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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfotek Global
Ronal Yulyanto Suladi
Universitas Budi Luhur
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Implementation of Database Distributed Sharding Horizontal Partition in MySQL. Case Study of Application of Food Serving On Kemkes Samidi Samidi; Ronal Yulyanto Suladi; Ario Bambang Lesmana
JURNAL SISFOTEK GLOBAL Vol 12, No 1 (2022): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v12i1.477

Abstract

In today's digital era, database systems are becoming a mandatory thing that is used in every industry field to improve the performance of the industry, over time with the increasing need for databases, single databases and single servers are considered less capable in handling data that continues to increase in number, so it often leads to a decline in performance. Therefore came the idea of centralized data storage that can be modified to accommodate the availability, scalability, reliability, and management of data. One way that can be done is to apply Distribute Database using Sharding Horizontal Partition technique and experimental methods using mariadb and spider engine. The test was carried out on the database of food processing sites (TPP) in the TPP application at the ministry of health. The test results prove that although in some journals about Sharding, more sharding is implemented on NoSQL DBMS such as MongoDB, influx dB. In this study it can be proven that sharding can also be done in relational DBMS such as MariaDB and MySQL, data can be distribute and load process too, test result successful although the performance of time query response is still better single database than distribute database.
Comparison of the RFM Model's Actual Value and Score Value for Clustering Samidi Samidi; Ronal Yulyanto Suladi; Dewi Kusumaningsih
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5416

Abstract

Clustering algorithms and Recency-Frequency-Money (RFM) models are widely implemented in various sectors of e-commerce, banking, telecommunications and other industries to obtain customer segmentation. The RFM model will assess a line of data which includes the recency and frequency of data appearance, as well as the monetary value of a transaction made by a customer. Choosing the right RFM model also influences the analysis of cluster results, the output of cluster results is more compact for the same clusters (inter-cluster) and separate for other clusters (intra-cluster). Through an experimental approach, this research aims to find the best data set transformation model between actual RFM values and RFM scores. The method used is to compare the actual RFM value model and the RFM score and use the silhouette score value as an indicator to obtain the best clustering results using the K-Means algorithm. The subject of this research is a stall-based e-Commerce application, where data was taken in the Wiradesa area, Central Java. The resulting data set consisted of 273,454 rows with 18 attributes from January 2022 to December 2022 by collecting historical data from shopping outlets to wholesalers. The analysis of the data set was carried out by transforming the data set using the RFM method into actual values and score values; then the dataset was used to obtain the best cluster data. The results of this research show that transaction data based on time (time series) can be transformed into data in the RFM model where the actual value is better than the RFM score model with a silhouette score = 0.624646 and the number of clusters (K) =3. The results of the clustering process also form a series of data with a cluster label, thus forming supervised learning data.
Co-Authors Ario Bambang Lesmana Dewi Kusumaningsih Samidi Samidi