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The paper is aimed to analyze the penal proportionality in Indonesia's environmental legislation. Primary data were collected from statutes in Indonesia's environmental legislation. The result showed that penal proportionality relies on the idea that the severity of criminal sanction needs to be proportionate to both the crime seriousness and culpability of the actor. The more serious the offense, the heavier the punishment. The environmental legislation failed to meet penal proportionality due to its inability to reckon the crime seriousness in determining the scale/weight of criminal sanction. To set penal proportionality, offenses in environmental legislation need to be organized based on their seriousness which requires a corollary of rank-ordering, where less serious offenses do not need to be sentenced with greater severity than the more serious ones. The models of criminalization-based environmental damage meet this principle, hence spacing of criminal sanction among the offenses rank need to be formulated to ensure the application of penal proportionality.

The issue of poor local government capacity has been considered one of the problems impeding the implementation of decentralization, especially in developing countries. This study addresses this issue in the case of Indonesia, a country that has implemented massive decentralization (administrative, fiscal, and political) over the last two decades. The study aims to provide empirical evidence for the impact of local government capacity on public service delivery in the decentralization regime. Local government capacity is measured based on a policy capacity framework at the organizational level that includes three types of capacities: analytical, operational, and political. The regression of the panel data model, estimated with the Hausman-Taylor method, reveals that government capacity in terms of interactions of three types of capacities has a positive impact on public service delivery. This finding indicates the three types of capacities are complementary and effectively improve local government's achievement in delivering public services. While operational capacity (including fiscal capacity) has long been reckoned in designing decentralization, this result gives empirical evidence that other critical capacities should be well considered, political and analytical capacities. It underpins the efforts to internalize local government capacity in designing and implementing decentralization programs.

Primates occur in 90 countries, but four—Brazil, Madagascar, Indonesia, and the Democratic Republic of the Congo (DRC)—harbor 65% of the world’s primate species (439) and 60% of these primates are Threatened, Endangered, or Critically Endangered (IUCN Red List of Threatened Species 2017-3). Considering their importance for global primate conservation, we examine the anthropogenic pressures each country is facing that place their primate populations at risk. Habitat loss and fragmentation are main threats to primates in Brazil, Madagascar, and Indonesia. However, in DRC hunting for the commercial bushmeat trade is the primary threat. Encroachment on primate habitats driven by local and global market demands for food and non-food commodities hunting, illegal trade, the proliferation of invasive species, and human and domestic-animal borne infectious diseases cause habitat loss, population declines, and extirpation. Modeling agricultural expansion in the 21st century for the four countries under a worst-case-scenario, showed a primate range contraction of 78% for Brazil, 72% for Indonesia, 62% for Madagascar, and 32% for DRC. These pressures unfold in the context of expanding human populations with low levels of development. Weak governance across these four countries may limit effective primate conservation planning. We examine landscape and local approaches to effective primate conservation policies and assess the distribution of protected areas and primates in each country. Primates in Brazil and Madagascar have 38% of their range inside protected areas, 17% in Indonesia and 14% in DRC, suggesting that the great majority of primate populations remain vulnerable. We list the key challenges faced by the four countries to avert primate extinctions now and in the future. In the short term, effective law enforcement to stop illegal hunting and illegal forest destruction is absolutely key. Long-term success can only be achieved by focusing local and global public awareness, and actively engaging with international organizations, multinational businesses and consumer nations to reduce unsustainable demands on the environment. Finally, the four primate range countries need to ensure that integrated, sustainable land-use planning for economic development includes the maintenance of biodiversity and intact, functional natural ecosystems.

Shrimps are aquaculture products highly needed by the people and this is the reason their growth needs to be monitored using underwater digital images. However, the large dimensions of the shrimp digital images usually make the processing difficult. Therefore, this research focuses on reducing the dimensions of underwater shrimp digital images without reducing their information through the application of the Principal Component Analysis (PCA) algorithm. This was achieved using 4 digital shrimp images extracted from video data with the number of columns 398 for each image. The results showed that 12 PCs were produced and this means the reduced digital images with new dimensions have 12 variable columns with data diversity distributed based on a total variance of 95.61%. Moreover, the original and reduced digital images were compared and the lowest value of MSE produced was 94.12, the minimum value of RMSE was 9.54, and the highest value of PSNR was 8.06 db, and they were obtained in the 4th digital image. The experiment was conducted using 3 devices which include I3, I7, and Google Colab processor computers and the fastest computational result was produced at 2.1 seconds by the Google Colab processor. This means the PCA algorithm is good for the reduction of digital image dimensions as indicated by the production of 12 PC as the new variable dimensions for the reduced underwater image of shrimps.

