PERBANDINGAN MODEL PENDEKATAN ARTIFICIAL INTELLIGENCE BERBASIS JARINGAN SARAF TIRUAN DAN MODEL KLASIK TERHADAP MINAT E-WALLET
DOI:
https://doi.org/10.31849/rp6n6460Keywords:
Jaringan Saraf Tiruan, Regresi Linier Berganda, Keamanan, Privasi dan E-TrustAbstract
Kemajuan teknologi yang semakin pesat membawa dampak perubahan pada seluruh aspek khususnya pada sektor pembayaran. Dompet elektronik atau e-wallet saat ini menjadi tren dimasyarakat karena fleksibilitas dan kenyamanannya. Namun adopsi e-wallet tidak lepas dari beberapa faktor yang mempengaruhi minat konsumen untuk menggunakannya. Penelitian ini bertujuan untuk menganalisis pengaruh keamanan, privasi dan e-trust terhadap minat penggunaan e-wallet di Indonesi, dengan membandingkan dua metode, yaitu Jaringan Saraf Tiruan dan Regresi Linear Berganda menggunakan SPSS dan MATLAB. Analisis perbandingan dilakukan untuk mengidentifikasi metode yang memiliki tingkat akurasi paling baik. Jumlah populasi yang digunakan dalam penlitian ini dihitung menggunakan rumus Lemeshow, yang menghasilkan 165 reponden. Hasil penelitian menujukan bahwa metode Jaringan Saraf Tiruan memberikan hasil analisis yang jauh lebih baik dibandingkan Regresi Linear Berganda dengan hasil nilai yang didapat di setiap variable mendekati angka target yang diharapkan, sedangkan regresi linear berganda memberikan hasil yang kurang optimal khususnya pada variable keamanan dan privasi.
References
[1] R. Arvyana, F. Damayanti, and T. S. Rochmah, “Analisis E-Wallet Berbasis TAM (Technology Adoption Model) di PT Telkom Indonesia Tbk,” Jurnal Lentera Bisnis , vol. 11, no. 3, p. 286, Oct. 2022, doi: 10.34127/jrlab.v11i3.622.
[2] M. S. Alif and A. R. Pratama, “Analisis Kesadaran Keamanan di Kalangan Pengguna E-Wallet di Indonesia,” 2021.
[3] I. T. Moon, M. Shamsuzzaman, M. M. R. Mridha, and A. S. Md. M. Rahaman, “Towards the Advancement of Cashless Transaction: A Security Analysis of Electronic Payment Systems,” Journal of Computer and Communications, vol. 10, no. 07, pp. 103–129, 2022, doi: 10.4236/jcc.2022.107007.
[4] S. Putri Andika and Agusnia Kria, “Pengaruh Diskon Belanja dan Keamanan Dalam Menggunakan Shopeepay Sebagai Sistem Pembayaran Terhadap Perilaku Konsumsi di Kalangan Mahasiswa Unitri,” Jurnal Ilmu Manajemen dan Akutansi, vol. 11, no. 1, pp. 110–118, Mar. 2023.
[5] S. Bodhi and D. Tan, “Keamana Data Pribadi dalam Sistem Pembayaran E-Wallet Terhadap Ancaman Penipuan dan Pengelabuhan (Cybercrime),” Unes Law Review, vol. 4, no. 3, pp. 297–307, Mar. 2022, doi: 10.31933/unesrev.v4i3.
[6] B. Barkah et al., “Pengaruh E-Service Quality, E-Trust, dan E-WOM Terhadap E-Satisfaction Pengguna Aplikasi Shopee Di Kota Pontianak,” 2021.
[7] N. Salshabia Analita and T. Indra Wijaksana, “Analisi Perbandingan E-Service Quality dan E-Trust Aplikasi LinkAja dengan Aplikasi Dana,” Jurnal Penelitian dan Kajian Ilmiah Universitas Muhammadiyah Sumatera Barat, vol. 14, no. 1, pp. 98–106, Oct. 2020.
