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All Journal IAES International Journal of Robotics and Automation (IJRA) Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal Teknologi dan Sistem Komputer JTT (Jurnal Teknologi Terpadu) Knowledge Engineering and Data Science Indonesian Journal of Artificial Intelligence and Data Mining Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Journal of Robotics and Control (JRC) Science in Information Technology Letters Signal and Image Processing Letters
Achmad Fanany Onnilita Gaffar
Politeknik Negeri Samarinda
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Jurnal Informatika

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Speech classification using combination virtual center of gravity and k-means clustering based on audio feature extraction Diah Kumalasari; Arief Bramanto Wicaksono Putra; Achmad Fanany Onnilita Gaffar
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17390

Abstract

Voice recognition can be done in a variety of ways. Sound patterns can be recognized by performing sound feature extraction. The trainer sound data is built from the best sound data selection using a correlation coefficient based on the level of similarity between sound data for optimal sound features. Extraction of voting features on this research using the Virtual Center of Gravity method. This method calculates the distance between the sound data against the center point of gravity with visualizations in the 3-dimensional form of white, black, and grey pattern spaces. The preprocessing process generates a complex number of data consisting of real numbers and imaginary numbers. The number will be calculated the distance to the Virtual Center of Gravity's pattern space using Euclidean Distance. The sound feature testing is done using K-Means Clustering by means of a speech classification data based sound. The results showed an accuracy of 92.5%.
Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment Mulyanto Mulyanto; Bedi Suprapty; Arief Bramanto Wicaksono Putra; Achmad Fanany Onnilita Gaffar
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a17389

Abstract

In addition to the focus of research selected as a Final Project material, the selection of lecturers as student's supervisor becomes very important. The lecturer's competence related to the focus of student research and the supervising style of lecturers is also very influential on the final results. Measurement of style appropriateness between students' learning styles and supervising lecturers' styles can benchmark the quality of the final project's implementation, especially higher education institutions. This study has applied fuzzy-based assessment to build objective perceptions of students' learning characteristics and lecturers' characteristics (Visual (V), Auditory (A), Kinesthetic (K)) as supervisors through questionnaire processing that has designed in such away. Hence, it is suitable for this study. The measuring technique of the percentage of overlapping areas under the curves and the correlation test between a pair of curves have been used as performance measurement metrics. In general, the study results indicate a significant level of coverage adequacy for all research variables regarding existing conditions. It means that the process of Final Project activities in terms of students' and lecturers' learning characteristics as supervisors and their distribution is at a reasonable level (88.38%). It has also been shown by the results of the correlation test of the appropriateness of choice, both supervisors selected by students (0.8657) and students chosen by lecturers (0.9897) who are at a very significant level of similarity. Correlation tests conducted for similarities between students' and lecturers' learning characteristics as supervisors show almost no significant correlation between them (0.4064).
Co-Authors Agusma Wajiansyah Agusma Wajiansyah Agusma Wajiansyah, Agusma Aji Prasetya Wibawa Alfiansyah, M Nur Andri Pranolo Anggri Sartika Wiguna Arrosied, Harun Diah Kumalasari Didi Susilo Budi Utomo Gaffar, Emmilya Umma Aziza Hargiani, Fransisca Xaveria Haviluddin Haviluddin Irwan Gani Karmita, Sisie Malani, Rheo Mirza Rafdi Rosada Muhammad Bagus Bintang Timur Muhammad Soleh Muhammad Taufiq Sumadi Mulyanto Mulyanto Pratiwi, Adinda Putri Purba, Muren Fiatra Denata Purnawansyah Purnawansyah Rayner Alfred Rihartanto Rihartanto Riris Kurnia Ningsih Rizka Indah Armianti Roman Voliansky RR. Ella Evrita Hestiandari Salsabila, Unik Hanifah Saputra, Arief Bramanto Wicaksono Sari, Risna Julia Suprapty, Bedi Supriadi Supriadi