Mengeksplorasi penggunaan Generatif AI Gen-Z dalam kreativitas musik menggunakan pendekatan difusi inovasi: Wawasan dari Papua

Charlie Christofel Watofa; Ratna Juita; Dedi I. Inan; Muhamad Indra

doi:10.24246/aiti.v23i1.134-151

Authors

Charlie Christofel Watofa Program Studi Teknik Informatika, Universitas Papua https://orcid.org/0009-0009-2267-6638
Ratna Juita Program Studi Teknik Informatika, Universitas Papua
Dedi I. Inan Program Studi Teknik Informatika, Universitas Papua
Muhamad Indra Program Studi Teknik Informatika, Universitas Papua

DOI:

https://doi.org/10.24246/aiti.v23i1.134-151

Keywords:

Diffusion of Innovation, cognitive learning outcomes, Artificial Intelligence, musical creativity

Abstract

This study examines the use of Artificial Intelligence (AI)-based musical instruments in supporting the evaluation of cognitive learning outcomes and musical creativity among Generation Z. Using the Diffusion of Innovation (DOI) approach, this study analyzes Gen Z's adoption and perception of AI-based musical instrument technology in the context of digital music creation and production. Although the use of AI in education continues to grow, research on its impact on cognitive-based music learning remains limited, particularly in Indonesia. Data was collected through an online survey of Gen Z respondents in the Papua region. This study evaluates the influence of innovation characteristics, such as relative advantage, ease of use, and technological compatibility, on learning outcomes. R-square analysis shows a moderate contribution of AI to learning indicators, with CA = 0.354, CL = 0.473, CP = 0.489, and LO = 0.591. The research results indicate that AI-based musical instruments not only enhance cognitive engagement but also strengthen participants' creativity in the music learning process. These findings provide significant contributions to the development of technology-based learning systems and the application of AI in creative education, particularly in the realm of digital music.

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Author Biographies

Ratna Juita, Program Studi Teknik Informatika, Universitas Papua

dosen teknik informatika di universitas papua

Dedi I. Inan, Program Studi Teknik Informatika, Universitas Papua

dosen teknik informatika universitas papua

Muhamad Indra, Program Studi Teknik Informatika, Universitas Papua

asisten dosen teknik informatika universita papua

References

J. F. Merchán Sánchez-Jara, S. González Gutiérrez, J. Cruz Rodríguez, and B. Syroyid Syroyid, “Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and Opportunities,” Educ. Sci., vol. 14, no. 11, p. 1171, Oct. 2024, doi: 10.3390/educsci14111171. DOI: https://doi.org/10.3390/educsci14111171

Y. Zhang, Beh Wen Fen, Chao Zhang, and Sheng Pi, “Transforming Music Education Through Artificial Intelligence: A Systematic Literature Review on Enhancing Music Teaching and Learning,” Int. J. Interact. Mob. Technol., vol. 18, no. 18, pp. 76–93, Sep. 2024, doi: 10.3991/ijim.v18i18.50545. DOI: https://doi.org/10.3991/ijim.v18i18.50545

B. Karyadi, “Pemanfaatan Kecerdasan Buatan Dalam Mendukung Pembelajaran Mandiri,” Educ. J. Teknol. Pendidik., vol. 8, no. 2, pp. 253–258, 2023, doi: 10.32832/educate.v8i02.14843.

T. Smith and T. W. Cawthon, “Generation Z Goes to College,” Coll. Student Aff. J., vol. 35, no. 1, pp. 101–102, 2017, doi: 10.1353/csj.2017.0008. DOI: https://doi.org/10.1353/csj.2017.0008

S. Mahbub, “Gen-Z and Generative AI : Shaping the Future of Learning and Creativity,” Cogniz. J. Multidiscip. Stud., vol. 4, no. 10, pp. 1–18, 2024, doi: 10.47760/cognizance.2024.v04i10.001. DOI: https://doi.org/10.47760/cognizance.2024.v04i10.001

E. Zhou and D. Lee, “Generative artificial intelligence, human creativity, and art,” PNAS Nexus, vol. 3, no. 3, Feb. 2024, doi: 10.1093/pnasnexus/pgae052. DOI: https://doi.org/10.1093/pnasnexus/pgae052

