Data science for digital culture improvement in higher education using K-means clustering and text analytics

Maylawati, Dian Sa’adillah; Priatna, Tedi; Sugilar, Hamdan; Ramdhani, Muhammad Ali

doi:10.11591/ijece.v10i5.pp4569-4580

Cited by 19 publications

(15 citation statements)

References 0 publications

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“…Salah satu teknik perhitungan yang dapat S diterapkan untuk menangani permasalahan tersebut diatas adalah dengan memanfaatkan teknik data mining dengan menggunakan metode Clustering. Data mining adalah teknik untuk menggali,menemukan dan mencari informasi penting atau wawasan pengetahuan (insight) tersembunyi dari database besar [2]. Salah satu tujuan data mining adalah untuk mencari pola atau trend tertentu yang hendak di capai sehingga dapat digunakan untuk memprediksi dan mendukung dalam hal pengambilan keputusan di kemudian waktu [3].…”

Section: Pendahuluanunclassified

Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas

Supriyadi

Triayudi

Sholihati

2021

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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Armada kendaraan truk merupakan salah satu aset utama dalam bisnis di bidang jasa transportasi. Evaluasi kinerja amada secara cepat dan akurat diperlukan untuk mendukung tercapainya produktivitas armada secara maksimal sehingga target perusahaan dapat mudah tercapai. Proses evaluasi kinerja armada yang masih dilakukan secara manual menyebabkan rumitnya proses pengolahan dan kurang akurat nya hasil evaluasi yang diperoleh, sehingga diperlukan suatu teknik pengolahan data secara cepat dan lebih akurat salah satunya dengan namun menerapkan teknik data mining menggunakan metode clustering. Metode Clustering akan digunakan untuk mengelompokkan setiap armada kendaraan berdasarkan produktivitas kinerjanya. Penelitian ini membandingkan penerapan Algoritma K-Means dan K-Medoids, yang kemudian dilakukan uji validitas terhadap hasil cluster yang terbentuk. Davies Bouldin Index sebagai metode dalam analisis klaster menghasilkan nilai validitas sebesar 0,67 untuk K-Means clustering dan 1,78 untuk K-Medoids. Berdasarkan nilai validitas yang dihasilkan Algoritma K-Means dipilih untuk diimplementasikan pada pembuatan aplikasi clustering armada kendaraan berbasis web karena paling relevan dengan nilai validitas DBI yang lebih rendah dari pada K-Medoids. Pengujian yang telah dilakukan terhadap hasil clustering pada aplikasi web didapatkan persentase kesesuaian sebesar 97 % baik dengan tool Rapidminer maupun dengan perhitungan secara manual

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Section: Pendahuluanunclassified

Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas

Supriyadi

Triayudi

Sholihati

2021

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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“…The algorithm defines certain data by a specific number if the clusters are determined a priori [14]. Despite all its advantages [34], k-means has some weaknesses, but these later ones do not affect our contribution to studying the similarity score between consumers' queries and published WSs. Our approach consists of using clustering through classification and filtration rather than similarity.…”

Section: Web Service Clusteringmentioning

confidence: 99%

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Fariss¹,

Allali²,

Asaidi³

et al. 2021

IJEECS

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Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.

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“…c. Random initialization of values for each cluster. d. Repeat steps a, b and c until the data convergence [22], [23]. e. Based on the above, the process behind K-means is applied…”

Section: K-meansmentioning

confidence: 99%

Forecasting model with machine learning in higher education ICFES exams

Cervera

Parra

Prado

2021

IJECE

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<span>In this paper, we proposed to make different forecasting models in the University education through the algorithms K-means, K-closest neighbor, neural network, and naïve Bayes, which apply to specific exams of engineering, licensed and scientific mathematical thinking in Saber Pro of Colombia. ICFES Saber Pro is an exam required for the degree of all students who carry out undergraduate programs in higher education. The Colombian government regulated this exam in 2009 in the decree 3963 intending to verify the development of competencies, knowledge level, and quality of the programs and institutions. The objective is to use data to convert into information, search patterns, and select the best variables and harness the potential of data (average 650.000 data per semester). The study has found that the combination of features was: women have greater participation (68%) in Mathematics, Engineering, and Teaching careers, the urban area continues to be the preferred place to apply for higher studies (94%), Internet use increased by 50% in the last year, the support of the family nucleus is still relevant for the support in the formation of the children.</span>

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Data science for digital culture improvement in higher education using K-means clustering and text analytics

Cited by 19 publications

References 0 publications

Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas

Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Forecasting model with machine learning in higher education ICFES exams

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