Article Per Year (5 Year)
p-Index From 2021 - 2026
1.817
P-Index
This Author published in this journals
All Journal Teknik dan Ilmu Komputer JURNAL ELEKTRO SABDAMAS Rekayasa: Jurnal Teknik Sipil Jurnal Ilmiah Teknik Mesin Poros Jurnal Pengabdian Masyarakat Charitas Cylinder : Jurnal Ilmiah Teknik Mesin Jurnal Serina Abdimas
Sandra Octaviani
Universitas Katolik Atmajaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : JURNAL ELEKTRO

 1 
Perancangan Jaringan Fiber To The Home Berbasis Gigabit Passive Optical Network Di Citra Garden Puri Cluste Denza Timothy Sutjipto, Marcellus; Octaviani, Sandra; Ghozali, Theresia; Windha Mahyastuty, Veronica; Kristina Yanti Hutapea, Duma
Jurnal Elektro Vol 16 No 1 (2023): Vol.16 No.1 April 2023: Jurnal Elektro
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v16i1.5129

Abstract

This research aims to design a GPON-based Fiber to The Home (FTTH) line in the Citra Garden Puri cluster Denza . The method used is to carry out field observations, by conducting surveys to determine the need for equipment that will be used in designing the route. After the survey is conducted, this project requires Google Earth Pro software to do the mapping of the spot that is used for the ODP and to create passages for the fiber. To make sure that all planning already meets the requirement, there are several calculationdone in this research such as; link loss budget, power link budget, power margin and risetime budget. The distance that were used in the calculation are calculated from the STO Cengkareng until the ONT on each customer. Based on the calculation, the result of the biggest loss for link loss budget are obtained from the upstream, in the amount of 22,0456 dB and the loss from downstream are equals to 21,5721 dB. The result that obtained for the upstream loss of power link budget are equals to -17,0456 dBm and -16,5172 dBm for downstream. The next calculation is power margin, from this project we obtained the lowest power margin for the upstream 6,9544 dB and 7,4288 dB for downstream. The last calculation obtained for this project is rise time budget, from the calculation the shortest data for the rise time budget upstream is 0,1001 ns and for the downstream is 0,1019 ns. Based on all the calculation, this project can be concluded as a success because all the calculation are between the boundary that is tolerated by PT Telkom.
Klasifikasi Gender Berdasarkan Gambar Menggunakan Metode Deep Learning Pada MATLAB Sachi, Haenuki; Wijayanti, Linda; Octaviani, Sandra
Jurnal Elektro Vol 16 No 2 (2023): Vol.16 No.2 Oktober 2023: Jurnal Elektro
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v16i2.5135

Abstract

In the present era, machine intelligence, also known as Artificial Intelligence (AI), is demanded not only to execute specific commands but also to recognize, analyze, or even make decisions, thereby providing desired outputs. By harnessing the power of AI, it is anticipated that desired outcomes will be more accurate and goal achievement will be optimized, minimizing losses. With the capabilities of AI in mind, a research study has been conducted on AI's ability to analyze and make decisions based on specific data. In this study, data in the form of images of men and women were utilized. The objective of this research is to analyze the ability of AI, particularly in gender classification. The method employed in designing this system is Deep Learning, with GoogLeNet as the Convolutional Neural Network utilized. In testing, the data accuracy ranged from 61.8% to 100% for the system without training algorithm options and from 97.5% to 100% for the system with training algorithm options. Testing was also carried out on a smaller set of training data and grayscale images, yielding lower accuracy ranges. From this research, it can be concluded that the quantity of training data, image preprocessing, and training algorithm options are crucial indicators for enhancing prediction accuracy.
Implementasi IoT dengan ESP 32 Untuk Pemantauan Kondisi Suhu Secara Jarak Jauh Menggunakan MQTT Pada AWS Austin, Calvin; Mulyadi, Melisa; Octaviani, Sandra
Jurnal Elektro Vol 15 No 2 (2022): Vol.15 No.2 Oktober 2022: Jurnal Elektro
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v15i2.5141

Abstract

The development of the internet of things (IoT) creates many new innovations in the industrial sector aimed at increasing effectiveness. One of them is to monitor the condition of industrial process machines remotely as discussed in this study. This monitoring can be done using a device connected to the internet. The design of this system requires a microcontroller with an ESP32 wifi module as a data receiver and sender. The data sent is data from the temperature sensor. The data is in the form of simulation data generated from the program and has a random number. Machine-to- server data communication uses the Message Queuing Telemetry Transport (MQTT) protocol. The entire machine-to-server, server-to-server communication system will be carried out in the cloud using a cloud computing platform, namely Amazon Web Services (AWS). The test results show that the payload and temperature data sent from the microcontroller can be stored in the database. To see the reliability of the system, two Normal and Stress Tests were carried out, with a success percentage of 100% fo r data storage to the database on two tests and 16.67% failure in sending data on the Stress Test. The two tests were arranged under different conditions.
Perbandingan Algoritma Machine Learning menggunakan Orange Data Mining untuk Klasifikasi Jenis Kendaraan pada Sistem Tilang Digital Pranadjaya, Egipta; Pangestu, Evan Sudira; Octaviani, Sandra; Darmawan, Marten; Sereati, Catherine Olivia
Jurnal Elektro Vol 17 No 1 (2024): Jurnal Elektro: April 2024
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v17i1.5429

Abstract

This paper discusses the application of the Orange Data Mining application to compare several machine learning algorithms for classifying vehicle types in digital ticket systems. This research compares and analyzes the logistic regression algorithm, Support Vector Machine (SVM) and Neural Network (NN) to solve vehicle classification problems in digital traffic tickets. The research results show that in the training process and based on the dataset used, the algorithms that have the highest level of accuracy are Logistic Regression, Neural Network and Support Vector Machine. Meanwhile, during the testing process, all algorithms in the model were able to carry out classification with 100% accuracy
Co-Authors ., Weimintoro Anthon de Fretes, Anthon de Fretes Austin, Calvin Bachri, Karel Octavianus Basuki, Widodo Widjaja Catherine Olivia Sereati Duma Kristina Yanti Gunawan, Harjadi Hadi Sutanto Harto Tanujaya Henry Cahya Tanijaya Indriati, Kumala Karangga, Yusaphat Phosa Lukas, Lukas Marcellio, Ricco Marten Darmawan Melisa Mulyadi Octavianus Bachri, Karel Pangestu, Evan Sudira Pranadjaya, Egipta Rippun G. Manalu, Ferry Rosehan Rosehan, Rosehan Sachi, Haenuki Steven Darmawan, Steven Sugioko, Andre Surya Permana, Rizky Theresia Ghozali Timothy Sutjipto, Marcellus Tjahjadi, Fanny Trifenaus Prabu Hidayat Veronica Windha Mahyastuty Wijayanti, Linda Windha Mahyastuty, Veronica