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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Teknologi Informatika dan Komputer
Gatot Soepriyono
Universitas Nasional
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Implementasi Data Mining dengan Algoritma Apriori dalam Menentukan Pola Pembelian Aksesoris Laptop Gatot Soepriyono; Agung Triayudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6555

Abstract

Consumer purchasing patterns are an important factor in the business world, which influences marketing strategies, stock management and company profits. In the context of the laptop accessories business, a deep understanding of consumer purchasing patterns is very necessary to increase operational efficiency and customer satisfaction. Data mining, as a powerful data analysis method, has become an effective tool in uncovering these patterns. One of the data mining algorithms that is often used to analyze association patterns is the Apriori algorithm. This research applies the Apriori algorithm to identify and analyze purchasing patterns for laptop accessories from transaction data obtained from a retail store. By analyzing this data, we can identify items that are frequently purchased together and purchasing patterns that may not be immediately apparent to humans. The results of this analysis provide valuable insight into consumer preferences, helping retail stores to design more effective marketing strategies. The results of this research can also be used to manage stock more efficiently. By knowing deeper purchasing patterns, retail stores can predict stock needs more accurately, reduce the risk of excess inventory, and optimize operational expenses. Thus, this research can help increase company profits and satisfy customers by providing accessories that suit their preferences. In the increasingly developing information era, the use of data mining and algorithms such as Apriori is becoming increasingly important. This research is an example of how data analysis can be used in the real world to support smarter and more efficient decision making in the laptop accessories business. As a result, a better understanding of consumer behavior and purchasing patterns can provide a strong foundation for developing successful business strategies.
Perbandingan Kinerja Algoritma Clustering Data Mining Untuk Prediksi Harga Saham Pada Reksadana dengan Davies Bouldin Index Gatot Soepriyono; Agung Triayudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6623

Abstract

Mutual funds are a container that can be used to accommodate funds from the public which will later be distributed to the owners of the company. The ease of investing in share prices cannot be separated from the ease of obtaining information. The share price that is very popular with the public is the share price for banks, whether privately owned or government owned. However, even though banks are very close and popular with capital market players, this does not rule out the possibility of a decline in share prices. This problem is not a problem that can be considered trivial and ignored, if you continuously experience losses from the capital market it will certainly give rise to distrust or a lack of interest in the public to participate in investing in companies. Predictions for stock prices must be done well and correctly and get accurate results, therefore it is necessary to use a special technique or method to help carry out the prediction process until results are obtained with a good level of accuracy. The expected prediction process is in line with the concept of data mining. The process of applying clustering for predictions is also considered very suitable, this is because in stock prices there is no target class for each data. The K-Means algorithm and K-Medoids algorithm are part of cluster data mining to be used to make predictions based on cluster formation. The purpose of the comparison is to get more reliable results, where these results can be seen from better algorithm performance. The performance measurement process for the K-Means and K-Medoids algorithms will later be assessed based on the Davies Bouldin Index (DBI). The results of the research show that the performance results of the K-Means algorithm are better than the K-Medoids algorithm. This is proven by the DBI value obtained from the K-Means algorithm being no more than 0.6, while in the K-Medoids algorithm the DBI value obtained is up to 5.822. Overall, each stock data has an optimal cluster based on the clustering process with the K-Means algorithm. The optimal cluster results in BMRI stock data, the optimal cluster is at K=4 with a DBI value of 0.501. In the BBNI stock data, the optimal cluster is at K=4 with a DBI value of 0.500. In the BBCA stock data, the optimal cluster is at K=3 with a DBI value of 0.441. In the BNGA stock data, the optimal cluster is at K=2 with a DBI value of 0.263. In the BDMN stock data the optimal cluster is at K=2 with a DBI value of 0.028 and in the MEGA stock data the optimal cluster is at K=4 with a DBI value of 0.353.
Co-Authors Agung Triayudi Andrianingsih Danis Setiawan Flipo Hariski Irfansyah Ismia Iwandini Muhammad Hafizh Lazuardi Novi Dian Nathasia Nur Hayati