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All Journal Journal of Economic, Bussines and Accounting (COSTING) Jurnal Scientia
Bagas Pratama
Universitas Gunadarma
Economics, Econometrics & Finance Education Mathematics

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STRATEGY TO INCREASE THE COMPETITIVENESS OF THE E-COMMERCE SYSTEM ON TOKOPEDIA USING SWOT ANALYSIS Bagas Pratama
Jurnal Scientia Vol. 12 No. 01 (2023): Education, Sosial science and Planning technique
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/scientia.v12i01.1189

Abstract

The development of information technology has entered a new phase which has affected various aspects of the life of the world community, including buying and selling. E-commerce has become the new prima donna in shopping for people around the world, including in Indonesia. The purpose of this research is to find out the weaknesses, strengths, opportunities and threats of Tokopedia. As well as knowing how Tokopedia implements its marketing strategy. The type of research used in this research is qualitative research. The results obtained are that Tokopedia is the safest business and has many conveniences in terms of service and service features and the various features have various benefits and can be easily accessed and access is certain and there are competitors in every business, there must be competitors. whatever, so is the online business.
Perbandingan Metode Extreme Gradient Boosting (XGBOOST) Dengan Long Short-Term Memory (LSTM) Untuk Prediksi Saham Pt. Bank Mandiri Tbk. (BMRI) Bagas Pratama; Lintang Yuniar Banowosari
Journal of Economic, Bussines and Accounting (COSTING) Vol 7 No 3 (2024): Journal of Economic, Bussines and Accounting (COSTING)
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/costing.v7i3.9473

Abstract

In the continuously evolving world of investments, achieving optimal investment results is the primary goal of every investor. The high profit potential in stock investments makes it an attractive choice. However, it is difficult to predict the direction of stock price movements, but many methods and ways are used to predict in terms of buying and selling, one of which is the result of the very rapid development of computing for machine learning, namely artificial intelligence techniques. In this study, the artificial intelligence methods used are Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM). This study uses a stock dataset of PT. Bank Mandiri Tbk. (BMRI) for 20 years, especially in the open price column. After testing, the results from the XGBoost method are obtained, namely the Coefficient of Determination (R2) value of 89.09%, indicating that the results are good and the Mean Absolute Percentage Error (MAPE) is 3.21%, indicating that the error percentage is low. While in the LSTM method, the R2 value is 98.44%, meaning that the prediction results have been predicted very well and the MAPE is 1.77%, indicating that the error percentage is very low. Keywords: XGBoost, Open Price, Prediction, LSTM, Stocks
Co-Authors Lintang Yuniar Banowosari