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  1. Home
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  3. Vol. 10, No. 3, August 2025
  4. Articles

Issue

Vol. 10, No. 3, August 2025

Issue Published : Jun 13, 2025
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Land Price Distribution Prediction in Jakarta Using Support Vector Machine with Feature Expansion and Kriging Interpolation

https://doi.org/10.22219/kinetik.v10i3.2216
Hadid Pilar Gautama
Telkom University
Sri Suryani Prasetiyowati
Telkom University
Yuliant Sibaroni
Telkom University

Corresponding Author(s) : Hadid Pilar Gautama

hadidgautama@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 3, August 2025
Article Published : Jun 13, 2025

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Abstract

Fluctuations in land prices over time are very significant, especially in big cities, one of which is Jakarta. The increase in land prices is influenced by high demand, location-related needs, ease of access to various public facilities and crowds. Uncontrolled prices and lack of information about the distribution of land prices cause buyers to get land that is not in accordance with their needs. This study develops a land price distribution prediction system for Jakarta for 2025-2026 using Support Vector Machine (SVM) with time-based feature expansion and spatial interpolation. The SVM model with RBF kernel demonstrated superior performance, achieving 93.14% accuracy for 2025 predictions using the t-1 model. For 2026 predictions, the t-2 model achieved 83.33% accuracy. This approach involves utilizing one to two years of historical data and systematically selected features, ensuring more accurate and relevant predictions. Ordinary kriging interpolation visualizations revealed a significant shift in land price distribution patterns, indicating a decline in affordable land availability and an increase in high-value properties across Jakarta. The integration of SVM and kriging interpolation, coupled with comprehensive evaluation metrics, provides a robust methodological framework for predicting urban land price distributions. This system offers practical implications for informed decision-making in Jakarta's dynamic land market, enabling stakeholders to make efficient, budget-based property decisions. The research contributes significantly to urban planning by providing a comprehensive tool for understanding and predicting land price trends, which can assist various stakeholders in making informed property investment decisions.

