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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF)
Ade Kurnia, Dian
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Computer Science & IT

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Pemanfaatan Chatgpt Dalam Pembelajaran Persamaan Dasar Akuntansi Rahaningsih, Nining; Suarna, Nana; Dienwati Nuris, Nisa; Ade Kurnia, Dian; Rasyid, Abdul; Frihandiansah, Riyandi
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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Abstract

Learning Accounting is an essential part of business and finance education. To ensure effective learning, an innovative approach is needed to optimize the understanding of concepts and practical applications in the field of accounting. One technology that has attracted attention in this context is Artificial Intelligence, particularly through the utilization of ChatGPT. However, there are still challenges in teaching accounting. Conventional learning systems are often less interactive and cannot provide adequate individual support to students. Therefore, a new approach is needed to enhance student interaction with instructors. The aim of this research is to analyze and implement the utilization of ChatGPT in the development of accounting educati to improve the understanding of accounting concepts and enhance students' ability to apply accounting knowledge. The research method employs an experimental approach involving a group of students in a learning program utilizing ChatGPT as a virtual assistant. ChatGPT provides explanations, answers questions, and provides feedback to students. To analyze and evaluate the effectiveness of using AI (ChatGPT) in accounting education, the System Usability Scale is used.The results of this research indicate that the use of AI, particularly through ChatGPT, in accounting education can provide significant benefits. Students experience improvements in their understanding of accounting concepts, practical abilities, better individual support, and increased motivation and interest in learning accounting, with an accuracy rate of 80.625%.
Hybrid Model for Speech Emotion Recognition using Mel-Frequency Cepstral Coefficients and Machine Learning Algorithms Nurdiawan, Odi; Ade Kurnia, Dian; Sudrajat, Dadang; Pratama, Irfan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5143

Abstract

Speech Emotion Recognition (SER) is a subfield of affective computing that focuses on identifying human emotions through voice signals. Accurate emotion classification is essential for developing intelligent systems capable of interacting naturally with users. However, challenges such as background noise, overlapping emotional features, and speaker variability often reduce model performance. This study aims to develop a lightweight hybrid SER model by combining Mel-Frequency Cepstral Coefficients (MFCC) as feature representations with three machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN). The methodology involves audio data preprocessing, MFCC-based feature extraction, and classification using the selected algorithms. The RAVDESS dataset, consisting of 1,440 English-language audio samples across four emotions (happy, angry, sad, neutral), was used with an 80/20 train-test split to ensure class balance.. Experimental results show that the KNN model achieved the highest performance, with an accuracy of 78.26%, precision of 85.09%, recall of 78.26%, and F1-score of 77.06%. The Decision Tree model produced comparable results, while the SVM model performed poorly across all metrics. These findings demonstrate that the proposed hybrid approach is effective for recognizing emotions in speech and offers a computationally efficient alternative to deep learning models. The integration of MFCC features with multiple machine learning classifiers provides a robust framework for real-time emotion recognition applications, especially in environments with limited computing resources.
Co-Authors Abdul Rasyid Dadang Sudrajat Dienwati Nuris, Nisa Frihandiansah, Riyandi Nining Rahaningsih Odi Nurdiawan Pratama, Irfan Suarna, Nana