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All Journal Journal of Informatics and Information Security
Priatna, Wowon Priatna
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Computer Science & IT

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Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning Rakhmat Purnomo; Priatna, Wowon Priatna; Tri Dharma Putra
Journal of Informatic and Information Security Vol. 2 No. 1 (2021): Juni 2021
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/v6cdp268

Abstract

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance.
Penerapan Algoritma K-Means untuk Mengetahui Pola Persediaan Barang pada Toko Raja Bekasi Intan Safira; Ratna Salkiawati; Priatna, Wowon Priatna
Journal of Informatic and Information Security Vol. 3 No. 1 (2022): Juni 2022
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/ykryzk32

Abstract

This study aims to determine how much the results of grouping goods affect the needs of consumers. Excess inventory will greatly fill the warehouse and be inefficient because of the expiration date on food products, beverages, etc. Currently Toko Raja still manages goods manually so it is not time efficient. To solve this problem, a technique is needed, namely data mining. The data mining technique that will be used in this research is the K-Means Clustering method. K-Means is one of the most popular algorithms because it is easy and simple to implement. However, the results of the clustering of K-Means are very dependent on theselection of the initial cluster center point. Calculation of accuracy in this study using the test results of the K-Means clustering method using the Davies-Bouldin Index (DBI) is 1.856 where the DBI value close to zero cluster is good enough. From the resulting accuracy, it can be concluded that the K-Means Clustering method can support the system well.
Co-Authors Intan Safira Rakhmat Purnomo Ratna Salkiawati Tri Dharma Putra