Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal G-Tech : Jurnal Teknologi Terapan
Alfredi Yoani
Universitas Airlangga, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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

Found 2 Documents
Search

 1 
Prediksi Jumlah Kejadian Banjir Bulanan di Indonesia Berdasarkan Analisis Long Short Term Memory Alfredi Yoani; Sediono Sediono; M. Fariz Fadillah Mardianto; Elly Pusporani
G-Tech: Jurnal Teknologi Terapan Vol 7 No 4 (2023): G-Tech, Vol. 7 No. 4 Oktober 2023
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v7i4.3346

Abstract

Floods are among the most common and dangerous natural disasters worldwide, leading to loss of life and economic instability. In Indonesia, floods have been the most frequently occurring natural disaster since 2009. The high frequency underscores the urgency of predicting the number of natural disaster events to assist the government and the public in taking appropriate mitigation measures, as well as contributing to the achievement of Sustainable Development Goal 15 regarding Terrestrial Ecosystems. The method used to predict the monthly occurrence of floods in Indonesia is Long Short Term Memory (LSTM). LSTM was chosen for its ability to process sequential data over a long period of time. Upon analysis, highly accurate forecasting results were obtained, with a Mean Absolute Percentage Error (MAPE) of 8.04%, a Root Mean Square Error (RMSE) of 5.991. The model is also proficient at estimating training data, with an value of 95.71%.
Pemodelan Indeks Kebahagiaan di Indonesia Berdasarkan Pendekatan Mixed Geographically Weighted Regression Alfredi Yoani; Fina Insyiroh; Leni Sartika Panjaitan; Toha Saifudin; Suliyanto
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3639

Abstract

The well-being of society, involving the fulfilment of basic needs and opportunities for education and employment, can be measured through the happiness index. This research aims to assist the Indonesian government in achieving Sustainable Development Goal 3 related to Health and Well-being. It is hoped that by comprehending these factors, the government can improve the health and well-being of the Indonesian population. The happiness index varies across different geographical regions due to factors such as culture, social dynamics, and the environment, which can have different impacts from one region to another. Given the randomness in data patterns stemming from the diverse provinces in Indonesia, this study employs the Mixed Geographically Weighted Regression (MGWR) method. Results reveal that the MGWR model, utilizing a fixed Gaussian kernel weight, yields the lowest Akaike’s Information Criterion Corrected and the highest at 87.2%, underscoring its precision in modeling the happiness index in Indonesia.
Co-Authors Elly Pusporani Fina Insyiroh Leni Sartika Panjaitan M. Fariz Fadillah Mardianto Sediono, Sediono Suliyanto Toha Saifudin