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Automated Air Conditioner Controler and Monitoring Based on Internet of Things Mas Aly Afandi; Silvi Nurandi; I Ketut Agung Enriko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 1 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.64563

Abstract

Air conditioner make electricity demand becomes higher over time. International Energy Agency (IEA) shows that electricity consumption for air conditioner will be the main trigger for the increase in world electricity demand in 2050. Higher electricity demand caused by inefficient usage of air conditioner due to human error factors. Human error that mostly happen is forget to turn off the air conditioner. This condition make air conditioner will be operate all day. This research is aim to reduce human error case that happened by making automated air conditioner controller and monitoring based on internet of things. This research use passive infrared sensor as an input to make sure air conditioner in the room is used or not and temperature sensor DHT 11 to make sure air conditioner operation. Internet of things technology is used to monitor the output from the system and control the device. Data test shows that the device works well. Air conditioner controller device works as the command and scenario that given. Error reading for temperature sensor is 0.29% and best configuration for infrared transmitter and passive infrared at radius 90°.
Automatic Temperature Control System on Smart Poultry Farm Using PID Method I Ketut Agung Enriko; Ryan Anugrah Putra; Estananto
Green Intelligent Systems and Applications Vol. 1 Iss. 1 (2021)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.768 KB) | DOI: 10.53623/gisa.v1i1.40

Abstract

Chicken farmers in Indonesia are facing a problem as a result of the country's harsh weather conditions. Poultry species are very susceptible to temperature and humidity fluctuations. As a result, an intelligent poultry farm is necessary to intelligently adjust the temperature in the chicken coop. A smart poultry farm is a concept in which farmers may automatically manage the temperature in the chicken coop, thereby improving the livestock's quality of life. The purpose of this research is to develop a chicken coop prototype that focuses on temperature control systems on smart poultry farms via the PID control approach. The PID control method is expected to allow the temperature control system to adapt to the temperature within the cage, thereby assisting chicken farmers in their tasks. The sensor utilized is a DHT22 sensor with a calibration accuracy of 96.88 percent. The PID response was found to be satisfactory for the system with Kp = 10, Ki = 0, and KD = 0.1, and the time necessary for the system to reach the specified temperature was 121 seconds with a 1.03 % inaccuracy.
Development of Catch-up TV Application on Internet TV Platform for RTSP/RTMP Streaming Protocol I Ketut Agung Enriko
Ultimatics : Jurnal Teknik Informatika Vol 7 No 1 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (684.765 KB) | DOI: 10.31937/ti.v7i1.342

Abstract

With the proliferation of video streaming services, now internet TV has become a popular service on the Internet. Catch-up TV is an attractive service in internet TV, where user who misses a live programme can watch it later. Meanwhile, not all streaming protocols, by default, are able to support catch-up TV application. This paper explains how to develop catch-up TV application in RTSP/RTMP streaming protocol, by doing shell-script programming and using Wowza streaming engine, on Linux-based streaming server. The research result has been implemented in UseeTV, one of the largest commercial internet TV service in Indonesia.
Weight-Loss Program Assistance System for Obesity Patients Based on Internet of Things (IoT) Technology I Gusti Bagus Astawa; I Ketut Agung Enriko
Ultimatics : Jurnal Teknik Informatika Vol 8 No 1 (2016): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (791.931 KB) | DOI: 10.31937/ti.v8i1.502

Abstract

These days, the use of Internet technology can be found in almost every sector of human life. One of the advanced Internet technologies is Internet of Things (IoT), that is a technology where devices can communicate via Internet connectivity. It is used in many vital industries like automotive, electricity, home automation, and healthcare. This study aims to implement IoT technology for healthcare sector, i.e. in helping obesity people to pursue their weight-loss program (WLP). The result is a system which consists of a smart weight scale, a mobile application, and food menu recommendation database in order to help obesity people in their WLP program. A trial to some obesity patients is performed to collect data. Index Terms—Internet of things; overweight; weight loss program; food recommendation
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%.
Improvement of Student Attendance System for Recording Student Surface Body Temperature Based on Internet of Things Mas Aly Afandi; I Ketut Agung Enriko; Muhammad Aulia Baihaqy
Elinvo (Electronics, Informatics, and Vocational Education) Vol 7, No 2 (2022): November 2022
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (658.395 KB) | DOI: 10.21831/elinvo.v7i2.53944

