Analisis Kualitas Layanan Sistem Drainase Wonocolo dan Ketintang dalam Menghadapi Curah Hujan Tinggi Berbasis Metode Text Mining, SERVQUAL, dan Kano

Wonocolo dan Ketintang di Surabaya Selatan sering mengalami genangan akibat curah hujan tinggi, yang mengganggu kenyamanan dan aktivitas masyarakat. Penelitian ini bertujuan mengevaluasi kualitas layanan sistem drainase untuk mengukur persepsi publik terhadap respons hujan ekstrem serta mengidentifikasi atribut layanan yang memengaruhi kepuasan. Metode integrasi text mining (analisis opini publik dari media sosial dan laporan sekunder), SERVQUAL (penilaian kesenjangan harapan• persepsi), dan model Kano (klasifikasi atribut kepuasan) diterapkan. Text mining mengidentifikasi 14 atribut dari 5 dimensi SERVQUAL, sementara SERVQUAL mengungkap gap rata-rata negatif pada dimensi reliability dan responsiveness. Model Kano mengklasifikasikan atribut sebagai one• dimensional, menunjukkan hubungan linear antara performa layanan dan kepuasan. Hasil menunjukkan layanan drainase belum optimal, dengan keluhan dominan pada kondisi keamanan drainase dan respons petugas lambat, yang menurunkan resiliensi hidraulik kawasan. Atribut prioritas seperti respons petugas bantu dan stabilitas sistem direkomendasikan sebagai dasar strategi perbaikan infrastruktur, termasuk rehabilitasi saluran dan integrasi teknologi cerdas untuk mitigasi genangan.

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Wonocolo and Ketintang areas in South Surabaya frequently experience waterlogging due to high rainfall, disrupting community comfort and activities. This study evaluates drainage system service quality to assess public perception of extreme rainfall response and identify satisfaction-influencing service attributes. An integrated method of text mining (public opinions from social media and secondary reports), SERVQUAL (expectation-perception gap assessment), and Kano model (satisfaction classification) is applied. Text mining identifies 14 attributes across 5 SERVQUAL dimensions, with SERVQUAL revealing negative average gaps in reliability and responsiveness dimensions. The Kano model classifies attributes as one-dimensional, indicating a linear performance• satisfaction relationship. Findings reveal suboptimal drainage services, with dominant complaints on drainage safety conditions and slow officer response, reducing hydraulic resilience in the area. Priority attributes, such as officer assistance responsiveness and system stability, are recommended as basesfor infrastructure improvement strategies, including channel rehabilitation and smart technology integration forflooding mitigation.