ISSN 1979-2794
e-ISSN: 2655-6634
OPTIMIZING BUDGET ALLOCATION FOR SUPPORTING
EDUCATIONAL FACILITIES IN ISLAMIC BOARDING SCHOOLS
Muhammad Ali Gunawan1 guns12380@gmail.
Sabilal Rosyad2 sabilfahnisan@yahoo.
Hawa Husna Ab Ghani3 husnaghani@unisza.
Sekolah Tinggi Aagama Islam Ki Ageng Pekalongan.
Pekalongan Sekolah Tinggi Aagama Islam Ki Ageng Pekalongan.
Pekalongan Universiti Sultan Zainal Abidin.
Terengganu.
Malaysia Abstrak Mengoptimalkan alokasi anggaran untuk fasilitas pendidikan di pesantren merupakan langkah penting dalam memastikan kualitas pendidikan yang berkelanjutan.
Penelitian ini bertujuan untuk mengoptimalkan alokasi anggaran guna mendukung fasilitas pendidikan di Pesantren Al-Qutub.
Wonopringgo.
Kabupaten Pekalongan, dengan pendekatan Bayesian Decision Making menggunakan metode Markov Chain Monte Carlo (MCMC).
Data dikumpulkan melalui angket, observasi, dan wawancara untuk memperoleh informasi tentang kondisi fasilitas, alokasi anggaran saat ini, serta kebutuhan tambahan fasilitas.
Analisis data menggunakan metode MCMC, memungkinkan pengambilan keputusan berdasarkan distribusi probabilistik dari data yang terkumpul.
Hasil penelitian menunjukkan bahwa alokasi anggaran yang dioptimalkan melalui model Bayesian menghasilkan distribusi yang lebih efektif, dengan fokus pada prioritas fasilitas yang mendesak dan secara langsung meningkatkan kualitas pendidikan.
Studi ini berkontribusi pada model analisis anggaran yang dapat diterapkan di lembaga pendidikan Islam di Indonesia.
Secara teoretis, penelitian ini memperkaya literatur tentang manajemen anggaran pendidikan di pesantren dengan pendekatan statistik Bayesian.
Secara praktis, penelitian ini membantu pengelola pesantren dalam mengalokasikan anggaran secara lebih efisien dan akurat sesuai dengan kebutuhan pendidikan.
Kata kunci: Optimasi Alokasi Anggaran.
Pesantren.
Pengambilan Keputusan Bayesian.
Markov Chain Monte Carlo (MCMC).
Fasilitas Pendidikan.
Muhammad Ali.
Sabilal.
Hawa.
Optimizing Budget Allocation For SupportingA.
Abstract Optimizing budget allocation for educational facilities in Islamic boarding schools is essential to ensure sustainable education quality.
This study aims to optimize budget allocation to support educational facilities at Al-Qutub Islamic Boarding School.
Wonopringgo.
Pekalongan Regency, using a Bayesian Decision Making approach with the Markov Chain Monte Carlo (MCMC) method.
Data were collected through questionnaires, observations, and interviews to gather insights on facility conditions, current budget allocation, and additional facility needs.
Data analysis employed the MCMC method, enabling decisionmaking based on the probabilistic distribution of the collected data.
The findings indicate that budget allocation optimized through the Bayesian model results in a more effective distribution, addressing urgent facility priorities that directly enhance educational quality.
This study contributes a budget analysis model applicable to Islamic educational institutions in Indonesia.
Theoretically, it enriches the literature on educational budget management in Islamic boarding schools using Bayesian statistical approaches, while practically, it assists school administrators in allocating budgets more efficiently and accurately in line with educational needs.
Keywords: Budget Allocation Optimization.
Islamic Boarding Schools.
Bayesian Decision Making.
Markov Chain Monte Carlo (MCMC).
Educational Facilities.
INTRODUCTION
The effective allocation of financial resources is crucial for Islamic boarding schools, known as Pondok Pesantren, to sustain and improve their educational facilities.
