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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Informatika dan Teknik Elektro Terapan JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Building of Informatics, Technology and Science Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Brahmana : Jurnal Penerapan Kecerdasan Buatan Journal La Multiapp Action Research Literate (ARL) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia The Indonesian Journal of Computer Science
Zahra, Amalia
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Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Journal : Building of Informatics, Technology and Science

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Multi Kelas Speaker Recognition Menggunakan Deep Learning dengan CN-Celeb Dataset Martulandi, Adipta; Zahra, Amalia
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2467

Abstract

Speaker recognition has been widely applied in various fields of human life such as Siri from Apple, Cortana from Microsoft, and Voice Assistant by Google. One of the problems when creating speaker recognition is related to the dataset used for the modeling process. The dataset used for creating the speaker recognition model is mostly data that cannot represent real-world conditions. The result is when implemented in the real-world conditions are not optimal. This study develops a speaker recognition model using deep learning (LSTM) with the CN-Celeb dataset. The CN-Celeb dataset is data taken directly from the real world so there is a lot of noise. The hope of using this dataset is that it can represent real world conditions. Model development uses 2 stacked LSTM for multi-class speaker recognition tasks. In addition, this study performs tuning hyperparameters with a grid search method to obtain the most optimal model configuration. The results showed that the EER value of the LSTM model was 10.13% better than the reference baseline paper of 15.52%. In addition, when compared with other studies that also used the CN-Celeb dataset but using different models, it was found that the LSTM model had promising results. From the results of study that has been carried out and also compared with other people's research, it was found that the LSTM model gave promising performance. The LSTM model is compared with the x-vectors, PLDA, TDNN, and transformers models
Market-Adaptive Stock Trading through B-WEMA Driven Proximal Policy Optimization Ichsan, Mulia; Zahra, Amalia
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9349

Abstract

Developing automated trading strategies that achieve stable returns while controlling risk remains a central threat in quantitative finance. Many reinforcement learning-based trading systems focus on reward maximization but provide limited justification for the choice of forecasting indicators and often lack comprehensive benchmarking against alternative strategies and risk measures. This essay addresses the problem of integrating a statistically grounded price-smoothing technique with a policy optimization scheme to improve sequential trading decisions under market uncertainty. We propose a hybrid model that combines Brown’s Weighted Exponential Moving Average (B-WEMA) as a trend-sensitive forecasting indicator with a Deep Reinforcement Learning agent trained using Proximal Policy Optimization (PPO). The role of B-WEMA is to provide structured price signals that reduce noise sensitivity, while PPO determines buy and sell actions through policy updates constrained for stable learning. The performance of the proposed model is evaluated over a 10-month trading horizon and compared with a buy-and-hold benchmark and an alternative reinforcement learning method, Advantage Actor-Critic (A2C), both implemented under the same experimental conditions. Empirical results show that the proposed B-WEMA-PPO framework achieved a cumulative return of 23.43% over the test period, outperforming both the benchmark and the A2C-based agent. In addition to cumulative return, risk-adjusted performance metrics, namely volatility and maximum drawdown, are reported to provide a balanced assessment of profitability and risk exposure. These findings suggest that incorporating structured exponential smoothing into policy optimization may enhance the stability and effectiveness of reinforcement learning-based trading strategies.
Co-Authors Al Mukarram, Khasyi Cahyadi, Jonathan Caprisiano, Ebenhaiser Jonathan Chandra, Wiryanata David David Fadhil, Muhammad Farhan Fadzlul Rahman, Ferry Faldi, Faldi Farhan Fadhil, Muhammad Fathur Rahman Hafni, Liza Halim, Rico Harits, Sayekti Hasibuan, Reski Wahyuni Hendra Saputra Henry, Michael Himawan, Randy Ichsan, Mulia Joko Pranoto, Wawan Karyawati, Tati Kristian Handoko Wijaya Sukardjoh Lubis, Tuti Alawiyah Martulandi, Adipta Maskur, Maimunah Michael, Stefanus Muhammad Manaqib, Muhammad Mukhlas, M. Anang Mustamirrun, Nurizki Naibaho, Agusti Frananda Alfonsus Novika, Andien Dwi Raharjo, Alim Ramadhansyah, Muhammad Hafizh Riftiarrasyid, Mohammad Faisal Roesmiatun Purnamadewi, Yasinta Rohani, Wilda Sadewo, Satrio Sams, Andrew Steven Setyoko, Andreas Dimas Seventina, Healthy Suma'inna, Suma'inna Supriani, Ria Rafita Vincent Vincent, Vincent William, Ezra