Garuda - Garba Rujukan Digital

Meningkatkan Variasi Tindakan Non-Playable Character Pada Game Survival Menggunakan Metode Markov Hendra Winata; Liliana Liliana; Hans Juwiantho
Jurnal Infra Vol 9, No 2 (2021)
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Digital games or often called Video games are common today. The development of game variants makes games never stop improving, especially in the Artificial Intelligence section. Each game has its own artificial intelligence so that many variations are generated and make a game unique. This research tries to make a variation of the actions taken by NPCs against players. In an effort to make these variations, the Markov Chain method is used to help state selection. Markov Chain method is combined with Finite-State Machine for NPC state selection. Based on the results of testing and questionnaires, 80.4% strongly agree and 19.6% agree that the resulting NPC has a large variety of actions. The results of the questionnaire also found that 69.6% were very unrealistic and 30.4% said that NPCs were unrealistic or did not imitate human behavior.

Penerapan Probabilistic FSM pada AI musuh dalam game ARPG untuk gerakan AI tidak monoton Nicolas Wiyendi; Djoni Haryadi Setiabudi; Hans Juwiantho
Jurnal Infra Vol 9, No 1 (2021)
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Game is really popular and becoming one of human aspects of life from child, Along with times the needs of game AI that’s not monotone has become more and more real, The problem that made a game monotone is the AI repeating its movement, with that the AI that doesn’t repeat its movement is made, this AI will make players not bored because of spam movement also motivated to play the game. Previous research has been made but with different genre and different random technic.Game will be developed with Probabilistic finite state machine methods combine with random shuffle bag. Probability will be used for showing the animation so it will make the animation that’s come out more than one, and random shuffle bag will be used for the decision making for the movement so its evenly divided and not repeated.Result of the testing shows that AI is not repeating the movement but it can repeat the pattern of the movement, Problem with repeating pattern can be solve with probability on animation that’s come out so with the same pattern movement player can see different movement.

Penerapan Metode Goal Oriented Action Planning untuk Agent AI pada Turn Based Tactics Video Game Ryan Chandra Kusuma; Liliana Liliana; Hans Juwiantho
Jurnal Infra Vol 9, No 2 (2021)
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In turn-based tactics game, difficulties often placed on resources owned by enemies. Players have to do repetitive action to counterbalance enemy’s resources. To make players spent more time on strategies rather than counterbalance enemy’s resources, goal-oriented action planning will be implemented for AI. It’s expected that AI GOAP even without extra resources can replace AI FSM with extra resources.Goal-Oriented Action Planning (GOAP) is a decision-making method that capable of making a character not only do what it will do, but also determine how to do it. A* is a method that looks for a path by exploring the minimum number of nodes with minimum cost solution. This research combines GOAP and A* search. GOAP in this research has several variations of actions based on health points. Result of the research shows that AI GOAP without extra resources has 33.33% winrate against AI FSM with extra resources, and 86.66% against AI FSM with extra resources but reduced power unit. The results of respondents from various players with different experiences show that the difficulty of AI FSM with extra resources is higher, the level of player satisfaction and AI’s realistic level is higher when fought against AI GOAP without extra resources.

Penerapan Finiste State Machine dan Atreus AI Behavior pada AI Musuh dalam Fighting Game Jong Jeffrey Wicaksono; Djoni Haryadi Setiabudi; Hans Juwiantho
Jurnal Infra Vol 8, No 2 (2020)
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Artificial Intelligence (AI) has already become an important part in game development. AI in game have a diverse job to give a direct experience to player. The lack of strategy in enemy AI inside fighting game can make players less motivated when playing. To solve this problem, AI is made which has variety of cooperate strategy that can make attract player’s motivationGame is developed using Unity3D Engine and using C# programming language. Finite State Machine method is used to develop AI which become player enemy and Atreus AI Behavior is given in order to have variety of strategies. Probability also added in AI decision making in order to make the AI less predictable.The results show that AI can run well using FSM and Atreus Behavior merging. Testing is also carried out on 15 players which have a background as a casual player or above. These 15 players are in charge to try the game. The results show that AI is more difficult to beaten and player is motivated to defeat the AI.

