Model Pendekatan Artificial Neural Network Dalam Estimasi Biaya Pembangunan Drainase Kabupaten Buton Selatan

RAHMAT, RAHMAT (2025) Model Pendekatan Artificial Neural Network Dalam Estimasi Biaya Pembangunan Drainase Kabupaten Buton Selatan. Masters thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Estimasi biaya pembangunan drainase di Kabupaten Buton Selatan masih sering mengalami deviasi yang signifikan akibat keterbatasan metode konvensional yang kurang mampu menangkap hubungan non-linear dan kompleksitas faktor teknis-ekonomis. Tingginya kerentanan wilayah terhadap banjir dan genangan, ditambah dengan kapasitas anggaran daerah yang terbatas, menuntut pendekatan estimasi yang lebih akurat dan adaptif. Oleh karena itu, penelitian ini mengusulkan penerapan Artificial Neural Network (ANN) sebagai solusi berbasis kecerdasan buatan untuk meningkatkan presisi perencanaan anggaran drainase. Penelitian ini menggunakan pendekatan kuantitatif dengan pengembangan model ANN berbasis data historis proyek drainase. Data primer dan sekunder dikumpulkan dari Dinas PUPR Kabupaten Buton Selatan, kemudian dilakukan augmentasi data untuk mengatasi keterbatasan dataset. Arsitektur ANN dirancang dengan tiga lapisan tersembunyi dan dioptimasi menggunakan algoritma Adam. Model dievaluasi dengan metrik R², MAE, MAPE, dan RMSE, serta dibandingkan dengan metode lain seperti Regresi Linier, Random Forest, dan SVR. Model ANN dengan arsitektur 7-32-16-8-1 menunjukkan kinerja terbaik dengan nilai R² sebesar 0,9978 dan MAE Rp 5.656.544 (3,04%), mengungguli metode pembanding secara signifikan. Analisis faktor dominan mengungkapkan bahwa harga satuan pasangan batu merupakan variabel paling berpengaruh terhadap biaya total. Hasil ini membuktikan bahwa ANN mampu memberikan estimasi biaya drainase yang lebih akurat dan dapat dijadikan alat pendukung keputusan bagi pemerintah daerah dalam penganggaran infrastruktur.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Estimasi Biaya, Drainase, Artificial Neural Network (ANN), Kabupaten Buton Selatan, Augmentasi Data, Permutation Importance, Akurasi Prediksi.
Divisions: Fakultas Teknik > magister teknik sipil
Depositing User: MTS Magister Teknik Sipil 2025
Date Deposited: 10 May 2002 16:13
Last Modified: 10 May 2002 16:13
URI: http://repository.untag-sby.ac.id/id/eprint/44267

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