Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni, Febri; Santoso, Agus Adi

doi:10.31598/sintechjournal.v3i1.528

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Pada penelitian sebelumnya [20] berhasil memanipulasi gambar yaitu pada gambar medis sinar-x dengan mengecilkan ukuran penyimpanan gambar, namun gambar yang dihasilkan tetap terlihat dengan jelas melalui perbandingan algoritma Run Length Encoding, Huffman, dan Lempel Ziv Welch. Pada saat memanipulasi gambar tidak hanya dengan mengecilkan ukuran gambar contohnya dengan meningkatkan kontras pada gambar atau mengubah dan menambahkan objek pada gambar [21] [22].…”

Section: Pendahuluanunclassified

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

Khairunnisak,

Fahmi,

Suhartono

2023

SINTECH Journal

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Abstract In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium. One of the media for hiding messages is pictures. However, steganography techniques can still be detected by steganalysis techniques. Steganalysis is a technique for analyzing hidden messages in steganography. Therefore this study applies image processing techniques with the Generative Adversarial Network algorithm model, which aims to manipulate images so that steganalysis techniques cannot detect hidden messages. Proof of the results of applying the Generative Adversarial Network algorithm using a web-based application containing message hiding and extraction functions. The results obtained are that the Generative Adversarial Network algorithm can be applied to create mock objects, and images can revive based on training data which is a model for how the algorithm works. In addition, the results of testing the Generative Adversarial Network algorithm were successfully applied to image steganography which functions to prevent steganalysis techniques from trying to detect messages in images. Future research is expected to be able to select steganographic images other than the results from the training data model according to the original size chosen randomly according to the selection of the user.

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Section: Pendahuluanunclassified

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

Khairunnisak,

Fahmi,

Suhartono

2023

SINTECH Journal

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“…Kernel berfungsi untuk mentransformasi data sehingga ruang dimensinya menjadi tinggi dan memisahkan data secara linear. SVM memiliki kelebihan dengan adanya ruang kernel yaitu dapat mengklasifikasikan model hanya dengan data yang terpilih saja [18]. Metode SVM mempunyai empat buah kernel polynomial, sigmoid, linear dan Radial Basis Function (RBF).…”

Section: Metode Support Vector Machineunclassified

Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine

Jochsen,

Angeline,

Herwindiati

et al. 2023

JoSYC

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One area that has a rich cultural heritage is Bali. Bali is very well known as a very beautiful place and is often visited by tourists in Indonesia and outside Indonesia. Temple buildings in Bali have unique characteristics that reflect the richness of Indonesian culture. So many tourists are interested in vacationing there. However, due to the uniqueness of each temple building there, there is a lack of knowledge about the buildings being seen, so the main aim of this design is to develop a system for recognizing historical temple buildings in Indonesia through building images. More broadly, this design contribution can be applied in the development of similar systems for other historical regions in Indonesia, enriching efforts to preserve and promote cultural heritage nationally. Thus, this design not only paves the way for innovation in the field of image recognition, but also has a positive impact in preserving valuable cultural property. The method used for recognition is Local Binary Pattern as texture feature extraction from temple building images, while Support Vector Machine with a polynomial kernel is used to recognize temple buildings. It is hoped that the combination of these two methods can provide good results in recognizing temple buildings with the correct classification level. The accuracy of this design model using 90 percent training data and 10 percent test data was 45.93 percent, while when using 80 percent training data and 20 percent test data, the accuracy dropped slightly to 43.96 percent. When using 90 percent training data, the recognition of historical buildings produces a precision of 59 percent, a recall value of 71 percent, and an f1-score of 57 percent. On the other hand, with 80 percent training data, the recognition of historical buildings produces 62 percent precision, 72 percent recall value, and 57 percent f1-score.

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“…Corona virus disease (COVID-19) merupakan virus Corona jenis baru yang menyebabkan infeksi dan menular, proses penyebaran bisa melalui cairan dari hidung ataupun air liur saat yang terinfeksi bersin atau batuk [1]. Virus COVID-19 kemudian menyebabkan pandemi.…”

Section: Pendahuluanunclassified

“…Kernel digunakan untuk mentransformasi data ke ruang dimensi yang lebih tinggi, dan disebut ruang kernel, berguna untuk memisahkan data secara linear [9]. Kelebihan SVM lainnya adalah dapat digunakan untuk data yang berdimensi tinggi, dengan adanya ruang kernel sehingga hanya data yang terpilih untuk mengklasifikasi model [1]. Belum ada kesimpulan yang pasti tentang kernel mana yang lebih baik atau buruk untuk aplikasi tertentu.…”

Section: Pendahuluanunclassified

Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19

2021

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In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86

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Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Cited by 3 publications

References 15 publications

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine

Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19

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