Keywords

Land Price Prediction Jakarta Support Vector Machine (SVM) Time Based Feature Expansion Kriging Interpolation
Pilar Gautama, H., Prasetiyowati, S. S., & Sibaroni, Y. (2025). Land Price Distribution Prediction in Jakarta Using Support Vector Machine with Feature Expansion and Kriging Interpolation. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(3). https://doi.org/10.22219/kinetik.v10i3.2216
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References
  1. Akhiat, Y., & Amjad, S. (2022). Feature Selection : A Review and Comparative Study Feature Selection : A Review and Comparative Study, (May). https://doi.org/10.1051/e3sconf/202235101046
  2. Amelia, U., Indra, J., & Masruriyah, A. F. N. (2022). Implementasi Algoritma Support Vector Machine (Svm) Untuk Prediksi Penyakit Stroke Dengan Atribut Berpengaruh. Scientific Student Journal for Information, Technology and Science, III(2), 254–259.
  3. Apostolidis-Afentoulis, V. (2015). SVM Classification with Linear and Rbf Kernels. ResearchGate, (July), 0–7. https://doi.org/10.13140/RG.2.1.3351.4083
  4. Athoillah, M., & Putri, R. K. (2019). Handwritten Arabic Numeral Character Recognition Using Multi Kernel Learning Support Vector Machine, 4(2), 99–106.
  5. Az-zahra, N. A., & Arianto, D. B. (n.d.). Analisis Perbandingan Algoritma Regresi Linear dan Decision Tree pada Prediksi Harga Rumah, 2–6.
  6. Baghaee, H. R., Mlakic, D., Nikolovski, S., & Dragicevic, T. (2020). Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8(3), 2385–2403. https://doi.org/10.1109/JESTPE.2019.2916621
  7. Bonci, B. A., Mccoy, E., & Cole, J. (2011). A research review : the importance of families and the home environment, (March).
  8. bps.go.id. (2023). Garis Kemiskinan, Jumlah, dan Persentase Penduduk Miskin di Daerah Menurut Kabupaten/Kota di Provinsi DKI Jakarta, 2022-2023. Retrieved December 22, 2024, from https://jakarta.bps.go.id/id/statistics-table/2/NjQ1IzI=/garis-kemiskinan-jumlah-dan-persentase-penduduk-miskin-di-daerah-menurut-kabupaten-kota-di-provinsi-dki-jakarta.html
  9. Chandrasa, N. A., Prasetyowati, S. S., & Sibaroni, Y. (2022). Land Price Classification Map in Jakarta Using Random Forest and Ordinary Kriging. Building of Informatics, Technology and Science (BITS), 4(2). https://doi.org/10.47065/bits.v4i2.1896
  10. Ding, S., Hua, X., & Yu, J. (2014). An overview on nonparallel hyperplane support vector machine algorithms, 975–982. https://doi.org/10.1007/s00521-013-1524-6
  11. Dooley, C. (2017). Data visualisation and machine learning web application with potential use in sports data analytics.
  12. Fadhlurrahman, I. (2024). 29% Penduduk DKI Jakarta ada di Kota Jakarta Timur pada Pertengahan 2024. Retrieved from https://databoks.katadata.co.id/demografi/statistik/a3a99d1fa4222a7/29-penduduk-dki-jakarta-ada-di-kota-jakarta-timur-pada-pertengahan-2024#:~:text=Menurut data kependudukan Direktorat Jenderal,juta jiwa pada Pertengahan 2024.
  13. Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A Review on Data Preprocessing Techniques Toward Ef fi cient and Reliable Knowledge Discovery From Building Operational Data, 9(March), 1–17. https://doi.org/10.3389/fenrg.2021.652801
  14. Ferreira, P., Le, D. C., & Zincir-Heywood, N. (2019). Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection. 15th International Conference on Network and Service Management, CNSM 2019. https://doi.org/10.23919/CNSM46954.2019.9012708
  15. Fluorida Fibrianda, M., & Bhawiyuga, A. (2018). Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(9), 3112–3123. Retrieved from http://j-ptiik.ub.ac.id
  16. Jakarta.bps.go.id. (2024). Luas Daerah Menurut Kabupaten/Kota. Retrieved December 22, 2024, from https://jakarta.bps.go.id/id/statistics-table/2/MzgjMg%3D%3D/luas-daerah-menurut-kabupaten-kota.html
  17. Jurnal, T., Dan, S., Pohan, F., & Adi, R. (2019). Ordinary kriging dalam penentuan lama penggalian tambang terbuka, 15(2), 130–136.
  18. Kali, D. A. S. (2024). Pemetaan Sebaran Hujan Rancangan Menggunakan Interpolasi, 04(01), 239–249.
  19. Li, C., Liu, K., & Wang, H. (2011). The incremental learning algorithm with support vector machine based on hyperplane-distance. Applied Intelligence, 34(1), 19–27. https://doi.org/10.1007/s10489-009-0176-9
