Journal of Data Science and Software Engineering

THE EFFECT THE EFFECT OF SPREADING FACTOR ON LORA TRANSMISSION

Thu, 29 Dec 2022 04:39:06 +0000

The conditions of a different area can affect the transmission of data so that
transmission is needed that is resistant to interference and in certain conditions a
device that can monitor several places is needed at once. The concept of Wireless
Sensor Network (WSN) is applied to meet these demands. This research is shown to
determine the effect of Spreading Factor (SF) on Long Range (LORA) transmission
on distance by analyzing Quality of Service (QOS). The test is divided into 2
conditions, namely: The Line of Sight (LOS) condition & Non-Line of Sight (NLOS)
condition. The test results show that the maximum distance that the LoRa
transmitter can reach is 1100m in LOS conditions while for NLOS conditions it can
only reach a distance of 300m. The QOS parameters used to consist of Delay,
Throughput, RSSI, & SNR. Spreading Factor (SF) affects Delay and Throughput, not
RSSI and SNR. The best value of Delay (9.64 ms), Throughput (667.60 Bps), and RSSI
( -94.25 dBm) is at Spreading Factor (SF) 6 and SNR (5.23 dB) is Spreading Factor
(SF) 8 and for the distance, the value of RSSI (-76.45 dBm) and SNR (5.23 dB) is at a
distance of 10m. This applies in LOS and NLOS conditions.

IMPLEMENTATION IMPLEMENTATION OF DATA TRANSMISSION WITH LONG RANGE COMMUNICATION MODULE (LORA) AND MQTT-SN PROTOCOL TO SUPPORT SOIL HUMIDITY SENSOR DATA TRANSMISSION

Thu, 29 Dec 2022 04:52:27 +0000

Wireless sensor network can help remote data transfer. Implementation of wireless
sensor network in IoT system must be done with a good planning because IoT system typically
have limited system resources. This limitation can affect performance of a wireless network
sensor. The purpose of this study is to find out the effect of node range to the data transfer
performance in terms of delay, throughput, RSSI, and SNR by using QOS (quality of service)
analysis for LoRa and MQTT protocol. The results of LoRa’s protocol delay are between 2,82
ms to 37,27 ms. Throughput between 0,61 Kb/s to 24,29 Kb/s. SNR between 2,7 dBm to 8,34
dBm, and RSSI between -74,92 dBm to -122,36 dBm. On the other hand, the results of MQTT’s
protocol delay are between 677,49 ms to 1182,69 ms. Throughput between 0,60 Kb/s to 1,12
Kb/s. SNR between 2,7 dBm to 8,34 dBm and RSSI between -74,92 dBm to -122,36 dBm.

IMPLEMENTATION OF THE MQTT-SN PROTOCOL ON THE INTERNET GATEWAY DEVICE WITH UDP DATA PACKAGE DELIVERY

Thu, 29 Dec 2022 00:00:00 +0000

Internet of Things (IoT) is one of the new trends in the world of technology that is likely to become a trend in the future, to be able to make this happen, communication protocols such as MQTT-SN are needed which is a variant of the MQTT protocol and the connection protocol that supports IoT is NB- IoT to support this. Unlike MQTT which uses TCP as its communication protocol, MQTT-SN uses UDP as its data communication protocol. The purpose of this study is to determine the results of Quality of Service on the value of delay and throughput at QoS levels 0, 1, and 2. There are 2 test scenarios, namely real-time test scenarios and phased test scenarios. The design of the instrument consists of sensor instruments, Raspberry Pi microcontrollers for internet gateway device, and NB-IoT modules to then be tested with scenarios to get test results. Based on the test results, the best QoS results for the delay parameter in the real-time scenario are QoS level 2 with a delay value of 1.602 seconds, while for the gradual scenario there is QoS 0 with a delay value of 1.622 seconds. Furthermore, the best QoS results for throughput parameters in real-time scenarios are found at QoS level 2 with a throughput value of 245.79 bits per second and in a phased scenario found at QoS level 1 with a throughput value of 286.42 bits per second.

