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All Journal Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Journal of Information Technology and Computer Science (JOINTECS) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Transaction on Informatics and Data Science
Totok Chamidy
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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Journal : Journal of Information Systems and Informatics

 1 
Multivariate LSTM for Drug Purchase Prediction in Pharmaceutical Management Brawijaya, Fanny; Almais, Agung Teguh Wibowo; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1313

Abstract

This study aims to develop a structured approach to predict the number of hospital drug purchases using deep learning techniques. The Multivariate Long Short-Term Memory (LSTM) model is designed to capture temporal and contextual patterns including transaction time, polyclinic type, and drug type to improve the efficiency of pharmaceutical management. The model was tested using outpatient transaction data at RSIA Fatimah Probolinggo hospital in East Java, Indonesia, through three configurations (A, B, and C) to determine the optimal parameters. The best model, the Model B1, produces a Mean Absolute Error (MAE) value of 10.239, Mean Absolute Percentage Error (MAPE) of 1.976%, and the Coefficient of Determination (R²) of 0.199, which indicates a high degree of accuracy. The results of the study prove that multivariate LSTM is able to model complex intervariable dependencies and provide superior results than conventional forecasting methods. In practical terms, this model can be used as a decision-making tool for hospital management in planning drug procurement, optimizing inventory, and preventing shortages and overstocks. The application of this model contributes to data-driven pharmaceutical supply chain planning in smart hospital management systems.
Stock Price Prediction Using Backpropagation ANN: Case Study of ADMR (2023–2025) Khozin, Muhammad; Abidin, Zainal; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1347

Abstract

This study develops an Artificial Neural Network (ANN) backpropagation model for predicting stock prices using ADMR stock data from 2023 to 2025, obtained from Yahoo Finance. Given the inherent volatility and unpredictability of stock prices, accurate forecasting plays a crucial role in investment decision-making. ANN models are particularly effective for capturing complex, non-linear relationships and patterns in financial data, which traditional statistical models may fail to address. In this research, various configurations were tested by adjusting the number of hidden neurons (5, 10, and 15) and learning rates (0.1, 0.3, and 0.5). The optimal model architecture was found to be 3-10-1, consisting of three input neurons, ten hidden neurons, and one output neuron, which achieved the best prediction performance with a Mean Absolute Percentage Error (MAPE) of 2.26%. This model was trained with a learning rate of 0.3 and completed in 915 iterations. However, the model's predictive capabilities are constrained by its reliance on historical stock prices alone, excluding external factors such as macroeconomic indicators, market sentiment, or trading volume, which may improve its generalization and overall accuracy. Future work could integrate these variables for better robustness and predictive power.
Co-Authors A Basid, Puspa Miladin Nuraida Safitri AA Sudharmawan, AA Adhitya Prayoga Permana Agung Teguh Wibowo Almais Ajib Hanani Akbar, Ismail Alviola, Nuril Afni Anisa Anisa Baehaqi Baskoro, Sentot Eko Brawijaya, Fanny Cahyo Crysdian Emha Ahdan Fahmi Elmuna Fresy Nugroho Hadziq Agasta, Hadziq Hariyanto, Dikky Cahyo Ismail Akbar Moh. Ainur Rohman Mohammad Faisal Fajar Fadilah Muhammad Faisal Muhammad Faisal Muhammad Khozin Ririen Kusumawati Saleh, Moh setyowati, Kurnia Dwi Sri Harini Suarti, Isa Suhartono Suhartono Suhartono Syahiduz Zaman Syauqi, A’la Syukur, Mohamad Arif Abdul Wardana, M. Dafa Zainal Abidin