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All Journal Telematika JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Mujiyanto, Mujiyanto
Universitas AMIKOM Yogyakarta
Computer Science & IT Education

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Measuring the Effectiveness of Multimedia Use in Micro-Credential Game Developer Programs through Simple Additive Weighting (SAW) yanto, Muji
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 2 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i2.76964

Abstract

This study aims to assess the effectiveness of multimedia use in the Micro-Credential Game Developer Program using the Simple Additive Weighting (SAW) method. Given the significant growth of the game industry in Indonesia, enhancing the quality of local games through human resource development via micro-credential programs is a primary focus. This quantitative research collected data from 101 lecturers and 22 students using a Likert Scale-based questionnaire to evaluate various learning media. The SAW calculation results indicate that Video and Audio media are rated as the most effective, achieving the highest scores of 1 (82.57% for Video and Audio) based on criteria such as ease of understanding, improvement in learning effectiveness, and motivation. Conversely, Teks media received the lowest score of 0.8 (64%), indicating challenges in material comprehension. The study's conclusions affirm the importance of audiovisual media in enhancing the quality of game development learning and provide recommendations for integrating these media into the game education curriculum. Despite offering valuable insights, the study acknowledges limitations related to the limited sample scope, suggesting a need for further research for broader exploration and validation of findings.
Comparison of Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in Forecasting Commodity Prices Mujiyanto, Mujiyanto; Nurindahsari, Susi; Nurul Izza, Rahmafatin
Telematika Vol 17, No 2: August (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i2.2932

Abstract

In this study, we compare the performance of both hybrid and non-hybrid forecasting models, explicitly focusing on Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in predicting commodity prices within the volatile market of Central Java, Indonesia. The primary objective is to evaluate which hybrid and non-hybrid models provide the most accurate and reliable forecasts under various conditions. Analyzing daily price data from the SiHaTi platform, an official service provided by Bank Indonesia, the Hybrid ARIMA-LSTM model emerges as the most accurate, achieving a forecast accuracy of 92.5%, compared to the 78.3% and 84.7% accuracies of Linear Regression and ARIMA, respectively. These findings underline the potential advantages of combining machine learning with statistical methods to improve predictions in dynamic market conditions, providing invaluable insights for policymakers and market analysts. However, it should be noted that only one hybrid model was compared, and future research should explore multiple hybrid models to ensure a comprehensive evaluation of their effectiveness.
Co-Authors Nurindahsari, Susi Nurul Izza, Rahmafatin