  20. Ma, M., Mei, S., Wan, S., Wang, Z., & Feng, D. (2019). Video summarization via nonlinear sparse dictionary selection. IEEE Access, 7(December), 11763–11774. https://doi.org/10.1109/ACCESS.2019.2891834
  21. May, L. G. (n.d.). Classification.
  22. Mu’tashim, M. L., Muhayat, T., Damayanti, S. A., Zaki, H. N., & Wirawan, R. (2021). Analisis Prediksi Harga Rumah Sesuai Spesifikasi Menggunakan Multiple Linear Regression. Informatik : Jurnal Ilmu Komputer, 17(3), 238. https://doi.org/10.52958/iftk.v17i3.3635
  23. Mukti, Y. I. (2020). Sistem Prediksi Lulus Tepat Waktu Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (Svm). JUTIM (Jurnal Teknik Informatika Musirawas), 5(2), 110–115. https://doi.org/10.32767/jutim.v5i2.1050
  24. Muldani, M. D., Prasetiyowati, S. S., & Sibaroni, Y. (2024). Air Pollution Classification Prediction Model with Deep Neural Network based on Time-Based Feature Expansion and Temporal Spatial Analysis, 6(2), 867–877. https://doi.org/10.47065/bits.v6i2.5675
  25. Murni, S., Widiyanto, D., & Dewi, C. N. P. (2022). Klasifikasi Citra Penyakit Daun Kopi Arabika Menggunakan Support Vector Machine (SVM) Dengan Seleksi Fitur Information Gain. Seminar Nasional Mahasiswa Ilmu Komputer Dan Aplikasinya (SENAMIKA), 700–709.
  26. Novak Zelenika, K., Vidaček, R., Ilijaš, T., & Pavić, P. (2017). Application of deterministic and stochastic geostatistical methods in petrophysical modelling – a case study of upper pannonian reservoir in sava depression. Geologia Croatica, 70(2), 105–114. https://doi.org/10.4154/gc.2017.10
  27. Nursiyono, J. A., & Dewi, D. M. (2021). Determinan Harga Tanah di Indonesia Menggunakan Big Data (Studi Kasus: www.lamudi.co.id). Jurnal Pertanahan, 11(2). https://doi.org/10.53686/jp.v11i2.105
  28. Prasetiyowati, S. S., & Sibaroni, Y. (2024). Unlocking the potential of Naive Bayes for spatio temporal classification: a novel approach to feature expansion. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00958-x
  29. Pratama, S. (2016). Prediksi Harga Tanah Menggunakan Algoritma Linear Regression. Technologia : Jurnal Ilmiah, 7(2), 122–130. https://doi.org/10.31602/tji.v7i2.624
  30. Purbolaksono, M. D., Irvan Tantowi, M., Imam Hidayat, A., & Adiwijaya, A. (2021). Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 393–399. https://doi.org/10.29207/resti.v5i2.3008
  31. Putra, C. D., Ramadhani, A., & Fatimah, E. (2021). Increasing Urban Heat Island area in Jakarta and it’s relation to land use changes. IOP Conference Series: Earth and Environmental Science, 737(1). https://doi.org/10.1088/1755-1315/737/1/012002
  32. Rais, A. N., Alfarobi, I., Hadi, S. W., & Kurniawan, W. (2024). ANALISA PREDIKSI HARGA JUAL RUMAH MENGGUNAKAN, 6(2), 417–423.
  33. Rokhman, K. A., Berlilana, B., & Arsi, P. (2021). Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online. Journal of Information System Management (JOISM), 3(1), 1–7. https://doi.org/10.24076/joism.2021v3i1.341
  34. Saiful, A. (2021). Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 8(1), 41–50. https://doi.org/10.35957/jatisi.v8i1.701
  35. Shousong, C., Xiaomin, G., Xiaoguang, W., & Ying, C. (2019). Notice of Removal: Research on Urban Land Price Assessment Based on Artificial Neural Network Model. IEEE Access, 7(June 2018), 180738–180748. https://doi.org/10.1109/ACCESS.2019.2958978
  36. Sitanggang, D., & Sherly, S. (2022). Model Prediksi Obesitas dengan Menggunakan Support Vector Machine. Jurnal Sistem Informasi Dan Ilmu Komputer Prima(JUSIKOM PRIMA), 5(2), 172–175. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2443
  37. Suhendar, A. H., Rohmawati, A. A., & Prasetyowati, S. S. (2024). Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate. Sinkron, 9(1), 1–9. https://doi.org/10.33395/sinkron.v9i1.13023
  38. Wang, S., Tang, J., & Liu, H. (2020). Encyclopedia of Machine Learning and Data Science. Encyclopedia of Machine Learning and Data Science, (October 2017). https://doi.org/10.1007/978-1-4899-7502-7
  39. Yustanti, W. (2012). Nihru Nafi’ Dzikrulloh1, Indriati2, Budi Darma Setiawan3 2017. Jurnal Matematika Statistika Dan Komputasi, 9(1), 57–68.
  40. Zulfahmi, I., Syahputra, H., Naibaho, S. I., Maulana, M. A., & Sinaga, E. P. (2023). Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree Untuk Deteksi Tingkat Depresi Mahasiswa. Bina Insani Ict Journal, 10(1), 52. https://doi.org/10.51211/biict.v10i1.2304
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References


