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Visualisasi dan Analisis Sebaran Data Sekolah (SD, SMP dan SMA) di Kota Bengkulu Menggunakan Geocoding R Alyudin, Dyah Rizky; Manurung, Parluhutan; Mandini Manessa, Masita Dwi
Justek : Jurnal Sains dan Teknologi Vol 7, No 2 (2024): Juni
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v7i2.22131

Abstract

Abstract:  Schools are a means of infrastructure needed to fulfill the law's educational obligations, so the distribution of schools needs to be a concern so that access to education for every citizen can be achieved. Analysis of school distribution is one way to see the needs of schools in an area through visualization of the distribution of school data in Indonesia, including Bengkulu City. However, access to school coordinates is limited, so a method is needed to obtain coordinate points for mapping and distribution analysis. Meanwhile, there is still little research regarding taking coordinate points from addresses for school data distribution in Indonesia, including Bengkulu City. Even though Geocoding with R is one way to get the coordinates of an address well. By using geocoding and visualization using the Google API, mapview, shiny and the ggplot function in R, we can show variations in the distribution of geocoding data so that distribution analysis can be carried out. The results of the visualization of the distribution of Bengkulu City school data look good, with the Muara Bangkahulu District lacking a high school, while Teluk Segara, Ratu Agung and Muara Bangkahulu lack a junior high school, and the Kampung Melayu and Sungai Serut Districts lack an elementary school. Visualizing the distribution of this data would be better done by combining four methods, namely Google API, mapview, shiny and ggplot because each method shows the advantages and disadvantages of the display.Abstrak: Sekolah menjadi suatu sarana prasarana yang diperlukan untuk memenuhi Undang-Undang dalam kewajiban pendidikan, sehingga sebaran sekolah perlu menjadi perhatian agar akses menerima pendidikan bagi setiap warga negara dapat terlaksana. Analisis sebaran sekolah menjadi salah satu cara untuk melihat kebutuhan sekolah di suatu wilayah melalui visualisasi sebaran data sekolah di Indonesia termasuk Kota Bengkulu. Akan tetapi, akses mengenai koordinat sekolah terbatas, sehingga diperlukan metode untuk mendapatkan titik koordinat untuk melakukan pemetaan dan analisis sebaran. Sementara itu, penelitian mengenai pengambilan titik koordinat dari alamat untuk sebaran data sekolah masih sedikit di Indonesia termasuk Kota Bengkulu. Padahal Geocoding dengan R adalah salah satu cara untuk mendapatkan koordinat dari suatu alamat dengan baik. Dengan menggunakan geocoding dan visualisasi menggunakan Google API, mapview, shiny dan fungsi ggplot di R, dapat memperlihatkan variasi sebaran data hasil geocoding sehingga analisis sebaran dapat dilakukan. Hasil visualisasi sebaran data sekolah Kota Bengkulu tampak baik dengan wilayah yang Kecamatan Muara Bangkahulu kekurangan SMA, sementara Teluk Segara, Ratu Agung dan Muara Bangkahulu kekurangan SMP, serta Kecamatan Kampung Melayu dan Sungai Serut kekurangan SD. Visualisasi sebaran data ini akan lebih baik dilakukan dengan mengkombinasikan dari empat metode yaitu Google API, mapview, shiny dan ggplot dikarenakan masing-masing metode menunjukkan kelebihan dan kekurangan tampilan.
Evaluating Climatic Niche Suitability for Bos javanicus Reintroduction in Cagar Alam Pananjung Pangandaran Using MaxEnt and Native-Habitat Benchmarks from Ujung Kulon and Alas Purwo Kautsar, Azhari Al; Mandini Manessa, Masita Dwi
Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments)
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Javan banteng (Bos javanicus) persists on Java mainly in a small number of protected-area strongholds, making robust climatic niche characterization important for conservation planning and for evaluating potential management or restoration targets. Here, we modeled banteng climatic suitability in southwestern Java using a MaxEnt (maxnet) framework calibrated with bioclimatic predictors from CHELSA and benchmark occurrence records from extant populations in Ujung Kulon National Park (UKNP) and Alas Purwo National Park (APNP). To contextualize transferability to non-occupied protected habitat, we also projected suitability to Cagar Alam Pananjung Pangandaran (CAPP) and quantified environmental novelty using the Multivariate Environmental Similarity Surface (MESS). Univariate comparisons indicated that all eight bioclimatic variables differed significantly among UKNP, APNP, and CAPP, supporting strong site-level climatic differentiation. Tuned MaxEnt models showed good discrimination under cross-validation, and projections revealed pronounced contrasts among sites: mean suitability was low in UKNP (≈0.13), high in APNP (≈0.80), and intermediate in CAPP (≈0.48). MESS values indicated that UKNP and APNP projections largely remained within the training climatic envelope, whereas CAPP exhibited localized environmental novelty (negative MESS), implying higher extrapolation risk and greater uncertainty in inference. Overall, our results suggest that APNP currently aligns most closely with the modeled climatic niche, while CAPP may contain partially suitable conditions but requires cautious interpretation and additional ecological validation (e.g., habitat structure, disturbance, and prey–human interactions) before being considered in conservation decision-making.