These institutions often face financial constraints due to their reliance on public support and limited internal revenue.
Strategic budgeting is therefore essential to ensure educational goals are met without compromising quality.
1 Although Islamic boarding school make significant contributions to both religious and general education, they often lack the necessary infrastructure to support a modern educational environment.
2 Budget 1 Amos.
Ephrahem.
, & Bhoke-Africanus.
Effectiveness of School HeadsAo Financial Management Skills in Provision of Quality Education in Secondary School.
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allocation in Islamic boarding school is essential not only for facility improvement but also for enhancing teaching quality and student outcomes.
Previous research has explored various methods of budget allocation in educational settings.
A particularly promising approach is Bayesian Decision Theory, which enables probabilistic assessments and dynamic adjustments based on continuously updated data.
4 Studies indicate that Bayesian models, such as those implemented through Markov Chain Monte Carlo (MCMC) methods, offer a more robust decision-making framework, especially in contexts with complex, multi-faceted needs like Islamic boarding 5 These statistical methods are valued for their ability to incorporate prior information and refine decisions over time, making them suitable for environments with financial limitations and diverse facility requirements.
Despite these advancements, a research gap remains in exploring how Bayesian models can optimize budget allocation specifically for Islamic boarding school.
While traditional schools may benefit from such methods.
Islamic boarding school face unique challenges tied to their dual role in providing both religious and general education.
Existing research rarely considers these distinctions, impacting the applicability of conventional budgetary models to Islamic boarding school.
Additionally, most studies have yet to adequately examine how Bayesian frameworks can directly support budget allocation decision-making in religiously affiliated institutions.
3 Mukti.
Budianti.
, & Hamdani.
The Financial Aspects of Islamic Education (An Idea to Improve the Quality of Islamic Education in Islamic Boarding School.
International Journal for Educational and Vocational Studies, 2.
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4 Francesco.
Di.
Bayesian Data Analysis of Imperfect Information for Decision-Theoretic Approaches to Structural Integrity Management.
5 Hanada.
, & Matsuura.
MCMC from Scratch: A Practical Introduction to Markov Chain Monte Carlo.
In MCMC from Scratch: A Practical Introduction to Markov Chain Monte Carlo.
Springer Nature.
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6 Lundeto.
Talibo.
, & Nento.
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AL-ISHLAH: Jurnal Pendidikan, 13.
, 2231Ae2240.
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org/10.
35445/alishlah.
Muhammad Ali.
Sabilal.
Hawa.
Optimizing Budget Allocation For SupportingA.
Addressing this gap is urgent given the role of Islamic boarding school in Indonesia's socio-educational landscape.
With increasing demands for educational quality and facility management.
Islamic boarding school administrators require reliable tools to effectively optimize their limited resources.
7 By focusing on budget allocation for educational facilities, this study responds to the pressing need for a systematic and evidence-based approach, specifically tailored to Islamic educational settings.
optimized budget framework suited to the unique structure and needs of Islamic boarding school is not only necessary but timely, considering evolving educational standards.
This study contributes to the field by proposing a practical model for budget allocation that leverages Bayesian Decision-Making through MCMC, tailored to the needs of Islamic boarding school.
Addressing the challenges of facility management and financial resource allocation in Islamic boarding school, this study aims to provide a replicable model that can benefit similar institutions across Indonesia and beyond.
The model is designed to support decision-makers in Islamic boarding school by providing a structured approach to budget distribution based on probabilistic data, thus reducing uncertainty and maximizing the utility of limited funds.
The primary objective of this study is to evaluate and implement a Bayesian approach to optimize budget allocation for educational facilities at Al-Qutub Islamic Boarding School in Wonopringgo.
Pekalongan.
This includes a practical application of MCMC to address various budgetary needs and facility requirements.
By combining Bayesian Decision Theory with real-world data from Islamic boarding school, this study aims to establish a robust decision-making framework that meets both current and future facility needs, supporting long-term educational improvements in Islamic boarding 7 Sulastri.
