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All Journal Journal of Applied Information, Communication and Technology (JAICT)
Fikri Nizar Gustiyana
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Engineering

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LoRaWAN Network Planning for ODP Door Monitoring in Banyumas Districts I Ketut Agung Enriko; Fikri Nizar Gustiyana; Gilang Hijrian Fahreja; Gede Candrayana Giri
JAICT Vol 8, No 1 (2023)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v8i1.3939

Abstract

This study aims to design a LoRaWAN network on the coverage side to find out how many gateways are needed and to design an IoT-based monitoring system at ODC doors to minimize damage due to vandalism or forced opening. The method used is a simulation using Atoll software version 3.40 and several stages of calculations to predict signal strength and quality in the Banyumas Regency area. This study uses a frequency of 920 MHz with a bandwidth of 125 kHz and a Spreading factor of 1 to 12. The results obtained are a comparison of the number of gateways, signal strength and signal quality based on variations in the spreading factor. SF 7 produces 104 gateways with a signal strength of -71.88 dBm and a signal quality of 9.43 dBm. spreading factor. SF 12 produces 48 gateways with a signal strength of -79.8 dBm and a signal quality of 10.78 dBm. The larger the SF used will improve signal quality but reduce signal strength and also fewer gateways.
Forecasting JPFA Share Price using Long Short Term Memory Neural Network I Ketut Agung Enriko; Fikri Nizar Gustiyana; Hedi Krishna
JAICT Vol 8, No 1 (2023)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v8i1.4285

Abstract

To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. Machine Learning currently has a good level of accuracy in processing and predicting data. In this study, we modeled data using the Long-Short Term Memory (LSTM) algorithm to predict the stock price of a company called Japfa Comfeed. The main objective of this journal is to analyze the level of accuracy of Machine Learning algorithms in predicting stock price data and to analyze the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction shown in mape values and the data model obtained on variations in epochs values. All optimization models show that the higher the epoch value, the lower the loss value. Adam's Optimization Model is the model with the highest accuracy value of 98.44%.
Co-Authors Gede Candrayana Giri Gilang Hijrian Fahreja Hedi Krishna I Ketut Agung Enriko