Dynamics Difficulty Adjusment Metode Evolutionary MCTS with Flexible Search Horizon pada Multi-Action Adversarial Games untuk Penyesuaian Tingkat Permainan Andhika Evantia Irawan; Liliana Liliana; Hans Juwiantho
Jurnal Infra Vol 9, No 1 (2021)
Publisher : Jurnal Infra

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Dynamic Difficulty Adjustment (DDA) is a method that modifies AI behavior to suit the player's abilities. So far, research on DDA in Monte Carlo Tree Search has been able to provide an appropriate level of challenge. However, the advantages of MCTS in finding solutions to long-term strategies have not been maximally implemented because so far it is only used in 2D real-time fighting games, which are short-term strategy game.This study combines DDA with evolutionary monte carlo tree search with flexible horizon (FH-EMCTS). FH-EMCTS is combination of vanilla MCTS with an Evolutionary algorithm. This method increases the length of the search space to certain extent. Giving DDA to FH-EMCTS is done by changing the way of selecting actions and assessing each node.The result of this research is that AI agents that use FH-EMCTS with DDA can be implemented into multi-action adversarial game and can provide balanced level of difficulty to other AI agents and humans. Based on the results of survey of AI agents against humans, it shows that the most fun and realistic AI agents are not the AI agents who have the best ability of winning percentage but AI agents who have win rate of around 50%.

Sistem Rekomendasi Tempat Makan Wilayah Solo Raya Berbasis Web dengan User Based Collaborative Filtering Menggunakan Fuzzy Conditional Probability Relation Christian Suryadi; Rolly Intan; Hans Juwiantho
Jurnal Infra Vol 9, No 2 (2021)
Publisher : Jurnal Infra

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Recommendation system is a system used to predict an object for users in the form of useful information based on the rating value. Recommendation system can be applied for food places. The method commonly used for recommendation system is User-Based Collaborative Filtering. This method is a technique used to predict an item that the user likes based on the same rating, by means of user to user.This study uses User-Based Collaborative Filtering method using Fuzzy Conditional Probability Relation to perform calculations between users. Testing is done by calculating the accuracy of the recommendations generated by the system for users. The survey will be used to find the accuracy value of the method.The results of this study is the accuracy values from the User-Based Collaborative Filtering method using Fuzzy Conditional Probability Relation. Based on the survey results, the accuracy obtained is 62.78%, the accuracy using a rating limit of 2 is 47%, with a rating limit of 3 is 69%, and with a limit of 4 is 83%. From the results of this accuracy, we can summarize that User-Based Collaborative Filtering using Fuzzy Conditional Probability Relation can produce results that are quite good and satisfactory to provide recommendations.

Pembuatan Website untuk Rekomendasi Smartphone Bryan Christiansen; Leo Willyanto; Hans Juwiantho
Jurnal Infra Vol 8, No 2 (2020)
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The era of digitalization is upon mankind. Humans as a social being are heading towards a faster way of exchanging information. With all accessible information known to man is on our fingertip, the use of gadgets is no longer a luxury, but becomes a necessity.The topic for this thesis is creating a website that can give those who need to buy smartphone but do not understand the details of difference of each product. Recommendations that are given to the user are based on their preferences like price or the size of the smartphone. The website converts the criteria of preferences that are inputted by the user into more detailed parameters.The result of this thesis can give users a list of smartphones recommendations. Via the list given, user can have a new perspective and options that are small in size to choose a smartphone that matches user’s criteria of preferences.

Adaptive AI Pada Survival Horror Game Menggunakan Fuzzy Leonard Evan Widodo; Rolly Intan; Hans Juwiantho
Jurnal Infra Vol 8, No 2 (2020)
Publisher : Jurnal Infra

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There are many methods used by developers to create artificial intelligence in their games. The method most often used by developers because of it’s flexibility is the scripting method. Scripting method is done by writing several rules in the source code in the form of if - then. The problem with this method is that the NPC’s artificial intelligence is easily exploited by gamer who have understood the pattern of the NPC’s behavior.Therefore the fuzzy logic is used to overcome these problems.The use of the fuzzy inference method is to make the AI to be adaptive to certain situation where depending on the situation the AI will give different decision making. In this case, Tsukamoto Fuzzy inference method is used in this design. In the method, all rules have their respective values which then will be calculated to look for the average score which then was used to determine which decision to make. Before constructing the fuzzy rules, determining the design of the game and the design of the map must be made first. After all are done, the appropriate fuzzy rules can be constructed.The results of the survey showed that the game that uses the Tsukamoto fuzzy inference method provides an adaptive decision according to the conditions at a moment in a game. Ninth of the players who played the game agreed that game A and game B had different decisions despite using the same AI as much as 77.2%, while those who said that AI was the same as much as 22.8%. From the results of this survey it can be said that the Tsukamoto fuzzy inference method can produce adaptive decisions based on the condition of the game at a certain moments.

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