<![CDATA[Abstrak. Ketimpangan akses pendidikan muncul ketika distribusi sekolah tidak selaras dengan persebaran penduduk usia sekolah. Meskipun kebijakan zonasi diterapkan untuk pemerataan akses, kajian yang mengintegrasikan Nearest Neighbor Analysis, Kernel Density Estimation, dan Spatial Mismatch Index masih terbatas. Penelitian ini menganalisis kesesuaian antara distribusi sekolah menengah negeri dan persebaran penduduk usia sekolah di Kota Manado untuk mengidentifikasi ketidaksesuaian spasial. Data meliputi 9 SMA dan 9 SMK negeri dan penduduk usia 15–19 tahun 2025, bersumber dari BPS, Kemendikdasmen, dan Kemendagri. Hasil menunjukkan Nearest Neighbor Ratio 0,77997 (z =−1,80268; p = 0,07148), mengindikasikan distribusi sekolah mengelompok di inti kota. Analisis SMI menunjukkan enam kecamatan mengalami defisit fasilitas, empat kecamatan relatif seimbang, dan satu kecamatan surplus layanan pendidikan. Korelasi Pearson r= 0,83 (p < 0,01) menunjukkan hubungan positif kuat, sedangkan Moran’s I= 0,0237 (p= 0,1422), menunjukkan tidak terbentuknya klaster spasial signifikan. Temuan ini menunjukkan spatial mismatch lokal antara distribusi sekolah dan kebutuhan demografis. Abstract: Inequality in educational access arises when the distribution of schools is not aligned with the distribution of the school-age population. Although zoning policies are implemented to equalize access, studies integrating Nearest Neighbor Analysis, Kernel Density Estimation, and the Spatial Mismatch Index are still limited in scope. This study aims to establish an agreement between the distribution of public secondary schools and the distribution of the school-age population in Manado City, identifying spatial mismatches. Data covering 9 public senior high schools and 9 public vocational high schools, and the population aged 15–19 in 2025, sourced from BPS, the Ministry of Elementary and Secondary Education, and the Ministry of Home Affairs. The results show a Nearest Neighbor Ratio of 0.77997 (z = −1.80268; p = 0.07148), indicating that the distribution of schools is clustered in the city core. The SMI analysis shows that six sub-districts experience a deficit in aid, four sub-districts are relatively balanced, and one sub-district has a surplus in educational services. Pearson's correlation r=0.83 (p<0.01) indicates a strong positive relationship, while Moran's I=0.0237 (p=0.1422) indicates no significant spatial cluster formation. These findings suggest a spatial mismatch between school distribution and demographic needs at the local level.Submitted: 2026-01-01 Revisions: 2026-03-02 Accepted: 2026-04-14 Published: 2026-09-15]]>
