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OPTIMALISASI SISTEM DRAINASE MIKRO DAS KALI BORO MELALUI ANALISIS HIDROLOGI HIDRAULIKA UNTUK KETAHANAN BANJIR PERKOTAAN Rahmi, Ardia Tiara; Pratiwi, Imasti Dhani; Wijayanti, Pipit; Utomowati, Rahning; Tjahjono, Gentur Adi; Ronggowulan, Lintang
Jurnal Riset Rekayasa Sipil Vol 9, No 1 (2025): September 2025
Publisher : Prodi Teknik Sipil Fakultas Teknik Universitas Sebelas Maret Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jrrs.v9i1.109485

Abstract

Urbanisasi yang masif di Kota Surakarta telah mendorong peningkatan area terbangun dan alih fungsi lahan di Daerah Aliran Sungai (DAS) Kali Boro, memicu eskalasi masalah genangan akibat sistem drainase eksisting yang inadekuat. Meskipun banyak studi drainase perkotaan modern mengadopsi model numerik canggih dan solusi berbasis alam, studi ini secara strategis memilih pendekatan hidrologi-hidraulika klasik (Log Pearson Tipe III, Mononobe, Rasional, Manning) untuk mengatasi keterbatasan data spesifik dan sumber daya di tingkat mikro perkotaan tropis, sekaligus menawarkan solusi yang relevan dan aplikatif bagi Pemerintah Kota Surakarta. Pendekatan ini mengisi kekosongan literatur terkait implementasi master plan drainase mikro komprehensif di kota tropis yang masih mengandalkan data primer dan metode yang terjangkau. Penelitian ini bertujuan untuk menganalisis kondisi drainase mikro eksisting, menghitung debit banjir rencana (T = 2, 5, 10 tahun), mengidentifikasi akar permasalahan, dan menyusun master plan drainase mikro di DAS Kali Boro. Hasil analisis menunjukkan bahwa kapasitas saluran drainase eksisting hanya mampu menampung sekitar 72,5% dari debit banjir rencana untuk kala ulang 5 tahun, diperparah oleh penyempitan, sedimentasi, dan penumpukan sampah. Implementasi master plan yang diusulkan, meliputi normalisasi, peningkatan dimensi saluran, perbaikan gorong-gorong, pengelolaan sedimen dan sampah, pembangunan sumur resapan, serta adopsi drainase berwawasan lingkungan, diproyeksikan mampu mengurangi luasan genangan hingga lebih dari 50% di area prioritas. Studi ini menegaskan relevansi pendekatan konvensional yang terjustifikasi dalam konteks lokal, memberikan kontribusi signifikan dalam perencanaan ketahanan banjir di DAS mikro perkotaan tropis, dan dapat menjadi acuan kebijakan adaptasi iklim Kota Surakarta.
GEOSPATIAL DEEP LEARNING: A CONTEXTUAL-CONSTRUCTIVE APPROACH TO GEOGRAPHY LEARNING TO EMPOWER SPATIAL THINKING SKILLS STUDENTS Prihadi, Singgih; Muryani, Chatarina; Sugiyanto, Sugiyanto; Noviani, Rita; Pratiwi, Imasti Dhani
GeoEco Vol 12, No 1 (2026): GeoEco January 2026
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v12i1.107180

Abstract

Geography education in the digital native era requires students to develop critical, creative, and innovative thinking through a spatial approach. Learning geography is not just about learning theory, but about learning deeply by optimising technology and geospatial data. This study aims to analyse the concepts and characteristics of Geospatial Deep Learning in geography education and to analyse the potential of Geospatial Deep Learning in empowering spatial thinking skills. This study employs a qualitative descriptive approach, utilising 10 informants of geography teachers and vice principals of senior high schools in Surakarta City. The sampling technique used is purposive sampling, as informants were selected based on the researcher's considerations. Data collection techniques include in-depth interviews and observations of teaching practices. The researcher employs an interactive data analysis model. The validity of the data used was triangulated by source and method. The research findings indicate that Geospatial Deep Learning in the context of high school geography education in Surakarta has already utilised geospatial data to enhance students' spatial thinking skills, though not yet to its full potential. Teachers have endeavoured to help students use data from Google Earth to strengthen their analysis. The potential of Geospatial Deep Learning in geography learning is significant, particularly in developing spatial thinking skills. Teachers noticed an improvement in spatial representation, which is evident in students' ability to visualise and manipulate spatial data in a more interesting, interactive, and informative way.