Kerangka Evaluasi Sistem Manajemen Pengetahuan Berbasis Kecerdasan Buatan pada Warisan Budaya Hindu

Penulis

  • Ni Putu Eka Merliana IAHN Tampung Penyang Palangka Raya
  • I Gede Agus Krisna Warmayana UHN I Gusti Bagus Sugriwa Denpasar
  • Ervantia Restulita L. Sigai IAHN Tampung Penyang Palangka Raya

Kata Kunci:

Kecerdasan Buatan, Sistem Manajemen Pengetahuan, Warisan Budaya Hindu, Representasi Pengetahuan Budaya, Kerangka Evaluasi

Abstrak

Perkembangan kecerdasan buatan telah memperluas pemanfaatan sistem manajemen pengetahuan dalam pengelolaan warisan budaya, dari sekadar penyimpanan dan pengorganisasian informasi menjadi penghubungan, pengolahan, interpretasi, dan penyajian kembali pengetahuan. Namun, evaluasi sistem berbasis kecerdasan buatan pada warisan budaya masih cenderung berfokus pada aspek teknis, sehingga belum sepenuhnya mempertimbangkan konteks, nilai, keaslian, dan keterpercayaan pengetahuan budaya. Kondisi ini menjadi penting dalam pengelolaan warisan budaya Hindu yang memiliki karakter pengetahuan yang beragam dan kontekstual. Tulisan ini bertujuan merumuskan kerangka konseptual evaluasi sistem manajemen pengetahuan berbasis kecerdasan buatan untuk warisan budaya Hindu melalui kajian literatur tahun 2022–2026. Hasil sintesis menunjukkan empat dimensi utama yang dapat digunakan sebagai dasar evaluasi, yaitu kesesuaian budaya, ketepatan konteks budaya, keaslian dan keterlacakan sumber, serta kepercayaan terhadap representasi pengetahuan budaya. Keempat dimensi tersebut selanjutnya dijabarkan ke dalam faktor-faktor pengukuran sebagai dasar awal pengembangan indikator dan instrumen evaluasi. Kerangka yang dihasilkan menegaskan bahwa kualitas sistem manajemen pengetahuan berbasis kecerdasan buatan tidak cukup ditentukan oleh kemampuan teknis dan ketepatan informasi, tetapi juga oleh kemampuannya mempertahankan makna, konteks, keaslian sumber, dan keterpercayaan representasi pengetahuan budaya. Kerangka ini dapat menjadi landasan bagi penelitian selanjutnya untuk mengembangkan, memvalidasi, dan menguji instrumen evaluasi secara empiris pada sistem manajemen pengetahuan warisan budaya Hindu.

Unduhan

Data unduhan belum tersedia.

Biografi Penulis

I Gede Agus Krisna Warmayana, UHN I Gusti Bagus Sugriwa Denpasar

Alavi, M., Leidner, D. E., & Mousavi, R. (2024). Knowledge Management Perspective of Generative Artificial Intelligence. Journal of the Association for Information Systems, 25(1), 1–12. https://doi.org/10.17705/1jais.00859

Böhm, K., & Durst, S. (2026). Knowledge management in the age of generative artificial intelligence – from SECI to GRAI. VINE Journal of Information and Knowledge Management Systems, 56(1), 106–121. https://doi.org/10.1108/VJIKMS-10-2024-0357

Chuyao, W., & Adzharuddin, N. A. (2025). Synthetic Authenticity and Audience Trust in AI-Generated Intangible Cultural Heritage: A Qualitative Multimodal Study. Journal of Media and Society, 8(2), 2025. https://doi.org/10.24191/ejoms.v8i2.6894

Cui, C. D. L., Cursi, S., Simeone, D., Yan, W., Fioravanti, A., & Currà, E. (2025). AI Assistant for Heritage Knowledge Management: Retrieving Integrated Data from Ontology-Driven HBIM and Archival Documents. In Lecture Notes in Civil Engineering: 764 LNCE (pp. 263–281). Springer. https://doi.org/10.1007/978-3-032-06974-0_14

Dinda, G., & Rahman, Z. (2025). Towards a digital future: A conceptual framework for manuscript preservation system in India under National Mission for Manuscripts (NMMs). Journal of Information Management, 12(1), 54–73. https://doi.org/10.5958/2348-1773.2025.00003.0

Ferro, S., Giovanelli, R., Leeson, M., De Bernardin, M., & Traviglia, A. (2025). A novel NLP-driven approach for enriching artefact descriptions, provenance, and entities in cultural heritage. Neural Computing and Applications, 37(25), 21275–21296. https://doi.org/10.1007/s00521-025-11449-2

