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All Journal COMPTON: Jurnal Ilmiah Pendidikan Fisika Jurnal Pendidikan dan Teknologi Indonesia Journal Software, Hardware and Information Technology Jurnal Media Informatika Jurnal Abdi Masyarakat Indonesia Al Khidma: Jurnal Pengabdian Masyarakat Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Dedikasi Saintek Jurnal Pengabdian Masyarakat Applied Science and Technology Research Journal DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat
Hastono, Tri
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Journal : Applied Science and Technology Research Journal

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Multi-Layer Perceptron Model for Dota 2 Game Results from UCI Using MLP Classifier Yudhistira, Galih; Aliya Widiastuti, Pika; Rahyuni, Rahyuni; Hastono, Tri; Harry Pratisto, Eko
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 2 No. 2 (2023): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v2i2.5797

Abstract

Dota 2 is a genre game Moba in the PC (Personal Computer) system battle arena game online (online) with multiplayer ( bringing together 2 players in 1 machine ). Game Dota 2 consists of 2 opposing teams To get the victory, every team has 5 players who can choose hero 1 from 121 different heroes. Study This discusses the use of the Multi-Layer Perceptron (MLP) model to predict the results Dota 2 game. The author uses the UCI dataset containing historical data of Dota 2 matches, processed and trained with the MLP model using MLPClassifier from the scikit learn Python library. The data preprocessing process includes normalization features and handling of missing data. Training involves hyperparameter selection and validation cross To prevent overfitting. Although the MLP model is successful in predicting results with accuracy high, the author takes notes room For improvement, like additional features or the use of more models complex. In research, This obtained results with Accuracy Train results: 68.06%, Accuracy Test: 58.00%, Accuracy Precision: 58.53%, Accuracy Recall: 73.50%, Accuracy f1: 63.39%.
Network Nerves Mock Backpropagation Prediction Graduation Student Elementary School With Practice Values Exam Rahmawati Sunaryo, Azzahra; Abelia, Nevanda; Hastono, Tri; Pratisto, Eko Harry
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 2 No. 2 (2023): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v2i2.5798

Abstract

The backpropagation method is a computer technique to help predict and sort data. This method is usually used to change the connection between parts of the computer's brain in the hidden layer. Meanwhile, the Nervous System Network (ANN) is an information-processing system that is very similar to the function of human brain cells. Value is a benchmark for a student's graduation, if the student's score is getting better, the more opportunities for the student's graduation. In predicting this pass using the method of Artificial Neural Networks (ANN), namely Backpropagation, and using Matlab software with the MSE (Mean Square Error) result of 0.099512.
Co-Authors Abdul Rouf Abelia, Nevanda Aliya Widiastuti, Pika Arisanto, Fauzan Bayu Gilang Purnomo Budisaputro, Crismantoro Ciptadi, Prahenusa Wahyu Danang Widyawarman Eko Harry Pratisto, Eko Harry Fahriza, Nayaka Anung Harry Pratisto, Eko Hartiningsih, Subekti Janah, Siti Nuraitul Jati, Gema Kharisma KHOIRUL ANAM Laksana, Feri Febria Muhammad Fairuzabadi Nurmala Nurmala Pambudi, Brevi Istu Puspasari, Diah Ayu Rahmawati Sunaryo, Azzahra Rahyuni Rahyuni Rifangi, Ahmad Shidqi, Faishal Sunaryo, Azzahra Rahmawati Syah, Firdiyan Wahyu Ciptadi, Prahenusa Warniasih, Kristina Widyawarman, Danang wulansari, Brilian Yudhistira, Galih