Sari.
Marlina.
Wijaya.
, & Hidayah.
Operational Financial Management of JamiAoatul QuraAowal Hufadz Islamic Boarding School Palembang.
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Linguistics, and Literature, 2.
, 181Ae190.
https://doi.
org/10.
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METHOD
This study utilized a mixed-methods approach, combining quantitative and qualitative data collection to assess the budgetary needs for educational facilities at AlQutub Islamic Boarding School.
Wonopringgo.
Pekalongan.
Quantitative data was obtained through structured questionnaires administered to administrators, teachers, and facility management staff, providing insights into current budget allocations and perceptions of facility needs.
This structured data served as a foundation to assess financial In addition, qualitative data from in-depth interviews and observations provided nuanced perspectives on operational challenges, facility requirements, and perceived impacts on education quality, aligning with literature that emphasizes multisource data for complex educational assessments.
8 This mixed-method approach allowed the study to capture both measurable data and contextual insights, essential for implementing the Bayesian Decision-Making framework.
Data analysis employed the Bayesian Decision-Making framework through the Markov Chain Monte Carlo (MCMC) method, executed in R for precise modelling.
R was selected for its statistical libraries that support Bayesian analysis, such as AurjagsAy and AuStanAy, enabling MCMC simulations to estimate optimal posterior distributions.
Recent studies have highlighted Bayesian methods' effectiveness in refining budget allocations under uncertainty, especially in resource-limited educational settings.
9 Using these tools, the model iteratively simulated budget allocation scenarios, allowing a probabilistic assessment of outcomes to address varying facility needs.
The MCMC method allowed the model to generate dynamic budget distributions that reflect the institutionAos unique 8 de Villiers.
Farooq.
, & Molinari.
Qualitative research interviews using online video technology Ae challenges and opportunities.
Meditari Accountancy Research, 30.
, 1764Ae1782.
https://doi.
org/10.
1108/MEDAR-03-2021-1252
Rens van de Schoot et al.
AuBayesian Statistics and Modelling,Ay Nature Reviews Methods Primers 1, no.
, https://doi.
org/10.
1038/s43586-020-00001-2.
Muhammad Ali.
Sabilal.
Hawa.
Optimizing Budget Allocation For SupportingA.
priorities, aligning with research that supports Bayesian methods for flexible, datainformed decisions in educational institutions.
The analysis process in R also involved comprehensive pre-processing of data, ensuring reliability for Bayesian analysis by handling missing values, standardizing responses, and merging datasets.
Visualization tools, particularly Auggplot2Ay and AubayesplotAy, facilitated interpretable representations of budget priorities, with recent studies recommending visual aids for decision-making in educational contexts to ensure accessible interpretations of complex data.
11 RAos adaptability thus supported a structured,12 reproducible approach to model validation,13 convergence checks,14 and output visualization,15 reinforcing the studyAos objectives of evidence-based, actionable recommendations for budget allocation.
This method provides a scalable model applicable to similar educational settings facing financial constraints.
RESULTS AND DISCUSSION
Before proceeding with further analysis, the descriptive statistics from the collected data provide essential insights into the fundamental characteristics of the study Table 1 presents a summary of the mean, standard deviation (SD), naive standard error (Naive SE), and time-series standard error (Time-series SE) for the primary 10 Tolba.
Ahlam.
Bayesian and Non-Bayesian Estimation Methods for Simulating the Parameter of the Akshaya Distribution.
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11 Tomaszewski.
Zarestky.
, & Gonzalez.
Planning Qualitative Research: Design and Decision Making for New Researchers.
International Journal of Qualitative Methods, 19.
https://doi.
org/10.
1177/1609406920967174
Barry J.
Grant.
Lars Skjyrven, and Xin Qiu Yao.
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Cheung et al.
Reporting Reliability.
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model parameters: mu .
ean perceptio.
and sigma .
erception variatio.
Additionally.
Table 1 shows the quantiles for each variable, including the median .