[8] I. Indriyanti, T. Wahyuni, E. Ermawati, N. Ichsan, and H. Fatah, “Analisis Perbandingan Metode TAM dan UTAUT dalam Mengukur Kesuksesan Penggunaan Aplikasi Ojek Online,” Jurnal Interkom: Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 14, no. 4, pp. 24–30, Jan. 2020, doi: 10.35969/interkom.v14i4.59.
[9] R. N. P. B. Puspaningrum and A. D. R. Atahau, “Penggunaan E-Wallet dalam Transaksi E-Commerce: Analisis Unified Theory of Acceptance and Use of Technology (UTAUT),” Jurnal Ekonomi Pendidikan dan Kewirausahaan , vol. 11, no. 2, pp. 191–208, Oct. 2023, doi: 10.26740/jepk.v11n2.p191-208.
[10] V. Nourani, H. Gökçekuş, and I. K. Umar, “Artificial intelligence based ensemble model for prediction of vehicular traffic noise,” Environ Res, vol. 180, Jan. 2020, doi: 10.1016/j.envres.2019.108852.
[11] N. Cavus, Y. B. Mohammed, and M. N. Yakubu, “An artificial intelligence‐based model for prediction of parameters affecting sustainable growth of mobile banking apps,” Sustainability (Switzerland), vol. 13, no. 11, Jun. 2021, doi: 10.3390/su13116206.
[12] N. Sudariana and M. M. Yoedani, “Analisis Statistik Regresi Linier Berganda,” Senima Transactions on Managemenr and Business, vol. 2, no. 2, Apr. 2022.
[13] J. Zupan, “Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them*,” Acta Chim Slov, 1994, [Online]. Available: https://www.researchgate.net/publication/251626579
[14] N. Cavus et al., “Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness,” Sustainability (Switzerland), vol. 14, no. 10, May 2022, doi: 10.3390/su14105826.
[15] H. Tamiru and M. O. Dinka, “Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia,” J Hydrol Reg Stud, vol. 36, Aug. 2021, doi: 10.1016/j.ejrh.2021.100855.
[16] N. Hasanah, M. Zainal Abidin, and U. Banjarmasin, “Pengaruh Keamanan dan Kemudahan Bertransaksi terhadap Minat Beli Menggunakan Dompet Digital Ovo pada Kalangan Mahasiswa di Banjarmasin,” Jurnal Ekonomi dan Bisnis, vol. 15, no. 2, pp. 405–424, Sep. 2022.
[17] L. T. Truc, “Empowering tomorrow: Unleashing the power of e-wallets with adoption readiness, personal innovativeness, and perceived risk to client’s intention,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 3, Sep. 2024, doi: 10.1016/j.joitmc.2024.100322.
[18] S. Devica, M. Septynaputri Widodo, P. Studi Manajemen Pemasaran, and P. Ubaya Jalan Ngagel Jaya Selatan, “Pengaruh Perceived of Benefit dan E-Trust terhadap Minat Menggunakan Qris,” Jurnal Bisnis Perspektif, vol. 15, no. 2, pp. 89–99, Jul. 2023, [Online]. Available: http://jurnal.ukdc.ac.id/index.php/BIP
[19] D. Nurhani and S. Harsono, “The Influence of E-service Quality and E-trust on BSI Mobile User Loyalty with Customer Satisfaction as a Mediator,” International Journal of Economics, Business and Management Research, vol. 08, no. 02, pp. 143–157, 2024, doi: 10.51505/ijebmr.2024.8212.
[20] E. N. Fitriyani, “Analisis Faktor yang Mempengaruhi Minat Menggunakan E-Wallet pada Generasi Milenial Muslim,” 2023.
[21] S. B. Utami, A. D. B. Bawono, and N. Sasongko, “Pengaruh Privasi, Keamanan, Keandalan, dan Transparansi Terhadap Minat Penggunaan Payment Fintech UMKM di Watukelir,” Widya Cipta: Jurnal Sekretari dan Manajemen, vol. 7, no. 2, pp. 228–239, Sep. 2023, doi: 10.31294/widyacipta.v7i2.15976.
