine whether a student is accepted or rejected in the scholarship selection process. From the result, ensemble learning model can be used by academics as a Decision Support System (DSS) for determining scholarship recipients. This model can also be used as a development for institutions in the academic field.

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“…People mainly rely on experience and domain knowledge (Peng, 2022). The classification of things is mainly in the qualitative sense, and it is difficult to achieve the quantitative sense (Buslim et al, 2021). However, it is for the classification problems, and the ancient traditional taxonomy based only on experience and field knowledge is powerless (Ravuri & Vasundra, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Evaluation Algorithm of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering

Qin

Wei-xuan

Davis

2023

International Journal of Web-Based Learning and Teaching Technologies

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In response to the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, this paper proposes an English teaching ability estimation algorithm based on big data fuzzy K-means clustering. Firstly, the article establishes a constraint parameter index analysis model. Secondly, quantitative recursive analysis is used to evaluate the capabilities of big data information models and achieve entropy feature extraction of capability constrained feature information. Finally, by integrating big data information fusion and K-means clustering algorithm, the article achieves clustering and integration of indicator parameters for English teaching ability, prepares corresponding teaching resource allocation plans, and evaluates English teaching ability. The experimental results show that using this method to evaluate English teaching ability has good information fusion analysis ability and improves the accuracy of teaching ability evaluation and the efficiency of teaching resource application.

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

Cited by 3 publications

References 4 publications

Aplikasi Dynamic Cluster pada K-Means BerbasisWeb untuk Klasifikasi Data Industri Rumahan

Aplikasi Dynamic Cluster pada K-Means BerbasisWeb untuk Klasifikasi Data Industri Rumahan

Ensemble learning techniques to improve the accuracy of predictive model performance in the scholarship selection process

The Evaluation Algorithm of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering

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