Implementasi Sistem Untuk Prediksi Harga Emas

Abstract

Gold is one of the investment commodities whose value continues to increase from year to year. The rise in gold prices will encourage investors to choose to invest in gold rather than the capital market. Investment in gold gives better results for the long term and with better purchasing power, so gold investment is an effective solution considering the value of money annually eroded by inflation. Such a state of economic instability is what drives many people, organizations and companies to invest in gold precious metals. Factors influencing the rise or fall of gold price according to Riefiyono (2010) are change of exchange rate (dollar exchange rate to rupiah), world political situation, domestic economic situation, and interest rate.
 
 The method used in this case is K-Nearest Neighbor (KNN) is a method that uses In the training phase, this algorithm only retains feature vectors and sample training data classification. In the classification phase, the same features are calculated for testing data (whose classification is unknown).
 
 The results obtained are successfully made an application for the prediction of gold prices by utilizing the method of Nearest Neighbor Retrieval. This application can help users in knowing the gold price prediction results are expensive or cheap with views in terms of economic situation, interest rates, political situation, and changes in exchange rates.

Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation

“…If the market demand for gold increases, the price tends to rise due to the limited supply of gold, and vice versa. In addition, geopolitical and security factors can also affect gold price fluctuations (Nugroho, 2018;Rakhmawati and Nurhalim, 2021). Political or security crises can cause global uncertainty and tension, thereby increasing market demand for gold as a reserve of value.…”

“…Meanwhile, there are articles that discuss machine learning algorithms, approaches, and techniques (Pragna et al, 2022;Radhamani et al, 2022;ul Sami and Junero, 2017;Tripurana et al, 2022) that applied machine learning technique to forecast financial financial indicators, with a primary focus on forecasting the price of gold. In another research, discuss the forecasting of gold prices using Generalized Autoregressive Conditional Heteroscedasticity (Garch) model (Haris, 2020); local polynomial nonparametric method equipped with GUI R (Hendrian et al, 2021); average-based fuzzy time series method (Hariwijaya et al, 2020); multiple linear regression method (Sravani et al, 2021); Nearest Neighbor Retrieval method (Nugroho, 2018); data mining techniques (Mahena et al, 2015). These research explores gold price forecasting in the context of making investment decisions in gold stocks.…”