A. D. Oktavia, D. I. Inan, R. N. Wurarah, and O. A. Fenetiruma, “Analisis Faktor-faktor Penentu Adopsi E-Wallet di Papua Barat: Extended UTAUT 2 dan Perceived Risk,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 587–600, 2024, doi: 10.57152/malcom.v4i2.1277. DOI: https://doi.org/10.57152/malcom.v4i2.1277

M. Sanganeria and R. Gala, “Tuning Music Education: AI-Powered Personalization in Learning Music,” no. NeurIPS, 2024, [Online]. Available: http://arxiv.org/abs/2412.13514

A. Skulmowski and K. M. Xu, “Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load,” Educ. Psychol. Rev., vol. 34, no. 1, pp. 171–196, Mar. 2022, doi: 10.1007/s10648-021-09624-7. DOI: https://doi.org/10.1007/s10648-021-09624-7

C. Zhai, S. Wibowo, and L. D. Li, “The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review,” Smart Learn. Environ., vol. 11, no. 1, p. 28, Jun. 2024, doi: 10.1186/s40561-024-00316-7. DOI: https://doi.org/10.1186/s40561-024-00316-7

Verett M. Rogers., “DIFFUSION OF INNOVATIONS. By Everett M. Rogers. New York: The Free Press of Glencoe, 1962. 367 pp. $6.50,” Soc. Forces, vol. 41, no. 4, pp. 415–416, May 1963, doi: 10.2307/2573300. DOI: https://doi.org/10.2307/2573300

R. O. Vincent, “Diffusion of Innovation in Social Networking Sites among Rebecca.vincent@gmail.com DOI empirically , using Social networking sites ( SNS ) as the target innovation . The study was conducted among students of the University of Agriculture , Abeokuta in Nig,” J. Comput. Sci., no. 4, pp. 361–372, 2010.

Engel Novita Ramadani and Dina Fitria Handayani, “Instrumen Penilaian Hasil Pembelajaran Kognitif Pada Tes Objektif,” J. Pendidik. Dan Ilmu Sos., vol. 2, no. 4, pp. 86–96, 2024, doi: 10.54066/jupendis.v2i4.2159. DOI: https://doi.org/10.54066/jupendis.v2i4.2159

J. Rahmawati, “PENGARUH PEMANFAATAN TEKNOLOGI KECERDASAN BUATAN DAN KETERAMPILAN BERPIKIR KRITIS TERHADAP KUALITAS HASIL BELAJAR MAHASISWA,” SINDORO CENDEKIA Pendidik., vol. 7, no. 9, 2024, doi: doi.org/10.9644/sindoro.v7i9.6541.

P. W. Azizah and M. Sanglise, “Apa Yang Memotivasi Seseorang Mengakses Aplikasi Mobile Laporkitong? Perspektif Teori Uses And Gratification (U&G) Dengan PLS-SEM,” J. Ris. Sist. Inf. Dan Tek. Inform. (JURASIK, vol. 9, no. 1, pp. 383–390, 2024, [Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jurasikMotivasiseseorangmengaksesaplikasimobileLaporKitong

B. I. Sappaile, N. Nuridayanti, L. Judijanto, and R. Rukimin, “Analisis Pengaruh Pembelajaran Adaptif Berbasis Kecerdasan Buatan terhadap Pencapaian Akademik Siswa Sekolah Menengah Atas di Era Digital,” J. Pendidik. West Sci., vol. 2, no. 01, pp. 25–31, 2024, doi: 10.58812/jpdws.v2i01.937. DOI: https://doi.org/10.58812/jpdws.v2i01.937

E. Erdfelder, F. FAul, A. Buchner, and A. G. Lang, “Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses,” Behav. Res. Methods, vol. 41, no. 4, pp. 1149–1160, 2009, doi: 10.3758/BRM.41.4.1149. DOI: https://doi.org/10.3758/BRM.41.4.1149

N. Wanma, D. I. Inan, and L. Y. Baisa, “Evaluasi User Experience Dan User Interface Aplikasi Laporkitong Dengan End User Computing Satisfaction,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 1, p. 304, 2024, doi: 10.35889/jutisi.v13i1.1789. DOI: https://doi.org/10.35889/jutisi.v13i1.1789