%) and quantile range .
5% to 97.
5%), which helps to understand the probability distribution of the data.
Table 1.
Descriptive Statistic and Quantiles for each variable Descriptive Statistic Mean Naive SE Time-series SE 2,720 0,148 0,001 0,001 1,470 0,107 0,001 0,001 Quantiles for each variable 2,50% 97,50% 2,431 2,621 2,721 2,82 3,003 1,277 1,395 1,462 1,538 1,693 Table 1 above, shows a mean .
value of 2.
720, indicating that respondentsAo perceptions are generally slightly above the midpoint on the scale used, suggesting a neutral to moderately positive tendency regarding the observed variable.
The standard deviation .
470 reflects moderate variation in respondentsAo perceptions, highlighting differences in their assessments or experiences related to facility budget allocation.
The naive standard error (Naive SE) for mu is 0.
001, suggesting that the sample mean provides a fairly accurate estimate of the population mean.
The time-series standard error (Timeseries SE) is similarly close, reinforcing the consistency of the sample mean from the collected data.
The quantile distribution for mu and sigma also demonstrates consistency and data The median .
%) of mu is 2.
721, aligning with the mean, while the 25% to 75% quantile range lies between 2.
621 and 2.
82, indicating that most respondents fall within a similar perception range.
For sigma, the median value is 1.
462, with the 25% to 75% quantile range between 1.
395 and 1.
538, showing a somewhat centralized distribution with acceptable variability.
The 2.
5% to 97.
5% quantile range further highlights the extreme data values without showing significant outliers, supporting the stability of results within the Bayesian model applied.
Muhammad Ali.
Sabilal.
Hawa.
Optimizing Budget Allocation For SupportingA.
To verify the convergence and posterior distribution of the primary parameters analyzed, namely mu .
ean perceptio.
and sigma .
erception variabilit.
, the following plots display the trace and density plots from the MCMC analysis for these parameters.
The trace plot examines whether the MCMC process has achieved convergence, while the density plot shows the shape of each parameterAos posterior distribution, indicating the most likely range of values.
These visualizations help confirm that the Bayesian model estimates are stable and reliable, supporting decision-making in facility budget allocation.
Figure 1.
Trace Plot and Density Plot Figure 1 shows the MCMC analysis results for the parameters mu and sigma.
The trace plots on the left indicate convergence for both parameters, mu and sigma, which appear stable and do not exhibit any patterns or trends, suggesting that the MCMC process has achieved good convergence.
The posterior distribution for mu .
peaks around 2.
while the distribution for sigma .
peaks around 1.
The density plots on the right display bell-shaped .
posterior distributions for both parameters, showing that the Istifkar: Jurnal Pendidikan Islam Vol.
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parameter estimates are within a stable and centered range without extreme dispersion.
This indicates that the data has a distribution centered around specific values with moderate variation, making it reliable for further decision-making within the Bayesian As a follow-up, the following posterior distribution plots display the estimated parameters mu and sigma to validate the analysis results.
Figure 2.
Posterior Distribution of Mu and Sigma The above plots show the posterior distributions for the parameters mu .
n the lef.
and sigma .
n the righ.
obtained from Bayesian analysis.
The distribution of mu peaks around 2.
7, indicating that the most likely mean value for this parameter is approximately at this point, with a range from about 2.
4 to 3.
The distribution of sigma peaks around 1.
4, reflecting moderate variability in the data, with a range from 2 to 1.
The symmetric bell-shaped form in both plots suggests a nearly normal distribution, indicating stable and consistent estimates for the mean and variability This implies that the Bayesian model has successfully provided reliable parameter estimates for further interpretation in the context of budget allocation and educational facility prioritization.
Muhammad Ali.
Sabilal.
Hawa.
Optimizing Budget Allocation For SupportingA.
Figure 3.
Distribution of Facility Urgency Perceptions Figure 3 above shows the distribution of facility urgency perceptions in an educational institution, measured on a Likert scale from 1 to 5.