[22] S. Damerianta, D. Mukodim, and A. Harmadi, “The influence of perceptions of usefulness, user ease, and security on interest in using fund e-wallet with e-trust as intervening variable,” Technium Social Sciences Journal, vol. 34, pp. 708–717, Aug. 2022, [Online]. Available: www.techniumscience.com
[23] Z. Zhou, C. Qiu, and Y. Zhang, “A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-49899-0.
[24] B. Baby, Z. Dawod, and M. Saeed Sharif, “Customer Churn Prediction Model Using Artificial Neural Networks (ANN): A Case Study in Banking,” in Proceedings: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, IEEE, Sep. 2023, p. 1.
[25] N. Cavus, Y. B. Mohammed, and M. N. Yakubu, “An artificial intelligence‐based model for prediction of parameters affecting sustainable growth of mobile banking apps,” Sustainability (Switzerland), vol. 13, no. 11, Jun. 2021, doi: 10.3390/su13116206.
[2] M. S. Alif and A. R. Pratama, “Analisis Kesadaran Keamanan di Kalangan Pengguna E-Wallet di Indonesia,” 2021.
[3] I. T. Moon, M. Shamsuzzaman, M. M. R. Mridha, and A. S. Md. M. Rahaman, “Towards the Advancement of Cashless Transaction: A Security Analysis of Electronic Payment Systems,” Journal of Computer and Communications, vol. 10, no. 07, pp. 103–129, 2022, doi: 10.4236/jcc.2022.107007.
[4] S. Putri Andika and Agusnia Kria, “Pengaruh Diskon Belanja dan Keamanan Dalam Menggunakan Shopeepay Sebagai Sistem Pembayaran Terhadap Perilaku Konsumsi di Kalangan Mahasiswa Unitri,” Jurnal Ilmu Manajemen dan Akutansi, vol. 11, no. 1, pp. 110–118, Mar. 2023.
[5] S. Bodhi and D. Tan, “Keamana Data Pribadi dalam Sistem Pembayaran E-Wallet Terhadap Ancaman Penipuan dan Pengelabuhan (Cybercrime),” Unes Law Review, vol. 4, no. 3, pp. 297–307, Mar. 2022, doi: 10.31933/unesrev.v4i3.
[6] B. Barkah et al., “Pengaruh E-Service Quality, E-Trust, dan E-WOM Terhadap E-Satisfaction Pengguna Aplikasi Shopee Di Kota Pontianak,” 2021.
[7] N. Salshabia Analita and T. Indra Wijaksana, “Analisi Perbandingan E-Service Quality dan E-Trust Aplikasi LinkAja dengan Aplikasi Dana,” Jurnal Penelitian dan Kajian Ilmiah Universitas Muhammadiyah Sumatera Barat, vol. 14, no. 1, pp. 98–106, Oct. 2020.
[8] I. Indriyanti, T. Wahyuni, E. Ermawati, N. Ichsan, and H. Fatah, “Analisis Perbandingan Metode TAM dan UTAUT dalam Mengukur Kesuksesan Penggunaan Aplikasi Ojek Online,” Jurnal Interkom: Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 14, no. 4, pp. 24–30, Jan. 2020, doi: 10.35969/interkom.v14i4.59.
[9] R. N. P. B. Puspaningrum and A. D. R. Atahau, “Penggunaan E-Wallet dalam Transaksi E-Commerce: Analisis Unified Theory of Acceptance and Use of Technology (UTAUT),” Jurnal Ekonomi Pendidikan dan Kewirausahaan , vol. 11, no. 2, pp. 191–208, Oct. 2023, doi: 10.26740/jepk.v11n2.p191-208.
[10] V. Nourani, H. Gökçekuş, and I. K. Umar, “Artificial intelligence based ensemble model for prediction of vehicular traffic noise,” Environ Res, vol. 180, Jan. 2020, doi: 10.1016/j.envres.2019.108852.
[11] N. Cavus, Y. B. Mohammed, and M. N. Yakubu, “An artificial intelligence‐based model for prediction of parameters affecting sustainable growth of mobile banking apps,” Sustainability (Switzerland), vol. 13, no. 11, Jun. 2021, doi: 10.3390/su13116206.
