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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Transformatika
Warsono Warsono
Lampung University
Computer Science & IT

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Calibration of Geomagnetic and Soil Temperatur Sensor for Earthquake Early Warning System Dodi Yudo Setyawan; Dona Yuliawati; Warsito Warsito; Warsono Warsono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i5.7592

Abstract

The study of Design of Earthquake Early Warning System for Real Time Using Geomagnetism and Total Electron Content with Fuzzy Logic through competitive grants scheme has obtained the prototype of the earthquake early warning system. However, it still needs improvements on in the calibration of thesensor system especially for MAG3110 sensor and DHT11 sensor. This calibration was done by adjusting the sensor system to the existing measuring devices standards in the Physics Department laboratory of the Sains Faculty Lampung University, to obtain measurement accuracyand to get a good result about where and when the earthquake would occurand how strong the earthquake would be. The calibration of MAG3110 sensor and DHT11 sensor obtained the standard correction results, the standard deviation of MAG3110 from 3 axes, namely x axis was 8.5, y axis was 2.66, and z axis was 1.9, whereas the standard deviation for DH11 sensor was 0.1161.
Robusta London Coffee Price Forecasting Analysis Using Recurrent Neural Network – Long Short Term Memory (RNN – LSTM) Ferzy Tryanda Nosa; Dian Kurniasari; Amanto Amanto; Warsono Warsono
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5482

Abstract

Coffee price forecasting has a significant role in preventing price fluctuations at a time. Therefore, a method is needed that can be used to forecast the price of coffee. This study discusses the analysis of coffee price forecasting using the Recurrent Neural Network – Long Short-Term Memory (RNN – LSTM) method. This study will be determined the best LSTM model that aims to get the results of forecasting the price of London robusta coffee with the smallest  Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. Using the LSTM model with units of 128 and dropouts of 0.1, forecasting the price of London robusta coffee has an RMSE value of 1,303 and MAPE of 3.53%. Therefore, the LSTM model can indicate the cost of London robusta coffee with an accuracy rate of 96.47%. 
Co-Authors Amanto Amanto Dian Kurniasari Dian Kurniasari Dona Yuliawati Warsito .