Application of knowledge-based system in automated data warehouse design

Data warehouse has become more and more popular for an enterprise as a data repository system.Yet tools to appropriately design its conceptual model are rarely available, even though this model is known as a key for the successful of the overall design. In this paper we propose an approach and a tool to guide the decision makers in designing data warehouse conceptual model based on the Entity Relationship (ER) model of the existing operational database systems. Using this approach, the ER model is automatically transformed into the multidimensional model

Kombinasi K-Nearest Neighbor (KNN) dan Relief-F untuk Meningkatkan Akurasi Pada Klasifikasi Data

Dalam penelitian ini, penulis mengusulkan proses peningkatan akurasi pada K-Nearest Neighbor (KNN) dengan kombinasi seleksi fitur menggunakan metode Relief-F. Adapun penyebab kurang maksimalnya akurasi pada K-Nearest Neighbor dibandingkan dengan metode klasifikasi lainnya disebabkan oleh pengaruh atribut yang kurang signifikan dan persentase pengaruh yang cenderung rendah dari suatu data dalam menentukan kelas pada data baru. Metode Relief-F digunakan untuk melakukan seleksi pada atribut yang korelasinya kurang baik dari data yang diujikan. Pengujian dari metode yang diusulkan yaitu membandingkan akurasi yang diperoleh dari metode KNN tanpa menggunakan seleksi fitur dengan KNN menggunakan seleksi fitur Relief-F. Hasil pengujian yang diperoleh yaitu metode yang diusulkan mampu meningkatkan akurasi klasifikasi dari KNN dengan peningkatan yang diperoleh yaitu sebesar 10.32% setelah dibandingkan dengan pengujian KNN tanpa seleksi fitur

Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds

Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tests on the test set show the highest accuracy value for feature 17 value of 64.96% in the test results obtained some important information, including; The test results on the MFCC coefficient values of twelve to twenty show overfitting. This is shown in the model training process which repeatedly produces high accuracy but produces low accuracy in the classification testing process. The feature assignment on MFCC shows that the higher the feature value assignment on MFCC causes a very large sound feature dimension. With the large number of features obtained, the MFCC method has a weakness in determining the number of features

Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA

Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

In the field of remote sensing, in addition to the weather forecast, atmospheric dynamics, oceans, cloud cumulonimbus, and Tornado are part of the phenomenon of chaos. Because in the clouds cumulonimbus, there are some layers with a gray border indicating irregular and uncertain. There is a boundary line on the layers of Cumulonimbus Clouds that could be identified based on the pixel where the differences in the intensity values are extremes. A cloud layer cumulonimbus with a gray edge border can be used as the basis for predicting the occurrence of a tornado based on a pixel location that has specific characteristics. In this research, a Supervised Image Classification algorithm with Spectral Angle Mapper was performed to get the minimum and maximum pixel intensity interval values based on spectral angles in cumulonimbus clouds. Spectral angles allow for quick mapping in determining the spectral similarities between two spectrums on cumulonimbus cloud layers. The spectral similarities are calculated by referring to the angle between the spectral forming the same dimensional vector space on the RGB color spectrum. Early detection in cumulonimbus cloud layers will indicate the occurrence of chaos phenomenon, which could be used to predict tornadoes. The results showed that the Spectral Angle Mapper approach gave minimum and maximum pixel intensity values interval of the Average Correlation Angle in the dataset image Cumulonimbus Cloud with a classification accuracy value of 95.83%

Data Security Using Multi-bit LSB and Modified Vernam Cipher

Data security is one of the most important aspects of today's information era. Some methods are used to secure important data from hackers. The LSB is a steganographic algorithm that is often used to store data in the last bit. In order to improve the security, we combine steganography with cryptography enables. In this research LSB is modified using the multi-bit LSB model. Modifications are made to the bits of each character, the rotation by a certain amount can randomize the plaintext content before cryptographic algorithm, Vernam is performed. The bit on LSB can be inserted data as much as 1, 2, 3 or 4 - bit information. The calculation results of MSE and PSNR values indicate that the use of 1-bit LSB is superior to that of 2-, 3-, or 4-bit LSB

Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q

Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q

Implementation Of Face-To-Face Online Learning System Based On Audio Video, Presentation And Chat Using The Moodle E-Learning Platform

Currently, the implementation of teaching and learning at SMP Negeri 1 Binjai Kwala Begumit was done in the classroom alternately. However, with the current condition of pandemic covid-19, the learning process no longer carried out fully in schools. The school has not been using information technology in the form of e-learning applications in the teaching and learning process. The school has difficulty in recording the existing teaching and learning process: assignments, exams, assessments, and other activities. Therefore the use of e-learning applications is now very much needed. With existing school facilities, such as internet facilities and the ICT teachers, training in developing and implementing e-learning for teaching staff become the best alternative so that learning process can be done properly