Akhiat, Y., & Amjad, S. (2022). Feature Selection : A Review and Comparative Study Feature Selection : A Review and Comparative Study, (May). https://doi.org/10.1051/e3sconf/202235101046

Amelia, U., Indra, J., & Masruriyah, A. F. N. (2022). Implementasi Algoritma Support Vector Machine (Svm) Untuk Prediksi Penyakit Stroke Dengan Atribut Berpengaruh. Scientific Student Journal for Information, Technology and Science, III(2), 254–259.

Apostolidis-Afentoulis, V. (2015). SVM Classification with Linear and Rbf Kernels. ResearchGate, (July), 0–7. https://doi.org/10.13140/RG.2.1.3351.4083

Athoillah, M., & Putri, R. K. (2019). Handwritten Arabic Numeral Character Recognition Using Multi Kernel Learning Support Vector Machine, 4(2), 99–106.

Az-zahra, N. A., & Arianto, D. B. (n.d.). Analisis Perbandingan Algoritma Regresi Linear dan Decision Tree pada Prediksi Harga Rumah, 2–6.

Baghaee, H. R., Mlakic, D., Nikolovski, S., & Dragicevic, T. (2020). Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8(3), 2385–2403. https://doi.org/10.1109/JESTPE.2019.2916621

Bonci, B. A., Mccoy, E., & Cole, J. (2011). A research review : the importance of families and the home environment, (March).

bps.go.id. (2023). Garis Kemiskinan, Jumlah, dan Persentase Penduduk Miskin di Daerah Menurut Kabupaten/Kota di Provinsi DKI Jakarta, 2022-2023. Retrieved December 22, 2024, from https://jakarta.bps.go.id/id/statistics-table/2/NjQ1IzI=/garis-kemiskinan-jumlah-dan-persentase-penduduk-miskin-di-daerah-menurut-kabupaten-kota-di-provinsi-dki-jakarta.html

Chandrasa, N. A., Prasetyowati, S. S., & Sibaroni, Y. (2022). Land Price Classification Map in Jakarta Using Random Forest and Ordinary Kriging. Building of Informatics, Technology and Science (BITS), 4(2). https://doi.org/10.47065/bits.v4i2.1896

Ding, S., Hua, X., & Yu, J. (2014). An overview on nonparallel hyperplane support vector machine algorithms, 975–982. https://doi.org/10.1007/s00521-013-1524-6

Dooley, C. (2017). Data visualisation and machine learning web application with potential use in sports data analytics.

Fadhlurrahman, I. (2024). 29% Penduduk DKI Jakarta ada di Kota Jakarta Timur pada Pertengahan 2024. Retrieved from https://databoks.katadata.co.id/demografi/statistik/a3a99d1fa4222a7/29-penduduk-dki-jakarta-ada-di-kota-jakarta-timur-pada-pertengahan-2024#:~:text=Menurut data kependudukan Direktorat Jenderal,juta jiwa pada Pertengahan 2024.

Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A Review on Data Preprocessing Techniques Toward Ef fi cient and Reliable Knowledge Discovery From Building Operational Data, 9(March), 1–17. https://doi.org/10.3389/fenrg.2021.652801

Ferreira, P., Le, D. C., & Zincir-Heywood, N. (2019). Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection. 15th International Conference on Network and Service Management, CNSM 2019. https://doi.org/10.23919/CNSM46954.2019.9012708

Fluorida Fibrianda, M., & Bhawiyuga, A. (2018). Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(9), 3112–3123. Retrieved from http://j-ptiik.ub.ac.id

Jakarta.bps.go.id. (2024). Luas Daerah Menurut Kabupaten/Kota. Retrieved December 22, 2024, from https://jakarta.bps.go.id/id/statistics-table/2/MzgjMg%3D%3D/luas-daerah-menurut-kabupaten-kota.html

Jurnal, T., Dan, S., Pohan, F., & Adi, R. (2019). Ordinary kriging dalam penentuan lama penggalian tambang terbuka, 15(2), 130–136.

Kali, D. A. S. (2024). Pemetaan Sebaran Hujan Rancangan Menggunakan Interpolasi, 04(01), 239–249.

Li, C., Liu, K., & Wang, H. (2011). The incremental learning algorithm with support vector machine based on hyperplane-distance. Applied Intelligence, 34(1), 19–27. https://doi.org/10.1007/s10489-009-0176-9

Ma, M., Mei, S., Wan, S., Wang, Z., & Feng, D. (2019). Video summarization via nonlinear sparse dictionary selection. IEEE Access, 7(December), 11763–11774. https://doi.org/10.1109/ACCESS.2019.2891834

May, L. G. (n.d.). Classification.

