Permonoputri, Aileen Salsabilla (2026) Evaluasi Kepatuhan Teknis Instalasi Pipa Bawah Laut Terhadap Peraturan Menteri Perhubungan Nomor 129 Tahun 2016 Berbasis Data Hidro-Akustik dan Random Forest. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Instalasi pipa bawah laut di Indonesia beroperasi dalam kondisi lingkungan laut yang dinamis dan berisiko tinggi, namun evaluasi teknis terhadap pemenuhan standar regulasi masih sangat terbatas. Peraturan Menteri Perhubungan Nomor 129 Tahun 2016 (PM 129/2016) mewajibkan pemendaman pipa pada perairan dangkal kurang dari 20 meter minimal sedalam 2 meter di bawah dasar perairan, namun kepatuhan instalasi di lapangan belum pernah diverifikasi secara sistematis. Penelitian ini mengevaluasi kepatuhan teknis instalasi pipa bawah laut terhadap ketentuan PM 129/2016 menggunakan data hidro-akustik dan pendekatan klasifikasi Random Forest. Data yang digunakan meliputi Singlebeam Echosounder (SBES) untuk kedalaman perairan dan morfologi dasar laut, Side Scan Sonar (SSS) untuk tekstur seabed dan identifikasi fitur berbahaya, serta Sub Bottom Profiler (SBP) untuk menentukan posisi dan kedalaman pemendaman pipa. Lima parameter hasil integrasi, yaitu kedalaman perairan, tekstur seabed, keberadaan fitur berbahaya, ketebalan sedimen penutup, dan kedalaman pipa digunakan sebagai variabel input model. Evaluasi manual terhadap 21 titik pengamatan menunjukkan bahwa hanya 4 titik (19,05%) memenuhi syarat kepatuhan, sedangkan 17 titik (80,95%) dikategorikan tidak patuh akibat kedalaman pemendaman yang tidak memenuhi ambang minimum regulasi. Ketidakpatuhan juga diduga dipengaruhi dinamika dasar perairan, yang ditandai keberadaan sandwave, guratan aktif, serta rata-rata ketebalan sedimen penutup yang tipis sebesar 0,71 m. Model Random Forest terbaik mencapai akurasi 93,33% dengan nilai Kappa 0,8485 pada Stratified K-Fold Validation, serta akurasi 100% dan Kappa 1,00 pada data testing. Hasil ini membuktikan bahwa integrasi data hidro-akustik dan machine learning dapat menjadi kerangka evaluasi kepatuhan yang objektif, sistematis, dan dapat direplikasi guna mendukung penegakan PM 129/2016.
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Submarine pipeline installations in Indonesia operate within dynamic and high-risk marine environments, yet systematic technical verification of regulatory compliance remains largely absent. The Regulation of the Minister of Transportation Number 129 of 2016 (PM 129/2016) mandates a minimum burial depth of 2 meters below the seabed for pipelines in waters shallower than 20 meters; however, adherence to this requirement has not been objectively assessed. This study evaluates the technical compliance of submarine pipeline installations with PM 129/2016 using hydroacoustic data and a Random Forest classification approach. Three instruments were employed: Singlebeam Echosounder (SBES) for water depth and seabed morphology, Side Scan Sonar (SSS) for seabed texture and hazardous feature identification, and Sub Bottom Profiler (SBP) for pipeline location and burial depth. Five integrated parameters water depth, seabed texture, presence of hazardous features, sediment cover thickness, and pipeline burial depth served as model input variables. Manual compliance assessment of 21 observation points revealed that only 4 points (19.05%) met the regulatory requirements, while 17 points (80.95%) were non-compliant due to insufficient burial depth. The elevated non-compliance rate is further attributed to dynamic seabed conditions, including sandwave features, active scours, and thin sediment cover averaging 0.71 m. The optimal Random Forest model achieved an accuracy of 93,33% and a Kappa of 0.8485 under Stratified K-Fold Cross Validation, and attained 100% accuracy with a Kappa of 1.00 on the test dataset. These results demonstrate that integrating hydroacoustic data with machine learning constitutes an objective, systematic, and replicable compliance evaluation framework supporting the enforcement of PM 129/2016.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Random Forest, Evaluasi Kepatuhan, Hidro-akustik, Pipa Bawah Laut, PM 129/2016 Random Forest, Compliance, Hydroacoustic, Submarine Pipeline, PM 129/2016 |
| Subjects: | Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QC Physics > QC100.5 Measuring instruments (General) Q Science > QE Geology > QE471 Sedimentary rocks. Sedimentology T Technology > TJ Mechanical engineering and machinery > TJ930 Pipelines (General). Underwater pipelines. |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Aileen Salsabilla Permonoputri |
| Date Deposited: | 29 Jun 2026 01:10 |
| Last Modified: | 29 Jun 2026 01:10 |
| URI: | http://repository.its.ac.id/id/eprint/134093 |
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