Vol.
No.
Desember 2025: 439Ae457 https://doi.
org/10.
22146/kawistara.
https://jurnal.
id/kawistara/index The Journal of Social Sciences and Humanities ISSN 2088-5415 (Prin.
| ISSN 2355-5777 (Onlin.
Submitted: 06-10-2024.
Revised: 18-12-2026.
Accepted:24-02-2026 The Role of Security Personnel and Village Information Systems to Reduce Crime Rates in Rural Areas Wida Reza Hardiyanti1* Faculty of Economics and Business of Universitas Gadjah Mada Muhammad Khairil Anwar2 Indonesia Open University *Corresponding author: wida.
h@mail.
ABSTRACT Crime rates in rural areas have been increasing in recent years.
This surge in criminal activity has fostered a climate of fear and insecurity among rural residents.
Several factors contribute to this phenomenon.
Many rural areas lack a sufficient number of security personnel, hindering effective deterrence and response to crime.
Moreover, community members patrol .
, often struggles with ineffectiveness due to a lack of organization and resources.
Furthermore, low awareness of basic security measures among rural residents leaves them vulnerable.
Village information system also need to consider as one of the factors which might be influence the crime activities in villages.
However, economic factors like poverty and unemployment can push individuals towards criminal activity.
This study investigates the impact of increasing security personnel on crime rates and crime reporting in Indonesian villages using panel data from the PODES survey and SUSENAS from 2018-2022.
The study employs a propensity score matching (PSM) model to examine the relationship between security personnel, crime rates, and crime reporting, considering other factors such as regulations, social assistance and socioeconomic factors.
The study found that increasing security personnel has a significant negative impact on crime rates, indicating that a higher number of security personnel leads to a lower crime rate in villages.
Additionally, enhancing village information system also has a significant positive impact on crime reporting which finally reduce crime.
This research breaks new ground by comprehensively analyzing the interconnected nature of crime and its reporting within rural Indonesian communities.
By demonstrating the effectiveness of increased security personnel and village information system will not only reducing crime rates but also encouraging reporting.
KEYWORDS Rural Crime.
Security Personnel.
Crime Reporting.
Panel Data.
Village Information System.
INTRODUCTION
and village potential data for spatial context National crime statistics in Indonesia indicate that the total number of recorded criminal incidents declined to 561,993 cases in 2024, with an overall crime rate of 204 incidents per 100,000 population, according to the Crime Statistics 2024/2025 report published by Badan Pusat Statistik (BPS), records, socio-economic survey results, (Badan Pusat Statistik [BPS], 2.
While these macro-level figures show a general downward trend compared with 2023, they mask important spatial distinctions: urban areas tend to experience more frequent and diverse criminal activity, such as petty theft and property crime associated with high population density and social inequality, in contrast to rural regions where absolute crime counts are lower and interpersonal CopyrightA 2025 Wida Reza Hardiyanti.
Muhammad Khairil Anwar.
This article is distributed under a Creative Commons Attribution-Share Alike 4.
0 International license.
Jurnal Kawistara is published by the Graduate School of Universitas Gadjah Mada.
or localized offenses are relatively more on whether these programs foster trust in prominent (BPS, 2025.
Maftuhin, 2.
law enforcement or reduce crime.
Through Although BPS do not yet include explicit crime rates per 100,000 population disaggregated by urban and rural areas, they do incorporate village potential data that covers crime presence and other community characteristics at the large-scale randomized control trials across six countries, the researchers found that community policing, as implemented in these regions, failed to significantly improve public trust in the police or lead to a noticeable reduction in crime rates.
village level, providing a foundation for rural Therefore, this research addresses four analysis (BPS, 2.
Criminological research critical policy challenges in rural Indonesia:
contextualizes these patterns by highlighting Insufficient Security Personnel how urban socio-economic conditionsAi Rural communities face critically low police- such as greater unemployment, inequality, to-population ratios, severely compromising and mobilityAicorrelate with higher crime incidence, whereas rural areas, despite enforcement responsiveness.
This personnel showing lower crime intensity, are influenced deficit undermines community safety and by factors such as limited infrastructure enables unchecked escalation of criminal and social services that can contribute to interpersonal deviance (Maftuhin, 2.
