Perancangan dan Pembuatan Serious Game Sebagai Simulasi Aktivitas Bisnis dan Akuntansi Menggunakan Pendekatan Agent-Based Modelling

Permainan memiliki efek positif dalam mendukung kemampuan motorik seseorang untuk berkembang. Salah satu bahasan menarik dalam permainan adalah simulasi bisnis yang memiliki kategori serious game untuk dijadikan alternatif media pembelajaran dalam memahami konsep dasar bisnis dan aktivitas akuntansi. Gameplay dibuat dengan menciptakan artificial bisnis level UKM yang melakukan aktivitas jual beli, dimana di dalamnya terdapat game world, pelanggan dan toko yang berinteraksi untuk menghasilkan fenomena sebuah pasar. Aktivitas jual beli menjadi dasar siklus akuntansi untuk pemain belajar dan memahami kejadian finansial. Setiap agent diberi attribute yang digunakan sebagai parameter untuk membentuk personality-trait dan pengambilan keputusan pembelian serta aktivitas yang membentuk tingkah laku sehari - hari melalui State Machine dan Rule Based System (RBS). Simulasi perilaku konsumen dalam pasar virtual digunakan sebagai trigger aktivitas utama bisnis yaitu transaksi penjualan. Kejadian finansial yang terjadi dari proses simulasi menghasilkan proses jurnal untuk memberikan pemain pengalaman dalam memproses transaksi dengan contoh sumber aktivitas buatan.Kata Kunci: Serious Game, Perilaku Konsumen, Agent-based Model, A Star, Stack Finite State Machine, Rule-Based System.

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Defense behavior of real time strategy games: comparison between HFSM and FSM

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v13.i2.pp634-642 ◽

2019 ◽

Vol 13 (2) ◽

pp. 634

Author(s):

Rahmat Fauzi ◽

Mochamad Hariadi ◽

Muharman Lubis ◽

Supeno Mardi Susiki Nugroho

Keyword(s):

Finite State Machine ◽

State Machine ◽

Test Results ◽

Defense Behavior ◽

Rule Based ◽

Rule Based System ◽

Strategy Games ◽

Special Skill ◽

Finite State ◽

Rts Game

<span>RTS Game is one of the popular genre in PC gaming, which has been played by various type of players frequently. In RTS game, NPC Defense Building (Tower) has attacking behavior to the closest enemy without considering certain enemy parameters. This causes the NPC Tower to be more predictable by the opponent and easily defeated if NPC attacked by enemies in the group. Thus, this research simulates NPC Tower using Hierarchical Finite State Machine (HFSM) method compared with Finite State Machine (FSM). In this study, NPC Tower detects enemies by seeing at four parameters namely NPC Tower Health, Enemy's Health, Enemy Type, and Tower Distance to enemies. NPC Tower will attack the most dangerous enemy according to the ‘Degree of Danger’ parameter. Then use the decision-making logic of the rule-based system. The output of NPC Tower are three type of behaviors namely Aggressive Attacking, Regular Attacking, and Attack with Special Skill. From the test results of 3 NPC Tower, Kamandaka NPC Tower with HFSM method is winning 8.92% compare to Kamandaka Tower with FSM method. For Gayatri Tower NPC obtained equal results using both HFSM and FSM. Meanwhile, Adikara NPC with HFSM method is 4.62% superior to Adikara Tower with FSM method.</span>

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Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Technologies ◽

10.3390/technologies6040110 ◽

2018 ◽

Vol 6 (4) ◽

pp. 110 ◽

Cited By ~ 4

Author(s):

Gadelhag Mohmed ◽

Ahmad Lotfi ◽

Amir Pourabdollah

Keyword(s):

Neural Networks ◽

Human Activities ◽

Finite State Machine ◽

Fuzzy Rule ◽

State Machine ◽

Machine Learning Techniques ◽

Rule Based ◽

Neuro Fuzzy ◽

Finite State ◽

Learning Capabilities

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

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DESAIN SERIOUS GAME SOSIALISASI BENCANA BERBASIS MODEL TEORI AKTIFITAS

Jurnal Mnemonic ◽

10.36040/mnemonic.v2i1.53 ◽

2019 ◽

Vol 2 (1) ◽

pp. 59-66

Author(s):

Fresy Nugroho ◽

Eko Mulyanto Yuniarno ◽

Mochamad Hariadi

Keyword(s):

Finite State Machine ◽

State Machine ◽

Serious Game ◽

Finite State

Sosialisasi bencana masih mengalami beberapa kendala antara lain kesulitan bila dilakukan secara offfline, mengingat untuk sosialisasi offline membutuhkan waktu khusus, memerlukan persiapan yang lama dan memerlukan alokasi dana khusus. Makalah ini memberikan sebuah pendekatan baru berupa sosialisasi bencana dalam bentuk serious game. Sehingga dinamakan serious game sosialisasi bencana. Dalam mendesain serious game sosialisasi bencana, menggunakan model teori aktivitas utnuk serious game dan tahapan sosialisasi bencana sebagaimana di isyaratkan dalam undang-undang tentang penanggulangan bencana dan diuraikan pusat vulkanologi dan mitigasi bencana geologi. Untuk membuat sebuah serious game dibutuhkan pula metode finite state machine untuk memudahkan mesin mengatur permainan. Visualisasi juga di rancang untuk menunjukkan capaian yang diperoleh pemain agar lebih menarik.

