Software engineering is concerned with the development and evolution of high-quality software systems in a systematic, controlled, and efficient manner. Software engineers are concerned with safety and reliability of the product as well as the cost and schedule of the development process. The lectures and the group projects will cover all aspects of the software life cycle, from development team management, problem specification and analysis, system design techniques, implementation and documentation practices, testing, to maintenance and evaluation of the final product.

Uml Distilled A Brief Guide To The Standard Object Modeling Language

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

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Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Factor Analysis and Principal Component Analysis

Along with the development of the Covid-19 pandemic, many responses and news were shared through social media. The new Covid-19 vaccination promoted by the government has raised pros and cons from the public. Public resistance to covid-19 vaccination will lead to a higher fatality rate. This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM). This research aims to study the public response to the acceptance of the vaccination program. The research result can be used to determine the direction of government policy. Data collection was taken via Twitter in the year 2021. The data then undergoes the preprocessing methods. Afterward, the data is processed using SVM classification. Finally, the result is evaluated by a confusion matrix. The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative. The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Government agencies in this modern era are greatly assisted by the rapid development of information technology, so transparency and speed are imperative in providing services to the public. The use of social media by government agencies is one of the innovations by maximizing technology, one of which is the Ministry of Health of the Republic of Indonesia which is the ministry in charge of organizing government affairs in the health sector. The information contained on the Twitter social media account of the Ministry of Health of the Republic of Indonesia is various types of information uploaded randomly, therefore Twitter users often cannot distinguish the types of information provided by the Twitter of the Ministry of Health of the Republic of Indonesia. Based on this case, Twitter users can provide responses or comments that often lead to pros and cons. The extreme gradient boosting (XGBoost) algorithm is a tree-based algorithm such as the decision tree algorithm, which uses an ensemble principle that combines several weak learning sets and makes a new model that is strong to produce strong predictions. In this study, our aim is to implement extreme gradient boosting to classify the tweet topics and analyze the sentiment towards comments made by the public on the tweets of the Ministry of Health of the Republic of Indonesia. The results of this study indicate the highest level of accuracy in the classification, i.e. 89.35% with a precision of 88.76%, and 88.58% for the recall value from 2243 tweets. Furthermore, the best accuracy in sentiment analysis was obtained at 91.22%, 89.17% for precision, and for recall at 89.06% with 304 comment data.

Classification and Sentiment Analysis on Tweets of the Ministry of Health Republic of Indonesia

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

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Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

The amplification of Internet technology primarily in cloud technology development is enabling small and medium enterprises (SME's) to utilize the affordable cost of information technology infrastructure and does not require a tremendous expenditure. Today many SME's ready to implements the Internet in their business processes as well as large-scale enterprise, particularly when the internet users also expanding. The data from http://statista.com illustrated that numeral of internet user in Indonesia rise drastically over the last three years. This research discusses the eagerness of Indonesian SME's, especially in Surabaya to utilize Point of Sales (POS) application based on cloud computing. The study related to the adoption of the POS done by qualitative research methodology. The completion of this investigation identified the essential factor of POS based on cloud computing adoption which appropriates with IT Users and Entrepreneur’s. The most significant factor in cloud computing adoption for point of sales is security and ease of use. Based on that result this study purpose the POS infrastructure for SME’s in Indonesia especially in Surabaya.

Cloud Computing-Based Point-of-Sales Readiness for Surabaya’s Small/Medium Enterprises

K-Nearest Neighbour (K-NN) is one of the popular classification algorithm, in this research K-NN use to classify internet traffic, the K-NN is appropriate for huge amounts of data and have more accurate classification, K-NN algorithm has a disadvantages in computation process because K-NN algorithm calculate the distance of all existing data in dataset. Clustering is one of the solution to conquer the K-NN weaknesses, clustering process should be done before the K-NN classification process, the clustering process does not need high computing time to conqest the data which have same characteristic, Fuzzy C-Mean is the clustering algorithm used in this research. The Fuzzy C-Mean algorithm no need to determine the first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered. The Fuzzy C-Mean has weakness in clustering results obtained are frequently not same even though the input of dataset was same because the initial dataset that of the Fuzzy C-Mean is less optimal, to optimize the initial datasets needs feature selection algorithm. Feature selection is a method to produce an optimum initial dataset Fuzzy C-Means. Feature selection algorithm in this research is Principal Component Analysis (PCA). PCA can reduce non significant attribute or feature to create optimal dataset and can improve performance for clustering and classification algorithm. The resultsof this research is the combination method of classification, clustering and feature selection of internet traffic dataset was successfully modeled internet traffic classification method that higher accuracy and faster performance.

https://www.beei.org/index.php/EEI/article/download/608/911

Improving K-NN Internet Traffic Classification Using Clustering and Principle Component Analysis

This research will use the algorithm K-Nearest Neighbour (K-NN) to classify internet data traffic, K-NN is suitable for large amounts of data and can produce a more accurate classification, K-NN algorithm has a weakness takes computing high because K-NN algorithm calculating the distance of all existing data. One solution to overcome these weaknesses is to do the clustering process before the classification process, because the clustering process does not require high computing time, clustering algorithm that can be used is Fuzzy C-Mean algorithm, the Fuzzy C-Mean algorithm does not need to be determined in first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered, but the algorithm Fuzzy C-Mean has the disadvantage of clustering results obtained are often not the same even though the same input data, this is because the initial dataset that of the Fuzzy C-Mean is not optimal, to optimize initial datasets in this research using feature selection algorithm, after main feature of dataset selected the output from fuzzy C-Mean become consistent. Selection of the features is a method that is expected to provide an initial dataset that is optimum for the algorithm Fuzzy C-Means. Algorithms for feature selection in this study used are Principal Component Analysis (PCA). PCA reduced non significant attribute to created optimal dataset and can improve performance clustering and classification algorithm. Results in this study is an combining method of classification, clustering and feature extraction of data, these three methods successfully modeled to generate a data classification method of internet bandwidth usage that has high accuracy and have a fast performance.

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Clustering and Feature Selection Technique for Improving Internet Traffic Classification Using K-NN

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

http://ijiis.org/index.php/IJIIS/article/download/65/27

Adi Suryaputra Paramita

Papers

Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Cloud Computing-Based Point-of-Sales Readiness for Surabaya’s Small/Medium Enterprises

Improving K-NN Internet Traffic Classification Using Clustering and Principle Component Analysis

Clustering and Feature Selection Technique for Improving Internet Traffic Classification Using K-NN

Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm