Prediksi Return Saham menggunakan Bidirectional LSTM dengan Optimisasi Cuckoo Search
Abstract
Abstrak-Membeli saham bisa dijadikan salah satu pertimbangan berinvestasi selain membeli emas, tanah, dan lainnya. Apalagi ketika terdapat saham yang memiliki risiko yang kecil namun memiliki return yang tinggi. Sudah banyak sekali penelitian mengenai saham menggunakan berbagai metode. Dimulai dari metode yang paling konvensional, hingga menggunakan Deep Learning. Deep Learning merupakan salah satu metode yang ramai dibicarakan, karena metode ini rata-rata menghasilkan model prediksi yang memiliki keakuratan tinggi. Oleh karena itu, dalam tugas akhir dilakukan prediksi return saham pada indeks IDX 30 dengan membangun model prediksi return saham menggunakan kombinasi metode Bidirectional LSTM dan Cuckoo Search Optimization. Terdapat total 20 data saham yang diuji pada tugas akhir ini. Pada pengujian pertama prediksi return saham didapatkan dari prediksi harga saham yang diproses menjadi prediksi return saham. Sedangkan pada pengujian kedua, prediksi return saham didapatkan dari data return saham. Pada pengujian pertama 15 dari 20 data saham memiliki nilai Root Mean Square Error dan Mean Average Error yang lebih kecil ketika hasil prediksi Bidirectional LSTM dikombinasikan dengan Cuckoo Search Optimization. Sedangkan pada pengujian kedua 8 dari 20 data saham memiliki nilai Root Mean Square Error dan Mean Average Error yang lebih kecil ketika hasil prediksi Bidirectional LSTM dioptimasi dengan Cuckoo Search Optimization.
Kata kunci - saham, deep learning, IDX 30, bidirectional LSTM, cuckoo search optimization
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