Rancang Bangun Aplikasi Game Survive This Pandemic sebagai Media Edukasi Pencegahan Virus Bagi Anak-Anak

Pandemi COVID-19 berdampak pada masyarakat yang menjadikan mereka tidak bisa beraktivitas di luar rumah, khususnya untuk anak-anak sehingga mereka tidak bisa bermaian ataupun sekolah seperti biasanya. Aktivitas belajar di rumah selama masa pandemi memunculkan sejumlah kendala, salah satunya yaitu anak-anak yang mengeluh bosan karena terlalu banyak menghabiskan waktunya dirumah dengan rutinitas yang itu-itu saja. Perkembangan zaman yang diiringi dengan teknologi yang semakin canggih dapat memberikan inovasi baru, salah satunya memudahkan pembelajaran untuk siswa, yaitu dengan adanya game edukasi tentang COVID-19. Banyaknya game edukasi yang ada saat ini dapat memudahkan guru dalam menyampaikan materi pembelajaran dengan cara yang unik dan menyenangkan. Oleh karena itu, peneliti memiliki tujuan untuk merancang dan menciptakan game edukasi tersebut yang “Survive this Pandemic!”. Game yang menggunakan beberapa karakter dengan tema COVID-19. Karakter utama dalam game memiliki tiga nyawa yang bisa digunakan untuk sampai ke garis finish. Game edukasi dan adventure dengan banyak jebakan dan teka-teki tentang COVID-19. Pembuatan game dilakukan dengan menggunakan engine Construct 2 dan menyajikan dua tingkat level permainan. Yang menjadi pembeda disetiap level adalah tingkat jebakan dan teka-tekinya, serta adanya sebuah achivment berupa atribut atau BP yang akan di dapatkan setelah mencapai garis finish. Adanya game edukasi ini diharapkan dapat membantu mengatasi rasa bosan pada anak serta meningkatkan pengetahuan mereka dalam menghadapi pandemi COVID-19

Detection of robots in computer games using neural networks

Computer games occupy a huge share in the field of entertainment, so it is important to improve methods to combat cheating. In this paper, we have used the own Targetman game to collect and analyze the data. The neural network model has been proposed and we solved the classification binary task "bot/not-bot" with one hot encoding of outcome variable

Not all the bots are created equal:the Ordering Turing Test for the labelling of bots in MMORPGs

This article contributes to the research on bots in Social Media. It takes as its starting point an emerging perspective which proposes that we should abandon the investigation of the Turing Test and the functional aspects of bots in favor of studying the authentic and cooperative relationship between humans and bots. Contrary to this view, this article argues that Turing Tests are one of the ways in which authentic relationships between humans and bots take place. To understand this, this article introduces the concept of Ordering Turing Tests: these are sort of Turing Tests proposed by social actors for purposes of achieving social order when bots produce deviant behavior. An Ordering Turing Test is method for labeling deviance, whereby social actors can use this test to tell apart rule-abiding humans and rule-breaking bots. Using examples from Massively Multiplayer Online Role-Playing Games, this article illustrates how Ordering Turing Tests are proposed and justified by players and service providers. Data for the research comes from scientific literature on Machine Learning proposed for the identification of bots and from game forums and other player produced paratexts from the case study of the game Runescape

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior

From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page