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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Mathematics, Computation and Statistics (JMATHCOS)
Dini, Sekti Kartika
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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GOLD PRICE PREDICTION IN INDONESIA BASED ON INTEREST RATE USING DISTRIBUTED LAG ALMON TRANSFORMATION Aqilah, Nanda Yumna; Dini, Sekti Kartika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1889-1898

Abstract

Gold is valued for its safety and profitability, driven by steady price changes and influenced by interest rates. Accurately predicting gold prices is very important to make the right investment decisions. This study aims to build a gold price model in Indonesia using the Almon transformation lag distribution and see gold price predictions based on the model that has been built. We used the data on gold prices and interest rates from January 2016 to December 2023. Based on the results of the analysis, the best Almon transformation model used in this study is the Almon model with a maximum lag length of 16 and the second polynomial degree. The prediction results have a MAPE of 16.49%, which shows that the Almon model can predict gold prices well for one year. This study contributes to the understanding of gold price dynamics amid economic variations. However, limitations in the model assumptions should be considered.
Application of Association Rule Method Using the ECLAT Algorithm on Over-the-Counter Drug Transaction Data at Pharmacy “X” Dini, Sekti Kartika; Fuadah, Sekar Ridho
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9187

Abstract

Health is a fundamental need for every human being that plays an important role in improving the quality of life and productivity of society. As one of the means of pharmaceutical services, pharmacies not only serve as places for drug distribution but also as abundant sources of information regarding community purchasing patterns for health products. In the current digital era, every transaction that occurs at a pharmacy generates high-value data that can be utilised for data-driven decision making. One of the relevant analytical approaches in this context is Market Basket Analysis (MBA). Association rule is a commonly used method in MBA. This method generates rules in the form of implications "if X, then Y" based on the frequency of item occurrences in the data. The algorithm that can be used to perform association rule mining is ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal). Based on the results of the descriptive analysis of non-prescription drug transaction data from Pharmacy "X," it is known that the drugs frequently purchased by consumers are those containing paracetamol. Next, the association rule with the ECLAT algorithm with a minimum support of 0,0004 and a minimum confidence of 0,5 produces three rules that reflect that these drugs are often purchased together by consumers of Pharmacy "X".
Co-Authors Aqilah, Nanda Yumna Fuadah, Sekar Ridho