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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah FIFO
Ali Herdian
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Stock Prediction for Indonesia Stock Exchange with Long Short-Term Memory Wahab, Abdi; Herdian, Ali; Wirawan, Dian; Jumaryadi, Yuwan; Alam, Syamsir; Fiade, Andrew
Jurnal Ilmiah FIFO Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i1.010

Abstract

Predicting stock prices through different analyses and techniques is highly challenging. The task is complicated further by fluctuating market conditions and the impact of news, necessitating the consideration of numerous factors. The advancements in machine learning and deep learning have led many researchers to use algorithms like RNN with LSTM for predictions. In this study, we aim to predict stock prices on the Indonesia Stock Exchange using LSTM, focusing on optimizing the hidden layer and activation function. We focus on some stock data with good liquidation in the Indonesia Stock Exchange. The comparison performance between models proposed in this research will be the method in this research. The result showed that the LSTM model with hyperbolic tan activation method performed better than the LSTM model with sigmoid activation method. The future research based on this research, we can compare several other activation methods.
Energy-Aware Multi-Objective Deployment Optimization of Wireless Sensor Networks Using Direct Radio Graph Medium (DRGM) Modelling Fandi Ali Mustika; Ali Herdian; Prastika Indriyanti; Muhammad Rifqi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12233

Abstract

Wireless Sensor Networks (WSNs) are widely deployed for large-scale environmental monitoring applications, particularly in remote and maritime areas where manual surveillance is costly and impractical. One of the major challenges in WSN deployment is achieving full sensing coverage and network connectivity while minimizing energy consumption and deployment density. This paper proposes an energy-aware multi-objective deployment optimization model based on Direct Radio Graph Medium (DRGM) modeling. The deployment problem is formulated as a multi-objective optimization task aiming to minimize the number of active sensor nodes while maintaining communication connectivity under predefined sensing and transmission constraints. A genetic algorithm–based optimization mechanism is employed to generate Pareto-optimal deployment solutions. The proposed model is evaluated using NS-2 simulations under various node densities and traffic rates. Simulation results show that the DRGM-based deployment achieves full coverage using only 10 sensor nodes, compared to 50–100 nodes in random deployment, corresponding to a node reduction of up to 90%. Furthermore, the proposed approach significantly reduces network power consumption and radio duty cycles, demonstrating its effectiveness for energy-efficient and scalable WSN deployment in large monitoring areas.
Co-Authors Abdi Wahab Andrew Fiade Dian Wirawan Fandi Ali Mustika Jumaryadi, Yuwan Muhammad Rifqi Prastika Indriyanti Syamsir Alam