<![CDATA[Abstrak Teknologi informasi menjadi solusi masalah layanan publik, termasuk pengolahan data besar lewat data warehouse dengan metode Nine Step Kimball model Skema Bintang. Data warehouse mendukung pengelolaan dan analisis data. Masalah muncul saat manajemen butuh informasi detail dan tepat waktu tentang data transaksional stunting, tapi di lapangan pihak manajemen kesulitan mendapat profil data stunting karena pengisian aplikasi belum lengkap. Karena itu perlu evaluasi penerapan aplikasi pemodelan data warehouse untuk penyajian data stunting. Tujuan penelitian mendeskripsikan evaluasi penerapan aplikasi Pemodelan Data Warehouse untuk penyajian data stunting di Desa Wilayah Kabupaten Kediri. Metode penelitian deskriptif kuantitatif dengan pendekatan cross sectional. Penelitian dilakukan Mei-Juni 2025 di Desa wilayah Kabupaten Kediri. Populasi seluruh kader kesehatan pengguna aplikasi dengan sampel 40 orang pakai total sampling. Data dikumpulkan lewat kuesioner PIECES. Uji validitas 35 item valid karena r-hitung > r-tabel 0,334 dan reliabilitas Alpha 0,928 > 0,70, jadi instrumen valid dan reliabel. Pengukuran pakai skala Likert dengan analisis statistik deskriptif.Hasil penelitian menunjukkan semua aspek PIECES kategori Baik: performance 90%, information 85%, economics 82,5%, efficiency 72,5%, service 65%, control 62,5%. Skor terendah ada pada control 62,5% dan service 65% sehingga perlu perbaikan. Rekomendasi: optimalkan pelatihan dan monitoring evaluasi petugas agar lebih terampil serta lakukan backup data berkala. Melalui rancang bangun warehouse stunting diharapkan jadi alat bantu penyajian data stunting yang valid, sehingga stakeholder SKPD dan pemerintah desa bisa menetapkan sasaran intervensi tepat dan cepat. Hasil penelitian bisa jadi bahan evaluasi Desa Wilayah Kabupaten Kediri dalam pemantauan dan penanganan stunting. Abstract Information technology is an effective solution for public service challenges, including the processing of large datasets through a data warehouse using the Nine Step Kimball model with a Star Schema design. A data warehouse supports data management and analysis. A problem arises when management requires detailed and timely information from stunting transactional data; however, in practice, managers struggle to obtain stunting profiles because data entry in the existing application remains incomplete. Therefore, an evaluation of the implementation of a data warehouse modeling application for stunting data presentation is necessary.This study aimed to describe the evaluation of the Data Warehouse Modeling application for stunting data presentation in villages in Kediri Regency. The research method was quantitative descriptive with a cross-sectional approach. The study was conducted from May to June 2025 in villages within Kediri Regency. The population consisted of all community health cadres using the application, with a total sample of 40 respondents selected through total sampling. Data were collected using a questionnaire based on the PIECES framework. Validity testing showed that 35 items were valid with r-count > r-table of 0.334, and reliability testing yielded Cronbach’s Alpha of 0.928 > 0.70, indicating that the instrument was valid and reliable. Measurements used a Likert scale, and data were analyzed using descriptive statistics.The results showed that all PIECES variables were in the “Good” category: performance 90%, information 85%, economics 82.5%, efficiency 72.5%, service 65%, and control 62.5%. The lowest scores were control 62.5% and service 65%, indicating areas for improvement. Recommendations include optimizing training and monitoring-evaluation for officers to improve competency and performing regular data backups. Through the development of a stunting data warehouse, the application is expected to serve as a valid tool for presenting stunting data, enabling Regional Work Units (SKPD) stakeholders and village governments to set intervention targets accurately and promptly. These findings can serve as evaluation material for villages in Kediri Regency for stunting monitoring and management.]]>
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