Until now, the pandemic conditions of Covid-19 are still ravaging the world, even in Indonesia and West Java. Various attempts have been made to stop it. West Java implements Large Scale Social Restrictions, is known as Pembatasan Sosial Skala Besar (PSBB). However, over time, a discourse emerged to loosen PSBB. One of the World Health Organization’s (WHO) requirements to loosen is the effective reproduction rate of Corona Virus cases below 1. Therefore, this study focuses on predicting the number of cases in West Java. The methods based on multi-layer perception (MLP) and linear regression (LR). The data were obtained from the C Covid -19 positive case from March to mid-August 2020 in West Java. The experiments show that MLP reaches optimal if it used 13 hidden layers with learning rate and momentum = 0.1. The MLP had a smaller error than LR. Both of them predict the number of cases in the next 30 days from August 14, 2020. The results show that West Java will still have an increase in the number of new cases of Covid -19.

In this current digital era, people tend to shop online. Because of that, there are currently many e-commerce companies that can satisfy the various needs of society in shopping. Each company certainly has a strategy to attract consumers to shop at their shopping place. One of the media commonly used to attract consumers is social media. Tokopedia is one of the biggest marketplaces in Indonesia and is also active in utilizing Twitter as their social media mean. Therefore, it is essential for Tokopedia to pay attention to tweet contents interesting enough to be publicized. By applying text mining using K-Means Clustering algorithm, it can be seen which types of tweet contents that are attractive for Tokopedia consumers. Out of 885 Tokopedia tweets that have been collected, a clustering is then done using K-means algorithm, resulting 48 cluster tweets. Then, from the 48 clusters, they are further grouped into 5 major groups. Based on the results of the grouping, it can be seen that the most interesting content deals with quiz prizes and the least attractive content is on lifestyle.

Object identification is a part of the field of computer science, namely, image processing, whose research continues to innovate. Object identification describes an object based on the main characteristics of the object. Many research innovations related to object identification have been carried out to obtain optimal identification results. The convolutional neural network (CNN) is one of the algorithms that is widely used by researchers in the field of object identification or object recognition in digital images. The purpose of this study was to analyze the development of object identification in the search for the best algorithm in terms of the speed and efficiency of identification. The article data used were obtained from several sources, namely, Dimensions AI, Science Direct, and Google Scholar. The database search results obtained 1041 articles in the form of publications from 2010–2021. Through a systematic literature review based on the articles obtained, 32 articles were selected. The evaluation of the articles was carried out in the form of article data visualization, object identification algorithm development, and the research objects used. CNN’s research innovation is growing rapidly, with improvements being made to the identification techniques in its algorithmic architecture. The use of the CNN algorithm in the identification of image objects, starting with the region CNN technique, is improved with Fast R-CNN, Faster-CNN, and Mask R-CNN. The object of research has developed from facial recognition and the identification of moving images to the introduction of ancient manuscripts that are useful for the development of history and tourism. The successful identification of ancient scripted texts will greatly assist the availability of such manuscripts in a digital format, which allows for further multidisciplinary research. The availability of ancient manuscripts in a digital format also helps the government to preserve culture and increase people’s understanding of the culture they have.

Spatial Autoregressive (SAR) models are used to model the relationship between variables within a specific region or location, considering the influence of neighboring variables, and have received considerable attention in recent years. However, when the impact of exogenous variables becomes notably pronounced, an alternative approach is warranted. Spatial Expansion, coupled with the Casetti model approach, serves as an extension of the SAR model, accommodating the influence of these exogenous variables. This modeling technique finds application in the realm of rainfall prediction, where exogenous factors, such as air temperature, humidity, solar irradiation, wind speed, and surface pressure, play pivotal roles. Consequently, this research aimed to combine the SAR and Spatial Expansion models through the Casetti model approach, leading to the creation of the Spatial Autoregressive Exogenous (SAR-X) model. The SAR-X was employed to forecast the rainfall patterns in the West Java region, utilizing data obtained from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) dataset. The practical execution of this research capitalized on the computational capabilities of the RStudio software version 2022.12.0. Within the framework of this investigation, a comprehensive and integrated RStudio script, seamlessly incorporated into the RShiny web application, was developed so that it is easy to use.

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.