PEMETAAN DISTRIBUSI SPASIAL LAMUN DI PULAU KELAPA, KABUPATEN BIMA MENGGUNAKAN CITRA SENTINEL-2A DAN GOOGLE EARTH ENGINE BERBASIS MACHINE LEARNING

IZZULHAQ RAMADAN, . (2026) PEMETAAN DISTRIBUSI SPASIAL LAMUN DI PULAU KELAPA, KABUPATEN BIMA MENGGUNAKAN CITRA SENTINEL-2A DAN GOOGLE EARTH ENGINE BERBASIS MACHINE LEARNING. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Ekosistem lamun memiliki peran penting sebagai habitat biota laut, pelindung pantai, dan penyerap karbon biru (blue carbon). Informasi mengenai distribusi spasial lamun di Pulau Kelapa, Kabupaten Bima masih terbatas sehingga diperlukan pemetaan yang mampu menggambarkan kondisi terkini secara akurat. Penelitian ini bertujuan memetakan distribusi spasial lamun menggunakan citra Sentinel-2A berbasis machine learning melalui google earth engine, menentukan algoritma klasifikasi terbaik, serta mengidentifikasi kondisi tutupan lamun berdasarkan survei lapangan. Penelitian menggunakan metode kuantitatif dengan pendekatan spasial. Klasifikasi citra dilakukan menggunakan algoritma random forest, support vector machine, dan classification and regression tree, kemudian dievaluasi menggunakan overall accuracy, producer accuracy, user accuracy, kappa coefficient, dan confusion matrix. Hasil terbaik digunakan untuk menyusun peta distribusi spasial lamun, sedangkan kondisi lamun dianalisis melalui pengukuran persentase tutupan pada tujuh stasiun menggunakan metode line transect. Hasil penelitian menunjukkan bahwa random forest menghasilkan akurasi tertinggi dengan nilai overall accuracy sebesar 0,87 dan kappa sebesar 0,80. Pola distribusi lamun di Pulau Kelapa cenderung mengikuti garis pantai dengan luas tutupan sebesar 61,16 hektar, yang didominasi pada zona kedalaman 0–1 meter seluas 50,88 hektar. Hasil survei lapangan mengidentifikasi empat jenis lamun, yaitu Thalassia hemprichii, Cymodocea rotundata, Halodule pinifolia, dan Halophila ovalis. Dominasi jenis lamun di Pulau Kelapa yaitu Thalassia hemprichii dengan rata-rata persentase tutupan sebesar 27,46%. Rata-rata persentase tutupan lamun secara keseluruhan sebesar 33,31% yang berkategori sedang. Hasil penelitian ini dapat menjadi dasar dalam kegiatan monitoring dan pengelolaan konservasi ekosistem lamun di Pulau Kelapa secara berkelanjutan. ***** Seagrass ecosystems play a vital role as habitats for marine biota, coastal protectors, and blue carbon sinks. Information regarding the spatial distribution of seagrass on Kelapa Island, Bima Regency, remains limited; therefore, accurate mapping of current conditions is required. This study aimed to map seagrass spatial distribution using Sentinel-2A imagery and machine learning via google earth engine, determine the optimal classification algorithm, and assess seagrass cover conditions through field surveys. A quantitative method with a spatial approach was employed. Image classification was performed using random forest, support vector machine, and classification and regression tree algorithms, with performance evaluated based on overall accuracy, producer accuracy, user accuracy, and the kappa coefficient. The best-performing model was used to generate the seagrass spatial distribution map, while seagrass conditions were analyzed by measuring percentage cover at seven stations using the line transect method. The results indicated that the random forest algorithm yielded the highest accuracy, with an overall accuracy of 0.87 and a kappa coefficient of 0.80. Seagrass distribution on Kelapa Island generally followed the coastline, covering a total area of 61.16 hectares, with the majority (50.88 hectares) located in the 0–1 meter depth zone. Field surveys identified four seagrass species: Thalassia hemprichii, Cymodocea rotundata, Halodule pinifolia, and Halophila ovalis. Thalassia hemprichii was the dominant species, with an average cover of 27.46%. The overall average seagrass cover was 33.31%, falling into the moderate category. These findings can serve as a basis for the sustainable monitoring and conservation management of the seagrass ecosystem on Kelapa Island.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Dr. Aris Munandar, S.Pd., M.Si; 2). Della Ayu Lestari, S.Si., M.Si.
Subjects:	Geografi, Antropologi > Geografi
Divisions:	FIS > S1 Geografi
Depositing User:	Users 34857 not found.
Date Deposited:	04 Aug 2026 08:04
Last Modified:	04 Aug 2026 08:04
URI:	http://repository.unj.ac.id/id/eprint/68474

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