IMPLEMENTATION OF LORA WITH TEMPERATURE SENSORS IN IRRIGATION AREA (CASE STUDY: MARTAPURA CITY)

Thu, 29 Dec 2022 06:11:20 +0000

This study applies to the concept of a Wireless Sensor Network (WSN) consisting of a transmitting instrument and a receiving instrument using Long Range (LoRa) data transmission with a frequency of 915 MHz and LoRa 920 MHz. The test is divided into 2 tropical weather conditions, namely when the weather is sunny and rainy. The test results show that the maximum distance that the LoRa transmitter can reach is 1 kilometer. The QoS (Quality of Service) parameters used to consist of Delay, Throughput, RSSI, & SNR. Based on the test results of the QoS parameters, both frequencies affect tropical weather conditions and increase as the distance of data collection increases. LoRa Frequency 915 MHz and Frequency 920 MHz have their respective differences and advantages, which are uncertain on weather conditions and data transmission distances.

IMPLEMENTATION OF HARALICK METHODS WITH RANDOM FOREST CLASSIFIER FOR IDENTIFICATION OF POTATO DISEASE IN LEAF IMAGES

Thu, 29 Dec 2022 07:19:44 +0000

Potato plants are one of the most widely grown food crops in the highlands of Indonesia. Besides being used as food, potatoes are now known to be used to fight free radicals, control blood sugar, and nourish the digestive system. Therefore, potatoes have good prospects for development. In connection with efforts to develop potatoes in Indonesia, there are obstacles, namely the attack of potato plants by disease. As for the disease in potato plants, one of the characteristics of knowing it is on the leaves. To identify the leaf image, the texture feature is an important feature to recognize the leaf from an image. This is because there are differences in texture between normal and diseased leaves. To perform image processing through texture features, one method that can be used is haralick. In this study, a system was created to identify the types of diseases present in potato leaves using the Haralick method with the Random Forest Classifier. The image used is 300 data consisting of 3 classes, namely Late Blight, Early Blight, and Health. In this study, the testing was carried out by dividing the training and testing data with a percentage of 70:30, 80:20, and 90:10. The highest accuracy value in this study was obtained by using a combination of 80:20 split data, which was 0.88. The 70:30 data split gets an accuracy of 0.85 and the 90:10 data split gets an accuracy of 0.87.

AN APPLICATION OF FUZZY NEUTROSOPHIC SOFT SETS METHOD FOR PREDICTING COVID-19 SURVEILLANCE STATUS

Lisnawati, Andi Farmadi, Dwi Kartini, Mohammad Reza Faisal, Rudy Herteno — Thu, 29 Dec 2022 07:45:32 +0000

Coronavirus Disease 2019 (COVID-19) is a new type of disease that has never been previously identified in humans and has been declared a pandemic. The diagnosis of the disease is complicated by the variety of symptoms and imaging findings and the severity of the disease at the time of presentation. Fuzzy Neutrosophic Soft Sets are able to handle many types of uncertainty data such as ambiguity, inaccuracy, ambiguity, and inconsistency. Therefore, Fuzzy Neutrosophic Soft Sets can be applied to overcome the uncertainty of symptoms in COVID-19 surveillance. This research was conducted by collecting and presenting the respondent's Neutrosophic value and Neutrosophic value as a knowledge base, then performed Fuzzy Neutrosophic Soft Sets operations (composition, complement, value function, and score function) to obtain the monitoring status of the predicted results. Furthermore, the monitoring status of the predicted results is compared with the actual monitoring status of the respondents to obtain the accuracy level of Fuzzy Neutrosophic Soft Sets. Based on testing of 12 respondents, with 7 respondents as training data and 5 respondents as testing data, the accuracy of the Fuzzy Neutrosophic Soft Sets method in the diagnosis of COVID-19 surveillance status was 80%.