In this chart, we can see those perceptions of facility urgency vary among respondents, with the majority tending to rate urgency at a low level .
, with the highest frequency around 30 respondents.
rating of 4 also shows a significant frequency, while the middle value .
and high value .
have relatively fewer respondents.
This distribution pattern indicates that most respondents consider facility improvements or upgrades as a low priority, or they may have limited urgent needs related to facilities.
However, the spread across the scale suggests that a small portion of respondents still view facility enhancements as an important, immediate priority.
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Figure 4.
Boxplot of Budget Adequacy The boxplot above illustrates respondents' perceptions of budget adequacy in an institution, using a Likert scale from 1 to 5.
The median perception value is 3, indicating that most respondents have a neutral view on budget adequacy, neither too low nor too The interquartile range (IQR) from the first quartile .
to the third quartile .
encompasses most respondentsAo perceptions, suggesting moderate variation in budget adequacy assessments.
The range from 1 to 5 shows that some respondents find the budget highly inadequate .
, while others perceive it as very adequate .
This distribution indicates that, while the majority of respondents have a neutral or moderately positive view on budget adequacy, there is significant variation in perceptions, which may influence future budget allocation planning decisions.
The following histogram illustrates respondents' perceptions of facility urgency, providing an initial overview of budget allocation priorities.
Muhammad Ali.
Sabilal.
Hawa.
Optimizing Budget Allocation For SupportingA.
Figure 5.
Comparison of Posterior Means for Budget Allocation Scenarios The chart above shows a comparison of posterior means for three budget allocation priority scenarios: budget adequacy, facility urgency, and impact on quality.
The results indicate that the highest posterior mean is in the budget adequacy scenario, followed by impact on quality, with facility urgency being the lowest.
This suggests that, based on the Bayesian analysis results, the Budget Adequacy scenario is considered most important by the model, indicating that sufficient budget allocation is viewed as the top The impact on quality scenario also has a high posterior mean, underscoring the importance of the budgetAos effect on educational quality.
Meanwhile.
Facility Urgency has a lower priority within this budget distribution.
This interpretation can support decisionmaking in budget allocation, with a primary focus on budget adequacy and its direct impact on educational quality.
DISCUSSION
The analysis results indicate that perceptions of budget adequacy hold a high priority in budget allocation.
This suggests that the majority of respondents consider Istifkar: Jurnal Pendidikan Islam Vol.
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adequate budget allocation to meet educational facility needs to be essential.
This study aligns with findings from,16 which show that effective budget allocation significantly impacts educational quality in resource-constrained environments.
When budget adequacy becomes a priority, it implies that basic needs must be fulfilled before moving towards further quality improvements.
In the context of Islamic boarding school, which generally have limited funding, it is crucial to ensure that available resources cover essential needs.
The posterior distribution results reveal that impact on quality, or the effect of the budget on educational quality, is also a high priority after budget adequacy.
These findings highlight the importance of allocating the budget not only to meet basic needs but also to directly enhance quality.
Research by shows that budget management focused on quality improvement yields significant impacts on student satisfaction and academic outcomes.
Improved educational quality through proper budget allocation not only enhances the learning environment but also boosts student motivation and teacher satisfaction, contributing to better overall results.
On the other hand, facility urgency ranks lower in priority within the Bayesian Although perceptions of facility urgency are important, the data shows that respondents consider facility needs as something that can be delayed compared to budget adequacy and impact on educational quality.
This perception arises from the belief that facilities can be gradually improved or upgraded without directly affecting the educational This finding is supported by research from which indicates that prioritizing facilities often depends on specific situations and may not always be a primary factor in Islamic education environments.
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Hawa.
Optimizing Budget Allocation For SupportingA.
According to an interview with the management of Al-Qutub Islamic boarding school, one respondent stated.
AuFacilities are important, but as long as basic needs, such as teacher salaries and class operations, are met, facility improvements can wait.
We always try to ensure that teaching and teacher welfare are the top priorities in budget allocation.