Weak Village Information Systems Furthermore, rural areas across Indonesia Fewer than 50% of Indonesian villages have experienced a disturbing rise in crime possess functional digital crime-reporting rates, encompassing a range of offenses systems (BPS, 2.
This infrastructure that include theft, drug trafficking, sexual gap creates significant delays in incident assault, and even mass violence.
This surge in criminal activity has fostered a climate of emergency response, allowing persistent fear and insecurity among rural populations, underreporting of crimes.
further aggravating already existing social and economic challenges.
According to the United Nations Office on Drugs and Crime (UNODC), crime in rural areas is not only a growing problem globally but also a critical issue in developing countries like Indonesia, where rural infrastructure and governance systems may not be as robust as those in urban areas (UNODC, 2.
However.
Blair et al.
conducted impact of community policing initiatives in the Global South, specifically focusing Kawistara.
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Desember 2025: 439Ai457 Ineffective Traditional Community Participation .
coordination, inadequate resources, and lack of formal training.
These limitations reduce their deterrent impact and prevent meaningful bridging of security gaps.
Socioeconomic Drivers Crime Persistent rural poverty (World Bank, 2.
Economic increases vulnerability to engagement in criminal activities as a means of survival.
illicit activities as survival mechanisms.
The absence of formal security structures leaves rural communities vulnerable to a range of criminal activities that would otherwise be deterred by a more significant law enforcement presence.
Moreover, many rural areas also lack basic awareness of security measures.
Villagers are often unaware of how to protect themselves or report crimes effectively.
This gap in knowledge These socioeconomic challenges, coupled with weak local governance and limited access to education, create fertile ground for criminal networks to operate in rural Drug trafficking, in particular, has been a growing problem in these areas, as organized crime groups exploit the lack of local enforcement and socioeconomic desperation to expand their operations.
makes them easy targets for criminals.
This study aims to analyze the impact who take advantage of their isolation and of increasing security personnel and village lack of preparedness.
Compounding these information systems on crime reduction and challenges is the underdevelopment of the improvement of crime reporting in rural village information systems, which are vital Indonesia.
The focus encompasses: .
The for tracking, reporting, and responding to influence of additional security personnel.
In many villages, there is a significant .
The role of information systems.
delay in communication between residents .
The synergy between these two factors and law enforcement, as many communities in enhancing village safety.
The research still rely on traditional, slow, or even informal employs panel data from the PODES survey methods of relaying information.
According and SUSENAS KOR for the period 2018Ae2022 to a study by Badan Pusat Statistik .
, to investigate the impact of enhancing law less than 50 percent of villages in Indonesia enforcement on crime rates and reporting have functioning digital information systems This study also takes into that could facilitate crime reporting and account other factors such as regulations.
The lack of a formal and accessible reporting mechanism hampers the ability of residents to report crimes in a socioeconomic conditions, which are known timely manner, allowing criminal activity to to influence crime dynamics in rural settings.
go unchecked for extended periods.
The study uses a Propensity Score Matching (PSM) model to estimate the effect of increasing security personnel on crime areas, where poverty and unemployment reduction and reporting in rural areas.
PSM
are persistent issues.
According to the helps control for selection bias by matching World Bank .
, rural poverty rates villages that received additional security in Indonesia remain significantly higher resources with similar villages that did not.
than in urban areas, with many families This allows for a more accurate estimation struggling to meet basic needs.
The lack of the true effect of increased security of economic opportunities often drives personnel on crime outcomes.
Wooldridge Another individuals, particularly young men, toward Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
Score and responsive service delivery, which Matching (PSM) is a reliable method for together enhance safety, accessibility, and aligning treatment and control groups based user satisfaction.
In this view, adequate and on observable characteristics, improving the inclusive public amenities, when combined analysis of non-experimental data.
Research with intelligent design and technological highlights several factors influencing crime innovation, not only improve environmental rates in villages, demonstrating a significant quality but also foster a sense of security and correlation between certain variables and belonging among diverse urban populations crime levels.
Increased security personnel, (Itair, 2.
Propensity for example, can reduce crime.
Fondel .