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Enhanced fuzzy finite state machine for human activity modelling and recognition

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-01917-z ◽

2020 ◽

Vol 11 (12) ◽

pp. 6077-6091 ◽

Cited By ~ 1

Author(s):

Gadelhag Mohmed ◽

Ahmad Lotfi ◽

Amir Pourabdollah

Keyword(s):

Neural Network ◽

Human Activity ◽

Finite State Machine ◽

Short Term Memory ◽

Building Blocks ◽

State Machine ◽

Activity Data ◽

Learning Capability ◽

Rule Based System ◽

Finite State

AbstractA challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved $$95.7\%$$ 95.7 % and $$97.6\%$$ 97.6 % for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were $$94.2\%$$ 94.2 % and $$99.3\%$$ 99.3 % , respectively.

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MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

Informatics and Education ◽

10.32517/0234-0453-2018-33-10-29-35 ◽

2018 ◽

pp. 29-35

Author(s):

N. V. Brovka ◽

P. P. Dyachuk ◽

M. V. Noskov ◽

I. P. Peregudova

Keyword(s):

Mathematical Model ◽

Finite State Machine ◽

Active Agent ◽

Adaptive Testing ◽

State Machine ◽

Learning Activities ◽

Independent Learning ◽

Complex Object ◽

Total Reward ◽

Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

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PENERAPAN FINITE STATE MACHINE UNTUK PENGENDALIAN ANIMASI PADA VIDEO GAME RPG NUSANTARA LEGACY

Jurnal Sistem Komputer Musirawas (JUSIKOM) ◽

10.32767/jusikom.v3i1.251 ◽

2018 ◽

Vol 3 (1) ◽

pp. 1

Author(s):

Mustofa Mustofa ◽

Sidiq Sidiq ◽

Eva Rahmawati

Keyword(s):

Life Cycle ◽

Video Game ◽

Finite State Machine ◽

Role Playing ◽

State Machine ◽

Game Development ◽

Development Life Cycle ◽

Finite State ◽

Role Playing Game

Perkembangan dunia yang dinamis mendorong percepatan perkembangan teknologi dan informasi. Dengan dorongan tersebut komputer yang dulunya dibuat hanya untuk membantu pekerjaan manusia sekarang berkembang menjadi sarana hiburan, permainan, komunikasi dan lain sebagainya. Dalam sektor hiburan salah satu industri yang sedang menjadi pusat perhatian adalah industri video game. Begitu banyaknya produk video game asing yang masuk ke dalam negeri ini memberikan tantangan kepada bangsa ini. Tentunya video game asing yang masuk ke negara ini membawa banyak unsur kebudayaan negara lain. Ini semakin membuat kebudayaan nusantara semakin tergeserkan dengan serangan kebudayaan asing melalui berbagai media. Maka dari itu peneliti mencoba untuk menerapkan Finite State Machine dalam merancang sebuah video game RPG (Role-Playing game) yang memperkenalkan kebudayaan. Dalam perancangan video game ini peneliti menggunakan metode GDLC(Game Development Life Cycle) agar penelitian ini berjalan secara sistematis. Dalam suatu perancangan video game tedapat banyak elemen, pada penelitian ini penulis lebih fokus pada pengendalian animasi karakter yang dimainkan pada video game ini. Dari perancangan yang dilakukan, disimpulkan bahwa Finite State Machine dapat digunakan untuk pengendalian animasi yang baik pada video game RPG. Diharapkan video game ini dapat menjadi salah satu media untuk mengenalkan kebudayaan nusantara

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Human action recognition using simple geometric features and a finite state machine

Image Processing & Communications ◽

10.2478/v10248-012-0079-y ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 49-60 ◽

Cited By ~ 2

Author(s):

Damian Dudzńiski ◽

Tomasz Kryjak ◽

Zbigniew Mikrut

Keyword(s):

Action Recognition ◽

Finite State Machine ◽

Recognition Rate ◽

Human Action Recognition ◽

Human Action ◽

Video Stream ◽

State Machine ◽

Recognition Algorithm ◽

Finite State ◽

Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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