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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.

Beberapa permainan yang telah dibuat oleh komunitas baik dalam tingkat pemula maupun tingkatmahir dengan menggunakan fitur perangkat bantu Construct 2.0 menjadi tantangan tersendiri. Beberaparancangan desainer permainan berbasis construct memiliki fitur layout yang bervariasi ragam sertamenyebabkan kebutuhan kapasitas memori yang lebih besar. Upaya yang akan dijadikan tematik padapenelitian in adalah pengamatan terhadap kapasitas kebutuhan memori khususnya sebagai upayaminimalisasi kapasitas memori yang dibutuhkan dengan menggunakan teknik single layout multilayer,dibandingkan dengan penggunaan teknik sebelumnya yaitu multi layout dan multilayer. Hal ini akandikomparasi dalam model tabulasi terkait. Adapun tujuan dalam penelitian pada kesempatan kali ini adalahMengimplementasikan salah teknik animasi gambar dengan teknik single layout. Mengetahui implementasiteknik single layout multilayer. Adapun kesimpulan yang dapat ditarik berdasarkan hasil dan analisapenelitian ini bahwa Teknik single layout multi layer mampu didesain dengan baik dengan menggunakanperangkat Construct 2.0, namun dalam pengamatan terhadap kapasitas memori sejauh pengamatan tidakmemberikan pengaruh secara signifikan, dimana hal ini dipengaruhi oleh beberapa hal diantaranya: sejumlahresolusi gambar yang terlibat (karakter, latar belakang, animasi), sejumlah media audio yang terlibat,sejumlah animasi dari sejumlah framenya, sejumlah event-action yang terlibat dalam permainan(kompleksitas permainan).

Academic data increases every year in line with the increase of students. Abundant data store is alsoan abundance of information. Data mining technology is a tool for extracting information on largedatabases and has been widely used in many domains. Predicting student performance (study evaluation) isan activity to determine a future state based on existing data. Data in the field of academic research hasbeen done with various methods and algorithms, but the use of algorithm SLIQ (Supervised Learning InQuest) has not been done.SLIQ is an algorithm developed by the IBM's Quest project team in 1996 for mining large datasets.SLIQ algorithm classify and predict the students performance, beginning with the data cleaning, conductedelection training and testing data. By calculating gini index of each attribute and then selecting thesmallest gini index data table is split according to the criteria until find the same class. From the results ofthe calculation process can produce a set of rules that can be used to predict student performance.From the experiment it can be concluded that the algorithm SLIQ with decision tree technique canbe used as an alternative in designing a system datamining applications. Tests conducted system showedthat the constructed model can be used to predict the performance of new students. The resulting accuracyof the model system in fact has a lower score than the accuracy of other applications that are used as acomparison of Tanagra. Advantages of the proposed system is in its design does not need complexcalculations in obtaining the gini index attributes.

Decision support systems based on fuzzy logic may be used to solve problems to find optimum solution on  fuzziness environmental. The differences with old decision support system is we can find optimum solution that more realities, because in fuzzy logic not use absolute  truthful right or false. Goal of this article is how to construct model base on decision support system based on  fuzzy logic.