High poverty levels often correlate and Cabrera et al.
found that security with increased crime, driven by individuals presence decreases theft and vandalism.
striving to meet their needs, as explained by This deterrent effect is echoed by Region Bell et al.
Community participation in Security Guarding .
and Titan Security social activities is crucial for reducing crime.
Europe.
Tical .
and Wo .
emphasized that Enhanced information systems in villages social capital plays a pivotal role in enhancing also positively impact crime reduction.
Well- public security, not only by fostering strong community bonds but also by shaping decision-making by enabling swift and both the perception and reality of safety.
precise actions, which plays a crucial role Defined through networks of mutual trust, in crime prevention.
Additionally.
Igwe shared norms, and reciprocity, social capital .
demonstrated crime prevention and facilitates cooperation between citizens control are essential for community safety, and authorities, enabling effective crime especially in rural areas with limited formal prevention and strengthening community law enforcement, making local community Easier communication access can involvement crucial.
Similarly.
Cabrera .
potentially lower crime rates through various and Bell et al.
revealed that individuals Easier family contact reduces receiving direct cash transfers (BLT) are less prison recidivism, demonstrating a positive inclined to engage in criminal activities, as impact on criminal behaviour (De Clair & financial assistance helps alleviate economic Dixon, 2.
Conversely.
Zhang and Yue pressures that might otherwise lead to crime.
noted that while internet censorship Improved access to education is linked doesnAot directly affect crime, it can provide to decreased crime.
Study found that better education reduces criminal involvement (Bell et al.
, 2.
Quality public facilities Beyond physical amenities, smart public Efficient communication among law enforcement agencies is crucial, as poor communication can hinder crime-fighting.
Crime real-time that crime tends to cluster in specific areas.
community-driven Simon and Jichova .
conducted an access to information useful for criminal Kawistara.
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Bell post-socialist city, revealing that both crime et al.
showed that higher income and incidents and crime harm are less spatially education levels reduce crime likelihood, clustered than typically observed in cities with unemploymentAos impact being marginal such as those in the US or UK.
Using the due to alternative income sources.
law of crime concentration at places and While street lighting is often believed the Cambridge Crime Harm Index at the to deter crime, empirical findings remain street segment level, their findings challenge Bonner .
, using a pre/post- conventional expectations, suggesting that comparison design in two micro-places, place-based strategiesAioften found only modest effects of improved effective in Western contextsAimay require lighting on crime reduction.
The study, which This highlights the importance applied both situational crime prevention of theory testing and contextual sensitivity and informal social control perspectives, when transferring crime prevention models revealed that while some benefits were across different urban and socio-political observed, the overall results did not support Places of worship, enhancing strong deterrent effects.
Moreover, the social capital and control, can reduce crime, absence of a control area and variation in as seen in WoAos .
study in Washington DC.
illumination levels limited the conclusiveness of the findings, suggesting the need for crime rates (Sikorsi et al.
, 2.
Smith more rigorous future evaluations.
However, .
examines the relationship between gaps in understanding policy impacts on marginalized populations and rural crime criminal behavior remain.
Short- and long- rates, highlighting how social and economic term effects, risk perception, interaction exclusion contributes to increased crime in mechanisms, and data quality are key rural areas.
The study finds that marginalized research areas needing attention.
Certain groups, due to factors like poverty and Micro-level limited access to resources, are more likely patterns vary by crime type.
Stable hotspots to be involved in or affected by crime.
Smith for drugs and robbery in high-crime Chicago argues that addressing the root causes of areas, while thefts were sporadic and short- marginalization is essential for reducing rural Focused deterrence strategies, such as crime rates and improving community well- Aupulling levers,Ay effectively reduce crime.
Education level inversely correlates with crime rates.
indicated that higher educational attainment and school quality significantly reduce crime.
Human, social, and criminal capital also impact crime choices.
There is significant effects of education .
uman capita.
and peer influence .
ocial capita.
on adult criminality.
Data And Method The data used in this study are secondary data from PODES .
and SUSENAS KOR .
PODES data, collected by BPS biennially, covers all Indonesian villages and provides insights into village potential, including indicators like the Geographic Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
Difficulty Index (IKG) and the evaluation of between improved information and security village development and funding.
PODES
, additional security personnel, respondents include village heads, sub- district heads, and regional secretaries.
reporting, security teams, and police post.
