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INDONESIA
Faktor Exacta
Published by Universitas Indraprasta PGRI
ISSN : 1979276X     EISSN : 2502339X     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Faktor Exacta is a peer review journal in the field of informatics. This journal was published in March (March, June, September, December) by Institute for Research and Community Service, University of Indraprasta PGRI, Indonesia. All newspapers will be read blind. Accepted papers will be available online (free access) and print version.
Arjuna Subject : -
Articles 7 Documents
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Search results for , issue "Vol 14, No 3 (2021)" : 7 Documents clear
Teknologi Pengolahan Citra Digital Untuk Ekstraksi Ciri pada Citra Daun untuk Identifikasi Tumbuhan Obat Trinugi Wira Harjanti; Himawan Himawan
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.9841

Abstract

The leaf image identification process depends on the feature extraction results. Each medicinal plant has different shapes and patterns of leaf venation. But for one type of medicinal plants have the same pattern of venation shape and pattern even though the size is different. One of the methods for extraction of leaf image form characteristics is by fractal-based feature extraction. Through fractal can be calculated the value of leaf dimensions and searched parts of leaves that have similarities between one part with other parts. As for the method of extracting the characteristics of venation pattern using B-Spline method. Benefits of research conducted is to help people identifying the types of medicinal plants found, knowing the benefits and ways of brewing. While the research contribution is prototype software application based on information technology that can be used by the people through mobile phones for the identification of medicinal plants. To identify or match the results of feature extraction on the leaf found whether included in the medicinal plant, conducted by Euclidean Distance method. In the experiments we used 1100 data consist of 55 variety of medicinal plants for each 20 samples.The experimental result show that the accuracy of identification using of fractal and b-spline is 85.30%.
Prediksi Analisis Penderita Covid19 di Indonesia dengan Metode Linier Regresi dan Unsupervised Learning Yana Cahyana; Amril Mutoi Siregar
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.10591

Abstract

Penyakit Covid-19 sekarang ini telah dinyatakan penyeakit pandemic, karena tingkat penyebaran dan resiko yang ditimbulkan sangat berbahaya. Berbagai langkah seperti program awareness, social distancing, dan contact tracing telah dilakukan untuk mengendalikan wabah COVID-19. Jika tidak ada vaksin, prediksi kasus yang dikonfirmasi, meninggal, dan pulih diperlukan untuk meningkatkan kapasitas sistem perawatan kesehatan dan mengendalikan penularan. Dalam studi ini, kasus kumulatif dan harian dikonfirmasi, meninggal, dan pulih di Indonesia. Analisisa tidak mempertimbangkan perubahan apa pun dalam tindakan pengendalian pemerintah. Informasi dari studi ini dapat memberikan informasi yang relevan kepada pemerintah dan pejabat Kesehatan dan masyarakat. Bagaimana tingkat kesembuhan terhadap terkonfirmasi, tingkat kematian terhadap jumlah penderita. Penelitian ini menggunakan model regresi dan clustering dengan K-means, menggunakan unsupervised learning dan supervised learning untuk membangun distribusi model. Hasil penelitian ini dengan metode regresi dengan R2 = 0.99 sedangkan untuk clustering denga K= interval 10 - 15 dilihat dari hasil metode elbow
Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear Za'imatun Niswati; Rahayuning Hardatin; Meia Noer Muslimah; Siti Nur Hasanah
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.10010

Abstract

Cervical cancer is one of the most deadly types of cancer in women. According to Ferlay et al. (2018) cervical cancer ranks second for the type of cancer that attacks women the most. Data from the Indonesian Ministry of Health, there are at least 15000 cases of cervical cancer every year in Indonesia. This cancer is a type of tumor that develops in the epithelial tissue of the cervix. In addition to HPV vaccination, cervical cancer detection can also be carried out with a Pap smear test and VIA examination supported by medical image tests such as CT scan, microscopic and MRI (Akbar et al. 2021). Pap smear test is a type of test to detect cervical cancer which is quite widely used because the cost of the test is cheaper than the HPV vaccination. This test is carried out by taking samples of uterine cells which are then analyzed for early detection of cervical cancer (BPJS Kesehatan 2020). Through a pap smear can be found the presence of HPV infection and abnormal cells that can turn into cancer cells. The purpose of this research is to apply the ResNet50 and ResNet101 architectures on pap smear images to identify cervical cancer and evaluate the performance of the ResNet50 and ResNet101 architectures in the classification of cervical cancer on pap smear images. In this study, CNN ResNet50 and ResNet101 were used to classify cervical cancer on pap smear images. This study has created two models to predict the grade of cervical cancer on pap smear images. The ResNet50 architecture gets 91% accuracy while the ResNet101 architecture gets 89%. Although the architecture of ResNet101 is more complex than ResNet50, but if viewed from the results of the model evaluation, ResNet101 has a worse performance. This is due to the relatively small training data when trained with a large architecture such as ResNet101, not necessarily resulting in better accuracy.
Analisis Model Pengukuran Tinggi Permukaan Air Dengan Metode Canny Edge Detection dan Image Contouring Sebagai Indikator Peringatan Dini Bencana Banjir Frederick Alexander; Imelda Imelda
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.9567

