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Yusuf Supriyanto

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Author-ID : 3731278

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Prediksi Harga Emas dengan Menggunakan Algoritma Support Vector Regression (Svr) dan Linear Regression (LR) Anisa Aulia; Bella Aprianti; Yusuf Supriyanto; Chaerur Rozikin
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 5 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Gold is a precious metal that is in great demand, because generally the value of gold tends to be stable and the price per gram will increase every year. Gold investment is divided into two, there are digital investments and physical investments. Sometimes in this gold investment investors will experience losses as well as gain profits. To minimize this, a technique is needed to predict the price of gold. Prediction technique is one of the techniques used in Machine Learning. This study aims to predict the price of gold using the Support Vector Regression (SVR) and Linear Regression (LR) algorithms as a comparison. The software that will be used is Jupyter Notebook using the Python programming language. The final result obtained is a graph of the gold price and of the MSE (Mean Squad Error) error on the the SVR Algorithm is 7.524505784357 and LR Algorithm is 4.04444791059

Prediksi Harga Minyak Kelapa Sawit Menggunakan Linear Regression Dan Random Forest Yusuf Supriyanto; M. Ilhamsyah; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 7 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Commodity export is an important activity because it can open up new market opportunities abroad. Besides being able to increase investment and foreign exchange for a country, palm oil is a plantation product that plays an important role in the Indonesian economy, palm oil is the largest foreign exchange earner. In palm oil exports, the volume tends to increase from 2016 to 2019 but when viewed from the export value of palm oil, it tends to fluctuate. Therefore it is necessary to predict the price of palm oil to help make commodity export decisions and also help palm oil investors in maximizing profits, in this study to predict the price of palm oil used data mining methods with the implementation of the Linear Regression and Random Forest algorithms using rapidminer, with data sharing scenarios training and testing is divided into three, namely 90:10, 80:20 and 70:30 to determine the performance of the algorithm. The data that will be used for research is historical data on palm oil prices taken from investing.com. From the results of the implementation of the algorithm obtained in the 90:10 data sharing scenario, the best algorithm is Random Forest with RMSE 25,106 results, in the second scenario with 80:20 data sharing the best algorithm is Linear Regression with RMSE 31,174, in the third scenario 70:30 Linear data sharing. regression has the best result with RMSE 30,227. then from the three scenarios, the Linear Regression algorithm gets the best performance

Co-Authors Anisa Aulia Bella Aprianti Chaerur Rozikin Ilhamsyah, M Ultach Enri

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