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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) eProceedings of Engineering
Anditya Arifianto
Universitas Telkom
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Deteksi Serangan Spoofing Pada Citra Wajah menggunakan Ekstraksi Ciri Local Derivative Pattern Ni Gusti Ayu Mirah Eka Darmayanti; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.213

Abstract

Pada penelitian ini, diusulkan sistem pendeteksi serangan spoofing pada citra wajah manusia menggunakan metode ekstraksi ciri Local Derivative Pattern (LDP). Metode klasifikasi yang digunakan adalah k-Nearest Neighbour (k-NN) dan Support Vector Machine (SVM). Penelitian ini menggunakan NUAA Imposter and Photograph Database sebagai datasetnya. Parameter optimal untuk ekstraksi ciri menggunakan LDP, adalah sebagai berikut: LDP orde ke-2 dengan radius bernilai 5 yang bersifat overlapping non-uniform menggunakan algoritma klasifikasi SVM dengan kernel Radial Basis Function. Performansi terbaik didapatkan menggunakan F1-Score sebesar 99.8%. Pola uniform pada LDP mempercepat waktu komputasi dengan rata-rata 2.09 detik, sedangkan waktu komputasi pola non-uniform yaitu 5.49 detik.
Pengenalan Angka Tulisan Tangan Menggunakan Diagonal Feature Extraction dan Artificial Neural Network Multilayer Perceptron M. Ardi Firmansyah; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.214

Abstract

Pada  penelitian  ini  dibangun  sistem pengenalan angka tulisan tangan menggunakan metode ekstraksi ciri diagonal  dan  Artificial Neural Network Multilayer Perceptron. Pada ekstraksi ciri diagonal, citra dibagi menjadi beberapa area yang sama besar. Pada tiap area dihitung rata-rata nilai piksel pada setiap diagonalnya kemudian dirata-ratakan untuk mendapatkan nilai ciri pada area tersebut.  Ciri diagonal dikombinasikan dengan nilai rata-rata horizontal dan  vertikal  pada  matriks  area  tersebut  untuk  memperkuat  informasi  pada citra. Metode  ini  mencapai  akurasi  sebesar  92.80%  pada  tahap  pengujian menggunakan  1000  dataset  C1  dan  92.60%  pada  tahap  pengujian  menggunakan 1000 dataset MNIST. Kombinasi fitur diagonal dan rata-rata horizontal menghasilkan akurasi tertinggi dalam mengenali angka tulisan tangan.
Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern M. Adhi Satria; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.215

Abstract

Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.
Pneumonia Classification from X-ray Images Using Residual Neural Network Abdan Hafidh Ahnafi; Anditya Arifianto; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 2 (2020): September, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.2.454

Abstract

Pneumonia is a virus, bacterium, and fungi infection disease which causes alveoli swelling and gets worse easily if it is not taken care of immediately. There are symptoms that can be recognized through x-ray images, for example the appearance of white mist in the lungs. A pneumonia classification system has already developed, but it still produced low accuracy. In this research we develop classification system by increasing the depth of CNN architecture using Residual Neural Network to improve accuracy from previous research. The dataset contains 2 classes which are pneumonia and normal, and trained to produce the best learning strategy with various scenarios. The model trained using data train that has been oversampling. The best scenario is achieved by ResNet152 architecture using dropout 0.5. This scenario achieved a result of 0.88 precision, 0.95 recall, 0.92 f1-score, and 0.89 of accuracy. The classification model on this research produces higher accuracy compared to the research of Enes Ayan, et.al. in 2019 which produced 0.87.
Classifying Skin Cancer in Digital Images Using Convolutional Neural Network with Augmentation Zeyhan Aliyah; Anditya Arifianto; Febryanti Sthevanie
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 2 (2020): September, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.2.455

Abstract

Skin cancer is a hazardous disease that can induces death if it is not taken care of immediately. The disease is hard to identified since the symptoms have similarities with other disease. An automatically classification system of skin cancer has been developed, but it still produced low accuracy. We use Convolutional Neural Network  to enhance the accuracy of the classification. There are 2 main scenarios conducted in this research using HAM10000 dataset which has 7 classes. We compared ResNet and VGGNet architectures and obtained ResNet50 with augmentation as the best model with the accuracy of 99% and 99% macro avg.
Co-Authors Abdan Hafidh Ahnafi Adam Geraldy Katab Ade Romadhony Agung Toto Wibowo Al Faraby, Said Bandy Cipta Nur Ramadhan Dea Taradipa Ardiagarianti Ema Rachmawati Enki Probo Sidhi Erwin Yulizar Fardani Febryanti Sthevanie Fransisca Arvevia Intan Angelia Friendly Halomoan Sipayung Halprin Abhirawa Jody Jody Jondri Jondri Kurniawan Nur Ramadhan Kurniawan Nur Ramadhani Kurniawan Nur Ramdhani M. Adhi Satria M. Ardi Firmansyah Muh Aswan Abidin1 Muhammad Miftah Muslim Zulfikar Naufal Dzaky Anwari Ni Gusti Ayu Mirah Eka Darmayanti Nurul Halimatul Azizah Pima Hani Safitri Rachmi Azanisa Putri Rama Patria Himawan Rita Rismala Riyad Rivandi Rizza Aulia Rahman Supriadi, Muhammad Fadhlan Tjokorda Agung Budi Wiharja Tjokorda Agung Budi Wirayuda Untari Novia Wisesty Varian Vianandha Zeyhan Aliyah