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The Strategic Development of Quality Improvement Land Data Incrementally Using Integrated PESTEL and SWOT Analysis in Indonesia

Author

Listed:
  • Nurul Huda

    (Department of Geodesy and Geomatics, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, West Java, Indonesia
    Bogor Regency National Land Agency (BPN Bogor II), Bogor 16820, West Java, Indonesia)

  • Andri Hernandi

    (Spatial System and Cadastre Research Group, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, West Java, Indonesia)

  • Irwan Gumilar

    (Geodetic Science, Engineering, and Innovation Research Group, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, West Java, Indonesia)

  • Irwan Meilano

    (Spatial System and Cadastre Research Group, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, West Java, Indonesia)

  • Lisa A. Cahyaningtyas

    (Geodetic Science, Engineering, and Innovation Research Group, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, West Java, Indonesia)

Abstract

Land registration is an important program in asset legalization with the vast land resources owned by Indonesia. The reality is that there are 48 million certificated lands out of an estimated 126 million certificates throughout Indonesia, so the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) made a breakthrough through the Complete Systematic Land Registration (PTSL) program. The grouping mechanism of the PTSL program is divided into four clusters, namely K-1, K-2, K-3, and K-4. Land parcels included in K-4 have problems, namely that they have been registered but not mapped/mapped incorrectly, so the idea of modeling the K-4 typology is needed for accelerating the improvement in land data quality (KW). The research location is in Bandung Regency, which is included in the top five land offices with the highest number of K-4 in West Java Province. This research method uses a mixed method, namely quantitative with a Slovin approach for the identification and clustering of K-4 typology and descriptive qualitative for justification of typology modeling in multiple aspects. The results of K-4 typology modeling were 128 clusters based on seven types of data criteria and obtained 4 clusters that matched the data sample, namely T-1, T-43, T-63, and T-128. The four clusters were justified against the K-4 concept in the Ministry of ATR/BPN, the FFP-LA concept, and the PESTLE framework. Dissemination of K-4 typology modeling is a breakthrough that can be implemented by the Ministry of ATR/BPN and its staff in various regions and the role of multi-concepts in this research can be an input for improving the K-4 concept that has been in effect so far.

Suggested Citation

  • Nurul Huda & Andri Hernandi & Irwan Gumilar & Irwan Meilano & Lisa A. Cahyaningtyas, 2024. "The Strategic Development of Quality Improvement Land Data Incrementally Using Integrated PESTEL and SWOT Analysis in Indonesia," Land, MDPI, vol. 13(10), pages 1-21, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1655-:d:1495652
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    References listed on IDEAS

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    1. Mengjie Liu & Qing Qin & Qingchi Zou & Yue Wang & Yali Wen, 2021. "Natural Resource Dependence of Communities around the Giant Panda Protected Land Based on Livelihood Capital," Agriculture, MDPI, vol. 11(11), pages 1-11, November.
    2. Uchendu Eugene Chigbu & Tobias Bendzko & Menare Royal Mabakeng & Elias Danyi Kuusaana & Derek Osei Tutu, 2021. "Fit-for-Purpose Land Administration from Theory to Practice: Three Demonstrative Case Studies of Local Land Administration Initiatives in Africa," Land, MDPI, vol. 10(5), pages 1-24, May.
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