SUSENAS data gathered semi-annually (March Septembe.
, health, housing, fertility, family planning, and crime rates.
The study integrates three complementary theoretical foundations:
Routine Activity Theory establishes that and more.
The March survey with a larger crime requires three elements: motivated sample, is representative at the district/ offenders, suitable targets, and absence of city level, while the September survey is representative at the provincial and national SUSENAS respondents are randomly selected households, with March involving 960 households and September Key SUSENAS include the poverty rate.
Gini Ratio.
Poverty Depth Index.
Poverty Severity Index.
Human Development Index (HDI).
School Participation Rate, and Literacy Rate.
This study is replication and modification from FondevilaAos .
work by combining household and community data, minimizing unobserved heterogeneity bias.
It examines the impact of increasing security personnel on village crime rates, noting the correlation Source: processed by authors .
Kawistara.
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Here, security personnel AuguardiansAy presence disrupts criminal opportunities.
Deterrence Theory posits that visible law enforcement discourages criminal intent through perceived risk of apprehension.
Increased security personnel heighten this perceived risk, reducing offense incidence.
Information Transparency Theory (Heald, 2.
argues that robust information systems build public trust and institutional Efficient crime reporting mechanisms empower communities and enable rapid law enforcement responses, creating a proactive crime management The enhanced information and security systems .
reatment grou.
to those without .
ontrol Given the qualitative nature of the probit, and propensity score matching (PSM) are used.
Empirical analysis (PSM.
Logit/ Probit model.
tests three core hypotheses:
H1:
Increased significantly reduce crime rates.
H2: Enhanced village information systems reducing crime.
H3: Security personnel and information systems interact synergistically, amplifying crime reduction beyond individual effects Table 1.
Variable Categories and Descriptions Variable Type Dependent Variable Independent Variable Independent Variable Name Crime Rate Description Crime rate in the village.
Security Personnel Number of security personnel in the village.
Variable Information System Quality of the crime reporting information system in the Control Variable Regulations Number of village regulations related to security.
Control Variable BLT (Cash Transfe.
Number of recipients of direct cash transfers in the village, serves as a proxy for poverty.
Control Variable Education Access to educational facilities in the village.
Control Variable Marginal Groups Presence or arrival of marginal groups .
omeless, sex workers, or beggar.
in the village.
Control Variable Public Facilities Quality of public facilities in the village.
Control Variable Poverty Rate Household poverty rate.
Employment Employment status .
hether employed or no.
Control Variable Social Activities Involvement in social activities.
Control Variable Communication Access to communication in the village.
Control Variable Community Cooperation Level of community cooperation in the village.
Control Variable Police Post Readily accessible police station.
Control Variable .
Source: various sources .
Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
Model Specification the treatment and control groups.
PSM is Crime Rate= a statistical method used to balance the Where: 0 is a constant term.
Xi is distribution of covariates between treatment the independent variable of the village and control groups so that the analysis of information system and additional officers, treatment effects is more accurate.
ZiAUis the control variable .
= 1a.
, and Propensity Score Matching (PSM) is a A is error term method commonly used in econometrics to Logistic Regression Model estimate causal treatment effects in non- Logistic regression is used to model the probability of occurrence of a binary event, in this case the crime rate in the village.
This model is suitable for use because the dependent variable is binary.
The logistic regression function is as follows:
P(Y=.
1X1 2X2 .
Where: Y is a binary dependent variable .
or example, crime rat.
X1.
X2, .
Xn is is the independent variable (Officers.
Information.
Regulations.
BLT.
Education.
Public Facilities.
Poverty.
Employment, involvement in social experimental settings, helping to address selection bias in observational data.
outlined by Wooldridge .
, the process begins with determining the propensity This involves using a logistic regression model to estimate the probability that each unit in the study will receive the treatment, based on observed covariates.
This estimated probability is the propensity score, which helps in balancing the treatment and control groups by accounting for the covariates that influence the treatment assignment.
After determining the propensity score, the next step is matching.
Wooldridge explains that units in the treatment group are matched with units in the control group that have similar propensity scores, ensuring Probit Regression Model that the treatment and control groups are Probit regression is similar to logistic regression but uses the cumulative function of the normal distribution to estimate probabilities.
