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INTEGRASI MODEL SPASIAL DAN SEMANTIK PERAN AGEN DALAM PENGGUNAAN LAHAN HUTAN RAWAN LONGSOR DI KABUPATEN KULON PROGO YOGYAKARTA

RINA DJUARIAH, Dr.Senawi, M.P.;Dr.Ir. Agus Setyarso, M.Sc.;Prof. Dr.Ir. M.Maksum Machfoeds, M.Sc.

2017 | Disertasi | S3 Ilmu Kehutanan

Program rehabilitasi DAS melalui rehabilitasi lahan di luar kawasan hutan telah berhasil menambah tutupan lahan hutan di wilayah perbukitan dan pegunungan Kabupaten Kulon Progo, namun peristiwa longsor masih terus terjadi. Adanya kejadian longsor memerlukan evaluasi lahan dan karena berada di lahan milik, maka peran pengguna lahan bagi dalam penanaman tanaman kehutanan perlu dicermati. Tujuan penelitian ini adalah: Menyusun model spasial arahan penggunaan dan pengelolaan lahan hutan rawan longsor berdasarkan hasil evaluasi lahan (kemampuan lahan dan arahan fungsi kawasan); Menganalisis peran multi agen dan interaksi antara faktor internal agen utama pemilik lahan dengan faktor eksternal, dari tingkat tapak sampai tingkat regional; Menyusun model semantik peran agen pemilik lahan dalam pengelolaan lahan hutan miliknya; Menganalisis daya adaptasi masyarakat dalam pengelolaan lahan hutan dan pemukiman di lahan rawan longsor. Metode penyusunan model spasial arahan penggunaan lahan menggunakan analisis spasial tumpang-susun peta dan skoring faktor penghambat hasil evaluasi lahan yang dianalisis secara deskriptif kualitatif. Arahan pengelolaan lahan di tingkat tapak disusun dengan mempertimbangkan bentuk lahan dan tingkat rawan longsor. Prosedur identifikasi tipe dan peran agen serta interaksi faktor internal dan eksternal agen di tingkat tapak dalam mengelola lahannya menggunakan observasi partisipasi. Peran agen dianalisis secara deduktif dan induktif dalam kerangka emik. Perilaku agen utama pemilik lahan di tingkat tapak dan daya adaptasi terhadap kerawanan longsor disusun dalam model semantik dan prosedur pengambilan keputusan yang direpresentasikan dalam aturan if-then condition. Validasi keabsahan data dilakukan dengan teknik triangulasi waktu, tempat, data dan nara sumber. Analisis kemampuan lahan menghasilkan rentang kelas kemampuan lahan I, II, III, IV, VI dan VII. Kesesuaian Tidak Sesuai untuk Pemukiman (Pm) di kelas VI dan VII, Sawah(Sw) di kelas IV, VI dan VII. Hasil analisis arahan fungsi Kawasan Lindung (KL) 5,06%, Kawasan Budidaya Tanaman Tahunan (KBTT) 24,61%, Kawasan Penyangga (KP) 41,16% dan Kawasan Budidaya Tanaman Semusim dan Pemukiman (KBTSP) 28,40%. Kesesuaian penggunaan lahan adalah Sesuai 65,40%, Tidak Sesuai Negatif (TSN) 29,40% dan Tidak Sesuai Positif (TSP) 3,69%. Arahan pengelolaan lahan mempertimbangkan bentuk lahan dan tingkat kerawanan longsor. Faktor-faktor yang mempengaruhi perilaku agen menanam tanaman kehutanan terdiri dari faktor internal: Kondisi lahan (kurangnya alternatif tanaman pangan karena faktor biofisik lahan dan iklim), usia lanjut petani (terkait tenaga kerja, kepala keluarga perempuan, alternatif sumber penghasilan lain); dan faktor eksternal: Permintaan pasar, program pemerintah dan peran LSM. Pola tanam tanaman kehutanan dipengaruhi kemiringan lahan dan daya adaptasi masyarakat tinggal di area rawan longsor dipengaruhi oleh kemampuannya tanggap bencana longsor.

Rehabilitation program on watershed area of Kulon Progo Regency that dominated by hilly and mountainuous area has succeeded to increase vegetation cover. However, the landslides on private forest were continuously occur. The existing landslide needs evaluation and the role of the community for tree planted on the private forest at the watershed area also needs pay attention. The objectives of this research were to composed a spatial model for land use and management on private forest that landslide-prone based on the result of land evaluation (land capability and referral function area); to analyzed the role of multi agent and the interaction between the internal factors of the main agent with external factors at the site level to the region level; to composed the semantic model of the role of land owner on private forest management; and then to analyzed the adaptability of the community to manage the private forest and settlements on the landslideprone land. Spatial analysis were used to determined the spatial model of land use and management on private forest through overlay and scoring factors that restricted the land used. The land management on site level determined by land form and land susceptibility. The role of multi agent and the influence factors on site level determined by observation of the agent and analyzed used deductive and inductive within an emic framework. The semantic model were determined by the behaviour of main agent and they adaptability on land susceptibility that used ifthen analyzed. Data validation were used triangulation technique of the time, place, data and key person. The results showed the wide range of land capability classes, i.e. I, II, III, IV, VI and VII. Result of Land Suitability Analysis showed in general were Suitable Land-use, but Not Suitable for settlement at class VI and VII and also for Paddy Field at class IV, VI and VII. The study area can be classified as protected zone 5,06%, buffer zone 41,16%, and cultivated zone 24,61%, where 65,40%, 29,40% and 3,69% for suitable used, positively not suitable and negatively not suitable respectively. The direction of land management depend on the landform and susceptibility of landslides. Factors that affect agent behaviour in forestry planting consists of internal factors i.e. the condition of the land (the lack of alternative food crops due to climate and land biophysical), the age of farmers (related to lack of labour), women as head of household, alternative sources of other income; and external factors: i.e. market demand, government programs and the role of NGOs. The pattern of tree planting influence by land slope and the adaptability of community where living on the landslide-prone area influence by the response ability to landslide disaster.

Kata Kunci : Peran Agen, Evaluasi Lahan, Model Spasial, Model Semantik, Rawan Longsor;Human behaviour, Landslide-prone, Land Evaluation, Spatial modelling, Semantic modelling