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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Edukasi dan Penelitian Informatika (JEPIN) EMITTER International Journal of Engineering Technology Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) eProceedings of Engineering

Kurniawan Nur Ramadhani

Universitas Telkom

Author-ID : 2464809

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 38 Documents Claim Missing Document

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Articles

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Deteksi Spoofing Wajah Manusia Berbasis Video menggunakan Metode Local Derivative Pattern-Three Orthogonal Planes Febryanti Sthevanie; Diah Ajeng Dwi Yuniasih; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 1 (2020): Maret, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.1.376

Saat ini banyak sistem yang menggunakan pengenalan wajah sebagai keamanan. Namun, penggunaan wajah tersebut masih memiliki kerentanan terhadap serangan spoofing, yaitu serangan dengan cara memalsukan foto atau video dari pengguna asli sistem tersebut. Untuk menghindari adanya tindakan kriminal tersebut, diusulkan sebuah sistem yang dapat mendeteksi serangan spoofing menggunakan metode Local Derivative Pattern dari Three Orthogonal Planes. Dataset yang digunakan adalah bersumber dari empat dataset publik yang berbeda yaitu Idiap Replay-Attack Database, MSU MFSD Database, Casia FASD Database dan NUAA Imposter Database yang berformat video. Dari hasil pengujian, pada skenario intra-dataset didapatkan performansi terbaik dengan rata-rata F1-Score 97.77% dan rata-rata HTER 8.47%, sedangkan pada skenario cross-dataset rata-rata F1-Score 74.77% dan rata-rata HTER 29.05%.

Deteksi Helm pada Video Pengendara Sepeda Motor menggunakan Ekstraksi Ciri Histogram of Oriented Gradients Febryanti Sthevanie; Anang Kurniawan; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 1 (2020): Maret, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.1.377

World Health Organization mencatat sampai tahun 2015 kecelakaan di jalan telah merenggut 1.2 juta jiwa tiap tahunnya. Kecelakaan paling banyak dialami oleh pengendara sepeda motor karena minimnya keamanan yang melindungi pengendara sepeda motor, dan juga rendahnya kesadaran pengguna sepeda motor untuk menggunakan perangkat keselamatan yang sesuai dengan Undang-Undang. Riset-riset te- lah dilakukan diantaranya membuat sistem pendeteksi helm pada pengendara sepeda motor menggunakan metode ekstraksi fitur HOG, SIFT, LBP yang dapat menghasilkan performansi rata-rata masing masing, 93%, 64%, 64% dengan menggunakan metode klasifikasi SVM. Pada penelitian ini dibuat sebuah sistem yang dapat mendeteksi helm pada pengendara sepeda motor secara otomatis menggunak- an ekstraksi fitur Histogram of Oriented Gradient. Hasil tertinggi yang didapatkan dari hasil pengujian adalah fmeasure 90.67%, menggunakan metode ekstraksi fitur HOG dengan kondisi ukuran cell 8x8 pixels dan jumlah 9 bins dengan sudut 180o. Hasil tersebut dicapai menggunakan metode klasifikasi SVM dengan kernel polynomial derajat 3.

Pengenalan Aksara Bali Menggunakan Metode Pyramid Histogram of Oriented Gradients Febryanti Sthevanie; I Putu Indra Aristya; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 1 (2020): Maret, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.1.378

Aksara Bali terdiri dari 18 aksara dasar (biasa disebut aksara Wianjana) yang masing-masing terdiri atas 7 aksara vokal (pengangge suara). Penulisan aksara Bali dapat ditulis pada kertas ataupun daun tal yang sudah dikeringkan dan memiliki tekstur yang kasar serta mudah sobek sehingga membuat sulit dibaca. Maka dari itu, dibuat sistem yang dapat mengenali aksara Bali pada daun tal untuk membantu dapat membaca aksara Bali. Sistem ini dibangun menggunakan metode Pyramid Histogram of Oriented Gradient (PHOG) sebagai metode ekstraksi ciri. Dataset yang digunakan adalah dataset dari AMADI Lontar Set yang berupa gambar berjumlah 19.383 gambar dengan 133 kelas. Pada pengujian didapatkan nilai f1-score terbaik pada PHOG level 3 dengan 6 bin orientasi dan klasifikasi menggunakan SVM kernel linear yaitu sebesar 66.49% dan akurasi sebesar 81.35%.

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

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.

Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance Indra Bayu Kusuma; Arida Kartika; Tjokorda Agung Budi W; Kurniawan Nur Ramadhani; Febryanti Sthevanie
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.134

Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection using texture analysis on input image is proposed. Texture analysis used in this paper is the Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV). To classified input as original or spoof K-Nearest Neighbor (KNN) used. Experiment used 5761 spoofs and 3362 original from NUAA Imposter dataset. The experimental result yielded a best success rate of 87.22% in term of accuracy with configuration of the system using LBPV and histogram equalization with ratio ð‘… = 7 and ð‘ƒ = 8.

