Jurnal Geodesi Undip最新文献

{"title":"PENILAIAN KAPASITAS COVID-19 DI KABUPATEN SUKOHARJO MENGUNAKAN SISTEM INFORMASI GEOGRAFIS","authors":"Diaz Amel Lolita, Arief Laila Nugraha, M. Awaluddin","doi":"10.14710/jgundip.2022.33138","DOIUrl":"https://doi.org/10.14710/jgundip.2022.33138","url":null,"abstract":"Covid-19 (coronavirus disease 2019) merupakan penyakit pernafasan menular yang baru ditemukan pada tahun 2019 di Kota Wuhan, Tiongkok dengan tingkat penyebaran yang sangat cepat dan memiliki tingkat kematian yang tinggi. Kasus Covid-19 menyebar hingga ke Kabupaten Sukoharjo pada tanggal 23 Maret 2020, ditemukan kasus positif Covid-19 pertama dan resmi menetapkan status kejadian luar biasa. Kasus kumulatif Covid-19 di Kabupaten Sukoharjo pada tanggal 31 Mei 2021 sebanyak 6.212 kasus positif, sedangkan pada bulan Juni mengalami peningkatan yang tinggi sehingga pada tanggal 5 Juli 2021 kasus positif sebesar 7.976 kasus. Peningkatan kasus yang tinggi merupakan salah satu indikator yang menunjukkan kurangnya kesiapan dalam menghadapi pandemi. Peningkatan kapasitas dapat dilakukan dengan penilaian kapasitas sehingga dapat diketahui prioritas wilayah penanganan. Parameter yang digunakan dalam penilaian kapasitas antara lain jangkauan fasilitas kesehatan, rasio tenaga kesehatan, jangkauan fasilitas isolasi terpusat, satuan tugas Covid-19 dan edukasi Covid-19. Penentuan nilai tiap parameter menggunakan metode Analytical Hierarchy Process (AHP) untuk memperoleh bobot dari hasil wawancara narasumber. Kapasitas dalam menangani Covid-19 di Kabupaten Sukoharjo belum sepenuhnya merata, seperti rumah sakit rujukan dan fasilitas isolasi terpusat yang hanya tersedia di kecamatan-kecamatan yang terdekat dengan area kota seperti Kecamatan Kartasura, Kecamatan Grogol, dan Kecamatan Sukoharjo. Kapasitas perlu dilakukan peningkatan terutama di wilayah prioritas, baik dengan menambah fasilitas kesehatan maupun meningkatkan kualitas fasilitas kesehatan yang telah ada seperti Kecamatan Weru, Kecamatan Bulu, Kecamatan Tawangsari dan Kecamatan Nguter.","PeriodicalId":257540,"journal":{"name":"Jurnal Geodesi Undip","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133847676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PENENTUAN POTENSI KAWASAN PERUNTUKAN INDUSTRI MENGGUNAKAN ANALYTICAL HIERARCHY PROCESS (AHP) DAN SISTEM INFORMASI GEOGRAFIS DI KABUPATEN JEPARA","authors":"Muhammad Faishal Labib, M. Awaluddin, Yasser Wahyuddin","doi":"10.14710/jgundip.2022.33209","DOIUrl":"https://doi.org/10.14710/jgundip.2022.33209","url":null,"abstract":"Kabupaten  Jepara mengalami peningkatan pertumbuhan industri ditinjau dari peningkatan investasi Penanaman Modal Asing (PMA). Penentuan yang berpotensi sebagai pengembangan industri dibutuhkan untuk mendorong pertumbuhan sektor industri agar lebih terarah dan terpadu. Metode Sistem Informasi Geografis (SIG) merupakan metode yang memanfaatkan aspek parameter spasial yang digunakan dalam menentukan lokasi potensial untuk pengembangan industri di Kabupaten Jepara. Terdapat sepuluh parameter spasial dalam penelitian ini yang digunakan dalam penentuan kawasan peruntukan industri di antaranya adalah kemiringan lereng, jenis tanah, penggunaan lahan, curah hujan, jarak ke jalan utama, jarak ke jaringan listrik, jarak ke jaringan sungai, jarak ke fasilitas umum, jarak ke pemukiman dan jarak dari pusat kota. Data tersebut kemudian dilakukan pembobotan dengan metode Analytical Hierarchy Process (AHP). Metode AHP