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All Journal ILKOM Jurnal Ilmiah JGGAG (Journal of Games, Game Art, and Gamification)
Handoyo, Alif Tri
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Arts Computer Science & IT Other

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Interactive Horror Story and the Narratives Implementation: Case Study on Interactive Story Games Developed by Supermassive Games Pratama, Galih Dea; Junior, Franz Adeta; Handoyo, Alif Tri; Majiid, Muhammad Rizki Nur; Ardiyanto, Elshad Ryan; Purnomo, Fredy
Journal of Games, Game Art, and Gamification Vol. 9 No. 2 (2024)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v9i2.10998

Abstract

Video game is one of many media designed to provide entertainment to its targeted users. In response to that, narrative that presents story to engage the users in many ways, such as through interactive story that responds to the actions taken by the players during the gameplay. To understand how the narrative is brought in the games, there is analytical framework specifically presented to break down the elements included in the games like storyworld, character, emotion, narrative interface and micro-narrative. In this research, the analytical framework is used to analyze the interactive horror story games developed by Supermassive Games, taking deeper look on the elements brought by each game. There are various implementations on each of the games, from the storyworld resembling numerous popular horror thriller media, presence of multiple playable characters, emergence of emotions like Fear and Anxiety among others, and the existence of micro-narratives to supplement the main story.
Multi Classification of Bacterial Microscopic Images Using Inception V3 Nurtanio, Ingrid; Bustamin, Anugrayani; Yohannes, Christoforus; Handoyo, Alif Tri
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1121.80-90

Abstract

Microorganisms such as bacteria are the main cause of various infectious diseases such as cholera, botulism, gonorrhea, Lyme disease, sore throat, tuberculosis and so on. Therefore, identification and classification of bacteria is very important in the world of medicine to help experts diagnose diseases suffered by patients. However, manual identification and classification of bacteria takes a long time and a professional individual. With the help of artificial intelligence, we can effectively and efficiently classify bacteria and save a lot of time and human labor. In this study, a system was created to classify bacteria from microscopic image samples. This system uses deep learning with the transfer learning method. Inception V3 architecture was modified and retained using 108 image samples labeled with five types of bacteria, namely Acinetobacter baumanii, Escherichia coli, Neisseria gonorrhoeae, Propionibacterium acnes and Veionella. The data is then divided into training and validation using the k-fold cross validation method. Furthermore, the features that have been extracted by the model are trained with the configuration of minibatchsize 5, maxepoch 5, initiallearnrate 0.0001, and validation frequency 3. The model is then tested with data validation by conducting ten experiments and getting an average accuracy value of 94.42%.
Co-Authors Ardiyanto, Elshad Ryan Bustamin, Anugrayani Fredy Purnomo Ingrid Nurtanio Junior, Franz Adeta Majiid, Muhammad Rizki Nur Pratama, Galih Dea Yohannes, Christoforus