Tulisan ini membahas polarisasi dalam politik elektoral di Indonesia, yang dalam banyak aspek sangat ditentukan oleh framing media dan interaksi para aktor dalam media sosial. Sejak tahun 2014, Indonesia mengalami polarisasi politik dalam derajat yang cukup mengkhawatirkan setiap kali berlangsung pemilihan pimpinan eksekutif ditingkat nasional maupun di ibukota Jakarta. Polarisasi ini cenderung belum memperoleh perhatian yang memadai dalam kajian politik Indonesia. Tulisan ini menyajikan pengamatan awal tentang polarisasi politik itu, dengan tujuan untuk mengidentifikasi peluang-peluang pendalaman riset. Mengingat polarisasi politik itu kemungkinan besar akan tetap hadir dalam sejumlah peristiwa elektoral utama di Indonesia (khususnya pemilihan presiden tahun 2019), penelitian-penelitian yang cukup intensif tentang fenomena ini masih sangat ditunggu dalam studi politik.
Currently, numerous types of cybercrime are organized through the internet. Hence, this study mainly focuses on phishing attacks. Although phishing was first used in 1996, it has become the most severe and dangerous cybercrime on the internet. Phishing utilizes email distortion as its underlying mechanism for tricky correspondences, followed by mock sites, to obtain the required data from people in question. Different studies have presented their work on the precaution, identification, and knowledge of phishing attacks; however, there is currently no complete and proper solution for frustrating them. Therefore, machine learning plays a vital role in defending against cybercrimes involving phishing attacks. The proposed study is based on the phishing URL-based dataset extracted from the famous dataset repository, which consists of phishing and legitimate URL attributes collected from 11000+ website datasets in vector form. After preprocessing, many machine learning algorithms have been applied and designed to prevent phishing URLs and provide protection to the user. This study uses machine learning models such as decision tree (DT), linear regression (LR), random forest (RF), naive Bayes (NB), gradient boosting classifier (GBM), K-neighbors classifier (KNN), support vector classifier (SVC), and proposed hybrid LSD model, which is a combination of logistic regression, support vector machine, and decision tree (LR+SVC+DT) with soft and hard voting, to defend against phishing attacks with high accuracy and efficiency. The canopy feature selection technique with cross fold valoidation and Grid Search Hyperparameter Optimization techniques are used with proposed LSD model. Furthermore, to evaluate the proposed approach, different evaluation parameters were adopted, such as the precision, accuracy, recall, F1-score, and specificity, to illustrate the effects and efficiency of the models. The results of the comparative analyses demonstrate that the proposed approach outperforms the other models and achieves the best results.
This paper discusses a dominant group in local politics of Sumenep that is based on a pesantren network that is usually referred to as the Bani Syarqawi. The author argues that the superiority of religious clerics (kyai) over the mass in Sumenep has been mainly based on their adaptability to the transformational change of their role from traditional-charismatic to rational-authoritative by means of educational excellence and genealogical network that is both powerful and full of conflict. The social significance of the superiority found its way through a social change that enabled the religious elite to be the dominant elite group in Sumenep when the power of their royal counterpart declined rapidly. Equipped with Weberian model of authority, the author argues that the formalistic tendency of elite theory can be balanced with a perspective that elite can develop and exercise power over the mass even they are outside the formal structure of politics.
The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.
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