Mu’tashim, M. L., Muhayat, T., Damayanti, S. A., Zaki, H. N., & Wirawan, R. (2021). Analisis Prediksi Harga Rumah Sesuai Spesifikasi Menggunakan Multiple Linear Regression. Informatik : Jurnal Ilmu Komputer, 17(3), 238. https://doi.org/10.52958/iftk.v17i3.3635

Mukti, Y. I. (2020). Sistem Prediksi Lulus Tepat Waktu Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (Svm). JUTIM (Jurnal Teknik Informatika Musirawas), 5(2), 110–115. https://doi.org/10.32767/jutim.v5i2.1050

Muldani, M. D., Prasetiyowati, S. S., & Sibaroni, Y. (2024). Air Pollution Classification Prediction Model with Deep Neural Network based on Time-Based Feature Expansion and Temporal Spatial Analysis, 6(2), 867–877. https://doi.org/10.47065/bits.v6i2.5675

Murni, S., Widiyanto, D., & Dewi, C. N. P. (2022). Klasifikasi Citra Penyakit Daun Kopi Arabika Menggunakan Support Vector Machine (SVM) Dengan Seleksi Fitur Information Gain. Seminar Nasional Mahasiswa Ilmu Komputer Dan Aplikasinya (SENAMIKA), 700–709.

Novak Zelenika, K., Vidaček, R., Ilijaš, T., & Pavić, P. (2017). Application of deterministic and stochastic geostatistical methods in petrophysical modelling – a case study of upper pannonian reservoir in sava depression. Geologia Croatica, 70(2), 105–114. https://doi.org/10.4154/gc.2017.10

Nursiyono, J. A., & Dewi, D. M. (2021). Determinan Harga Tanah di Indonesia Menggunakan Big Data (Studi Kasus: www.lamudi.co.id). Jurnal Pertanahan, 11(2). https://doi.org/10.53686/jp.v11i2.105

Prasetiyowati, S. S., & Sibaroni, Y. (2024). Unlocking the potential of Naive Bayes for spatio temporal classification: a novel approach to feature expansion. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00958-x

Pratama, S. (2016). Prediksi Harga Tanah Menggunakan Algoritma Linear Regression. Technologia : Jurnal Ilmiah, 7(2), 122–130. https://doi.org/10.31602/tji.v7i2.624

Purbolaksono, M. D., Irvan Tantowi, M., Imam Hidayat, A., & Adiwijaya, A. (2021). Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 393–399. https://doi.org/10.29207/resti.v5i2.3008

Putra, C. D., Ramadhani, A., & Fatimah, E. (2021). Increasing Urban Heat Island area in Jakarta and it’s relation to land use changes. IOP Conference Series: Earth and Environmental Science, 737(1). https://doi.org/10.1088/1755-1315/737/1/012002

Rais, A. N., Alfarobi, I., Hadi, S. W., & Kurniawan, W. (2024). ANALISA PREDIKSI HARGA JUAL RUMAH MENGGUNAKAN, 6(2), 417–423.

Rokhman, K. A., Berlilana, B., & Arsi, P. (2021). Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online. Journal of Information System Management (JOISM), 3(1), 1–7. https://doi.org/10.24076/joism.2021v3i1.341

Saiful, A. (2021). Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 8(1), 41–50. https://doi.org/10.35957/jatisi.v8i1.701

Shousong, C., Xiaomin, G., Xiaoguang, W., & Ying, C. (2019). Notice of Removal: Research on Urban Land Price Assessment Based on Artificial Neural Network Model. IEEE Access, 7(June 2018), 180738–180748. https://doi.org/10.1109/ACCESS.2019.2958978

Sitanggang, D., & Sherly, S. (2022). Model Prediksi Obesitas dengan Menggunakan Support Vector Machine. Jurnal Sistem Informasi Dan Ilmu Komputer Prima(JUSIKOM PRIMA), 5(2), 172–175. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2443

Suhendar, A. H., Rohmawati, A. A., & Prasetyowati, S. S. (2024). Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate. Sinkron, 9(1), 1–9. https://doi.org/10.33395/sinkron.v9i1.13023

Wang, S., Tang, J., & Liu, H. (2020). Encyclopedia of Machine Learning and Data Science. Encyclopedia of Machine Learning and Data Science, (October 2017). https://doi.org/10.1007/978-1-4899-7502-7

Yustanti, W. (2012). Nihru Nafi’ Dzikrulloh1, Indriati2, Budi Darma Setiawan3 2017. Jurnal Matematika Statistika Dan Komputasi, 9(1), 57–68.

Zulfahmi, I., Syahputra, H., Naibaho, S. I., Maulana, M. A., & Sinaga, E. P. (2023). Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree Untuk Deteksi Tingkat Depresi Mahasiswa. Bina Insani Ict Journal, 10(1), 52. https://doi.org/10.51211/biict.v10i1.2304

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