The probit regression function is as follows:
comparable on observed covariates.
Various P(Y=.
X) = .
1X1 2X2 .
treatment effect is estimated by comparing Where: is the cumulative distribution function of the standard normal distribution.
Propensity Score Matching (PSM) Study uses Propensity Score Matching (PSM) to reduce bias that may arise from Kawistara.
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Finally, once matching is complete, the the outcomes of the treatment group with those of the matched control group.
Wooldridge .
matching ensures that the treatment and control groups are balanced on observed covariates, the difference in outcomes between the two groups can be interpreted may be more appropriate.
On the other hand, as the causal effect of the treatment, thus if interpretation via the odds ratio is more providing a more robust estimate of the important, the Logit model is usually more treatment effect.
PSM helps in minimizing selection Model Selection: The choice between Probit and Logit models often depends on the nature of the data being analyzed and interpretation preferences.
If a normal bias and allows a fairer evaluation of the effect of treatment .
ncreased information systems and/or improved information system.
on crime rates.
distribution better fits the data, a Probit model Table 2.
Model Selection Basis Model Description Logit Logistic regression (Logi.
is Advantages A a statistical technique used to model the probability of Logit model assumes that the error terms follow a logistic distribution.
Coefficients in the Logit model can be directly a binary event .
, yes/no, interpreted as the logarithm of the odds ratio, success/failur.
This model facilitating interpretation in the context of risk or uses the logit function to link a binary dependent A Simplicity in Computation: The Logit model is variable with one or more usually easier to implement and compute, and independent variables.
converges faster on many datasets.
Robustness to Model Misspecification: The Logit model tends to be more robust against incorrect model specification or errors in the distribution of independent variables.
Probit Probit regression is a A Coefficients in the Probit model are easier to statistical method similar interpret in the context of cumulative probability to logistic regression, but it from the standard normal distribution.
uses the cumulative normal The Probit model tends to handle extreme values distribution function to or outliers better due to the nature of the normal estimate the probability of a binary event.
PSM
Propensity Score Matching A Reducing selection bias: Ensures the treatment (PSM) is a statistical method and control groups are comparable in terms of used to reduce selection confounding characteristics.
bias by balancing the A distribution of confounding variables .
Internal validity: Improves the internal validity of treatment effect estimates.
Flexible use: Can be applied to various types of between the treatment and observational studies where randomization is not control groups.
Source: Wooldridge .
Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
and estimation was conducted to evaluate the impact of increasing security personnel accessibility to police posts, the presence and improving village information systems of community institutions, public facilities, on crime rates in rural areas from 2018 to marginalized groups, village regulations.
The main variables analyzed include and socioeconomic factors like poverty and employment levels.
A Propensity Score Matching (PSM) DISCUSSIONS Table 3.
Comparison Results between Logit and Probit Model:
The Impact of Village Information System on Crime
Dependent variable Crime
Logit
Probit
Coefficient Coefficient Village information system
939***
Dummy security 763*** .
Public facility 320*** .
Community institution 807*** (-24.
Gotong royong
706***
(-4.
Accessible police station
650***
(-156.
Regulation 00669*** (-10.
Police station
804***
(-222.
Marginal 720*** .
Internet signal
486***
Dummy work
0977***
(-17.
Dummy assistance 270*** .
Standard error is in parentheses, significance level: *10%, **5%, ***1% Kawistara.
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system in this case.
This method allows for Model 1: The Impact of Security Personnel Table 4.
1 Step 1: Performing Logit Estimation for Propensity Score Matching Estimation Variable Mean Treated Mean Control T-test p>.
Security Public Facility Community institution Gotong royong Readily accessible police station Regulation Police station presence Marginal Dummy work Dummy assistance Table 4.
2 Step 2: Performing Matching with Propensity Score Matching (PSM) Variable Sample Treated Controls Difference T-stat
Crime
Unmatched Crime
ATT
Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
Table 4.