Foka, A., & Griffin, G. (2024). AI, Cultural Heritage, and Bias: Some Key Queries That Arise from the Use of GenAI. Heritage, 7(11), 6125–6136. https://doi.org/10.3390/heritage7110287

Foka, A., Griffin, G., Ortiz Pablo, D., Rajkowska, P., & Badri, S. (2025). Tracing the bias loop: AI, cultural heritage and bias-mitigating in practice. AI and Society, 40(8), 5835–5847. https://doi.org/10.1007/s00146-025-02349-z

Harmanda, T. T., Hidayanto, A. N., Aji, R. F., & Ilin, I. (2026). Artificial intelligence adoption in the cultural heritage sector: A systematic review of trends, application domains, and challenges. Computers in Human Behavior Reports, 23(May), 101147. https://doi.org/10.1016/j.chbr.2026.101147

Iddamalgoda, C., Ng, K. H., & Koleva, B. (2026). Generative AI for Supporting Cultural Learning and Reflection: A Study on Technology User Acceptance. International Journal of Human–Computer Interaction, 42(1), 197–210. https://doi.org/10.1080/10447318.2025.2505777

Kaczorowska-Spychalska, D., Kotula, N., Mazurek, G., & Sułkowski, Ł. (2024). Generative Ai As Source of Change of Knowledge Management Paradigm. Human Technology, 20(1), 131–154. https://doi.org/10.14254/1795-6889.2024.20-1.7

Kong, L., & Seong, D. (2026). Source accountability in AI-mediated heritage interpretation under controlled data-source contamination. Npj Heritage Science. https://doi.org/10.1038/s40494-026-02926-5

Kraev, M., & Luchev, D. (2025). Analyzing Knowledge Graph Innovations and Emerging AI technologies for Cultural Heritage Data Management. Digital Presentation and Preservation of Cultural and Scientific Heritage, 15, 247–258. https://doi.org/10.55630/dipp.2025.15.23

Lewis, J. E., Whaanga, H., & Yolgörmez, C. (2025). Abundant intelligences: placing AI within Indigenous knowledge frameworks. AI and Society, 40(4), 2141–2157. https://doi.org/10.1007/s00146-024-02099-4

Münster, S., Maiwald, F., di Lenardo, I., Henriksson, J., Isaac, A., Graf, M. M., Beck, C., & Oomen, J. (2024). Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe. Heritage, 7(2), 794–816. https://doi.org/10.3390/heritage7020038

Natarajan, S., Mathur, S., Sidheekh, S., Stammer, W., & Kersting, K. (2025). Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28594–28600. https://doi.org/10.1609/aaai.v39i27.35083

Ocón, D., Yin, C., & Luna, J. (2026). Artificial insights or historical fidelity? Crafting an ethical framework for the use of GenAI in the restoration, reconstruction and recreation of movable cultural heritage. AI and Society, 41(1), 121–134. https://doi.org/10.1007/s00146-025-02454-z

Oruc, P. (2025). Cultural Heritage as AI Training Data. IIC International Review of Intellectual Property and Competition Law, 56(8), 1455–1459. https://doi.org/10.1007/s40319-025-01659-5

Pan, S., & Shi, Q. (2025). Exploring the Evolution of Museum Knowledge Organization Systems. Cataloging and Classification Quarterly, 63(6–7), 475–493. https://doi.org/10.1080/01639374.2025.2544142

Paolanti, M., Frontoni, E., & Pierdicca, R. (2026). Towards trustworthy AI in cultural heritage. Npj Heritage Science, 14(1), 1--? https://doi.org/10.1038/s40494-026-02403-z

Patnaik, D. I., Priyadarshini, P., Mishra, D., & Chaudhuri, S. D. (2025). Text to Tech: Preserving Ancient Hindu Texts in the Digital Realm. Indian Journal of Information Sources and Services, 15(1), 42–46. https://doi.org/10.51983/ijiss-2025.IJISS.15.1.07

Schimmenti, A., Pasqual, V., Vitali, F., & van Erp, M. (2026). Knowledge graphs generation from cultural heritage texts: combining LLMs and ontological engineering for scholarly debates. Journal of Documentation, 82(7), 206–250. https://doi.org/10.1108/JD-07-2025-0203

Shao, P. (2026). AI-Driven Digital Re-Creation Path for Intangible Cultural Heritage. International Journal of Knowledge Management, 22(1), 1–15. https://doi.org/10.4018/ijkm.413112

Spennemann, D. H. R. (2024). Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models. Heritage, 7(3), 1453–1471. https://doi.org/10.3390/heritage7030070

Stacchio, L., Balloni, E., Gorgoglione, L., Mancini, A., Giovanola, B., Tiribelli, S., & Zingaretti, P. (2024). An ethical framework for trustworthy Neural Rendering applied in cultural heritage and creative industries. Frontiers in Computer Science, 6(1459807). https://doi.org/10.3389/fcomp.2024.1459807