Klayar Beach, a key site within Indonesia's Gunung Sewu UNESCO Global Geopark, faces growing tourism pressures threatening its ecological integrity. This study evaluates sustainable visitation thresholds by integrating carryin g capacity analysis with beach quality assessment, addressing critical gaps in coastal geopark management literature. Using a descriptive-quantitative method, the study integrates beach carrying capacity analysis, including Physical Carrying Capacity (PCC), Real Carrying Capacity (RCC), and Effective Carrying Capacity (ECC), with beach quality indicators, such as accessibility, environmental quality, comfort, and infrastructure. Data were collected using field observations, surveys, GIS mapping, and stakeholder interviews. The result showed that while the beach has high environmental quality (score: 0.93), optimal water quality (1), and no industrial pollution, vendor waste management requires improvement. Poor facilities (score=0.5) are due to inadequate restrooms, limited shade, insufficient safety equipment, and significant limitations in facilities and comfort. Although the PCC exceeds 113,000 visits/day, the ECC is significantly lower at 6,060 visits/day, showing a gap between physical capacity and management capability. The beach was divided into five zones with distinct characteristics and capacities: Zones 1-2 show high development potential (ECC 547-256 visits/day), while Zones 4-5 require strict conservation measures (ECC 105-68 visits/day). The study shows the need for visitor limits, improved infrastructure, and community-based tourism strategies to balance conservation with economic growth. By aligning visitor management with zone-specific capacities, Klayar Beach can mitigate over-tourism and enhance sustainable use. This approach can serve as a model for other coastal geoparks facing similar challenges, promoting long-term environmental and socio-economic resilience. In this regard, a novel framework was provided for integrating carrying capacity and beach quality assessments in geopark contexts, contributing to more sustainable coastal tourism management in Indonesia and beyond.

The Javan gibbon ( Hylobates moloch ) is endemic to the island of Java and its distribution is restricted from the western tip of Java to the Dieng Mountains in Central Java. Unlike the other known habitats that hold a large population of Javan gibbons, the Dieng Mountains have not been protected and experience various threats. This study, which was conducted in 2018 and 2021, aimed to provide an update of the current density and population size of Javan gibbons in Dieng after the most recent study in 2010 and to investigate their relationships with habitat characteristics (vegetation and elevation). The triangulation method and a new acoustic spatial capture-recapture method were used to estimate group density. A new approach for extrapolation, based on the habitat suitability model, was also developed to calculate population size. The results show that the Javan gibbon population in the Dieng Mountains has most likely increased. The mean group density in each habitat type was high: 2.15 groups/km 2 in the low suitable habitat and 5.55 groups/km 2 in the high suitable habitat. The mean group size (3.95 groups/km 2 , n = 20) was higher than those reported in previous studies. The overall population size was estimated to be 1092 gibbons. This population increase might indicate the success of conservation efforts during the last decade. However, more effort should be made to ensure the long-term future of this threatened species. Although the density significantly differed between habitat suitability types, it was not influenced by the vegetation structure or elevation. A combination of multiple variables will probably have a greater effect on density variation.

The COVID-19 pandemic was impacting the health and economy around the world. All countries have taken measures to control the spread of the epidemic. Because it is not known when the epidemic will end in several countries, then the prediction of the COVID-19 pandemic is a very important challenge. This study has predicted the temporal evolution of the COVID-19 pandemic in several countries using the logistic growth model. This model has analyzed several countries to describe the epidemic situation of these countries. The time interval of the actual data used as a comparison with the prediction results of this model was starting in the firstly confirmed COVID-19 cases to December 2020. This study examined an approach to the complexity spread of the COVID-19 pandemic using the logistic growth model formed from an ordinary differential equation. This model described the time-dependent population growth rate characterized by the three parameters of the analytical solution. The non-linear least-squares method was used to estimate the three parameters. These parameters described the rate growth constant of infected cases and the total number of confirmed cases in the final phase of the epidemic. This model is applied to the spread of the COVID-19 pandemic in several countries. The prediction results show the spread dynamics of COVID-19 infected cases which are characterized by time-dependent dynamics. In this study, the proposed model provides estimates for the model parameters that are good for predicting the COVID-19 pandemic because they correspond to actual data for all analyzed countries. It is based on the coefficient of determination, R2, and the R2 value of more than 95% which is obtained from the non-linear curves for all analyzed countries. It shows that this model has the potential to contribute to better public health policy-making in the prevention of the COVID-19 pandemic.

A simple model for predicting Coronavirus Disease 2019 (COVID-19) epidemic is presented in this study. The prediction model is presented based on the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to describe the epidemic time evolution of infectious diseases. The original version of the Kermack and McKendrick model is used in this study. This included the daily rates of infection spread by infected individuals when these individuals interact with a susceptible population, which is denoted by the parameter β, while the recovery rates to determine the number of recovered individuals is expressed by the parameter γ. The parameters estimation of the three-compartment SIR model is determined through using a mathematical sequential reduction process from the logistic growth model equation. As the parameters are the basic characteristics of epidemic time evolution, the model is always tested and applied to the latest actual data of confirmed COVID-19 cases. It seems that this simple model is still reliable enough to describe the dynamics of the COVID-19 epidemic, not only qualitatively but also quantitatively with a high degree of correlation between actual data and prediction results. Therefore, it is possible to apply this model to predict cases of COVID-19 in several countries. In addition, the parameter characteristics of the classic SIR model can provide information on how these parameters reflect the efforts by each country to prevent the spread of the COVID-19 outbreak. This is clearly seen from the changes of the parameters shown by the classic SIR model.

This study aims to examine the legislation and execution of fines weight formulation and alternative sanctions for economic crimes offenders using doctrinal and empirical legal research. The results showed that the Economic Laws for natural persons set fines ranging from IDR 5-200 billion. Corporations have unequal fine patterns, hence the maximum fine weight for individuals and corporations violated the principle of punishment proportionality. The implemented fine weight does not follow the rules and is similar for individual and corporate prisoners without adapting the perpetrator's characteristics and offenses. As a result, fine execution by the public prosecutor was ineffective because inmates prefer to serve short prison sentences than pay state treasury fines. The convicts did not pay the fines and preferred a prison sentence for various reasons ranging from the large fines to economic consideration. Hence, the rules of the fine should focus on the convict's possibility to pay imposed fines executed by the public prosecutor and consider the nature of the perpetrators and offenses.