• Heinrich Rakuasa Departemen Geografi FMIPA Universitas Indonesia
  • S Supriatna Departemen Geografi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Indonesia
  • Mangapul Parlindungan Tambunan Departemen Geografi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Indonesia
  • Melianus Salakory Program Studi Pendidikan Geografi, FKIP, Universitas Pattimura
  • Wiclif. S. Pinoa Program Studi Pendidikan Geografi, FKIP, Universitas Pattimura



Spatial Analysis, Landslide Prone, Ambon City, SMORPH


The geographical condition of Ambon City, which is 75% a hilly area resulted in most communities building in marbled areas with slopes above 20%, which has the potential to threaten life and landslide disasters. This study simply looked at the influence of slopes and slope shapes in Ambon City that can be analyzed using geographic information systems (GIS) to map areas that have the potential for landslides. Identification and mapping of potential landslide areas have an important role as an effort in overcoming and anticipating the occurrence of landslide disasters. This study aimed to analyze the spread of potential landslide areas in Ambon City based on the results of SMORPH modeling. The study used the slope morphology or SMORPH method, which has a better degree of accuracy than the Storie Index and SINMAP methods to identify and classify potential landslide areas based on the matrix between slope shape and slope angle. This study resulted in 4 levels of landslide potential areas, namely very low, low, medium and high potential. Areas with high landslide potential dominate the northern and southern parts of Ambon City. In the region, most landslides occur in the form of sunken and convex slopes. The region has a hilly and mountainous topography with a steep slope. The results of this research using the SMORPH method can illustrate that the slope of the increasingly higher slope accompanied by the shape of a convex or concave slope will cause the potential for landslides that are higher in the region.


Aditian, A., Kubota, T. and Shinohara, Y. 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101-111, doi:. 10.1016/j.geomorph.2018.06.006.

Bhunia, G.S., and Shit, P.K. 2022. Geospatial Technology for Multi-hazard Risk Assessment. Springer International Publishing, pp. 1–18, doi:. 10.1007/978-3-030-75197-5_1.

BNPB. 2020. Indeks Resiko Bencana Indonesia. Badan Nasional Penanggulangan Bencana.

Djalante, R., Garschagen, M., Thomalla, F. and Shaw, R. 2017. Disaster Risk Reduction in Indonesia: Progress, Challenges, and Issues. Springer International Publishing, Switzerland.

Felita, G., Afdal, A. dan Marzuki, M. 2022. Kajian suseptibilitas magnetik tanah sebagai indikator longsor di Gunung Nago, Padang. Jurnal Fisika Unand 11(1):75-81, doi:10.25077/jfu.11.1.75%20–%2081.2022.

Hamida, F.N., dan Widyasamratri, H. 2019. Risiko kawasan longsor dalam ipaya mitigasi bencana menggunakan sistem informasi geografis. Pondasi 24(1):67-89, doi:10.30659/pondasi.v24i1.4997.

Harist, M.C., Afif, H.A., Putri, D.N. and Shidiq, I.P.A. 2018. GIS modelling based on slope and morphology for landslide potential area in Wonosobo, Central Java. MATEC Web of Conferences 229: 03004, doi: 10.1051/matecconf/201822903004.

Jakob, M. 2022. Chapter 14-Landslides in a changing climate. Landslide Hazards, Risks, and Disasters (Second Edition), Hazards and Disasters Series 2022, Pages 505-579, Elsevier, doi:. 10.1016/B978-0-12-818464-6.00003-2.

Khalil, Baja, S., Azikin, B., Hamzah, S. and Alimuddin, I. 2020. Typology of spatial based landslide disaster control in Pare-pare City South Sulawesi. International Journal of Advanced Research in Engineering and Technology 11(10):123-138, doi:10.34218/ ijaret. 11.10.2020.012.

McColl, S.T. 2022. Chapter 2 - Landslide causes and triggers. Landslide Hazards, Risks, and Disasters (Second Edition): Hazards and Disasters Series 2022, Pages 13-41, Elsevier, doi: 10.1016/B978-0-12-818464-6.00011-1.

Mufidawati, H., Damayanti, A. and Supriatna. 2021. Vegetative conservation for landslide mitigation in bungaya sub-district, gowa regency, south sulawesi province. IOP Conference Series: Earth and Environmental Science 683(1):012064, doi:10.1088/1755-1315/683/1/012064.

Nguyen, H.T., Wiatr, T., Fernandez-Steeger, T.M., Reicherter, K.R., Rodrigues, D.M.M. and Azzam, R. 2012. Landslide hazard and cascading effects following the extreme rainfall event on Madeira Island (February 2010). Natural Hazards 65(1):doi:10.1007/s11069-012-0387-y.

Paronuzzi, P., Del Fabbro, M. and Bolla, A. 2022. Soil moisture profiles of unsaturated colluvial slopes susceptible to rainfall-induced landslides. Geosciences (Switzerland) 12(,1): 6, doi: 10.3390/geosciences12010006.

Persichillo, M. G., Bordoni, M., Cavalli, M., Crema, S. and Meisina, C. 2018. The role of human activities on sediment connectivity of shallow landslides. Catena 160: 261-274, doi:. 10.1016/j.catena.2017.09.025

Phong, T.V., Dam, N.D., Trinh, P.T., Dung, N.V., Hieu, N., Tran, C.Q., Van, T.D., Nguyen, Q.C., Prakash, I. and Pham, B.T. 2022. GIS-based logistic regression application for landslide susceptibility mapping in Son La hydropower reservoir basin. Publisher: Springer Singapore.

Rakuasa, H. dan Rifai, A. 2021. Pemetaan Kerentanan Bencana Tanah Longsor Berbasis Sistem Informasi Geografis di Kota Ambon. Prosiding Seminar Nasional Geomatika, pp. 327–336, doi:10.24895/SNG.2020.0-0.1148.

Ramdhoni, F., Damayanti, A. and Indra, T.L. 2020. SMORPH application for landslide identification in Kebumen Regency. IOP Conference Series: Earth and Environmental Science 451(1):012013, doi: 10.1088/1755-1315/451/1/012013.

Salunkhe, A.A., Gobinath, R. and Makkar, S. 2022. Chapter 19 - Soft computing applications in rainfall-induced landslide analysis and protection—Recent trends, techniques, and opportunities. Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management 2022, Pages 271-287, doi:10.1016/B978-0-323-89861-4.00036-1.

Skilodimou, H.D., Bathrellos, G.D., Koskeridou, E., Soukis, K. and Rozos, D. 2018. Physical and anthropogenic factors related to landslide activity in the Northern Peloponnese, Greece. Land 7(3), doi:. 10.3390/land7030085.

Souisa, M., Hendrajaya, L. and Handayani, G. 2016. Landslide hazard and risk assessment for Ambon city using landslide inventory and geographic information system. Journal of Physics: Conference Series 739: 012078, doi: 10.1088/1742-6596/739/1/012078.

Triwahyuni, L., Sobirin, S. and Saraswati, R. 2017. Analisis Spasial Wilayah Potensi Longsor dengan Metode SINMAP dan SMORPH di Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta. Prosiding Lokakarya dan Seminar Nasional Penelitian 69-76, doi:. 10.35313/irwns.v8i3.701.




How to Cite

Rakuasa, H., Supriatna, S., Tambunan, M. P. ., Salakory, M. ., & Pinoa, W. S. . (2022). ANALISIS SPASIAL DAERAH POTENSI RAWAN LONGSOR DI KOTA AMBON DENGAN MENGGUNAKAN METODE SMORPH. Jurnal Tanah Dan Sumberdaya Lahan, 9(2), 213–221.