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All Journal Elkom: Jurnal Elektronika dan Komputer JAST
Jenny Putri Hapsari
Universitas Islam Sultan Agung Semarang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Design of Monitoring Lighting Counter Based on Android Application (Case Study at Tower BTS INDOSAT OOREDOO Site Utara Jepara 14JPA010) Deki Renjaka Ardiyanto; Jenny Putri Hapsari; Eka Nuryanto Budisusila
Journal of Applied Science and Technology Vol 2, No 01 (2022): Januari 2022
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/jast.2.01.7-15

Abstract

The need for information through a monitoring system on BTS tower equipment is very necessary, including monitoring lightning strike protection. To find out whether there is a strike on the BTS, usually, technicians have to come to the BTS location to see the impact of the strike in the form of damage to the arrester, or other damage and even seek information from residents around the BTS location. The location of the BTS location is far from the head office, of course this is not effective.This research is the development of a counter lighting system designed to monitor and detect lightning strikes from a distance. By using Arduino Uno, ethernet shield, current sensor and android smart phone. The information displayed on the android smart phone is the location of the BTS tower, the time and magnitude of the strike current. The lighting protection monitoring tool based on android operation using the PZEM-004T module is a system designed and utilized to read the presence of lightning strikes which include the current flowing in the BTS tower grounding system.The test results show that the lighting protection monitoring tool using the PZEM-004T module can measure and display the current value and when a strike occurs, in real time and stored in the database. Based on the test results, it can be concluded that the results of the design of the lighting protection monitoring tool have a deviation (error) of 9,64% and a current of 12,5%.
Implementasi Convolutional Neural Network berbasis Transfer Learning untuk Klasifikasi Acute Lymphoblastic Leukemia Rana Adinda Manalus Fata; Jenny Putri Hapsari
Elkom: Jurnal Elektronika dan Komputer Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3679

Abstract

Leukemia is a cancer that originates in human blood cells. The most common type of leukemia (97%), with an incidence of 4–4.5 cases per 100,000 children per year, is Acute Lymphoblastic Leukemia (ALL). This indicates that leukemia can progress rapidly and become fatal for the patient within a few months. Therefore, a supporting method is needed that can classify blood cells automatically, quickly, and accurately. This method is a Convolutional Neural Network (CNN) using the EfficientNet-B3 architecture as a pre-trained model or for transfer learning. This dataset consists of 3,527 blood cell images that have been preprocessed to a size of 224x224x3 and image enhancement has been applied. The images were trained on the pre-trained model and then combined with Global Average Pooling (GAP), Batch Normalization, Dense, and Softmax layers until the model could classify the images into the ALL or HEM classes. The results of the study show that the EfficientNet-B3 architecture is capable of classifying white blood cell images into the ALL and HEM classes through the transfer learning process. The best hyperparameter configuration for optimal results includes a learning rate of 0.0001 and the RMSProp optimizer. The model achieved the best training accuracy of 100% at epoch 30 and a batch size of 16, while the best testing accuracy was 96% at epoch 50 and a batch size of 16. Additionally, the precision, recall, and F1-score were 96%, 94%, and 95%, respectively.
Co-Authors Deki Renjaka Ardiyanto Eka Nuryanto Budi Susila Rana Adinda Manalus Fata