digunakan untuk mengetahui besar bobot yang mempengaruhi pada masing-masing parameter. Hasil penelitian ini yaitu peta potensial lahan kawasan peruntukan industri. Berdasarkan hasil pembobotan diperoleh lima kelas klasifikasi tanah yaitu sangat sesuai (S1) sebanyak 1,027 %, sesuai (S2) sebesar 14,402 %, cukup sesuai (S3) sebesar 33,472%, kurang sesuai (N1) sebesar 37,624 % serta tidak sesuai sebesar (N2) 13,475 %. Hasil dari intersect peta potensi lahan KPI dengan RTRW diperoleh wilayah yang sangat sesuai sebesar 31.655596 ha. Kata Kunci: AHP, SIG, Potensi Lahan Industri","PeriodicalId":257540,"journal":{"name":"Jurnal Geodesi Undip","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130526677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALISIS PEMODELAN FASE TUMBUH PADI MENGGUNAKAN CITRA SYNTHETIC APERTURE RADAR C-BAND SENTINEL-1","authors":"Ilhaam Cahya Nirtanto, Yudo Prasetyo, Bandi Sasmito","doi":"10.14710/jgundip.2022.34404","DOIUrl":"https://doi.org/10.14710/jgundip.2022.34404","url":null,"abstract":"Oryza Sativa atau yang biasa dikenal dengan nama Padi adalah tanaman yang dapat menghasilkan beras yang mana merupakan bahan makanan pokok terpenting di Indonesia. Tingkat produksi padi dapat diestimasi dengan pemantauan fase pertumbuhan padi. Identifikasi pertumbuhan padi dapat dilakukan dengan cepat dan luas dengan pengindraan jauh yang bersifat multitemporal. Teknik ini sudah banyak dilakukan untuk pemantauan fase pertumbuhan padi dengan jenis sensor optis. Namun, Indonesia memiliki liputan awan yang tinggi, sehingga sedikitnya ketersediaan citra optis yang bebas awan untuk pemetaan dan pemantauan area pertumbuhan padi. Keberadaan teknologi radar terutama SAR, memberi potensi yang baik untuk dikaji dalam pemantauan padi dengan ketersediaannya data bebas dari liputan awan.Pada penelitian ini, pemodelan fase pertumbuhan padi didapatkan dengan menggunakan citra SAR-C Sentinel-1 GRDH. Karakteristik nilai hamburan balik pada polarisasi VH meningkat selaras dengan fase pertumbuhan padi, berbeda dengan polarisasi VV yang lebih stagnan. Algoritma yang digunakan yaitu klasifikasi terselia Maximum Likelihood dan Support Vector Machine. Penggunaan kedua klasifikasi dengan tipe yang berbeda, pada Maximum Likelihood yang termasuk jenis parametrik dan Support Vector Machine untuk yang non-parametrik.Hasil pemetaan fase tumbuh padi melihatkan algoritma SVM lebih optimal untuk 4 kelas fase yaitu vegetatif awal, vegetatif, generatif dan pematangan. Akurasi dari klasifikasi yang paling optimal didapatkan pada kisaran 72,9% dengan kappa 0,6 hingga akurasi 0,98% dengan kappa 0.95. Penelitian pemodelan fase pertumbuhan padi ini diharapkan dapat digunakan untuk pemantauan laju produksi padi yang berfungsi untuk manajemen lahan yang baik agar hasil produktivitas yang optimal.","PeriodicalId":257540,"journal":{"name":"Jurnal Geodesi Undip","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALISIS DAMPAK KEBAKARAN HUTAN TERHADAP PERUBAHAN TUTUPAN LAHAN DAN HABITAT KAWASAN LINDUNG DI TAMAN NASIONAL WAY KAMBAS MENGGUNAKAN METODE POLARIMETRIK","authors":"Muhammad Farhan Aryasatya, Yudo Prasetyo, Yasser Wahyuddin","doi":"10.14710/jgundip.2022.34400","DOIUrl":"https://doi.org/10.14710/jgundip.2022.34400","url":null,"abstract":"","PeriodicalId":257540,"journal":{"name":"Jurnal Geodesi Undip","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130504115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALYSIS OF LAND USE CHANGES