ANALISIS POLA KORELASI HARGA DAN VOLUME SAHAM BBCA MENGGUNAKAN ALGORITMA APRIORI - Noviyanto Firmansyah, Heru; Alwani, Yazid; Henandra Eka Putra, Dias; Verdinand, Rhenal; Arifin, Muhammad (J-ENSITEC, 2025)
This study will determine the stock movement pattern of PT Bank Central Asia Tbk (BBCA) using Association Rule Mining according to the Apriori algorithm. The main focus is to investigate the correlation of the lowest price, the highest price, and the trading volume of BBCA shares in one time period. Past stock data is first processed and discretized into categorical data: lowest and highest, on each attribute. The data mining process is carried out using WEKA software to generate association rules with very high confidence values and lift values. Mining produces the top 10 association rules, some of which have 100% confidence values and lift values greater than 1, indicating a very strong relationship between the attributes. For example, it is found that when the lowest price is in the "high" category, the highest price is also high with absolute confidence. These observations indicate that BBCA stock behavior can be inferred through correlations between simple technical variables. This can help investors and market analysts to identify hidden patterns and make informed decisions based on evidence. This approach in the future can be complemented with other technical indicators such as Moving Average and RSI for more accurate predictions. This method offers practical value for investors by revealing hidden patterns and supporting data-driven decision-making, especially when combined with other technical tools like Moving Average and RSI.