Analisis Eksploratif Sampah Laut di Wilayah Pesisir Studi Kasus Pantai Labuhan Haji, Nusa Tenggara Barat, Indonesia
Bibliographic record
Abstract
Latar belakang:Indonesia merupakan negara kepulauan dengan garis pantai terpanjang kedua di dunia setelah Kanada, dengan panjang mencapai 81.000 kilometer atau 14% dari garis pantai di dunia, yang menempatkannya pada posisi rentan terhadap masalah sampah laut. Sampah laut merupakan masalah lingkungan yang signifikan, terutama di kawasan pesisir Indonesia, termasuk di Pantai Labuhan Haji, Lombok Timur. Pantai Labuhan Haji, Lombok Timur mengalami pencemaran serius dengan timbulan sampah mencapai 9,18 ton (0,77 kg/m²) pada 2021, dipicu oleh aktivitas wisata, dermaga, dan kurangnya infrastruktur pengelolaan sampah. Penelitian ini bertujuan untuk mengidentifikasi komposisi dan kepadatan sampah laut serta menganalisis hubungan antara komposisi dan kepadatan sampah dengan indeks kebersihan pantai (general index). Metode: Penelitian kuantitatif deskriptif menggunakan metode transek garis (KLHK 2020) periode Januari-Februari 2025. Populasi adalah seluruh sampah di garis pantai sepanjang 1500 m; sampel pada area 4.500 m² (300×15 m) mencakup 20% pantai. Teknik systematic random sampling dengan 5 subtransek, masing-masing 5 kotak 1×1 m² dipilih acak. Sampah dikategorikan makro (2,5 cm-1 m) dan meso (0,5-2,5 cm). Analisis meliputi perhitungan komposisi (%), kepadatan (item/m²), General Index (GI), dan regresi linear berganda (α=0,05).Hasil: Hasil penelitian menunjukkan sampah plastik mendominasi dengan berat 4606,84 gram (76,10% dari total sampah), diikuti sampah kain sebesar 1317,25 gram (21,76%). Jenis sampah lainnya (karet, kertas, kardus, busa plastik, dan logam) hanya berkontribusi kurang dari 3%. Total sampah terkumpul mencapai 2.899 item, terdiri dari 2.432 item sampah makro dan 467 item sampah meso. Kepadatan sampah makro mencapai 97,28 item/m², sementara sampah meso 18,68 item/m². Hasil perhitungan General Index (GI) sebesar 463,84 mengklasifikasikan Pantai Labuhan Haji dalam kategori "Sangat Kotor" (GI > 20). Analisis regresi dengan R² = 1,000 dan F = 130.620,650 (p < 0,05) menjelaskan seluruh variasi indeks kebersihan pantai, dimana kepadatan sampah berpengaruh sangat signifikan (β = 3,940; p = 0,000), namun komposisi sampah tidak signifikan (β = 0,095; p = 0,115).Simpulan: Pencemaran sampah laut di Pantai Labuhan Haji tergolong “Sangat Kotor” dengan dominasi sampah plastik, dimana hipotesis penelitian menunjukkan sebagian diterima karena hanya kepadatan sampah yang berpengaruh signifikan namun komposisi sampah tidak berpengaruh signifikan terhadap indeks kebersihan pantai, yang mencerminkan pengelolaan sampah buruk dan mengancam ekosistem lokal. Pengurangan kepadatan sampah harus menjadi prioritas strategi pengelolaan kebersihan pantai. ABSTRACT Title: Exploratory Analysis Of Marine Debris in Coastal Areas: A Case Study of Labuhan haji Beach, West Nusa Tenggara, IndonesiaBackground: Indonesia is an archipelagic nation with the second-longest coastline in the world after Canada, stretching 81,000 kilometers or comprising 14% of the world's coastlines, which places it in a vulnerable position regarding marine debris issues. Marine debris represents a significant environmental problem, especially in Indonesia's coastal areas, including Labuhan Haji Beach, East Lombok. Labuhan Haji Beach, East Lombok, experiences serious pollution with debris accumulation reaching 9.18 tons (0.77 kg/m²) in 2021, driven by tourism activities, port operations, and inadequate waste management infrastructure. This research aims to identify the composition and density of marine debris and analyze the relationship between debris composition and density with beach cleanliness index (general index).Method: A quantitative descriptive study using line transect method (KLHK 2020 guidelines) was conducted during January-February 2025. The population consisted of all debris along the 1500 m coastline; samples were collected from a 4,500 m² area (300×15 m) covering 20% of the beach. Systematic random sampling technique was employed with 5 subtransects, each with 5 randomly selected 1×1 m² plots. Debris was categorized as macro (2.5 cm-1 m) and meso (0.5-2.5 cm). Analysis included calculation of composition (%), density (items/m²), General Index (GI), and multiple linear regression (α=0.05).Result: results indicated that plastic waste dominated with a weight of 4,606.84 grams (76.10% of total debris), followed by textile waste at 1,317.25 grams (21.76%). Other debris types (rubber, paper, cardboard, plastic foam, and metal) contributed less than 3% collectively. Total collected debris reached 2,899 items, comprising 2,432 macro debris items and 467 meso debris items. Macro debris density reached 97.28 items/m², while meso debris density was 18.68 items/m². The General Index (GI) calculation of 463.84 classified Labuhan Haji Beach in the "Very Dirty" category (GI > 20). The regression analysis with R² = 1.000 and F = 130,620.650 (p < 0.05) explained the entire variation in the beach cleanliness index, in which waste density had a highly significant effect (β = 3.940; p = 0.000), but waste composition was not significant (β = 0.095; p = 0.115).Conclusion: Marine debris pollution at Labuhan Haji Beach is classified as "Very Dirty" with plastic waste dominance, in which the research hypothesis was partially accepted because only waste density had a significant effect on the beach cleanliness index, while waste composition did not have a significant effect, reflecting poor waste management that threatens the local ecosystem. Reducing waste density should be the priority in beach cleanliness management strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".