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Muhamad Fuat Asnawi
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Clean Energy and Smart Technology
Published by CV Nature Creative Innovation
ISSN : -     EISSN : 29642647     DOI : https://doi.org/10.58641/cest
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mechanical Engineering
The Clean Energy and Smart Technology (CEST) journal aims to provide a platform for researchers, academicians, and professionals to publish high-quality articles that explore innovative ideas and solutions in the field of energy and technology. It focuses on fostering interdisciplinary research and practical applications that address current challenges and opportunities in energy efficiency, sustainability, and technological advancements. Scope The journal covers a wide range of topics, including but not limited to: Energy and Cost Efficiency: Analysis of cost-saving measures and efficient energy utilization in various sectors. Social Impact of Energy and Technology: Studies on how energy and technological developments affect societal well-being. Sustainable and Smart Architecture: Design and development of energy-efficient and smart buildings. Environmental and Safety Concerns: Investigation of hazardous substances in residential and industrial environments. Structural Innovations: Research on earthquake-resistant technologies and advanced materials. Legal and Policy Frameworks: Examination of laws and regulations governing energy and technology implementation. Renewable Energy Development: Exploration of new and renewable energy sources and technologies. Multidisciplinary Applications: Cross-cutting studies that integrate energy and technology with fields such as informatics, engineering, physics, and chemistry.
Arjuna Subject : Energi (semua kategori) - Energi (Lain-Lain)
Articles 41 Documents
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IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM IN MOBILE APPLICATION-BASED VOICE EMOTION CLASSIFICATION SYSTEM Naufal Ammar Raihan; Muhamad Fuat Asnawi; Iman Ahmad Ihsannuddin; Nahar Mardiyantoro; Muhammad Alif Muwafiq Baihaqy
Clean Energy and Smart Technology Vol. 4 No. 2 (2026): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v4i2.211

Abstract

The ability of machines to recognize emotions from voice is known as Speech Emotion Recognition (SER). This study developed a voice emotion classification system using a Convolutional Neural Network (CNN) and implemented it in the form of an Android mobile application. The main problem raised is how to recognize human emotions through voice signals accurately, efficiently, and in real-time on mobile devices. The study was conducted with two training stages, namely pre-training using the RAVDESS dataset and fine-tuning with the IndoWaveSentiment dataset. Audio data was converted into a 128×128×1 Mel-spectrogram to be input to the CNN. The CNN model consists of three convolution and pooling blocks, as well as dense and softmax layers. After training, the model was converted to TensorFlow Lite format and integrated with the Android application through a client-server architecture using Flask. The test results showed that the system was able to recognize neutral, happy, disappointed, and surprised emotions with a high level of accuracy both on test data but not as good on live recorded voice. The system also features a SQLite-based history feature. Test results showed 96% accuracy on external test data and 55% on live recorded audio, with an average accuracy of 75.5%. This indicates the model performs very well in structured conditions, but still needs improvement for real-world input.

Page 5 of 5 | Total Record : 41

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