Penggunaan metode optimized random forest dalam menganalisis prediksi tren saham Indonesia = The use of optimized random forest method in analyzing the prediction of stock trends in Indonesia

Darmawan, Anak Agung Ayu Diva Shanty (2024) Penggunaan metode optimized random forest dalam menganalisis prediksi tren saham Indonesia = The use of optimized random forest method in analyzing the prediction of stock trends in Indonesia. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Saham adalah tanda kepemilikan dari suatu individu atau suatu lembaga yang merupakan instrumen keuangan berjangka panjang dan dapat diperjualbelikan. Penelitian ini menggunakan optimized random forest sebagai metode utama untuk membantu dalam memprediksi tren saham sektor energi. Saham yang digunakan adalah ADRO.JK, INDY.JK, PTBA.JK, TOBA.JK, dan UNTR.JK. Langkah pertama adalah mencari technical indicator akan digunakan sebagai variabel untuk membantu dalam memprediksi tren saham dengan menggunakan harga penutup yang sudah disesuaikan dari saham. Model akan dibangun dari dua data yaitu data berdasarkan saham dan data berdasarkan tahun. Terdapat empat tahapan dalam pembangunan model: model random forest, model optimized random forest, model random forest dengan feature importance, serta model optimized random forest dengan feature importance. Model akan dievaluasi berdasasrkan nilai akurasi, F1, dan nilai AUC. Hasil yang diperoleh dari metode optimized random forest akan dibandingkan dengan hasil yang didapatkan dari metode random forest. Penelitian menunjukkan bahwa optimized random forest merupakan metode yang lebih akurat untuk digunakan dalam memprediksi tren saham. Selain itu, hasil prediksi model terbaik juga akan digunakan dalam sebuah simulasi perdagangan saham. / Stocks are ownership symbols of an individual or an institution, which are long-term financial instruments and can be traded. This research employs the optimized random forest as the primary method to assist in predicting stock trends in the energy sector. The stocks used are ADRO.JK, INDY.JK, PTBA.JK, TOBA.JK, and UNTR.JK. The first step involves identifying the technical indicators that will be used as variables to aid in predicting stock trends, using adjusted closing prices of the stocks. The model will be constructed using two sets of data: stock-based data and year-based data. There are four stages in the model development: random forest model, optimized random forest model, random forest model with feature importance, and optimized random forest model with feature importance. The model will be evaluated based on accuracy, F1 score, and AUC value. The results obtained from the optimized random forest method will be compared with those from the random forest method. The research demonstrates that the optimized random forest is a more accurate method for predicting stock trends. Furthermore, the predictions from the best-performing model will be used in a stock trading simulation.

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