- In this era, we need to extract the text needed to visualize or need knowledge from a large collection of document texts. Text mining is the process of obtaining high-quality information from text. High-quality information obtained because of attention to patterns and trends by reading statistical patterns. In the process of extracting the text, we need to pay for the words offered to give value/weight to the terms provided in a document. The weight given to the term depends on the method used. In weighting many words such as algorithms for example such as TF, IDF, RF, TF-IDF, TF.RF, TF.CHI, WIDF. This research will be analyzed and compared with the TF-IDF, TF.RF, and WIDF algorithms. For the test method, the naïve Bayes classification method will be used and the valuation analysis using the confusion matrix. With a dataset used as many as 130 documents in which 100 data transfer and 30 test data. Based on the analysis of the results of the classification that has been done, it can determine the weighting of TF.RF with naif classification is better than weighting TF.IDF and WIDF with Accuracy values of 98.67%, Precision 93.81%, and Recall 96.67%.Keywords - Text Mining, TF-IDF, TF-RF, WIDF, Classification, Naïve Bayes. Abstract - Pada era sekarang ini pemanfaatan text mining sangatlah diperlukan untuk mevisualkan atau mengevaluasi pengetahuan dari kumpulan besar dari teks dokumen. Text mining adalah proses untuk memperoleh informasi berkualitas tinggi dari teks. Informasi berkualitas tinggi biasanya didapatkan karena memperhatikan pola dan tren dengan cara mempelajari pola statistik. Pada proses teks mining terdapat pembobobtan kata yang bertujuan untuk memberikan nilai/bobot pada term yang terdapat pada suatu dokumen. Bobot yang diberikan pada term tergantung kepada metode yang digunakan. Dalam pembobotan kata banyak sekali terdapat algoritma-algoritma contohnya seperti TF, Idf, RF, TF-IDF, TF.RF, TF.CHI, WIDF. Pada penelitian ini akan dianalisis dan dibandingkan algoritma TF-IDF, TF.RF, dan WIDF. Untuk metode pengujiannya akan digunakan metode klasifikasi naïve bayes dan analisis perbandingannya menggunakan confussion matrix. Dengan dataset yang digunakan sebanyak 130 dokumen yang mana 100 data traning dan 30 data uji. Berdasarkan analisa pada hasil klasifikasi yang telah dilakukan, dapat disimpulkan bahwa pembobotan TF.RF dengan klasifikasi Naïve bayes lebih baik dari pembobotan TF.IDF dan WIDF dengan nilai Accuracy 98,67%, Precision 93,81%, dan Recall 96,67%. Kata Kunci - Text Mining, TF-IDF, TF-RF, WIDF, Klasifikasi, Naïve Bayes.

Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.

The development of unmanned aerial vehicles has been identified as a potential source of a weapon for causing operational disruptions against critical infrastructures. To mitigate and neutralise the threat posed by the misuse of drones against malicious and terrorist activity, this paper presents a holistic design of a long-range autonomous drone detection platform. The novelty of the proposed system lies in the confluence between the design of hardware and software components to effective and efficient localisation of the intruder objects. The research presented in the paper proposes the design and validation of a situation awareness component which is interfaced with the hardware component for controlling the focal length of the camera. The continuous stream of media data obtained from the region of vulnerability is processed using the object detection that is built on region based fully connected neural network. The novelty of the proposed system relies on the processing of multi-threaded dual-media input streams that are evaluated to mitigate the latency of the system. Upon the successful detection of malicious drones, the system logs the occurrence of intruders that consists of both event description and the associated media evidence for the deployment of the mitigation strategy. The analytics platform that controls the signalling of the low-cost sensing equipment contains the NVIDIA GeForce GTX 1080 for detecting drones. The experimental testbeds developed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, drone flight path, tradeoff between the angle of coverage against the distance of coverage. The validation of the proposed system has resulted in yielding a range of intruder drone detection by 250m with an accuracy of 95.5%.

Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.