Development of a Fourier Transform-Based Gamelan Signal Processing Simulation Using Python and GNU Octave to Support Problem-Solving Skills and Independent Learning

Authors

  • Aditya Yoga Purnama Universitas Sarjanawiyata Tamansiswa
  • Fitria Sulistyowati Universitas Sarjanawiyata Tamansiswa
  • Widodo Widodo Universitas Sarjanawiyata Tamansiswa
  • Alfat Khaharsyah Universitas Sarjanawiyata Tamansiswa
  • Ragil Saputri Universitas Negeri Yogyakarta
  • Rudolfus Soares Universitas Sarjanawiyata Tamansiswa
  • Radhiatul Amalia Universitas Sarjanawiyata Tamansiswa

DOI:

https://doi.org/10.58230/edutech.v3i3.85

Keywords:

Fourier transform, GNU Octave, Learning independence, Problem-solving skills, Python, Simulation

Abstract

Science education today requires integrating technological competencies to prepare students for global competition; however, unsupportive learning habits and limited use of technology contribute to low levels of problem-solving skills and learning independence. This study aims to develop a simulation of gamelan musical instrument signal processing using open-source computational platforms, namely Python and GNU Octave. The research employed a research and development (R&D) approach based on the Borg and Gall model, limited to the initial product development stage. Fourier Transform analysis was applied to convert sound signals from the time domain into the frequency domain. The simulation successfully identified the gamelan’s natural frequency at 328 Hz via spectral analysis, with consistent results on both platforms. The developed simulation provides visualization of time-series waveforms and frequency spectra and is intended as a computational learning medium for physics instruction, particularly in wave topics. Although field testing and effectiveness evaluation were not conducted, the simulation is designed to support the development of students’ problem-solving skills and learning independence in future implementations.

Downloads

Download data is not yet available.

References

Auning, C. and Auning, M. (2023) ‘Students’ explanations of a complex natural phenomenon using mathematical modelling as a design feature in a model-based inquiry unit’, Nordic Studies in Science Education, 19(1), pp. 62–77. Available at: https://doi.org/10.5617/NORDINA.8965.

Baist, A., Firmansyah, M.A. and Pamungkas, A.S. (2019) ‘Desain Bahan Ajar Komputasi Matematika Berbantuan Software Mathematica Untuk Mengembangkan Kemandirian Belajar Mahasiswa’, FIBONACCI: Jurnal Pendidikan Matematika dan Matematika, 5(1), p. 29. Available at: https://doi.org/10.24853/fbc.5.1.29-36.

Beauval, C., Scotti, O. and Bonilla, F. (2006) ‘The role of seismicity models in probabilistic seismic hazard estimation: Comparison of a zoning and a smoothing approach’, Geophysical Journal International, 165(2), pp. 584–595. Available at: https://doi.org/10.1111/j.1365-246X.2006.02945.x.

Beckers, J., Dolmans, D. and van Merriënboer, J. (2016) ‘e-Portfolios enhancing students’ self-directed learning: A systematic review of influencing factors’, Australasian Journal of Educational Technology, 32(2), pp. 32–46. Available at: https://doi.org/10.14742/ajet.2528.

Bignardi, S. et al. (2018) ‘OpenHVSR - Processing toolkit: Enhanced HVSR processing of distributed microtremor measurements and spatial variation of their informative content’, Computers and Geosciences, 120, pp. 10–20. Available at: https://doi.org/10.1016/j.cageo.2018.07.006.

Brougham, D. and Haar, J. (2018) ‘Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace’, Journal of Management and Organization, 24(2), pp. 239–257. Available at: https://doi.org/10.1017/jmo.2016.55.

Davis, J. et al. (2012) ‘Smart manufacturing, manufacturing intelligence and demand-dynamic performance’, Computers and Chemical Engineering, 47, pp. 145–156. Available at: https://doi.org/10.1016/j.compchemeng.2012.06.037.

Duval, R. (1999) ‘Representation, Vision And Visualization: Cognitive Functions In Mathematical Thinking’. Basıc Issues For Learnıng’, Proceedings of the 21st North American PME Conference, 91(2), pp. 266–297.

Hapsari, N. (2016) ‘Pengembangan e-modul pengayaan materi pertumbuhan dan perkembangan untuk meningkatkan kemandirian hasil belajar’, Jurnal Pendidikan BIologi, 5(5), pp. 23–31.

Israel, M. et al. (2015) ‘Empowering K–12 Students With Disabilities to Learn Computational Thinking and Computer Programming’, Teaching Exceptional Children, 48(1), pp. 45–53. Available at: https://doi.org/10.1177/0040059915594790.

Kismiati, D.A. (2020) ‘Implementasi E-Modul Pengayaan Isolasi dan Karakterisasi Bakteri dalam Meningkatkan Kemandirian Belajar Siswa SMA’, ALVEOLI: Jurnal Pendidikan Biologi, 1(1), pp. 1–10. Available at: https://doi.org/10.35719/alveoli.v1i1.1.

Lavrentieva, O.O. et al. (2019) ‘Theoretical and methodical aspects of the organization of students’ independent study activities together with the use of ICT and tools’, CEUR Workshop Proceedings, 2433(1), pp. 102–125. Available at: https://doi.org/10.55056/cte.371.