3 Step 3: Comparing PSM Covariates Mean Treated Variable Mean Control %Bias T-test p>.
Security Officer Public Facility Community institution Gotong royong Readily accessible police station Regulation Police station Marginal Dummy work Dummy assistance Covariate Balance Variables security, public facility, community The covariate balance results show the institution, and others also show similar average covariate values for treated and distributions between treated and control control groups before and after matching, along with t-tests and p-values to evaluate indicating successful matching in creating the similarity of covariate distributions.
covariate balance.
non-significant p-values.
Table 4.
4 Step 4: Average Treatment Effect Result Variable Coefficient .
% Conf.
Interva.
Village information system
06439614***
06501578 to -0.
Constant
03101578***
25E-.
03210473 to -0.
The Propensity Score Matching (PSM) before and after matching.
In the unmatched estimation provides a clear picture of the sample, the treated value is -0.
74, while the differences in the main outcome, covariate control value is 0.
071, resulting in a difference In the matched sample (ATT).
The PSM results highlight the the treated value remains -0.
74, but the differences in the main outcome, all crime, control value changes to 0.
181, resulting in a Kawistara.
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This indicates that after Score Matching, the difference in crime matching, the difference between treated rates between villages with and without an and control groups significantly decreases information system significantly decreases.
(Table 4.
Additionally.
The Propensity Score Matching regression results show the linear regression outcomes of the treatment variable after matching to estimate the adjusted treatment between the two groups becomes more The linear regression also shows that the treatment effect on the measured outcome is significant.
Improvements in the village information significant negative effect of the treatment system are significantly related to increased on crime rates.
Village information system crime reporting.
Furthermore.
Hood and variable also indicating a significant negative Di xon .
suggest that an efficient effect on crime rate in rural areas.
The information system boosts administrative effectiveness of adding security officers is efficiency and responsiveness in handling shown by the study of (Bako, 2018.
David, crime cases, making the reporting process 2.
, which indicates that increasing the faster and more accurate.
The integration number of security officers can reduce the of community security systems also has a crime rate due to an increase in patrols and significant impact.
Community participation crime prevention.
Adding security officers in activities like neighbourhood watch can increase residentsAo sense of security, strengthens social networks and collective thereby reducing crime incidents (Bako.
An active community security system empowers residents to engage in Result Overall, indicate that after conducting Propensity environmentAos which in turn increases crime reporting.
Model 2: The Impact of Village Security System Table 5.
1 Step One: Logit Estimation for Propensity Score Estimation Variable Mean Treated Mean Control T-test p>.
Security Public Facility Community institution Gotong royong Easy to access the police Regulation Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
Variable Mean Treated Mean Control T-test p>.
Police station presence Marginal Dummy work Dummy assistance In the first stage, the logit model is The results of logistic regression show used to estimate the propensity score that several variables have a significant based on several variables.
The estimation effect on security with the coefficients and results show that the variables security, standard errors listed.
The variable village public facilities, ease of reaching police information system has a coefficient of 3.
posts, regulations, presence of police posts, which means that an increase in this variable marginal, communication, and work have significantly increases security.
Likewise, a significant influence.
The community the variables public facilities and community institution variable has a significant negative institutions, indicating a significant positive influence, while the community mutual cooperation variable and the aid dummy are analysis shows that several variables have a not significant.
These results indicate that significant effect on security.
Logistic most variables have a significant effect on the propensity score.
Table 5.
2 Step Two: Performing Matching with Propensity Score Matching (PSM) Variable Sample Treated Controls Difference T-stat
Crime
Unmatched Crime
ATT
The results of the covariate balance the treated group and the control group.
evaluation show that most variables have The matching results show that the all crime variable has an average difference between difference between the treated group and the treated group and the control group.
the control group.
The regulation variable In the unmatched sample, the difference is has a variance ratio outside the limits .
201 with a standard error of 0.
001 and a .
, indicating a different variance between t-statistic of 155.
66, indicating a significant t-test Kawistara.
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In the Average Treatment effect on on the treated group is significant but with a the Treated (ATT), the difference is 0.
170 with lower level of confidence than the unmatched a standard error of 0.
110 and a t-statistic of sample (Table 5.
54, indicating that the effect of treatment Table 5.
3 Step Three: Comparing PSM Covariates Balance Mean Treated Variable Mean Control %Bias T-test p>.
Security Officer Public Facility Community institution Gotong royong Regulation Police station presence Marginal Dummy work Dummy assistance Easy to access the police Table 5.
