Publication Search

This research is an evaluative study of the old urban area of Kesawan in Medan City, which has been part of the city’s revitalization efforts initiated by the Medan City Government. Kesawan possesses strong historical and colonial architectural character but has undergone functional and visual degradation due to uncontrolled modern urban development. One of the key issues identified is the presence of inactive urban spaces, disconnected from the public realm and lacking social meaning—phenomena recognized in urban theory as lost space. This concept serves as the foundation for evaluating the effectiveness of the revitalization program implemented since 2021. The study employs a qualitative approach using a single-case study method, focusing on Kesawan as a complex urban space. Data were collected through field observations, in-depth interviews with five categories of informants (building owners, visitors, security personnel, street vendors, and architects), and visual documentation. The analysis adopts the theoretical framework of Finding Lost Space by Roger Trancik (1986), which comprises three main approaches: Figure-Ground Theory, Linkage Theory, and Place Theory. These were further elaborated into six evaluative indicators: connectivity, continuity and circulation, enclosure, accessibility, visual orientation, and the meaning and perception of space by the public. The findings indicate that revitalization has brought significant visual improvements and physical enhancements, particularly along the main corridor of Jalan Ahmad Yani I–VII. However, many secondary streets and non-priority areas still exhibit characteristics of lost space, such as disconnected pedestrian paths, underutilized voids, weak spatial integration, and limited social engagement. These conditions suggest that the revitalization outcomes remain uneven and predominantly cosmetic in certain areas. The study recommends integrating spatial and social approaches in future urban revitalization policies to ensure that public space functions can be restored holistically, sustainably, and contextually in line with local identity.

Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .

Indonesia is an agricultural country with great potential in the agricultural sector, both as a source of livelihood and as an economic driver. This sector has an important function in food security, improving farmers' welfare, and preserving the environment. Given Indonesia's regional diversity, it is important to develop policies that suit the agricultural characteristics of each region. This research uses the clustering method, a technique that groups data based on similar characteristics so that objects in one group (cluster) have high similarities, while between clusters they are different. This research applies the clustering method to group provinces in Indonesia based on agricultural sector factors in 2023, using hierarchical and non-hierarchical techniques. The results show that the optimum clusters obtained using both single linkage and K-Means formed five optimal clusters. The Java Island cluster shows high productivity, while Riau, East Kalimantan and North Kalimantan have quite high productivity with minimal labor. The largest clusters have a lot of unmanaged land, increasing employment opportunities. Significant variables influence grouping.

Poverty means the inability to fulfill the basic needs of family members, both food and non-food.  In this study, we will analyze several indicators that are assumed to be factors that influence poverty in East Java in 2023, including East Java in 2023, including the percentage of poor people, life expectancy, average years of schooling, and unemployment rate. life expectancy, average years of schooling, and open unemployment rate using cluster analysis to group kabupatens. cluster analysis to group districts/cities into clusters based on the factors that influence poverty. factors that influence poverty. The data used is secondary data obtained through the Central Bureau of Statistics (BPS) website as much as 38 data. Then the data obtained were analyzed for data characteristics, multivariate normal distribution assumption test, independent assumption test, and cluster analysis. assumption test, multivariate normal distribution, independent assumption test, cluster analysis hierarchical, and non-hierarchical cluster analysis, and selection of the best method to determine the optimum cluster. optimum cluster. So that the results obtained data characteristics tend not to be equal, fulfill the multivariate normal distribution assumption test, dependent data. At Hierarchical clustering results obtained the grouping of districts/cities in East Java based on the factors that influence poverty into 5 based on factors that influence poverty into 5 clusters, with 7 districts/municipalities in cluster 1, 16 districts/municipalities in cluster 2, 10 districts/municipalities in cluster 3, 4 districts/municipalities in cluster 4. districts/municipalities in cluster 3, 4 districts/municipalities in cluster 4, and 1 district/municipality in cluster 5. Based on these results, differences in characteristics between clusters indicates that there are significant variations in poverty factors in each region. The results of the non-hierarchical clustering resulted in the grouping of districts/municipalities in East Java based on the factors affecting poverty into 2 clusters, with 13 clusters. factors that influence poverty as many as 2 clusters, with 13 cluster 1, 25 districts/cities in cluster 2. Also, the results of the ANOVA test results obtained the results of all variables of the factors that influencing poverty in districts/municipalities in East Java Province significantly on poverty.

Perjalanan wisata selalu berkaitan dengan bagaimana seorang wisatawan mengunjungi obyek wisata berdasar kebutuhan informasi tentang rute yang harus dilalui ke obyek wisata yang diinginkan dan transportasi apa saja yang bisa digunakan berdasar waktu yang dimiliki. Petunjuk arah juga sangat diperlukan untuk kemudahan akses perjalanan.             Model analisis pre-defined singkle-linkage dilakukan untuk menentukan obyek mana saja yang memiliki lokasi berdekatan bisa membentuk satu klaster dan kunjungan dilakukan berdasar jarak paling dekat dengan klaster menuju klaster berikutnya sesuai dengan kelompok jenis obyek wisata. Perhitungan jarak antar obyek dengan pengguna menggunakan metode Euclidean Distance untuk mencari jarak berdasar ketetanggaan terdekat.              Hasil analisis penentuan obyek wisata di kota Semarang dengan metode pre- defined single-linkage divisualisasikan menggunakan dendrogram yang bisa menggambarkan kelompok obyek dalam satu klaster berdasar ketetanggaan terdekat. Dari dendrogram bisa dilihat jumlah klaster yang terbentuk dari 14 obyek wisata.