Konstruksi Pola Fraktal Berdasarkan Bentuk Dasar Persegi Menggunakan Transformasi Affine

Abstrak: Geometri fraktal, juga dikenal sebagai "geometri alam", adalah jenis geometri yang mempelajari geometri tidak beraturan. Karakteristik utama dari geometri fraktal adalah self-similarity, yaitu bagian lain dari fraktal memiliki bentuk yang serupa pada skala yang berbeda. Penelitian ini bertujuan untuk membangun pola fraktal berdasarkan bentuk dasar persegi dan menggunakan dua jenis transformasi affine, yaitu dilatasi dan translasi. Parameter yang dapat diubah untuk transformasi adalah skala. Implementasi pembuatan program dilakukan dengan menggunakan bahasa pemrograman Python. Dengan membandingkan hasil dari enam iterasi untuk skala 0,5 dan 0,45, diperoleh perbedaan secara visual baru terlihat jelas dari iterasi 3.Kata kunci: geometri fraktal, transformasi, persegiAbstract: Fractal geometry, also known as "natural geometry", is a type of geometry that studies irregular geometries. The main characteristic of fractal geometry is self-similarity, i.e. other parts of the fractal have a similar shape at different scales. This study aims to build a fractal pattern based on a basic shape of a square and use two types of affine transformations, which are dilation and translation. The parameter that can vary for the transformation is the scale. The implementation of making the program is carried out using the Python programming language. By comparing the results of the six iterations for a scale of 0.5 and 0.45, the visual differences are only clearly visible from iteration 3.Keywords: fractal geometry, transformation, squar

Hierarchical Neural Network Implementation: Emotion Recognition for Food Security Comments on Twitter

Modern Hierarchical Neural Network (HNN) implementation combines several deep learning algorithms working together, connected in a hierarchy layer. For this HNN architecture to work well, the problem and the data must be in a hierarchical format. Emotion recognition is the best example of a layered problem where each emotion is attached to a sentiment. This research proposes an HNN model to solve the emotion recognition problem with three deep learning, one for the sentiment in the first layer and two models for the emotion prediction in the second layer. There are two combinations to be compared, full-LSTM and full-CNN. Surprisingly, the overall HNN performance for both combinations is similar, and both are below a control model without HNN architecture. However, solving the emotion recognition problems in the food security domain was still possible despite poor performance. The application result creates a rough estimation of what people feel about the current food security trend