Hasil Pencarian  ::  Simpan CSV :: Kembali

"This edition provides students and practitioners with a solid foundation for understanding the conceptual nature and practice of crime analysis to assist police in preventing and reducing crime and disorder. Author Rachel Boba Santos offers an in-depth description of this emerging field, as well as guidelines and techniques for conducting crime analysis supported by evidence-based research, real world application, and recent innovations in the field."

"Menganalisis hubungan antara jumlah tindak kejahatan dan faktor-faktor yang mempengaruhinya menjadi topik penelitian yang menarik karena jumlah tindak kejahatan di Indonesia dalam sepuluh tahun terakhir cenderung meningkat. Untuk meningkatkan kualitas keamanan masyarakat maka pemerintah perlu memahami faktor-faktor apa saja yang dapat memicu tindakan kejahatan. Dibandingkan dengan metode analisis regresi klasik, metode Geographically Weighted Regression GWR lebih diunggulkan karena dapat menangani masalah ketidak stasioneran spasial yang biasanya terjadi pada data fenomena-fenomena sosial. Ketidakstasioneran spasial adalah situasi dimana hubungan antar peubah berbeda-beda secara signifikan disetiap lokasi observasi. Hal tersebut mengakibatkan hasil analisis regresi klasik menjadi tidak akurat di beberapa lokasi. GWR menangani masalah tersebut dengan membangun model regresi di setiap titik observasi sehingga memungkinkan parameter regresi menjadi berbeda di setiap lokasi observasi. Penelitian ini menggunakan jumlah tindak kejahatan y sebagai peubah terikat dan peubah bebasnya adalah jumlah penduduk buta huruf x1, jumlah pengangguran x2, jumlah penduduk miskin x3, kepadatan penduduk x4, dan jumlah korban NAPZA x5. Penelitian ini menggunakan data sekunder yang dihimpun oleh POLRI, BPS, dan Dinsos di Jawa Tengah pada tahun 2015. Terdapat dua fungsi pembobot spasial GWR yang akan dibandingkan yaitu Kernel Gaussian dan Kernel Bisquare. Hasil penelitian menunjukkan fungsi Kernel Gaussian lebih baik dibanding Kernel Bisquare berdasarkan skor AIC dan R2. Hasil analisis menggunakan GWR menghasilkan model untuk 35 kabupaten/kota di Jawa Tengah.

---

Analyzing the relationship between number of crime cases and factors defined became an interesting research topic over the last ten years. The total number of crime in Indonesia didn rsquo t show a consistent decrease. In order to upgrade people safeness quality, the government need to know the factors influence people committing crime acts. Rather than using classical regression analysis, Geographically Weighted Regression GWR was preferable since it gave a better representative model by effectively resolve spatial non stationary problem which is generally exist in spatial data of social phenomenon. Spatial non stationary is a situation when the relationship between variables are significantly different in each location of observation point, so that classic regression analysis will result a misleading interpretation in some location. GWR handled the spatial non stationary problem by generating a single model in each observation point which allow different relationship to exist at different point in space. This study used number of crime cases y as the dependent variable and the factors which affect the number of crime cases as independent variables that consist of the number of illiterates x1 , the number of unemployed x2, the number of poor population x3, population density x4, the number of victims of drug x5. This study used secondary data collected by POLRI, BPS, and Social ministry of Indonesia in Central Java during 2015. Two spatial weighting functions were compared i.e. Kernel Gaussian and Kernel Bisquare and the study result indicated that Kernel Gaussian was batter according to score of R2 and AIC. GWR generated model for 35 city regency in Central Java. "

"This book reports on research conducted over the past eight years, in which efforts have been made to pioneer the combination of techniques from behavioural geography with Geographic Information Systems (GIS) in order to map the fear of crime.The first part of the book outlines the history of research into fear of crime, with an emphasis on the many approaches that have been used to investigate the problem and the need for a spatially-explicit approach. The second part provides a technical break down of the GIS-based techniques used to map fear of crime and summarises key findings from two separate study sites. Issues discussed include fear of crime in relation to housing prices and disorder, the use of fear mapping as a means with which to monitor the impact of Closed Circuit Television (CCTV) and fear mapping in transit environments."

"Aimed at readers with a knowledge of Geographic Information Systems (GIS) but no formal training in computer science, this book provides a clear and accessible introduction to how GIS store and process spatial data. This updated edition includes two new chapters on databases and future developments, substantial additional material on raster imagery, and revisions throughout that incorporate up-to-date applications such as GPS on mobile devices and Internet-based services. The chapter on future technologies includes discussions of 3D GIS, handling time in GIS, spatial SQL, and handling imprecise geographies"-"

"PLN merupakan salah satu perusahaan BUMN yang memiliki aset terbesar di Indonesia mencapai Rp 1.613 triliun yang digunakan untuk membangkitkan dan menyalurkan listrik kepelanggan. PLN berkomitmen untuk dapat mengelola asetnya dengan cermat dan baik dengan mengimplementasikan teknologi yang ada. Hal ini sejalan dengan semangat aspirasi PLN 2024 yaitu : Green, Lean, Innovative dan Customer Focused. Dengan dorongan semangat berinovasi ini, kemudian PT. PLN (Persero) UP3 Palu ingin menampilkan semua data aset untuk dapat memvisualiasi data aset distribusi tersebut secara geospasial melalui sistem GIS dan terintegrasi dengan data aset di EAM Maximo. Kemudian data tersebut ditampilkan melalui suatu website di GIS Korporat PLN. Dari implementasi yang dilakukan telah berhasil menampilkan sebanyak 79.450 tiang, 4.282,8 KMS JTM, dan 4.299 Gardu distribusi. Selain itu, penggambaran electrical connectivity antara data aset jaringan distribusi mencapai 3.755 aset JTM dan MVCable atau 93% dari total aset JTM MVCable sebesar 4.034 sampai bulan Nopember 2022. Dengan adanya integrasi ini menjadi basis data yang valid akan keberadaan dan kondisi dari aset distribusi tersebut. Selain itu juga, hal ini memberi dampak positif bagi perusahaan karena para karyawan dapat mengakses data aset distribusi secara luas dan membantu pengambilan keputusan dalam rangka meningkatkan pelayanan PLN.

---

PLN is one of the state-owned companies that has the largest assets in Indonesia, reaching IDR 1,613 trillion, which is used to generate and distribute electricity to customers. PLN is committed to being able to manage its assets carefully and properly by implementing existing technology. This is in line with the spirit of PLN's aspirations for 2024 : Green, Lean, Innovative and Customer Focused. With the encouragement of spirit of innovation, then PT. PLN (Persero) UP3 Palu wants to display all asset data to be able to visualize the distribution asset data geospatially through the GIS system and integrated with asset data in EAM Maximo. Then the data is displayed through a website on the PLN Corporate GIS. With this implementation, it has succeeded in showing 79,450 poles, 4,282.8 KMS JTM, and 4,299 distribution substations. In addition, the depiction of electrical connectivity between distribution network asset data reaches 3,755 JTM and MVCable assets or 93% of the total JTM MVCable assets until November 2022. With this integration, it becomes a valid database for the existence and condition of these distribution assets. Apart from that, this has a positive impact on the company because employees can access data on distribution assets widely and help make decisions in order to improve PLN services"