Ay This statement aligns with the analysis results, indicating that as long as basic needs are met, the primary focus can be directed toward quality and operational aspects of education rather than facility upgrades.
The histogram results for facility urgency perceptions show that most respondents rate the urgency of facilities as low.
This distribution confirms that the majority of respondents do not feel an urgent need for facility improvements at the moment.
This is consistent with research suggesting that in Islamic boarding school settings, there is often a greater focus on the substance of teaching than on physical infrastructure.
19 Physical facilities that are perceived as less urgent can be a reasonable choice in a resourceconstrained environment where administrators prioritize direct teaching aspects.
In an interview with the management of Al-Qutub Islamic boarding school, one administrator stated.
AuWe understand the importance of adequate facilities, but for us, the primary priority is ensuring that teaching and learning activities run smoothly.
Facility improvements will be carried out gradually as budget allows.
Ay This statement reflects a common view among Islamic boarding school administrators, who prioritize the smoothness of the learning process and teaching quality over physical facility This shows that, as long as educational goals are met, facility improvements can be postponed or carried out gradually according to budget constraints.
The boxplot analysis for budget adequacy shows that respondents hold neutral to moderately positive views on existing budget adequacy.
This indicates that most basic operational needs are met, though there is room for improvement.
These findings suggest Adri Lundeto.
Ishak Talibo, and Shinta Nento.
AuChallenges and Learning Strategies of Islamic Education in Islamic Boarding Schools in the Industrial Revolution Era 4.
0,Ay AL-ISHLAH:
Jurnal Pendidikan 13, no.
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35445/alishlah.
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that Islamic boarding school have enough funds to cover basic operations but may lack sufficient funds for expansion or further facility upgrades.
Research by confirms that Islamic boarding school often need to prioritize their budgets very carefully to ensure that basic operational needs remain the main focus.
In the context of the Bayesian model, the emphasis on budget adequacy and impact on educational quality demonstrates an approach consistent with literature on decision-making in resource-constrained educational environments.
Bayesian Decision Theory allows Islamic boarding school administrators to assess needs and priorities probabilistically, helping to address uncertainty in budget allocation With this model.
Islamic boarding school can allocate funds optimally by considering the probability of more beneficial outcomes, making the allocation more efficient and relevant to fundamental needs.
The scenario comparison in this analysis also highlights the flexibility of Bayesian Decision-Making in adjusting budget allocation priorities according to different contextual In this scenario, budget adequacy and impact on quality receive primary attention, while facility urgency ranks lower.
This flexibility allows Islamic boarding school to make adaptive decisions aligned with changing conditions and short-term or long-term needs.
This is crucial because conditions in Islamic boarding school can vary widely depending on economic factors, student numbers, and community support, as explained by in the context of Islamic educational settings.
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Optimizing Budget Allocation For SupportingA.
Theoretically, this study enriches the literature on educational budget management in Islamic boarding schools by demonstrating the application of Bayesian Decision Making through the MCMC method, highlighting its potential in handling budget allocation under resource constraints and uncertainty.
Practically, the findings provide a replicable budgeting model that administrators of Islamic boarding schools can adopt to prioritize essential educational needs effectively, ensuring that limited funds are allocated to areas with the most significant impact on educational quality.
This approach not only supports better financial decision-making but also aligns with the institutional goals of sustaining both religious and academic education within a constrained budget environment.
CONCLUSION
The analysis results indicate that budget adequacy and its direct impact on educational quality are the top priorities, while facility urgency ranks lower.
These findings confirm that the Bayesian model helps Islamic boarding school allocate their budgets more efficiently and effectively, thereby supporting the sustainability and overall improvement of educational quality.
A limitation of this study lies in its focus on a single Islamic boarding school, so the results may not fully represent all Islamic boarding school.
Future research is recommended to expand the sample and include additional variables, such as stakeholder participation, to gain a more comprehensive view of budget allocation needs within Islamic boarding school environments.
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