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All Journal Scientific Journal of Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Riset Komunikasi Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Abdidas Community: Jurnal Pengabdian Pada Masyarakat Journal of Future Artificial Intelligence and Technologies
Erna Zuni Astuti
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : Scientific Journal of Informatics

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Fuzzy Smart Reward for Serious Game Activity Design Haryanto, Hanny; Rosyidah, Umi; Kardianawati, Acun; Astuti, Erna Zuni; Dolphina, Erlin; Haryanto, Ronny
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.44051

Abstract

Purpose:  Serious game has been widely considered to be a potential learning tool, due to its main advantage to provide a fun experience in learning. The experience is supported mainly by in-game activities, where feedback is given in the form of rewards. However, rewards often don't work well due to various factors, for example, rewards are always the same, so they are monotonous. We use Appreciative Learning as underlying concept for activity design and fuzzy logic to create the reward behavior, called Fuzzy Smart Reward.Methods: We use Appreciative Learning as underlying concept for activity design and fuzzy logic to create the reward behavior. Appreciative Learning activities consists of Discovery, Dream, Design and Destiny. We propose fuzzy-based smart reward for those activities. The smart reward takes player achievement in each activity as input for the fuzzy inference system and give the dynamic reward as output.Result: A game prototype is developed as a test subject. The result shows that the smart reward could dynamically adjust the reward based on game conditions and player performance. Test conducted using Game Experience Questionnaire get the score 3.3 out of 4.Novelty:  There aren't many studies on dynamic rewards in structured reward systems; the majority of studies remove dynamic rewards from reward systems. In our research, a "smart reward" is a dynamic reward in a structured reward system that is created using artificial intelligence and is based on activities for appreciative learning. The use of Fuzzy Logic for structured reward behavior is also very rare. 
Capital Optical Character Recognition Using Neural Network Based on Gaussian Filter Astuti, Erna Zuni; Sari, Christy Atika; Syabilla, Mutiara; Sutrisno, Hendra; Rachmawanto, Eko Hari; Doheir, Mohamed
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.43438

Abstract

Purpose: As digital technology advances, society needs to convert physical text into digital text. There are now many methods available for doing this. One of them is OCR (Optical Character Recognition), which can scan images [1]–[4] containing writing and turn them into digital text, making it easier to copy written text from an image. Text recognition in images is complex due to variations in text size, color, font, orientation, background, and lighting conditions.Methods: The technique of text recognition or optical character recognition (OCR) in images can be done using several methods, one of which is a neural network or artificial neural network. The artificial neural network method can help a computer make intelligent decisions with limited human assistance. Intelligent decisions can be made because the neural network can learn and model the relationship between nonlinear and complex input and output data. In this research, the scaled conjugated gradient is applied for optimization. SCG is very effective in finding the minimum value of a complex function, but it takes longer than some other optimization algorithms.Result/Findings: The dataset used is an image with a size of 28 x 28 which is changed in dimension to 784 x 1. This research uses 4000 epochs and obtained the best validation result at epoch 3506 with a value of 0.0087446. Results: From the statistical test results, the effect of perceived usefulness on ease of use has the highest level of influence, obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtained a test value of 1.2.Novelty:  In this article, Gaussian filter is used as feature extraction to improve yield. Character detection results using a Gaussian filter are known to be almost 10% higher than those using only a neural network. The result with the Neural Network alone is 82.2%, while the Neural Network-Gaussian Filter produces 92.1%.
Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS Astuti, Erna Zuni; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47364

Abstract

Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.
Co-Authors Abdullah, Usman Zahid Abdussalam Abdussalam, Abdussalam Acun Kardianawati Afaf Firmansyah, Daffa Agustina, Feri Ali, Rabei Raad Arni Ernawati Astuti, Yani Parti Cahaya Jatmoko Christy Atika Sari De Rosal Ignatius Moses Setiadi Doheir, Mohamed Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erlin Dolphina Guruh Fajar Shidik Hanny Haryanto Haryanto, Ronny Heru Lestiawan Ifan Rizqa Isinkaye, Folasade Olubusola Lalang Erawan Ningrum, Novita Kurnia Noor Ageng Setiyanto, Noor Ageng Oktaridha, Harwinanda Oktavianto, Damar Patriantoro, Teguh Hartono Prasongko, Akbar Bagus Pulung Nurtantio Andono Rahmanti Asmarani Setiawan, Marcell Adi Sinaga, Daurat Sindhu Rakasiwi Soelistyowati, Diah Sutrisno, Hendra Syabilla, Mutiara T. Sutojo Umi Rosyidah Wellia Shinta Sari Widya Laksana, Deddi Award Yaacob, Noorayisahbe Mohd Yulianto, Lukas Zainal Arifin