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All Journal Jurnal Tekno Engineering, Mathematics and Computer Science Journal (EMACS)
Pamungkas, Prayoga Yudha
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Forensic-Based Investigation Optimization to Solving Traveling Salesman Problem Pamungkas, Prayoga Yudha; Latifah, Umi; Nafi'ah, Roikhanatun
Jurnal Tekno Vol. 22 No. 1 (2025): JURNAL TEKNO
Publisher : Direktorat Riset dan Pengabdian pada Masyarakat Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/33050y35

Abstract

The FBI Optimization (FBIO) represents one of the most novel metaheuristics which has been experimented with to solve Traveling Salesman Problem (TSP), demonstrating superior performance compared to tradition-al algorithms. Unlike conventional approaches that start with exploration and gradually shift to exploitation, FBIO maintains a dominant exploration phase throughout its iterations. Beginning with 100% exploration and tapering to approximately 90% dominance in exploration by the end of the process, FBIO effectively navigates the solution space, uncovering more promising routes. This exploration-centric approach enables FBIO to achieve solutions that are 8.39% closer to the near-optimal result compared to its counterparts. The algorithm’s enhanced performance in TSP highlights its potential applicability to other combinatorial optimization challenges. By prioritizing exploration, FBIO offers a robust framework for addressing complex prob-lems and ensures a comprehensive search of the solution space. Its ability to deliver high quality near-optimal solutions makes FBIO a valuable tool for future research, presenting new opportunities for solving various optimization problems and advancing practical problem-solving methodologies
Advancing Indonesian Audio Emotion Classification: A Comparative Study Using IndoWaveSentiment Majiid, Muhammad Rizki Nur; Setiawan, Karli Eka; Pamungkas, Prayoga Yudha; Annas, Taufiq; Setiawan, Nicholas Lorenzo
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13415

Abstract

This study addresses the critical gap in Indonesian Speech Emotion Recognition (SER) by evaluating machine learning models on the IndoWaveSentiment dataset, a novel corpus of 300 high-fidelity recordings capturing five emotions (neutral, happy, surprised, disgusted, disappointed) from native speakers. The research aims to identify optimal classification techniques and acoustic features for Indonesian SER, given the language’s unique linguistic characteristics and the scarcity of annotated resources. Six models, Logistic Regression, KNN, Gradient Boosting, Random Forest, Naive Bayes, and SVC, were trained on 45 acoustic features, including spectral contrast, MFCCs, and zero crossing rate, extracted using Librosa. Results demonstrated Random Forest as the top performer (90% accuracy), followed by Gradient Boosting (85%) and Logistic Regression (75%), with spectral contrast (contrast2, contrast7) and MFCC1 emerging as the most discriminative features. The findings highlight the efficacy of ensemble methods in capturing nuanced emotional cues in Indonesian speech, outperforming prior studies on locally sourced datasets. Practical implications include applications in customer service analytics and mental health tools, though limitations such as the dataset’s-controlled conditions and fixed sentence structure necessitate caution in real-world deployment. Future work should expand the dataset to include regional dialects, spontaneous speech, and hybrid architectures like CNN-LSTMs. This study establishes foundational benchmarks for Indonesian SER, advocating for culturally informed models to enhance human-computer interaction in underrepresented linguistic contexts.
Co-Authors Annas, Taufiq Majiid, Muhammad Rizki Nur Nafi'ah, Roikhanatun Setiawan, Karli Eka Setiawan, Nicholas Lorenzo Umi Latifah