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INDONESIA
Journal of Information System, Applied, Management, Accounting and Research
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta
ISSN : 25988700     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
JISIMAR (Journal of Information System, Applied, Management, Accounting and Research), terbit empat kali setahun pada bulan Februari, Mei, Agustus dan November, memuat naskah hasil pemikiran dan hasil penelitian di bidang Teknologi Informasi, Sistem Informasi, Sistem Informasi Manajemen, Sistem Informasi Akuntansi, Ilmu Manajemen dan Manajemen Terapan, Manajemen Sumber Daya, Sistem Manajemen Enterprise, Akuntansi.
Arjuna Subject : -
Articles 632 Documents
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Sistem Prediksi Hasil Produksi Tikus Putih Dengan Algoritma Forecasting Regresi Berganda (Studi Kasus: Rasa Farm) Pratama, Moch Frendika; Supriadi, Irwin; Abadi, Iwan
Journal of Information System, Applied, Management, Accounting and Research Vol 9 No 3 (2025): JISAMAR (Journal of Information System, Applied, Management, Accounting and Resea
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v9i3.1849

Abstract

White rat farming has an important role in scientific research and the pharmaceutical industry. However, promising to predict white rat populations each harvest cycle is often a challenge for breeders. This research aims to design and implement a prediction system for white rat production at Rasa Farm using a multiple regression forecasting algorithm. This method was chosen because it is able to analyze the relationship between independent variables and dependent variables in predicting white rat populations.This system was developed on a web basis to make it easier for breeders to upload data, analyze information and get population predictions automatically. The data used includes main factors such as the number of male and female broodstock, mortality rates, daily feed consumption, and environmental temperature. The test results show that the multiple regression algorithm is able to provide fairly accurate estimates with a low error rate (MAPE).This system is expected to help farmers in planning production, optimizing resources, and increasing efficiency in managing white rat farms. For further development, the integration of IoT technology and more complex machine learning methods can be applied to increase prediction accuracy. Keywords:Production prediction, white mice, multiple regression, forecasting, web-based system
Model Prediksi Harga Saham BJBR Menggunakan Long Short-Term Memory (LSTM) untuk Mendukung Keputusan Investasi Susanti, Sussy; Kuraesin, Aneu
Journal of Information System, Applied, Management, Accounting and Research Vol 9 No 3 (2025): JISAMAR (Journal of Information System, Applied, Management, Accounting and Resea
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v9i3.2047

Abstract

Stocks are one of the most popular investment instruments among the public due to their potential for long-term returns through price appreciation and dividend distributions; however, stock price movements are heavily influenced by various factors such as macroeconomic conditions, market sentiment, and corporate actions, making accurate forecasting essential for investors to minimize risk and maximize profit. PT Bank BJB Tbk (ticker code: BJBR), a major bank in Indonesia that operates both conventional and Sharia-based services, has shown high volatility over the past few. Therefore, this research aims to develop a stock price prediction model for BJBR using the Long Short-Term Memory (LSTM) approach, a variant of Recurrent Neural Networks (RNN) well-suited for time series data. Historical closing price data from January 2020 to June 2025 were collected, preprocessed through normalization, dataset division, and transformation into supervised learning format, and then used to train an LSTM model with a two-layer architecture and dropout layers to prevent overfitting. The model was trained using the Adam optimizer and evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Evaluation results showed that the model achieved a high level of accuracy, with an R² value of 0.9643 on the test data, while visualizations of predicted versus actual prices demonstrated a strong alignment, proving that the LSTM model is effective in capturing temporal patterns in financial time series data and can serve as a valuable tool for data-driven investment decision-making.

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