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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Generic
Vindriani, Marsella
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Computer Science & IT Engineering

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Aero-Track: Perangkat Lunak Perekam Data Penerbangan Aeronautika Fathan, Muhammad Rifqi; Aditya, Aditya; Afriansyah, Indra Gifari; Yousnaidi, Rani Silvani; Passarela, Rossi; Arsalan, Osvari; Kurniati, Rizki; Vindriani, Marsella
Generic Vol 15 No 1 (2023): Vol 15, No 1 (2023)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v15i1.146

Abstract

Kecelakaan pesawat terbang bisa terjadi pada semua fase penerbangan, Pada tahun 2014, di Indonesia sendiri sudah terjadi kecelakaan penerbangan berjumlah 84 kali. Maka dari itu, kami mengembangkan sebuah perangkat lunak bernama Aero-Track untuk merekam data penerbangan dengan kriteria spesifik mengenai area dan dan fase penerbangan. Perangkat lunak ini sudah diuji coba dengan merekam data penerbangan pada bandara Sultan Syarif Kasim II, bandara Sultan Mahmud Badaruddin II dan Bandara Sultan Hasanuddin. Data dari hasil perekaman tersebut sudah dapat dijadikan bahan analisis terkait pola dan karakteristik penerbangan.
Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest Abdurahman; Vindriani, Marsella; Prasetyo, Aditya Putra Perdana; Sukemi; Buchari, M. Ali; Sembiring, Sarmayanta; Firnando, Ricy; Isnanto, Rahmat Fadli; Exaudi, Kemahyanto; Dudifa, Aldi; Riyuda, Rafki Sahasika
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.
Co-Authors Abdurahman Aditya Aditya Aditya Putra Perdana Prasetyo Afriansyah, Indra Gifari Buchari, M. Ali Dudifa, Aldi Fathan, Muhammad Rifqi Kemahyanto Exaudi Osvari Arsalan Passarela, Rossi Rahmat Fadli Isnanto Ricy Firnando Riyuda, Rafki Sahasika Rizki Kurniati Sarmayanta Sembiring Sukemi Yousnaidi, Rani Silvani