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This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.

BLDC motor applications used in various forms in instrumentation, robotics, household, and transportation. One application of transportation equipment used as a propeller of electric bicycle vehicles. The value of the bicycle vehicle adjusted to the speed set, the amount that has determined. The purpose used in this study is to improve the efficiency of the regulation of BLDC motors on electric bicycles. Indicators of increasing performance are increasingly steady-state errors, and transient response required. The method used in this research is to do mathematical modeling in the form of transfer and optimization function equations. The model used is the model with the structure of the transfer function, while the optimization method used in this study is the Ziegler-Nichols method and firefly algorithm. The firefly algorithm is used in this study to obtain optimal Kp, Ki, and Kd values. The results showed that the firefly algorithm achieved better performance compared to the Ziegler-Nichols method.

Decision Support System couple the intellectual resource of individuals with the capabilities of the computer to improve the quality of decision. It's a computer based support system for management decision makers who deal with semi-structured problem. An integrated decision support system for use in an machine mollen product has been developed. It incorporates a linear Programming model that represents the contribution optimal and optimizes the production water pump and mollen machine. An optimization model is performed using a management scient model called linear programming approach in older to determine media selection. To use this model, the DSS needs ti interface with another software. Mathematical Programming is a technique used in mathematical models, particularly optimization models, to assist in decision making. The Simplex Method is "a systematic procedure for generating and testing candidate vertex solutions to a linear program." (Gill, Murray, and Wright, p. -137) It begins at an arbitrary corner of the solution set. At each iteration, the Simplex Method selects the variable that will produce the largest change towards the minimum (or maximum) solution. The  development of computer programs to be used as Decision Support Systems involves several tasks such as mathematical modeling, technical and data collection and development of a user friendly interface.

When creating databases for GIS-applications often existing maps are scanned and vectorised for used. However, vectorisation becomes obsolete when GIS-objects can be referred to both in theme and geometry in a raster environment. This article shows to use model spatial data raster and vector for GIS - applications in both the graphical and image structure. Geographical data must first be converted into a computer- readable format before it can be used in a GIS. Spatial data are "elements that can be stored in map form." These elements correspond to a uniquely defined location on the Earth's surface. Spatial data have also been describe as “any data concerning phenomenon a really distributed” in two or more dimensions. (Peuquet and Marble, I990.) Data model is the rules to convert real geographical variation into discrete objects. There are two main GIS data models - vector and raster. Each of the two data models has specific types of data, analysis and displays that can handle better than the other system. The vector model represents geographical reality as a series of discrete objects or features, classified as points, line's or areas (polygons). The geographical co-ordinates describing the locations of these features are stored in the computer database which lies at the heart of the GIS. In the raster model a regular grid of cells, or pixels, is used to encode the features found on the earth's surface. Each pixel has a number associated with it representing; the value of a geographical phenomenon, such as terrain elevation, soil type or biomass. Layers of raster grids covering the same region can be built up to represent further variables.