Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal JURTEKSI J-Intech (Journal of Information and Technology)
I Gede Wiarta Sena
Institut Informatika Indonesia (IKADO) Surabaya
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan dan Pembuatan Sistem Monitoring Aktifitas Karyawan Berbasis WEB pada PT. HRL INTERNASIONAL Titasari Rahmawati; I Gede Wiarta Sena; Nicholas Manuel Albert
J-INTECH (Journal of Information and Technology) Vol 10 No 2 (2022): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v10i2.803

Abstract

In a company, monitoring employee activities is one of the most important processes to ensure the continuity of the desired business process. The problem faced at this time is that there is no monitoring process that is properly systemized because the employee monitoring process is done manually. Based on the problems above, this study aims to create a web-based employee activity monitoring system at PT. HRL International. The method used is System Development Life Cycle (SDLC) Waterfall and in the model design process data flow diagrams will be used. Based on the results of the black box testing of the functional system, the results are by the planned design, so this system can help solve problems that exist in PT. HTL INTERNASIONAL.
MOBILE LEGEND GAME PREDICTION USING MACHINE LEARNING REGRESSION METHOD I Gede Wiarta Sena; Andi W. R. Emanuel
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 9, No 2 (2023): Maret 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v9i2.1866

Abstract

Abstract: A research institute explains that with 83.7 million people using the Internet, Indonesia is among the top 20 internet users globally. Various individual or group activities require an internet network, one of which is playing games, for developments in the gaming sector, especially the MOBA (Massive Online Battle Arena) genre game, is being hotly discussed. There are various kinds of MOBA genre games, one of which is the Mobile Legends game. Many E-Sport Mobile Legends teams, especially in Asia, make this phenomenon a business space to generate large profits. In this study, the researcher recommends a good machine learning algorithm to predict the outcome of Mobile Legends matches. Of the 600 match history data analyzed, this study recommends the Artificial Neural Network (ANN) and Random Forest (RF) algorithms as the right algorithms to predict the outcome of the match. Prediction results from each algorithm can reach 82% and 80% accuracy. These findings can help the E-sports analysis team build their match strategy.            Keywords: artificial neural networ; machine learning; mobile legend; prediction; random forest  Abstrak: Sebuah lembaga penelitian menjelaskan bahwa dengan 83,7 juta penduduk yang menggunakan Internet, Indonesia termasuk di dalam 20 besar pengguna internet secara global. Berbagai aktivitas individu atau kelompok membutuhkan jaringan internet, salah satunya adalah bermain game, untuk perkembangan pada sektor game khususnya game bergenre MOBA (Massive Online Battle Arena) sedang hangat diperbincangkan. Ada berbagai macam game bergenre MOBA, salah satunya game Mobile Legends. Banyak tim E-Sport Mobile Legends khususnya di asia menjadikan fenomena ini sebagai ruang bisnis untuk menghasilkan keuntungnya yang besar. Dalam penelitian ini, peneliti merekomendasikan algoritma pembelajaran mesin yang baik untuk memprediksi hasil pertandingan Mobile Legends. Dari 600 data riwayat pertandingan yang dianalisis, penelitian ini merekomendasikan algoritma Artificial Neural Network (ANN) dan Random Forest (RF) sebagai algoritma yang tepat untuk memprediksi hasil pertandingan. Hasil prediksi dari masing-masing algoritma dapat mencapai 82% dan akurasi 80%. Temuan ini dapat membantu tim analisis E-sports membangun strategi pertandingan mereka. Kata kunci: artificial neural network; machine learning; mobile legend; prediksi; random forest 
Co-Authors Andi W. R. Emanuel Nicholas Manuel Albert Titasari Rahmawati