Storey, V. C. (2025). Knowledge Management in a World of Generative AI: Impact and Implications. ACM Transactions on Management Information Systems, 16(3), 1–14. https://doi.org/10.1145/3719209

Tiribelli, S., Pansoni, S., Frontoni, E., & Giovanola, B. (2024). Ethics of Artificial Intelligence for Cultural Heritage: Opportunities and Challenges. IEEE Transactions on Technology and Society, 5(3), 293–305. https://doi.org/10.1109/TTS.2024.3432407

Wang, H., Gong, Y., Zhang, Y., & Li, F. (2025). Artificial Intelligence for Sustainable Cultural Heritage: Practical Guidelines and Case-Based Evidence. Sustainability (Switzerland), 17(20), 9192. https://doi.org/10.3390/su17209192

Wang, Y., & Zhang, M. (2025). CIDOC CRM-Based Knowledge Graph Construction for Cultural Heritage Using Large Language Models. Applied Sciences, 15(22), 12063. https://doi.org/10.3390/app152212063

Yuan, H., Li, Y., Wang, B., Liu, K., & Zhang, J. (2025). Knowledge graph-based intelligent question answering system for ancient Chinese costume heritage. Npj Heritage Science, 13(1), 198. https://doi.org/10.1038/s40494-025-01776-x

Referensi

Alavi, M., Leidner, D. E., & Mousavi, R. (2024). Knowledge Management Perspective of Generative Artificial Intelligence. Journal of the Association for Information Systems, 25(1), 1–12. https://doi.org/10.17705/1jais.00859

Böhm, K., & Durst, S. (2026). Knowledge management in the age of generative artificial intelligence – from SECI to GRAI. VINE Journal of Information and Knowledge Management Systems, 56(1), 106–121. https://doi.org/10.1108/VJIKMS-10-2024-0357

Chuyao, W., & Adzharuddin, N. A. (2025). Synthetic Authenticity and Audience Trust in AI-Generated Intangible Cultural Heritage: A Qualitative Multimodal Study. Journal of Media and Society, 8(2), 2025. https://doi.org/10.24191/ejoms.v8i2.6894

Cui, C. D. L., Cursi, S., Simeone, D., Yan, W., Fioravanti, A., & Currà, E. (2025). AI Assistant for Heritage Knowledge Management: Retrieving Integrated Data from Ontology-Driven HBIM and Archival Documents. In Lecture Notes in Civil Engineering: 764 LNCE (pp. 263–281). Springer. https://doi.org/10.1007/978-3-032-06974-0_14

Dinda, G., & Rahman, Z. (2025). Towards a digital future: A conceptual framework for manuscript preservation system in India under National Mission for Manuscripts (NMMs). Journal of Information Management, 12(1), 54–73. https://doi.org/10.5958/2348-1773.2025.00003.0

Ferro, S., Giovanelli, R., Leeson, M., De Bernardin, M., & Traviglia, A. (2025). A novel NLP-driven approach for enriching artefact descriptions, provenance, and entities in cultural heritage. Neural Computing and Applications, 37(25), 21275–21296. https://doi.org/10.1007/s00521-025-11449-2

Foka, A., & Griffin, G. (2024). AI, Cultural Heritage, and Bias: Some Key Queries That Arise from the Use of GenAI. Heritage, 7(11), 6125–6136. https://doi.org/10.3390/heritage7110287

Foka, A., Griffin, G., Ortiz Pablo, D., Rajkowska, P., & Badri, S. (2025). Tracing the bias loop: AI, cultural heritage and bias-mitigating in practice. AI and Society, 40(8), 5835–5847. https://doi.org/10.1007/s00146-025-02349-z

Harmanda, T. T., Hidayanto, A. N., Aji, R. F., & Ilin, I. (2026). Artificial intelligence adoption in the cultural heritage sector: A systematic review of trends, application domains, and challenges. Computers in Human Behavior Reports, 23(May), 101147. https://doi.org/10.1016/j.chbr.2026.101147

Iddamalgoda, C., Ng, K. H., & Koleva, B. (2026). Generative AI for Supporting Cultural Learning and Reflection: A Study on Technology User Acceptance. International Journal of Human–Computer Interaction, 42(1), 197–210. https://doi.org/10.1080/10447318.2025.2505777

Kaczorowska-Spychalska, D., Kotula, N., Mazurek, G., & Su?kowski, ?. (2024). Generative Ai As Source of Change of Knowledge Management Paradigm. Human Technology, 20(1), 131–154. https://doi.org/10.14254/1795-6889.2024.20-1.7

