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p-Index From 2021 - 2026
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All Journal Jurnal Teknik Industri Terintegrasi (JUTIN) Journal of Mathematics and Applied Statistics
Fara Triadi
Unknown Affiliation
Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering

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Designing a Web Application for Recognizing Past Learning Using the Laravel Framework Jaya, Arsan Kumala; Hanif, Abdullah; Triadi, Fara; Biabdillah, Fajerin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.239

Abstract

This study aims to provide information on the application design process using the Laravel framework. This study aims to design a web application that can help higher education institutions manage students who take prior learning recognition (RPL) classes effectively and efficiently. The problem often faced by universities is the difficulty in recording the formal/non-formal education history of RPL students. This application is expected to provide a solution by providing features such as recording education history, training history, conference history, award history, organizational history, and employment history. The system development method used in the design is the System Development Life Cycle (SDLC) by utilizing the Laravel framework as a framework for the system development process. The expected results of this study are a web application that is user-friendly, reliable, and able to increase the efficiency of student data collection in universities.
Identification of Speech Recognition Using K-Nearest Neighbor Method Hanif, Abdullah; Triadi, Fara; Jaya, Arsan Kumala; Hartanto, Subhan; Basir, Azhar
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 9 No. 1 (2026): January
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v9i1.56699

Abstract

Speech is a part of the human that has unique characteristics so that it can be distinguished from one person with someone else. Speech delivered, has a variety of information so that in its application it can be used to carry out voice commands using speech. In signal processing, Mel Frequency Cepstrum Coefficient (MFCC) is a method used for feature extraction. In this study, MFCC is used as a feature extraction method using Matlab R2017a and K-Nearest Neighbor (KNN) software used to identify and classify voice commands spoken by the speaker using speech pattern patterns obtained from the MFCC. This study uses 10 training data for each voice command word consisting of open, close, message and gallery, and 5 test data for each voice command word. Voice data is used using different words and different speakers. This research yields an accuracy level of 60% in voice Buka, 60% in voice Tutup, 60% in voice Pesan and 65% in voice Galeri.
Co-Authors Basir, Azhar Fajerin Biabdillah Hanif, Abdullah Hartanto, Subhan Jaya, Arsan Kumala