C. F. Risdiyanto, D. I. Inan, R. N. Wurarah, and O. A. Fenetiruma, “Analisis Faktor-faktor Pendukung dan Penghambat Beralih Mengadopsi Mobile Banking di Papua Barat Memanfaatkan PLS-SEM dan Perspektif Status Quo Bias,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 637–646, 2024, doi: 10.57152/malcom.v4i2.1289. DOI: https://doi.org/10.57152/malcom.v4i2.1289

M. P. Pura and P. N. Madiawati, “Pengaruh Promotion Mix Dan Gaya Hidup Terhadap Keputusan Pembelian Di Shopee Dengan Perilaku Konsumen Sebagai Variabel Intervening,” JEMMA (Journal Econ. Manag. Accounting), vol. 4, no. 2, p. 204, 2021, doi: 10.35914/jemma.v4i2.752. DOI: https://doi.org/10.35914/jemma.v4i2.752

A. N. Solihat, G. Gumilar, A. Srigustini, K. Kurniawan, and A. Suparman, “Dampak Pembelajaran Daring Terhadap Stres Akademik Dan Learning Outcome,” J. Ekon. Pendidik. Dan Kewirausahaan, vol. 11, no. 1, pp. 85–102, 2023, doi: 10.26740/jepk.v11n1.p85-102. DOI: https://doi.org/10.26740/jepk.v11n1.p85-102

Ulfah and Opan Arifudin, “Pengaruh Aspek Kognitif, Afektif, Dan Psikomotor Terhadap Hasil Belajar Peserta Didik,” J. Al-Amar, vol. 2, no. 1, pp. 1–9, 2021.

D. I. Inan et al., “Service quality and self-determination theory towards continuance usage intention of mobile banking,” J. Sci. Technol. Policy Manag., vol. 14, no. 2, pp. 303–328, 2023, doi: 10.1108/JSTPM-01-2021-0005. DOI: https://doi.org/10.1108/JSTPM-01-2021-0005

J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, 2015, doi: 10.1007/s11747-014-0403-8. DOI: https://doi.org/10.1007/s11747-014-0403-8

Hair J, A. R, Babin B, and Black W, “Multivariate Data Analysis.pdf,” Australia : Cengage, vol. 7 edition. p. 758, 2014.

W. W. Chin and P. R. Newsted, “The partial least squares approach to structural equation modeling. Modern methods for business research,” Stat. Strateg. Small Sample Res., no. April, pp. 295-336., 1998, [Online]. Available: http://books.google.com.sg/books?hl=en&lr=&id=EDZ5AgAAQBAJ&oi=fnd&pg=PA295&dq=chin+1998+PLS&ots=47qB7ro0np&sig=rihQBibvT6S-Lsj1H9txe9dX6Zk#v=onepage&q&f=false

J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. in Classroom Companion: Business. Cham: Springer International Publishing, 2021. doi: 10.1007/978-3-030-80519-7. DOI: https://doi.org/10.1007/978-3-030-80519-7

W. W. Chin, The Partial Least Squares Approach to Structural Equation Modeling, no. April. Lawrence Erlbaum Associates, 1998.

P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee, and N. P. Podsakoff, “Common method biases in behavioral research: A critical review of the literature and recommended remedies.,” J. Appl. Psychol., vol. 88, no. 5, pp. 879–903, 2003, doi: 10.1037/0021-9010.88.5.879. DOI: https://doi.org/10.1037/0021-9010.88.5.879

S. B. MacKenzie and P. M. Podsakoff, “Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies,” J. Retail., vol. 88, no. 4, pp. 542–555, Dec. 2012, doi: 10.1016/j.jretai.2012.08.001. DOI: https://doi.org/10.1016/j.jretai.2012.08.001

T. Teo, “Factors influencing teachers’ intention to use technology: Model development and test,” Comput. Educ., vol. 57, no. 4, pp. 2432–2440, Dec. 2011, doi: 10.1016/j.compedu.2011.06.008. DOI: https://doi.org/10.1016/j.compedu.2011.06.008

H. Taherdoost, “A review of technology acceptance and adoption models and theories,” Procedia Manuf., vol. 22, pp. 960–967, 2018, doi: 10.1016/j.promfg.2018.03.137. DOI: https://doi.org/10.1016/j.promfg.2018.03.137

D. Henriksen, P. Mishra, and P. Fisser, “Infusing creativity and technology in 21st century education: A systemic view for change,” Educ. Technol. Soc., vol. 19, no. 3, pp. 27–37, 2016.