Kong, L., & Seong, D. (2026). Source accountability in AI-mediated heritage interpretation under controlled data-source contamination. Npj Heritage Science. https://doi.org/10.1038/s40494-026-02926-5

Kraev, M., & Luchev, D. (2025). Analyzing Knowledge Graph Innovations and Emerging AI technologies for Cultural Heritage Data Management. Digital Presentation and Preservation of Cultural and Scientific Heritage, 15, 247–258. https://doi.org/10.55630/dipp.2025.15.23

Lewis, J. E., Whaanga, H., & Yolgörmez, C. (2025). Abundant intelligences: placing AI within Indigenous knowledge frameworks. AI and Society, 40(4), 2141–2157. https://doi.org/10.1007/s00146-024-02099-4

Münster, S., Maiwald, F., di Lenardo, I., Henriksson, J., Isaac, A., Graf, M. M., Beck, C., & Oomen, J. (2024). Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe. Heritage, 7(2), 794–816. https://doi.org/10.3390/heritage7020038

Natarajan, S., Mathur, S., Sidheekh, S., Stammer, W., & Kersting, K. (2025). Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28594–28600. https://doi.org/10.1609/aaai.v39i27.35083

Ocón, D., Yin, C., & Luna, J. (2026). Artificial insights or historical fidelity? Crafting an ethical framework for the use of GenAI in the restoration, reconstruction and recreation of movable cultural heritage. AI and Society, 41(1), 121–134. https://doi.org/10.1007/s00146-025-02454-z

Oruc, P. (2025). Cultural Heritage as AI Training Data. IIC International Review of Intellectual Property and Competition Law, 56(8), 1455–1459. https://doi.org/10.1007/s40319-025-01659-5

Pan, S., & Shi, Q. (2025). Exploring the Evolution of Museum Knowledge Organization Systems. Cataloging and Classification Quarterly, 63(6–7), 475–493. https://doi.org/10.1080/01639374.2025.2544142

Paolanti, M., Frontoni, E., & Pierdicca, R. (2026). Towards trustworthy AI in cultural heritage. Npj Heritage Science, 14(1), 1--? https://doi.org/10.1038/s40494-026-02403-z

Patnaik, D. I., Priyadarshini, P., Mishra, D., & Chaudhuri, S. D. (2025). Text to Tech: Preserving Ancient Hindu Texts in the Digital Realm. Indian Journal of Information Sources and Services, 15(1), 42–46. https://doi.org/10.51983/ijiss-2025.IJISS.15.1.07

Schimmenti, A., Pasqual, V., Vitali, F., & van Erp, M. (2026). Knowledge graphs generation from cultural heritage texts: combining LLMs and ontological engineering for scholarly debates. Journal of Documentation, 82(7), 206–250. https://doi.org/10.1108/JD-07-2025-0203

Shao, P. (2026). AI-Driven Digital Re-Creation Path for Intangible Cultural Heritage. International Journal of Knowledge Management, 22(1), 1–15. https://doi.org/10.4018/ijkm.413112

Spennemann, D. H. R. (2024). Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models. Heritage, 7(3), 1453–1471. https://doi.org/10.3390/heritage7030070

Stacchio, L., Balloni, E., Gorgoglione, L., Mancini, A., Giovanola, B., Tiribelli, S., & Zingaretti, P. (2024). An ethical framework for trustworthy Neural Rendering applied in cultural heritage and creative industries. Frontiers in Computer Science, 6(1459807). https://doi.org/10.3389/fcomp.2024.1459807

Storey, V. C. (2025). Knowledge Management in a World of Generative AI: Impact and Implications. ACM Transactions on Management Information Systems, 16(3), 1–14. https://doi.org/10.1145/3719209

Tiribelli, S., Pansoni, S., Frontoni, E., & Giovanola, B. (2024). Ethics of Artificial Intelligence for Cultural Heritage: Opportunities and Challenges. IEEE Transactions on Technology and Society, 5(3), 293–305. https://doi.org/10.1109/TTS.2024.3432407

Wang, H., Gong, Y., Zhang, Y., & Li, F. (2025). Artificial Intelligence for Sustainable Cultural Heritage: Practical Guidelines and Case-Based Evidence. Sustainability (Switzerland), 17(20), 9192. https://doi.org/10.3390/su17209192

Wang, Y., & Zhang, M. (2025). CIDOC CRM-Based Knowledge Graph Construction for Cultural Heritage Using Large Language Models. Applied Sciences, 15(22), 12063. https://doi.org/10.3390/app152212063

Yuan, H., Li, Y., Wang, B., Liu, K., & Zhang, J. (2025). Knowledge graph-based intelligent question answering system for ancient Chinese costume heritage. Npj Heritage Science, 13(1), 198. https://doi.org/10.1038/s40494-025-01776-x

Diterbitkan

2026-10-16