[12] N. Sudariana and M. M. Yoedani, “Analisis Statistik Regresi Linier Berganda,” Senima Transactions on Managemenr and Business, vol. 2, no. 2, Apr. 2022.
[13] J. Zupan, “Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them*,” Acta Chim Slov, 1994, [Online]. Available: https://www.researchgate.net/publication/251626579
[14] N. Cavus et al., “Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness,” Sustainability (Switzerland), vol. 14, no. 10, May 2022, doi: 10.3390/su14105826.
[15] H. Tamiru and M. O. Dinka, “Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia,” J Hydrol Reg Stud, vol. 36, Aug. 2021, doi: 10.1016/j.ejrh.2021.100855.
[16] N. Hasanah, M. Zainal Abidin, and U. Banjarmasin, “Pengaruh Keamanan dan Kemudahan Bertransaksi terhadap Minat Beli Menggunakan Dompet Digital Ovo pada Kalangan Mahasiswa di Banjarmasin,” Jurnal Ekonomi dan Bisnis, vol. 15, no. 2, pp. 405–424, Sep. 2022.
[17] L. T. Truc, “Empowering tomorrow: Unleashing the power of e-wallets with adoption readiness, personal innovativeness, and perceived risk to client’s intention,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 3, Sep. 2024, doi: 10.1016/j.joitmc.2024.100322.
[18] S. Devica, M. Septynaputri Widodo, P. Studi Manajemen Pemasaran, and P. Ubaya Jalan Ngagel Jaya Selatan, “Pengaruh Perceived of Benefit dan E-Trust terhadap Minat Menggunakan Qris,” Jurnal Bisnis Perspektif, vol. 15, no. 2, pp. 89–99, Jul. 2023, [Online]. Available: http://jurnal.ukdc.ac.id/index.php/BIP
[19] D. Nurhani and S. Harsono, “The Influence of E-service Quality and E-trust on BSI Mobile User Loyalty with Customer Satisfaction as a Mediator,” International Journal of Economics, Business and Management Research, vol. 08, no. 02, pp. 143–157, 2024, doi: 10.51505/ijebmr.2024.8212.
[20] E. N. Fitriyani, “Analisis Faktor yang Mempengaruhi Minat Menggunakan E-Wallet pada Generasi Milenial Muslim,” 2023.
[21] S. B. Utami, A. D. B. Bawono, and N. Sasongko, “Pengaruh Privasi, Keamanan, Keandalan, dan Transparansi Terhadap Minat Penggunaan Payment Fintech UMKM di Watukelir,” Widya Cipta: Jurnal Sekretari dan Manajemen, vol. 7, no. 2, pp. 228–239, Sep. 2023, doi: 10.31294/widyacipta.v7i2.15976.
[22] S. Damerianta, D. Mukodim, and A. Harmadi, “The influence of perceptions of usefulness, user ease, and security on interest in using fund e-wallet with e-trust as intervening variable,” Technium Social Sciences Journal, vol. 34, pp. 708–717, Aug. 2022, [Online]. Available: www.techniumscience.com
[23] Z. Zhou, C. Qiu, and Y. Zhang, “A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-49899-0.
[24] B. Baby, Z. Dawod, and M. Saeed Sharif, “Customer Churn Prediction Model Using Artificial Neural Networks (ANN): A Case Study in Banking,” in Proceedings: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, IEEE, Sep. 2023, p. 1.
[25] N. Cavus, Y. B. Mohammed, and M. N. Yakubu, “An artificial intelligence‐based model for prediction of parameters affecting sustainable growth of mobile banking apps,” Sustainability (Switzerland), vol. 13, no. 11, Jun. 2021, doi: 10.3390/su13116206.
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[1]
“PERBANDINGAN MODEL PENDEKATAN ARTIFICIAL INTELLIGENCE BERBASIS JARINGAN SARAF TIRUAN DAN MODEL KLASIK TERHADAP MINAT E-WALLET”, zn, vol. 7, no. 2, pp. 570–583, May 2025, doi: 10.31849/rp6n6460.
