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Dengue Fever (DF) continues to be a major public health threat in Indonesia, especially in urban areas with high population density, such as Purwokerto City. This study aims to develop a predictive model to identify high-risk areas for DF outbreaks by integrating Machine Learning (ML) algorithms and Geographic Information Systems (GIS). The research utilizes historical dengue case data, meteorological parameters (rainfall, temperature, humidity), and population density as predictive variables. Three ML classification algorithms—Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM)—were implemented to develop risk prediction models. Extensive data preprocessing, feature selection, and spatial integration were applied to ensure model robustness. The results show that the SVM model outperformed other methods, achieving the highest accuracy, precision, recall, and F1-score in classifying dengue risk zones. Risk maps generated through GIS visualization successfully identify priority areas for targeted interventions. The novelty of this research lies in the combination of local epidemiological data, multi-algorithm comparison, and geospatial mapping to improve early warning systems for DF in Purwokerto. This integrated approach is expected to support more effective prevention strategies and enhance public health preparedness.

Maritime piracy has long been a significant threat to global shipping, with profound economic and legal implications. This journal explores the economic effects and legal actions associated with maritime piracy. The economic impact of piracy is substantial, costing the international economy between $7 billion and $12 billion annually. The rise in piracy has led to higher ransom demands, with some payments reaching record amounts, and has significantly increased the cost of maritime insurance, particularly in high-risk zones. From a legal perspective, the journal examines the evolution of piracy laws, highlighting the United Nations Convention on the Law of the Sea (UNCLOS) and its definition of piracy. The legal framework under UNCLOS includes three main elements: acts committed for private ends, occurring on the high seas, and involving two ships. The journal also discusses the concept of universal jurisdiction, which allows any state to prosecute pirates, though prosecution must follow the domestic laws of the capturing state.The International Maritime Organization (IMO) has introduced several conventions and initiatives, such as the Djibouti Code of Conduct, to enhance maritime security. This journal will focus on the economical effect and the suitable legal actions to punish and prevent the acts of piracy.

The method fuzzy is one part of the fuzzy inference system that is useful for drawing conclusions or the best decision in uncertain problems. Currently, about 200 countries in the world are experiencing the Covid-19 pandemic. Various policies were carried out to overcome this. In addition to the health protocols that are always implemented, the community must also know the conditions of the area where they live so that they can anticipate any activities carried out. The method is used fuzzy to determine the risk zone for the spread of Covid-19 in North Sumatra Province based on 3 variables, namely positive cases, suspected cases, and dead cases. The data used in this study is weekly Covid-19 data from March 2021 to July 2021 for 4 selected areas, namely Medan City, Pematang Siantar City, Simalungun Regency, and Central Tapanuli Regency. All variables are represented using triangular curves and their membership functions are determined. Then the implication function of the min is used to determine the rules used. From the results of the implication function, the composition between all the rules obtained is carried out by taking the maximum rule then that value is used to modify the fuzzy area so that a new membership function is obtained. The method is used centroid to obtain a crisp or the final result is the value of the risk zone. This research also utilizes the Matlab to calculate the results. The results from the use of the Mamdani method will be compared with the real risk zone data so as to produce a 75% average percentage similarity for the data for 4 regions in North Sumatra so it can be concluded that the model made is good enough to determine the risk zone for the spread of Covid-19 in North Sumatra Province.