Abstract

Student attendance is a system used for tracking student activity in school. Many methods are used to develop student attendance systems, such as Quick Response (QR) Code systems, Radio Frequency Identification (RFID) systems, fingerprint systems, and so on. The COVID-19 pandemic has driven technological development, especially in the student attendance system. Measuring human surface body temperature has become a protocol that must be done before entering school. Student attendance systems need to expand the function not only for attendance but also for monitoring student surface body temperature. This research aims to improve student attendance systems by adding surface body temperature measurements and recording during student presence. Recording data can be done by using the internet of things. Student presence data will be sent to school databases throughout the internet. This system uses RFID technology for student presence and a non-contact thermal sensor for temperature measurement. According to data research, non-contact thermal sensors provide a temperature reading with an average error of 1.69%, a minimum error value of 0.96%, and a maximum error value of 2.57% with a range error value of 0.35°C – 0.95°C. RFID test shows that the optimum distance for the system to read an RFID card is 0 – 2cm. The System also successfully sent presence data to the student school database through the internet. This study concludes that developed systems can track student attendance by recording the student’s surface body temperature while in presence. Further work will be focused on managing data networks if this system is used with many users in the school.
Komparasi Hasil Optimasi Pada Prediksi Harga Saham PT. Telkom Indonesia Menggunakan Algoritma Long Short Term Memory I Ketut Agung Enriko; Fikri Nizar Gustiyana; Rahmat Hardian Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5822

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. One of the steps to achieve this can be using a prediction system based on machine learning. There are several algorithms that can be used to predict stock values, one of which is the Long-Short Term Memory (LSTM) algorithm. This study aims to compare several optimization models, namely the Adam, SGD and RMSprop optimization models to analyze the accuracy of the LSTM algorithm in predicting stock price data and analyzing 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 as shown in the Mean Absolute Percentage Error (MAPE) value and the data model obtained on variations in epochs values. Adam's optimization model shows that the higher the epoch value, the lower the loss value. The lower the loss value, the higher the prediction accuracy of the resulting stock data. Adam's Optimization Model is also the model with the highest accuracy value of 98.45%.
IMPLEMENTATION AND ANALYSIS OF THE INTERNET OF THINGS SYSTEM FOR ELECTRICAL ENERGY MONITORING AT INSTITUT TEKNOLOGI TELKOM PURWOKERTO Agung Enriko I Ketut; Mas Aly Afandi; Herryawan Pujiharsono; Fikri Nizar Gustiyana; Hedi Krishna; Filbert H. Juwono
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.1027

Abstract

Measurement of electric power usage is carried out using simple measuring instruments and the recording is still manual so that the data obtained is not real-time and accurate. This research aims to implement an electrical energy monitoring system using the Internet of Things (IoT) to obtain real-time information related to electrical energy in the education industry. This research uses an Industrial Grade Power Meter to get a more accurate measurement value. To connect the Power Meter device with the IoT system, this research uses Modbus RS485 communication and a mini PC to process data from the meter, so that the data can be sent to a server using the MQTT communication protocol, and displayed on the Dashboard. The test results of this study indicate that the monitoring system can be implemented and the system runs well with end-to-end measurement results. From the measurement results, the current value (3 phase average) has an average deviation of 0.001 Amperes, Voltage (3 phase average) has an average deviation of 0.519 V, Power factor has an average deviation of 0.012, Active power has a deviation average of 0.000 kW, reactive power with an average deviation of 0.000 kVAR, apparent power with an average deviation of 0.000 kVA and frequency with an average deviation of 0.124 Hz. Then the MQTT protocol has a quality of service with index 4 based on TIPHON standardization on delay, throughput, and packet loss parameters, and index 3 based on TIPHON standardization on jitter parameters.