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All Journal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Journal of Informatics and Information Security
Tri Dharma Putra
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Computer Science & IT

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Journal : Journal of Informatics and Information Security

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Perancangan Sistem Booking Lapangan Badminton Berbasis Web Menggunakan Algoritma  First In First Out Muhammad Imron Yusup; Tri Dharma Putra; Andy Achmad Hendharsetiawan
Journal of Informatic and Information Security Vol. 4 No. 1 (2023): Juni 2023
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/ymybg645

Abstract

Badminton is a sport that is in great demand by people, from children to adults. GOR WGN Sport Center is one of the businesses engaged in badminton sports field rental services located in Bekasi City. In terms of field rental services, they still use the manual method by visiting the location directly, which is less effective. With this lack of information, customers do not know the field rental schedule in real time, so information related to the schedule is urgently needed. For this reason, in leasing the field at the WGN Sport Center Sport Center, an information system related to website-based field rental is needed which will make it easier for customers to find information in the field booking process and field rental schedules in real time. The algorithm used is the First In First Out (FIFO) algorithm. In the FIFO algorithm data buffering is regulated and manipulated based on the fact that the first incoming data will be processed first, that is, in its implementation, the first customer to make a field booking will be served first.
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 Teknik Informatika, Fakultas Teknik 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 Pengelompokkan Minat Konsumen Gas LPG Pada Pangkalan Sudiawati Annisa Wulandari; Tri Dharma Putra; Srisulistiowati, Dwi Budi
Journal of Informatic and Information Security Vol. 3 No. 1 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/vfx5ae35

Abstract

Pangkalan Sudiawati is one of the shops engaged in the sale of LPG Gas products domiciled in Harapan Indah, North Bekasi. The goods sold at the Sudiawati base are 3 Kg and 12 Kg LPG Gas. These problems can be solved by using one of the techniques in data mining, namely the K-Means Clustering algorithm. This research is intended to assist Pangkalan Sudiawati in selling 3 Kg and 12 Kg LPG gas to consumers, to group sales data in order to maximize stock management. The data is processed by manual calculations using the K-Means algorithm and using Microsoft Excel 2019 Software. These results can be used to improve stock managementand sales strategies at Pangkalan Sudiawati.
Co-Authors Andy Achmad Hendharsetiawan Annisa Wulandari Dwi Budi Srisulistiowati Muhammad Imron Yusup Priatna, Wowon Priatna Rafie Hanifan Rakhmat Purnomo