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Journal : Journal of Applied Information, Communication and Technology (JAICT)

Rear Dump Truck Measurement Design Using Laser For Loading Process Automation Muhammad Irwan Yanwari; I Ketut Agung Enriko
JAICT Vol 7, No 2 (2022)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v7i2.3648

Abstract

Transportation of goods using trucks is an irreplaceable method. However, it is undeniable that the truck-based method of transporting goods often attracts controversies, such as over-loading and products containing hazardous chemicals. This makes the need for automation of loading goods to trucks so that intentional or unintentional errors such as overloading and damage caused when an accident occurs in the process of loading chemical goods can be minimized. Of the four stages proposed in the automation process, namely (1) Trucks enter the cargo loading area, (2) Measurement of the volume or capacity of the vessel, (3) Calculation of the ideal arrangement of products on the vessel, and (4) The process of loading goods using a robotic devices. This article contains the design process for measuring the volume or capacity of the vessel. The measurement process is carried out using a plus (+) laser module and the steps taken in the measurement process are scanning the vessel that is highlighted using the laser module at several angles using a camera and calculating the length, width, and height of the body using trigonometric formulas. With the automation of loading goods, it is hoped that human intervention in the loading and unloading process can be eliminated so that errors that may occur can be minimized.
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%.
Implementation Control And Monitoring System Water Quality of Koi Fish Ponds Based On the Internet Of Things Enriko, I Ketut Agung
JAICT Vol. 9 No. 1 (2024)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v9i1.5379

Abstract

Koi fish is one of the ornamental fish that is in great demand and has a fairly high price. Water quality plays an important role in the success of keeping koi fish. The quality of koi fish water must be at an ideal temperature of 25-30 °C and an acidity level or pH of 7-8 pH. The level of salt contained in water for koi fish must also be considered. A pond with a size of 200 x 50 x 100 cm requires a salt content of 1 to 2 ppm. Giving this salt is done to prevent the growth of bacteria in the koi pond which can come at any time. Ignorance of pond owners about the value and condition of water quality can disrupt the health of koi fish which can cause death. Based on these problems, the authors created a water quality control and monitoring system in koi fish ponds. The system created consists of a pH sensor, temperature sensor, and salinity sensor, and uses the Message Queuing Telemetry Transport (MQTT) protocol. The process of sending data to the IoT platform using a WiFi network. Based on the temperature sensor test, there is an average error of 1.4% with a sensor accuracy level of 98.6%. Testing the pH sensor and salinity sensor using the linear regression method. As for the pH sensor, the average error is 2% with an accuracy rate of 98%. The results of the salinity sensor test obtained an average error value of 7.6% with an accuracy rate of 92.3%. Then in the MQTT protocol, the parameters for delay and jitter have a bad category, while throughput has a moderate category, and packet loss has a very good category according to the TIPHON standard.