Abstract

Flood disaster remains a natural phenomenon that often occurs in Indonesia, especially in the Wisma Tajur Housing Complex area, Tangerang City which causes property losses including the safety of the souls of the affected community. The difficulty experienced so far is how to measure the water level to obtain alert status information as an indicator of flood warning. As a solution in overcoming these problems, this research proposes a method based on digital image processing with canny edge detection algorithms and image contouring in an effort to measure river water level. Canny edge detection and image contouring were chosen due to their accuracy in detecting the edges of the image and the ease of the computation process. The steps taken in this research are to conduct a simulation experiment of measuring the water level using a water container that can describe the situation in the river, then doing field testing. Canny edge detection produces an outline which can then be detected by the contour, then water level measurements can be made on the bounding rectangle that is formed and changes dynamically with fluctuations in water level. The contribution of this research is the use of black measuring lines that are processed using thresholding techniques to facilitate the process of measuring water level using a combination of canny edge detection and image contouring techniques as well as adding attributes / features using threshold, MinVal, and MaxVal values on the canny edge. Sampling testing produces an accuracy of 99.96%, prototype testing produces 100% accuracy, and direct testing produces an accuracy of 99.85%.
ALGORITMA NEURAL NETWORK BACKPROPAGATION UNTUK PREDIKSI HARGA SAHAM PADA TIGA GOLONGAN PERUSAHAAN BERDASARKAN KAPITALISASINYA Nopri Santi; Suryarini Widodo
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.9365

Abstract

Stock is one type of investment where investors can gain profits in the form of capital gains and dividends. Types of shares based on the level of capitalization are divided into 3 types, namely the first layer (blue chips), the second layer, and the third layer. One of the techniques that investors use in order to make a profit is technical analysis, which is using data of past stock prices and volumes based on the assumption that trends can recur following historical data patterns. Based on the assumptions of technical analysis, it is possible to use data mining to predict stock prices. In this study, stock price predictions will be carried out by comparing three types of companies based on their capitalization, for first layer stocks using PT. Bank Central Asia Tbk (BBCA), the second layer using PT. XL Axiata Tbk (EXCL), and third layer using PT Pembangunan Graha Lestari Indah Tbk. The data mining algorithm that will be used is the Neural Network Backpropagation method. The attributes used as predictors are open, high, low, and volume, while the objective attribute is close. This study aims to determine whether daily stock historical data can be used to predict stock prices using the Neural Network Backpropagation method and how to compare the results of predictions between 3 companies with different capitalization levels. The result of RMSE for BBCA by using the most optimal combination of parameters and 3 hidden layer is 123.84. The result of RMSE for EXCL by using the most optimal combination of parameters and hidden layer 2 is 37.36. The result of RMSE for PGLI by using the most optimal combination of parameters and hidden layer 6 is 6.16. So that the backpropagation neural network algorithm is most optimally applied to third layer companies, PT. Pembangunan Graha Lestari Indah Tbk because the RMSE value is the smallest.
RANCANG BANGUN PROTOTYPE PENGENDALIAN LENGAN ROBOT (ROBOTIC ARM) SEBAGAI PEMINDAH BARANG BERBASIS INTERNET OF THINGS Syah Alam; Gunawan Tjahjadi; Nur Rahma Yenita; Supriyadi Supriyadi
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.9807

Abstract

This study proposes a prototype design of a robot arm control system based on the Internet of Things (IoT) by utilizing the NodeMCU microcontroller and the Blynk application. The NodeMCU microcontroller functions as a control system combined with four servo motors that are positioned as mechanical drives at the base, shoulder, elbow and grip of the robot arm. The angle settings of each motor are 180 °, 90 °, 60 ° and 90 ° which are used as actuators for lifting, gripping and moving loads. To control the robotic arm, the blynk application can be accessed via smartphone. From the results of the design and testing, it was found that the maximum load that could be moved was 20 grams with a transfer time of 46 seconds and a speed of 0.0054 seconds. The max-imum distance for moving goods is 25 cm and the types of goods being moved are those that have a rough sur-face. This research is useful as a solution for moving goods that can be controlled remotely
ANALISIS SENTIMEN PENGARUH PEMBELAJARAN DARING TERHADAP MOTIVASI BELAJAR DI MASA PANDEMI MENGGUNAKAN NAIVE BAYES DAN SVM Ariansyah Ariansyah; Mira Kusmira
Faktor Exacta Vol 14, No 3 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i3.10325

Abstract

The COVID-19 pandemic in Indonesia has had a huge impact on the education sector. Where it is today, it must implement and adapt a new learning model called online. motivation in learning is very important because it can improve achievements. In this case there are many pros and cons about online learning, many people's opinions on social media, especially Twitter about the influence of online learning on learning motivation. This study aims to analyze the influence between online learning and learning motivation. Public opinion on Twitter is used as a sentiment analysis to find out what people think about online learning on learning motivations whether positive or negative. The data used are tweets in indonesian with the keywords "online learning", "distance learning" and "motivational learning", with the number of datasets as many as 455 tweets are classified into 2 parts namely agreement and disagreement. The classification in this study used naive bayes classification algorithm method and Support Vector Machine (SVM) by preprocessing data using tokenize, transform case, filtering and stemming. Data is processed using rapidminer application. The highest accuracy result of this study was by the classification algorithm method support vector machine (SVM) with accuracy 97.22%, precision 94.72%, recall 100% and error 2.78%.

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