scholarly journals Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)

2021 ◽  
pp. 243-248
Author(s):  
Yoni Aswan ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Motor Vehicle ◽  
Motor Vehicles ◽  
Hierarchical Data ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Mentawai Islands ◽  
Or Groups ◽  
Different Characteristics

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.

scholarly journals Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)

2021 ◽  
pp. 245-250
Author(s):  
Yoni Aswan ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Motor Vehicle ◽  
Motor Vehicles ◽  
Hierarchical Data ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Mentawai Islands ◽  
Or Groups ◽  
Different Characteristics

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.

scholarly journals The pohuwato resort policy efforts in responding the threating of motor vehicles in the district region

2019 ◽  
Vol 3 (2) ◽  
pp. 182-186
Author(s):  
MUHAMMAD RIZAL LAMPATTA
Keyword(s):  
Preventive Measures ◽  
Motor Vehicle ◽  
Crime Scene ◽  
Information Networks ◽  
Motor Vehicles ◽  
Motor Vehicle Theft ◽  
Internal Constraints ◽  
Vehicle Theft ◽  
External Constraints ◽  
Motorized Vehicles

Efforts are being made to combat criminal acts of theft in the Pohuwato Regional Police jurisdiction, namely through Pre-emptive efforts, preventive measures and repressive efforts. Operations carried out to combat motor vehicle theft, namely: K2YD Patrol (enhanced police activities), competitive Patrols, and Ranmor Patrol. (2) The Constraints Faced by the Police in Addressing Motorized Vehicles In the Pohuwato Regional Police's Legal Area are Internal Constraints consisting of incomplete Facilities and Infrastructures, Interrupted Information Networks, inadequate members of the police in terms of demand, and support for voting. And External Constraints which consist of a lack of evidence and elections, communities that support the Police, and supporting facilities at the crime scene are inadequate.

scholarly journals ALAT PEMANTAU KENDARAAN BERBASIS GPS DENGAN FITUR LOKAL DAN SMS PINTAR

2013 ◽  
Vol 1 ◽  
pp. 25
Author(s):  
Teguh Wibowo
Keyword(s):  
Global Positioning System ◽  
Automotive Industry ◽  
Motor Vehicle ◽  
Motor Vehicles ◽  
Positioning System ◽  
Motor Vehicle Theft ◽  
Local Language ◽  
Vehicle Theft ◽  
Global Positioning

Nowadays, the development of the automotive industry in Indonesia is increasing followed by high number of motor vehicle theft. In order to reduce the motor vehicle theft case, a Global Positioning System (GPS) based instrument in motor vehicle is investigated to monitor the location of vehicles. This instrument will be run if there are a phone signal from provider and GPS signal from satellite. The method is that the owner sends a text to the destination number installed in the instrument. There are some codes needed to be paired to the instrument. If the code is matched, instrument will reply the text automatically based on the program installed in the instrument. This instrument has many advantages, such as could give the location accurately with an error less than 5 meters, could turn off the vehicle, could give an information if the vehicle is stolen, and also could record the conversations while the vehicles are in use. This instrument is also improved by installing the local language (Indonesian) and features which meet the owner requirements. Generally, it could be concluded that this instrument really helps the rider or owner from motor vehicle theft. Good response and accurate location given by the instrument verified by the finding of lost vehicle showed that this instrument could be applied and important if it is applied in the motor vehicles.

scholarly journals Could Crime Risk Be Propagated across Crime Types?

2019 ◽  
Vol 8 (5) ◽  
pp. 203 ◽  
Author(s):  
Zengli Wang ◽  
Hong Zhang
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Theft ◽  
Vehicle Theft ◽  
Crime Type ◽  
Increased Risk ◽  
Short Period ◽  
Public Areas ◽  
Crime Types ◽  
Crime Risk ◽  
Temporal Interaction

It has long been acknowledged that crimes of the same type tend to be committed at the same location or proximity in a short period. However, the investigation of whether this phenomenon exists across crime types remains limited. The spatial-temporal clustered patterns for two types of crimes in public areas (pocket-picking and vehicle/motor vehicle theft) are separately examined. Compared with existing research, this study contributes to current research from three aspects: (1) The repeat and near-repeat phenomenon exists in two types of crimes in a large Chinese city. (2) A significant spatial-temporal interaction between pocket-picking and vehicle/motor vehicle theft exists within a range of 100 m. Some cross-crime type interactions seem to have a stronger ability of prediction than does single-crime type interaction. (3) A risk-avoiding activity is identified after spatial-temporal hotspots of another crime type. The spatial extent with increased risk is limited to a certain distance from the previous hotspots. The experimental results are analyzed and interpreted with current criminology theories.

Sign in / Sign up

Export Citation Format

Share Document