4 Step Four: Average Treatment Effect Result Variable Coefficient .
% Conf.
Interva.
Village information system
0643961***
06501578 to -0.
Constant
0310157***
25E-.
03210473 to -0.
The result showed that improvement The role of information technology in of the security system in the village shows crime prevention is explained by Rabbi et a significant effect on reducing the crime .
, who demonstrate that effective The implementation of the village integration of information systems in the information system, measured through the e-commerce sector can enhance digital village information system has a significant security and prevent criminal activities, correlation with increased reporting of crime The cases so that it can reduce the crime rate in the village (Table 5.
technology supported by strong managerial Wida Reza Hardiyanti and Muhammad Khairil Anwar Ai The Role of Security Personnel and Village Information .
data that effective information sharing is critical protection and operational security but for fostering public trust.
The research also promotes more sustainable and ethical concludes that improving these processes business practices.
Thus, well-implemented is essential for enhancing public confidence and strengthening the effectiveness of operational efficiency but also play a critical community policing in Europe.
Therefore, role in building a secure and responsible residents feel more protected and are more digital ecosystem.
This causes the crime willing to report crimes that occur.
leadershipAinot detection rate to be high so that criminals think twice about committing crimes due to the high probability of being caught and Ultimately, this leads to a decrease in crime rates.
Integration CONCLUSIONS Initial findings from the research indicate that increasing the number of security personnel in rural areas has a significant a higher police presence leads to lower crime This finding is consistent with the to increase community participation in deterrence theory, which suggests that the maintaining environmental security.
This visibility and presence of law enforcement finding is in accordance with research by discourage potential offenders from engaging Felson and Clarke .
which states that in criminal activity.
Furthermore, the study community involvement in local security shows that improving village information systems can significantly reduce crime rates.
systems has a significant positive effect The addition of security officers and on crime reporting.
Villages with better improvements to village information systems information infrastructure saw higher rates have been shown to have a significant impact of crime reporting, which in turn facilitated on increasing crime reporting.
Based on quicker responses from law enforcement the theory of social control put forward and, ultimately, a reduction in crime.
These by Hirschi .
, the stronger the social findings align with the broader literature ties and control in a community, the lower on crime prevention, which emphasizes the the crime rate in the area.
The addition of importance of timely information flow and security officers increases social supervision communication between communities and and formal control in village communities, law enforcement agencies in curbing crime.
negative impact on crime rates, meaning that which in turn reduces opportunities for In addition, the community-based crime eradication model, as studied by Aston et al.
explore the role of information sharing in community policing across Europe and its influence on public confidence in law enforcement.
Result found Kawistara.
Vol.
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Desember 2025: 439Ai457 Implication The implementation of robust village information systems has yielded several important implications for improving public safety and governance at the local level.
Drawing from empirical findings, four key outcomes emerge.
First, crime reporting efficiency has significantly improved.
The adoption of digital systems streamlines the reporting process, reducing communitiesAo reliance on slow or informal communication channels.
As a result, crime reporting has increased 7% ( = 0.
577, p < 0.
, enabling law enforcement agencies to respond more quickly and effectively to incidents.
Policy Recommendation The implications for enhancing rural security in Indonesia.
Investment in village information systems transforms passive communities into proactive, digitally connected networks that support crime reporting.
This transformation creates a virtuous cycle: more reports lead to faster police responses, which build public Second, these systems have led to trust, in turn encouraging more reports.
greater transparency and public trust.
With Furthermore, these systems serve as a real-time tracking and public access to complement to physical security measures crime data, village residents are empowered by offering a form of digital guardianship to monitor institutional actions, fostering a that strengthens the communityAos resilience sense of accountability among authorities.
against crime.
This openness helps reassure residents that their reports will be taken seriously, thereby reducing the tendency to underreport criminal activity.
Third, perceive a higher risk of detectionAia core principle of Deterrence Theory.
more crimes are reported and publicly increases, which in turn has a preventive effect on potential offenders.
Fourth.
Aston.
OAoNeill.
Hail.
and Wooff.
, 2023.
Information sharing in community policing in deterrence through visibility.
By making BIBLIOGRAPHY