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, square

Many world phenomena lead to nonlinear equations systems. For some applications, the non-linear equations which construct the non-linear equations system are interpolation functions. However, the interpolation functions are usually not represented as mathematical expressions but as computer programs in specific programming languages. The research proposed using the relaxed Newton method to solve the non-linear equations system that contained interpolation functions. The interpolation functions were represented in the R programming language. Then, the experiment used the Spline interpolation function to construct a two-dimensional non-linear equations system. Eleven initial guesses, maximum of ten-time iterations, and 10-7 precision were applied. The solution of the non-linear equations system and the iteration needed on each initial guess were observed. The experiment shows that the proposed method works well for solving the non-linear equations system constructed by Spline interpolation functions. By observing the initial guesses used in the experiment, there are four possible results: true solution, spurious solution, false solution, and no solution. Applying 11 initial guesses have five initial guesses resulting in true solutions, one initial guess in spurious solution, three initial guesses in false solutions, and one initial guess in no solution. The discussions imply that this method can be generalized to the three-dimensional non-linear equations system or higher dimensions.

<p>Numerous advanced techniques including machine learning models are widely used in landslide susceptibility zoning which result in very high accuracy. In some cases, very high accuracy represents an overfitting in the model, where a model adapts very well to the training data but poorly for the test or new data.  Cross Validation (CV) strategies are often employed to reduce overfitting in a machine learning model. Several cross validation techniques have been developed recently as a part of machine learning workflow.  However, the preference of choosing one cross validation method to another is still unclear in landslide susceptibility zoning. To illustrate this issue, the authors reproduce non CV, standard V-fold CV, and several spatial CV techniques using a benchmark dataset in Italy to train, validate and test an XgBoost model using 26 landslide controlling factors. The variation of RoC validation, RoC testing, and confusion matrix were used to detect the potency of model overfitting. The preference of using a CV technique for a benchmark data in Italy will be discussed further. The result is expected to provide guidance for choosing CV technique in landslide susceptibility zoning based on slope unit and machine learning workflow.</p>

Akuisisi data, bertujuan untuk mengambil data awal, merupakan salah satu tahapan dalam metodologi penambangan data. Data awal akan diproses menjadi data akhir yang digunakan untuk proses pemodelan, seperti pembuatan model untuk memprediksi potensi terjadinya tanah longsor. Data prediksi curah hujan yang disediakan oleh Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) dapat digunakan untuk pemodelan tersebut. Data akan disimpan di komputer lokal dengan menggunakan alat atau aplikasi otomasi yang bernama Apache Airflow. Proses akuisisi data dari server BMKG ke komputer lokal dijalankan secara otomatis dalam dua kali sehari, yaitu pada pukul 00.00 dan 12.00. Terdapat dua task yang dibuat di Directed Acyclic Graph (DAG) untuk proses ini, yaitu task pertama sebagai sensor ketersediaan data dan task kedua yang melakukan proses utama. Status dari DAG pada Apache Airflow juga dapat diketahui secara cepat, misalnya status telah berhasil, gagal, atau sedang berjalan. Apache Airflow juga menyediakan log yang dapat diakses untuk mengetahui alasan kegagalan suatu task. Hasil dari penelitian ini adalah terdapat pipeline pada aplikasi otomasi Apache Airflow untuk membantu proses akuisisi data secara periodik.

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