In the package delivery industry, exemplified by J&T Express Manado, optimizing courier distribution routes is essential for customer satisfaction, cost reduction, and on-time deliveries. The Traveling Salesman Problem (TSP) is a valuable tool for finding efficient routes to visit all delivery points once. This study employed the Genetic Algorithm and Nearest Neighbor Algorithm to tackle the TSP, aiming to identify the shortest routes and minimize distribution distances for J&T Express Manado's couriers using geographical coordinates. The Genetic Algorithm resulted in a distribution route of 41.20678 km, while the Nearest Neighbor Algorithm achieved a shorter route of 38.10361 km. For J&T Express Manado, our findings indicate that the Nearest Neighbor Algorithm excels in identifying the shortest courier distribution route and requires significantly less computational time. This study offers insights for J&T Express Manado and similar courier services, enabling them to enhance distribution operations, potentially reducing costs and improving efficiency. It also underscores the practical advantages of the Nearest Neighbor Algorithm in addressing TSP challenges within the industry

Penelitian ini dilakukan untuk menentukan model kesejahteraan keluarga yang dipengaruhi oleh jumlah penduduk dan infrastruktur di kota Manado menggunakan regresi spasial dan regresi terboboti spasial, menganalisis faktor-faktor yang mempengaruhinya serta mengkaji efekti?tas metode regresi spasial dalam menganalisis kasus tersebut. Analisis yang digunakan yaitu Spatial Error Model (SEM) dilanjutkan dengan Geographicaly Weighted Regression (GWR). Hasil analisis menunjukkan bahwa variabel prediktor yang mempengaruhi variabel respon adalah jumlah penduduk, jumlah sekolah dan sarana kesehatan. Kriteria penilaian lebih memilih Model GWR untuk menjelaskan kesejahteraan keluarga karena memiliki nilai AIC lebih kecil dibandingkan menggunakan model SEM.