AND URBAN LAND VALUE ZONE REVIEWED FROM THE RDTR PLAN BANDUNG (CASE STUDY OF RANCASARI DISTRICT)","authors":"Mohamad Adityo Ragil Sulistyo, A. P. Wijaya, Yasser Wahyuddin","doi":"10.14710/jgundip.2022.34402","DOIUrl":"https://doi.org/10.14710/jgundip.2022.34402","url":null,"abstract":"The city of Bandung, which is located in West Java Province, is known as the Flower City and consists of 31 sub-districts. One of the sub-districts in the city of Bandung which has various supporting facilities such as housing, rice fields, shopping centers, hospitals, and schools is Rancasari District. Rancasari Subdistrict, which is located in the south of Bandung City, every year always experiences rapid population growth and development from various aspects. These rapid changes and developments are also inseparable from the influence of detailed urban spatial plans and zoning regulations. The residential area is one of the things that is being considered, followed by other sectors that are starting to be built, such as trade and services, the main road that connects between cities, and the busy traffic causes the area to become congested. As a result, green land such as rice fields is reduced so that there is a lack of useful drainage to prevent flooding. In this study, it will be seen how changes in land value zones are affected by RDTR and zoning, in Rancasari District by referring to the Land Value Zone Map and Land Use Map so that they can identify and map the changes that occur every year to the land. The result of processing, in 2015, the highest land price in Cipamokolan Village was Rp. 2,602,000 namely in the Mixed class, Natural Protection, Public Services, Offices, Local Protection, and Housing. Meanwhile, in Derwati Village, the highest land price is Rp. 6,297,000 in Industry & Warehousing, Natural Protection, Public Services, Trade & Services, Offices, Local Protection, and Housing classes. In Manjahlega Village, the highest land price is Rp. 6,297,000 in General Services, Trade & Services, Offices, and Housing classes. In Mekarjaya Village, the highest land price is Rp. 6,297,000 in the Public Service, Trade & Services, Local Protection, and Housing classes. In 2020, the highest land price in Cipamokolan Village is Rp. 17,276,000 in Natural Protection, Public Services, Trade & Services, Offices, Local Protection, and Housing classes. In Derwati Village, the highest land price is Rp. 10,092,000 in Industry & Warehousing, Natural Protection, Public Services, Trade & Services, Offices, Local Protection, and Housing classes. In Manjahlega Village, the highest land price is Rp. 17,276,000 in Mixed, Natural Protection, Public Service, Local Protection, and Housing classes. Mekarjaya Village has the highest land price of Rp. 10,092,000 in the Public Service, Trade & Services, Local Protection, and Housing classes. 2. Land use in Rancasari District has a total area of 692,224 Ha. The distribution of land use in Rancasari Subdistrict in 2015 and 2020 was the largest class, namely the Residential Zone class, namely 462,187 Ha and 448,597 Ha, while the smallest in 2015 and 2020 was in the Bus Terminal/Angkot area class of 0.002 Ha and the Public Facilities class. area of 0.040 Ha. The land use classes in the Rancasari District are increasin","PeriodicalId":257540,"journal":{"name":"Jurnal Geodesi Undip","volume":"698 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131051180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}