Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics
Putra, Raditya Amanta
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimalisasi Portofolio Saham Syariah Berbasis Prediksi Menggunakan Long Short-Term Memory (LSTM) Nurmawati, Erna; Abyasa, Rayhan; Putra, Raditya Amanta
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8421

Abstract

Saham merupakan salah satu jenis investasi aset finansial yang berpotensi untuk memberikan tingkat imbal balik yang tinggi sehingga menjadi salah satu instrument investasi yang popular. Salah satu jenis saham yang popular di Indonesia adalah saham syariah yang didukung kuat dengan ajaran agama islam (shariah compliant). Saham syariah mempunyai kinerja yang baik jika dibandingkan dengan saham konvensional ketika terjadi krisis keuangan ditandai dengan risiko indeks yang lebih kecil. Investor saham selalu menginginkan hasil timbal balik yang maksimal dengan risiko seminimal mungkin. Keinginan tersebut dapat tercapai dengan menyeleksi saham dengan return terbesar lalu melakukan optimalisasi pada potofolio saham. Salah satu metode seleksi saham yang dapat dilakukan adalah dengan memprediksi harga saham dengan menggunakan model LSTM pada indeks JII. Saham dengan return terbesar sesuai dengan hasil prediksi akan dimasukkan ke dalam satu portofolio yang akan dioptimalisasi dengan metode Mean-Variance (MV) dan Equal Weight (EW) yang akan diambil metode terbaik. Sebagai pembanding, portofolio dengan saham yang dipilih secara acak akan dibentuk dan dibandingkan hasilnya. Hasil penelitian menunjukkan portofolio yang dibentuk dengan menggunakan prediksi model LSTM dan metode optimalisasi MV memiliki keseimbangan dalam nilai mean return bulanan, standar deviasi bulanan, sharpe ratio bulanan, serta simulasi investasi sepanjang tahun 2023.
Prediction-based Stock Portfolio Optimization Using Bidirectional Long Short-Term Memory (BiLSTM) and LSTM Putra, Raditya Amanta; Nurmawati, Erna
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.5941

Abstract

Purpose: Investment is the allocation of funds with the aim of obtaining profits in the future. An example of the investment instruments with high returns and high risks are stocks. The risks associated with the investment can be reduced by forming a portfolio of quality stocks optimized through mean-variance (MV). This is necessary because successful selection of high-quality stocks depends on the future performance which can be determined through accurate price prediction. Methods: Stock price can be predicted through the adoption of different forms of deep learning methods. Therefore, BiLSTM and LSTM models were applied in this research using the stocks listed on the LQ45 index as case study. Result: The utilization of LSTM and BiLSTM models for stock price prediction produced favorable outcomes. It was observed that BiLSTM outperformed LSTM by achieving an average MAPE value of 2.1765, MAE of 104.05, and RMSE of 139.04. The model was subsequently applied to predict a set of stocks with the most promising returns which were later incorporated into the portfolio and further optimized using the Mean-Variance (MV). The results from the optimization and evaluation of the portfolio showed that the BiLSTM+MV strategy proposed had the highest Sharpe Ratio value at k=4 compared to the other models. The stocks found in the optimal portfolio were BRPT with a weight of 19.7%, ACES had 16.9%, MAPI 11.8%, and BMRI at 51.6%. Novelty: This research conducted a novel comparison of LSTM and BiLSTM models for the prediction of stock prices of companies listed in the LQ45 index which were further used to construct a portfolio. Past research showed that the development of portfolios based on predictions was not popular.
Co-Authors Abyasa, Rayhan Nurmawati, Erna