<p><em>The Bomba textile is one of the textile fabrics in Indonesia used in a province called Sulawesi Tengah. Bomba Textile has a unique pattern and has a philosophical meaning in human life in Sulawesi Tengah. Bomba Textile has many motif patterns and varied colors. The problem in this research is the difficulty in classifying every The Bomba textile motif in each class. Data classification is needed to recognize the motif of each Bomba textile pattern and to cluster it into the appropriate class. The features used to classify the Bomba textile motif is the textural feature. Texture features obtained from Gray-Level Co-occurrence matrices (GLCM) method consisting of energy, contrast, homogeneity and correlation with four angles 0</em><em>°</em><em>, 45</em><em>°</em><em>, 90</em><em>°</em><em>, and 135</em><em>°</em><em>. This research will implement Quadratic Vector Machine (QSVM) method with texture feature on Bomba textile pattern. The use of a single texture feature with angles 90</em><em>°</em><em> has an accuracy of 90.3%. The incorporation of texture features by involving all features at all angles can improve the accuracy of the classification model. This research produces a model of motif classification on the Bomba textile which has the classification accuracy of 94.6% and error rate of 5.4%.</em></p>

This study aims to utilize artificial neural networks to distinguish batik motifs and non-batik fabric motifs. Several important steps are needed, namely the process of acquiring batik and non-batik images, pre-transforming batik and non-batik images to gray scale forms, texture feature extraction in gray scale images and detection of motifs using networks artificial nerve. Image acquisition is done by collecting batik and not batik images from several different motifs. Processing data sets is divided into 70% as training data and 30% as testing data. Artificial neural network models used in this research use the Backpropagation learning algorithm by comparing the Scaled conjugate gradient algorithm (trainscg) training method and the Levenberg-Marquardt algorithm (trainlm) training method. The results obtained for the accuracy using the Scaled conjugate gradient algorithm (trainscg) training method were higher with an accuracy value of 84.12%, compared to the Levenberg-Marquardt algorithm (trainlm) method by 86.11%.

<p><em>Smart City comes as a strategy to reduce the problem due to rapid urban growth and urbanization. The concept of Smart City is needed to ensure the conditions of a habitable City in the context of rapidly growing urban population growth. The urgency of this challenge prompted many cities to begin to find smarter ways of managing urban areas. One way to make the concept of the smart city is to make the city an icon that is sustainable and livable. This study aims to provide the necessary information in building and developing a city through the smart city approach. This paper clarifies the meaning of the word "smart" in the city context through an approach based on an in-depth literature review of the relevant study. This study will identify the main factors and characteristics that characterize smart cities. The method used to obtain various factors and the characteristics of the Smart City in the arrangement of a region is done by studying various kinds of the literature of various concepts and components in the Smart City. The results obtained in this study there is a concept of Smart City in urban planning by mapping various factors and characteristics in the Smart City. </em></p><p><strong>Keywords</strong><em>: Smart City, Urban planning, smart city characteristic </em></p>

After the enforcement of The Basic Agrarian Law, land law in Indonesia has the character of unification, meaning that only one land law applied that is national land law. Enforcing one particular land law at various society as in Indonesia, can generate injustice. Therefore, the Decision Of People Consultative Assembly Number of IX/MPR/2001 Section 4 letter c opens the opportunity of pluralism in unification sounding: Respecting law supremacy by accommodating varieties in law unification. What is meant by " varieties in law unification" is not clarified.According to the writer, the meaning of varietiesin unification specially land law is : enforcing adjacently two peripheral laws that is, national land law applied all over Indonesia regions and local adat land law. In areas having customary rights, adat law is fully applied adat law, but in areas without customary rights, national land law is use instead.

This study aims to empirically examine the differences of perception between generation (generation gap) in fraud prevention, which was analyzed by seven variables, namely tone of the top, anti-fraud training, code of conduct, whistle blowing system (WBS), segregation of duties, fraud risk assessment and background checking. This study used 398 questionnaires consisting of 149 gen Z respondents, 87 gen X respondents, 154 gen Y/Millennials respondents and 8 Boomers respondents. The analytical method used was Mann-Whitney test by SPSS v.26.0. The results show that there was generation gap between generations Z and X (in the tone of the top variable), between generations Z and Y (in the tone of the top variable and WBS) and between Generations X and Y (in the tone of the top variable). However, there were no generation gap between generation Z and boomers, X and boomers and Z and boomers in the overall fraud prevention variables studied. The limitation of this study was the limited number of respondents, especially from the boomer’s generation. This research is expected to provide benefits for determining anti-fraud prevention strategies that are in accordance with the character of each generation.