ADAPTASI ALAT UKUR ONLINE SELF-DISCLOSURE DALAM INTERAKSI DENGAN ARTIFICIAL INTELLIGENCE PADA GENERASI Z
DOI:
https://doi.org/10.51878/paedagogy.v6i3.12979Keywords:
Online Self-Disclosure, Artificial Intelligence, Generasi Z, Psikologi DigitalAbstract
The increasing intensity of reports of emotional disorders and high individual resistance in accessing professional psychological services due to social stigma are the background to this research. These operational issues have triggered a shift in the preferences of the younger generation to seek alternative, non-traditional support spaces through the interaction of artificial intelligence technology. The main focus of this scientific study is to adapt and validate the psychometric structure of the Online Self-Disclosure Scale instrument in the context of subject self-disclosure to conversational artificial intelligence. The steps for developing this measurement tool (scale development) were operated through a Google Forms digital survey reaching 432 respondents from the Generation Z group in the Greater Jakarta area. Sampling was carried out using a non-probability sampling method with the criteria for subjects aged 18 to 25 years, executed through a purposive sampling technique. Statistical parameter analysis was processed interactively using content validity requirements tests, internal reliability, and Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) modeling. Quantitative data from the empirical evaluation showed that this 17-item instrument met the four-dimensional model's structural suitability (Goodness of Fit) very well, as indicated by the CFI index achievement of 0.957, TLI of 0.948, and GFI of 0.994. The estimated McDonald's omega reliability index was at 0.941 and Cronbach's alpha of 0.939. The main conclusion confirms that this adapted scale draft has proven valid, reliable, and stable for measuring digital psychology phenomena on an ongoing basis.
ABSTRAK
Meningkatnya intensitas pelaporan gangguan emosional serta tingginya resistensi individu dalam mengakses layanan psikologis profesional akibat stigma sosial melatarbelakangi penelitian ini. Masalah operasional tersebut memicu pergeseran preferensi generasi muda untuk mencari ruang dukungan alternatif nontradisional melalui interaksi teknologi kecerdasan buatan. Fokus utama kajian ilmiah ini adalah mengadaptasi serta memvalidasi struktur psikometrik instrumen Online Self-Disclosure Scale dalam konteks keterbukaan diri subjek terhadap conversational artificial intelligence. Langkah-langkah pengembangan alat ukur (scale development) ini dioperasikan melalui survei digital Google Forms dengan menjangkau 432 responden dari kelompok Generasi Z di wilayah Jabodetabek. Penarikan sampel dijalankan menggunakan metode non-probability sampling dengan kriteria subjek berusia 18 hingga 25 tahun yang dieksekusi melalui teknik purposive sampling. Analisis parameter statistik diproses secara interaktif menggunakan uji persyaratan validitas isi, reliabilitas internal, serta pemodelan Exploratory Factor Analysis (EFA) dan Confirmatory Factor Analysis (CFA). Data kuantitatif hasil evaluasi empiris menunjukkan instrumen 17 butir ini memenuhi kesesuaian struktur model empat dimensi (Goodness of Fit) yang sangat baik, ditunjukkan oleh capaian indeks CFI sebesar 0,957, TLI sebesar 0,948, dan GFI sebesar 0,994. Estimasi indeks reliabilitas McDonald’s omega berada pada parameter 0,941 serta Cronbach’s alpha sebesar 0,939. Simpulan utama menegaskan bahwa draf skala adaptasi ini terbukti sahih, andal, dan stabil untuk mengukur fenomena psikologi digital secara berkelanjutan.
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References
Aidha, F. A., Khoirunnisa, K., & A’yun, S. Q. (2025). Integrasi kecerdasan buatan dan kecerdasan emosional dalam dialog digital: Tinjauan psikolinguistik terhadap interaksi generasi modern. GHANCARAN: Jurnal Pendidikan Bahasa dan Sastra Indonesia, 7(1), 45–56. https://doi.org/10.19105/ghancaran.vi.21712
Balcombe, L. (2026). Digital mental health post COVID-19: The era of AI chatbots. Encyclopedia, 6(2), 32–42. https://doi.org/10.3390/encyclopedia6020032
Campbell, M., Barthwal, A., Joshi, S., Shouli, A., & Shrestha, A. K. (2025). Investigation of the privacy concerns in AI systems for young digital citizens: A comparative stakeholder analysis (Naskah pracetak). ArXiv.org. https://doi.org/10.48550/arxiv.2501.13321
Carlier, C., Niemeijer, K., Mestdagh, M., Bauwens, M., Vanbrabant, P., Geurts, L., Waterschoot, T. van, & Kuppens, P. (2021). In search of state and trait emotion markers in mobile-sensed language: Field study. JMIR Mental Health, 9(2), Artikel e31724. https://doi.org/10.2196/31724
Christanto, H. J., & Sediyono, E. (2020). Analisa tingkat usability berdasarkan human computer interaction untuk sistem pemesanan tiket online kereta api. Jurnal Sistem Informasi Bisnis, 10(2), 163–172. https://doi.org/10.21456/vol10iss2pp163-172