Magana, A.J. and Silva Coutinho, G. (2017) ‘Modelling and simulation practices for a computational thinking-enabled engineering workforce’, Computer Applications in Engineering Education, 25(1), pp. 62–78. Available at: https://doi.org/10.1002/cae.21779.

Maimun and Bahtiar (2022) ‘European Journal of Educational Research’, European Journal of Educational Research, 11(3), pp. 1245–1257.

Orive-Miguel, D. et al. (2019) ‘Improving localization of deep inclusions in time-resolved diffuse optical tomography’, Applied Sciences (Switzerland), 9(24), pp. 1–27. Available at: https://doi.org/10.3390/app9245468.

Priambodo, A.S. (2019) ‘Studi Komparasi Simulasi Sistem Kendali Pid pada Matlab, Gnu Octave, Scilab, dan Spyder’, Elinvo (Electronics, Informatics, and Vocational Education), 4(2), pp. 169–175. Available at: https://doi.org/10.21831/elinvo.v4i2.28347.

Purnama, A.Y. et al. (2021) ‘Simulasi Difraksi Fraunhofer Menggunakan Media Spreadsheet dan GNU Octave Sebagai Alternatif Pembelajaran dimasa Pandemi’, 5(2), pp. 1–8.

Puspitasari, R.D. et al. (2018) ‘Kemandirian Belajar Fisika Pada Peserta Didik Dengan Pembelajaran Berbasis Proyek’, Indonesian Journal of Science and Mathematics Education, 01(1), pp. 1–12.

Rahpeyma, S. et al. (2016) ‘Detailed site effect estimation in the presence of strong velocity reversals within a small-aperture strong-motion array in Iceland’, Soil Dynamics and Earthquake Engineering, 89, pp. 136–151. Available at: https://doi.org/10.1016/j.soildyn.2016.07.001.

Sanita, N., Elisa, E. and Susanna, S. (2021) ‘Hubungan Kemandirian Belajar Terhadap Hasil Belajar Siswa pada Pembelajaran Fisika di SMAN 1 Syamtalira Bayu’, Jurnal Serambi Akademica, 9(6), pp. 857–864. Available at: http://www.ojs.serambimekkah.ac.id/serambi-akademika/article/view/3086.

Satoh, T., Kawase, H. and Matsushima, S. (2001) ‘Differences between site characteristics obtained from microtremors, S-waves, P-waves, and codas’, Bulletin of the Seismological Society of America, 91(2), pp. 313–334. Available at: https://doi.org/10.1785/0119990149.

Schmidt, H.G., Rotgans, J.I. and Yew, E.H.J. (2011) ‘The process of problem-based learning: What works and why’, Medical Education, 45(8), pp. 792–806. Available at: https://doi.org/10.1111/j.1365-2923.2011.04035.x.

Semuels, A. (2020) ‘Millions of Americans Have Lost Jobs in the Pandemic-And Robots and AI Are Replacing Them Faster Than Ever Jarvis the robotic butler on duty at the Grand Hotel in Sunnyvale, Calif., on July 30 Cayce Clifford for TIME’.

Shell, D.F. et al. (2017) ‘Improving students’ learning and achievement in CS classrooms through computational creativity exercises that integrate computational and creative thinking’, Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, pp. 543–548. Available at: https://doi.org/10.1145/3017680.3017718.

Sulistyowati, F. and Harini, E. (2021) ‘A Preliminary Study: Why Develop High Order Thinking Skills Worksheets Based on the 3N’, Journal of Medives : Journal of Mathematics Education IKIP Veteran Semarang, 5(1), p. 105. Available at: https://doi.org/10.31331/medivesveteran.v5i1.1462.

Sung, Y.T., Chang, K.E. and Liu, T.C. (2016) ‘The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis’, Computers and Education, 94, pp. 252–275. Available at: https://doi.org/10.1016/j.compedu.2015.11.008.

Thabet, M. (2019) ‘Site-Specific Relationships between Bedrock Depth and HVSR Fundamental Resonance Frequency Using KiK-NET Data from Japan’, Pure and Applied Geophysics, 176(11), pp. 4809–4831. Available at: https://doi.org/10.1007/s00024-019-02256-7.

Widiartini, N.K. and Sudirtha, I.G. (2019) ‘Effect of KWL learning method (know-want-learn) and self-assessment on student learning independence vocational high school’, International journal of social sciences and humanities, 3(2), pp. 277–284. Available at: https://doi.org/10.29332/ijssh.v3n2.331.

Yuliawati, W.S., Rasimeng, S. and Karyanto (2009) ‘PENGOLAHAN DATA MIKROTREMOR BERDASARKAN METODE HVSR DENGAN MENGGUNAKAN MATLAB’, Jurnal Geofisika Eksplorasi, 5(1), pp. 45–59.

Zhang, Z. et al. (2018) ‘Simulation of the microtremor H/V spectrum based on the theory of surface wave propagation in a layered half-space’, Acta Geophysica, 66(2), pp. 121–130. Available at: https://doi.org/10.1007/s11600-018-0112-7.

Downloads

Published

10-04-2026

How to Cite

Purnama, A. Y., Sulistyowati, F., Widodo, W., Khaharsyah, A., Saputri, R., Soares, R., & Amalia, R. (2026). Development of a Fourier Transform-Based Gamelan Signal Processing Simulation Using Python and GNU Octave to Support Problem-Solving Skills and Independent Learning. Educational Journal of Learning Technology, 3(3), 192–207. https://doi.org/10.58230/edutech.v3i3.85