Implikasi Perkembangan Guna Lahan Terhadap Kerusakan Jalan Soekarno-Hatta Di Kelurahan Bontang Lestari Kota Bontang
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis dampak perubahan tata guna lahan dan peningkatan volume kendaraan terhadap kerusakan Jalan Soekarno-Hatta di Kelurahan Bontang Lestari, Kota Bontang, Kalimantan Timur. Metode penelitian menggunakan pendekatan deskriptif kuantitatif dengan teknik analisis data meliputi analisis kuantitatif (deskriptif statistik, indeks kerusakan jalan, dan beban lalu lintas), analisis kualitatif (kebijakan dan SWOT), serta analisis spasial berbasis citra satelit Sentinel-2 dan Sistem Informasi Geografis (GIS). Hasil penelitian menunjukkan bahwa 30% ruas jalan mengalami kerusakan berat hingga sedang, terutama disebabkan oleh peningkatan volume kendaraan berat yang melebihi kapasitas desain awal, perubahan tata guna lahan (penambahan 23,7% lahan terbangun dalam 6 tahun), dan faktor lingkungan seperti longsoran bahu jalan. Analisis SWOT mengidentifikasi kekuatan seperti potensi teknologi digital dan dukungan kebijakan, namun juga kelemahan seperti keterbatasan anggaran dan koordinasi antar instansi. Berdasarkan temuan tersebut, penelitian merekomendasikan strategi terintegrasi, termasuk pembangunan jalur alternatif untuk kendaraan berat, perbaikan sistem drainase, penegakan regulasi beban kendaraan, rehabilitasi jalan, serta pemanfaatan teknologi pemantauan real-time. Implementasi strategi ini diharapkan dapat meningkatkan kualitas infrastruktur jalan, mendukung mobilitas berkelanjutan, dan memperkuat pertumbuhan ekonomi di Bontang Lestari. This study aims to analyze the impact of land-use changes and increased vehicle volume on road degradation along Soekarno-Hatta Road in Bontang Lestari Subdistrict, Bontang City, East Kalimantan. The research employs a quantitative descriptive approach, utilizing data analysis techniques such as quantitative analysis (descriptive statistics, pavement condition index, and traffic load analysis), qualitative analysis (policy evaluation and SWOT analysis), and spatial analysis based on Sentinel-2 satellite imagery and Geographic Information Systems (GIS). The findings reveal that 30% of the road sections suffer from moderate to severe damage, primarily due to heavy vehicle traffic exceeding the road's initial design capacity, land-use changes (a 23.7% increase in built-up areas over six years), and environmental factors such as shoulder landslides. The SWOT analysis identifies strengths such as digital technology potential and policy support, alongside weaknesses like budget constraints and inter-agency coordination issues. Based on these findings, the study recommends integrated strategies, including the construction of alternative routes for heavy vehicles, drainage system improvements, stricter enforcement of vehicle load regulations, road rehabilitation, and the adoption of real-time monitoring technology. The implementation of these strategies is expected to enhance road infrastructure quality, support sustainable mobility, and strengthen economic growth in Bontang Lestari.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".