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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC)
Ronsen Purba
Universitas Mikroskil, Medan
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation Sio Jurnalis Pipin; Ronsen Purba; Heru Kurniawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4014

Abstract

Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction. It captures temporal dependencies, identifies non-linear relationships, and deciphers complex trends in stock price data. This study employs deep learning techniques with the RNN-LSTM model optimized using Adaptive Moment Estimation (Adam) to enhance stock price prediction accuracy by leveraging historical stock price data and technical factors. Data preprocessing, including handling missing values and data normalization, aids the model in navigating the dataset's intricacies. Test results utilizing the Mean Squared Error (MSE) metric reveal the model's ability to produce predictions that closely resemble actual stock prices, with a low loss value of 0109012. The model also exhibits good predictive accuracy, as evidenced by a favorable Mean Percentage Error (MPE) score of 1.74% between predicted and actual values. These findings hold valuable implications for assisting investors and financial practitioners in managing complexity and uncertainty within the stock market
Analisis Sentimen dan Evolusi Topik terhadap Program Makan Bergizi Gratis Menggunakan IndoBERT dan cDTM Muhammad Hamzah Fauzi; Ronsen Purba
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10085

Abstract

This study aims to analyze public sentiment and the development of discussion topics related to the MBG program. Sentiment analysis was conducted using the IndoBERT model, while evolution topic analysis used the Continuous-Time Dynamic Topic Model (cDTM). The evaluation results showed that the IndoBERT model was able to classify sentiment with an accuracy value of 92.5% and an F1-score of 0.924. Integration between IndoBERT and cDTM showed a dominance of negative sentiment, especially in topics related to program implementation, while positive sentiment appeared more often in topics related to health and nutrition. The integration of sentiment and temporal topic analysis provides a more comprehensive understanding of the dynamics of public opinion regarding the MBG program.
Co-Authors Heru Kurniawan Muhammad Hamzah Fauzi Sio Jurnalis Pipin