Eling, F. (2025). The psychological impact of digital isolation: How AI-driven social interactions shape human behavior and mental well-being. International Journal of Research and Innovation in Social Science, 9(4), 3697–3705. https://doi.org/10.47772/ijrss.2025.90400265
Hadi, C. (2024). Optimalisasi interdependensi AI-manusia pada kesehatan mental melalui kerangka kerja kemitraan. Kajian Psikologi dan Kesehatan Mental, 2(1), 55–66. https://doi.org/10.35912/kpkm.v2i1.5513
Irawan, D., Puspitasari, A. A., Astuti, S. W. W., & Widyastuti, A. (2022). Persepsi keamanan, kepercayaan, dan akuntabilitas perusahaan terhadap niat berdonasi melalui fintech crowdfunding. ISOQUANT: Jurnal Ekonomi, Manajemen dan Akuntansi, 6(1), 73–89. https://doi.org/10.24269/iso.v6i1.1035
Iswanto, E. D., & Ayubi, D. (2023). The relationship of mental health literacy to help-seeking behavior: Systematic review. Journal of Social Research, 2(3), 755–764. https://doi.org/10.55324/josr.v2i3.726
Jacobson, N. C., Summers, B. J., & Wilhelm, S. (2020). Digital biomarkers of social anxiety severity: Digital phenotyping using passive smartphone sensors. Journal of Medical Internet Research, 22(5), Artikel e16875. https://doi.org/10.2196/16875
Kim, Y. (2024). AI health counselor’s self-disclosure: Its impact on user responses based on individual and cultural variation. International Journal of Human–Computer Interaction, 41(4), 2184–2197. https://doi.org/10.1080/10447318.2024.2316373
Kristian, J., & Setyawan, I. R. (2024). Peningkatan kualitas keputusan investasi melalui literasi keuangan digital. Jurnal Manajemen Bisnis dan Kewirausahaan, 8(2), 468–482. https://doi.org/10.24912/jmbk.v8i2.29695
Lee, J., Lee, D., Lee, J., Lee, J., & Lee, J. (2022). Influence of rapport and social presence with an AI psychotherapy chatbot on users’ self-disclosure. International Journal of Human–Computer Interaction, 40(7), 1620–1631. https://doi.org/10.1080/10447318.2022.2146227
Meng, J., & Dai, Y. (2021). Emotional support from AI chatbots: Should a supportive partner self-disclose or not? Journal of Computer-Mediated Communication?, 26(4), 207–222. https://doi.org/10.1093/jcmc/zmab005
Merwin, E. R., Hagen, A. C., Keebler, J. R., & Forbes, C. E. (2025). Self-disclosure to AI: People provide personal information to AI and humans equivalently. Computers in Human Behavior: Artificial Humans, 5, Artikel 100180. https://doi.org/10.1016/j.chbah.2025.100180
Peng, R. X., & Chen, J. (2025). Meme-ingful connections: Unleashing the power of memes, GIFs, and emojis in relationship-oriented online communication. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 19(4), Artikel 11. https://doi.org/10.5817/cp2025-4-11
Perry, A. C. (2026). Beyond conversation: How AI companion use is associated with communication and help-seeking behavior (Naskah repositori). Open Science Framework. https://doi.org/10.17605/osf.io/5xzjs
Querci, I., Barbarossa, C., Romani, S., & Ricotta, F. (2022). Explaining how algorithms work reduces consumers’ concerns regarding the collection of personal data and promotes AI technology adoption. Psychology and Marketing, 39(10), 1888–1901. https://doi.org/10.1002/mar.21705
Said, N. A., & Al?Said, K. (2020). Assessment of acceptance and user experience of human-computer interaction with a computer interface. International Journal of Interactive Mobile Technologies (iJIM), 14(11), 107–120. https://doi.org/10.3991/ijim.v14i11.13943
Saputra, M. C., & Andajani, E. (2023). Analysis of factors influencing intention to adopt battery electric vehicle in Indonesia. ADI Journal on Recent Innovation (AJRI), 5(2), 100–109. https://doi.org/10.34306/ajri.v5i2.993
Shih, H., & Liu, W. (2023). Beyond the trade-offs on Facebook: The underlying mechanisms of privacy choices. Information Systems and E-Business Management, 21(2), 353–387. https://doi.org/10.1007/s10257-023-00622-6
Vagnetti, R., Camp, N., Story, M., Belaid, K. A., Mitra, S., Zecca, M., Nuovo, A. D., & Magistro, D. (2024). Instruments for measuring psychological dimensions in human-robot interaction: Systematic review of psychometric properties. Journal of Medical Internet Research, 26, Artikel e55597. https://doi.org/10.2196/55597
Wodong, G. M. A., & Utami, M. S. (2023). The role of mental health knowledge and perceived public stigma in predicting attitudes towards seeking formal psychological help. Jurnal Psikologi, 50(1), 1–12. https://doi.org/10.22146/jpsi.71727
Wong, L., Tan, G. W., Ooi, K., & Dwivedi, Y. K. (2023). The role of institutional and self in the formation of trust in artificial intelligence technologies. Internet Research, 34(2), 343–370. https://doi.org/10.1108/intr-07-2021-0446
Zhang, S., Zhao, X., Nan, D., & Kim, J. H. (2024). Beyond learning with cold machine: Interpersonal communication skills as anthropomorphic cue of AI instructor. International Journal of Educational Technology in Higher Education, 21(1), Artikel 54. https://doi.org/10.1186/s41239-024-00465-2
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