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All Journal JMECS (Journal of Measurements, Electronics, Communications, and Systems) eProceedings of Engineering
Ratna Sari
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Skin Cancer Classification Malignant and Benign Using Convolutional Neural Network Nur Alyyu; Ratna Sari; R.Yunendah Nur Fuadah; Nor Kumalasari Caecar Pratiwi; Sofia Saidah
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol 9 No 2 (2022): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v9i2.5724

Abstract

Skin cancer is one of the most deadly cancers. This cancer ranks third after cervical cancer and breast cancer in Indonesia. In detecting skin cancer, a dermatologist can carry out a biopsy. However, carrying out a biopsy requires a long time and preparation. Innovations to classify and detect skin cancer using artificial neural networks are overgrowing in helping doctors so that prompt and appropriate treatment can be carried out. The purpose of this project was to develop a system to classifying skin cancer using Convolutional Neural Networks (CNNs) and the ResNet50 architecture. This research examined the extent of system performance results using accuracy, recall, precision, and f1-score by doing several trials by changing the hyperparameters. The dataset used in this study was obtained online through Kaggle, with two classes, malignant and benign, divided into 80% training data and 20% test data. Based on the testing result, the best hyperparameter system was obtained using AdaMax optimizer, the learning rate was 0.0001, the batch size was 64, and the epoch was 50. In this research, The performance results values were 99% for precission, recall and f1-score. Simulation results show that this method with highly optimized hyperparameters can accurately classify malignant and benign skin cancer.
Design And Implementation Of An Mqtt-Based Internet Of Drone Things For Swarm Drone Ratna Sari; Nyoman Bogi Aditya Karna; Arif Indra Irawan
eProceedings of Engineering Vol 10, No 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Swarm drones are drones that communicate witheach other, the more drones that communicate, the heavier thecommunication network, therefore the aim of this research is tobe able to make light communication between two drones usingthe Message Queuing Telemetry Transport (MQTT) dataprotocol. This thesis uses NodeMCU tools. The protocol used isMQTT in the use of communication between drones. Because ofthe shortcomings of the HTTP protocol, the MQTT serverprotocol must be implemented to support the development of theIoT platform. MQTT is a lightweight and simple communicationprotocol. The plan outcome is that two drones will be able tocommunicate reliably utilizing the MQTT data protocol. Afterconducting the test, the results of testing network quality with QoSparameters are delay, jitter, packet loss, and throughput. Theaverage delay of MQTT QoS 0 is 0.103 s, MQTT QoS 1 is 0.111s, and HTTP is 0.124 s. HTTP and MQTT QoS 0 get 0% packetloss, while MQTT QoS 1 gets 0.1% packet loss. Throughput on theMQTT protocol is faster than the HTTP protocol. And the networkquality of the MQTT protocol is better than HTTP.Keyword— MQTT,HTTP, IoT, Subscribe, Publish andBroker
Co-Authors Arif Indra Irawan Nor Kumalasari Caecar Pratiwi Nur Alyyu Nyoman Bogi Aditya Karna R. Yunendah Nur Fu’adah SOFIA SAIDAH