Pengenalan Logo Kendaraan Menggunakan Metode Local Binary Pattern dan Random Forest Alda Putri Utami; Febryanti Sthevanie; Kurniawan Nur Ramadhani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The vehicle logo is one of the features that can be used to identify a vehicle. Even so, a lot of Intelligent Transport System which are developed nowadays has yet to use a vehicle logo recognition system as one of its vehicle identification tools. Hence there are still cases of traffic crimes that haven't been able to be examined by the system, such as cases of counterfeiting vehicle license plates. Vehicle logo recognition itself could be done by using various feature extraction and classification methods. This research project uses the Local Binary Pattern feature extraction method which is often used for many kinds of image recognition systems. Then, the classification method used is Random Forest which is known to be effective and accurate for various classification problems. The data used for this study were as many as 2000 vehicle logo images consisting of 5 brand classes, namely Honda, Kia, Mazda, Mitsubishi, and Toyota. The results of the tests carried out obtained the best accuracy value of 88.89% for the front view logo image dataset, 77.03% for the side view logo image dataset, and 83% for the dataset with both types of images.

MODIFIKASI FUNGSI DENSITY PADA ALGORITMA ANT CLUSTERING Kurniawan Nur Ramadhani; Febryanti Sthevanie
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 1 No. 2 (2015)
Publisher : Universitas Widyatama

[INA]Clustering merupakan salah satu tugas dalam data mining untuk mengelompokkan data berdasarkan kemiripan karakteristik. Pada penelitian ini, akan diusulkan sebuah modifikasi pada algoritma Ant Clustering untuk mempercepat proses komputasi. Modifikasi dilakukan pada fungsi density dengan mempertimbangkan batasan pemisahan spasial. Dari hasil percobaan yang dilakukan dengan data sejumlah 800 baris dan jumlah iterasi sebanyak 1000, didapatkan bahwa modifikasi fungsi density pada algoritma Ant Clustering berhasil meningkatkan kecepatan dengan nilai akurasi yang tidak terlalu berbeda dengan algoritma Ant Clustering standar.[EN]Clustering is one of the tasks in data mining to group data based on similar characteristics. In this study, will be proposed a modification on Ant Clustering algorithm to speed up the process of computing. Modifications carried on by considering the density function limits the spatial separation. From the results of experiments conducted with a number of data lines 800 and the number of iterations of 1000, it was found that the density modification function on Ant Clustering algorithms managed to increase the speed with accuracy values that are not too different from Ant Clustering algorithm standard.

DETEKSI DAN REKOGNISI RAMBU-RAMBU LALU LINTAS DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Kurniawan Nur Ramadhani; M.Syahrul Mubarok; Agnes Dirgahayu Palit
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 3 No. 2 (2017)
Publisher : Universitas Widyatama

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

Video Based Fire Detection Method Using CNN and YOLO Version 4 Muhammad Salman Farhan; Febryanti Sthevanie; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.654

Fire detection is one of the technological efforts to prevent fire incidents. This is very important because the damage caused by fires can be minimized by having a fire detector. There are two types of fire detection, namely traditional-based and computer vision-based. Traditional-based fire detection has many shortcomings, one of which requires a close fire distance for activation. Hence, computer vision-based fire detection is made to cover the shortcomings of traditional-based fire detection. Therefore, in this study, we propose a video-based fire detection using a Convolutional Neural Network (CNN) Deep Learning approach supported by You Only Look Once (YOLO) object detection model version four. This study uses a dataset of various fire scenarios in the form of images and videos. The fire detection built in this study has an accuracy of above 90% with an average detection speed of 34.17 Frame Per Second (FPS).

Fire Detection on Video Using ViBe Algorithm and LBP-TOP Kurniawan Nur Ramadhani; Febryanti Sthevanie; Gamma Kosala; Ketut Sudyatmika Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4164

In this research, we built a system to detect fire using the ViBe (Visual Background Extractor) algorithm to extract dynamic targets. The ViBe algorithm is better at detecting moving target objects such as flame combustion. In this research we combined the ViBe algorithm with three frame differencing to gain better results on movement object. The HSI color space model was applied after the movement object was obtained. We used Local Binary Pattern-Three Orthogonal Planes to obtain the feature extraction to be classified with Support Vector Machine. Our result has shown that the proposed system were able to detect the fire using the LBP-TOP and ViBe algorithm methods with an average accuracy rate of 88.10%, and the best accuracy was 90.37%. The parameters used to achieve this accuracy in the feature extraction process were T=120, Radius=2, and frame gap=15, then the threshold value parameter for three-frame difference parameter was 25.

Co-Authors Abdan Hafidh Ahnafi Abdul Rahim Achmad Hussein Sundawa Kartamihardja Ade Saepul Mugni Agnes Dirgahayu Palit Agnes Dirgahayu Palit Alda Putri Utami Amalina, Neneng Nur Anang Kurniawan Anditya Arifianto Arida Kartika Arie Ardiyanti Suryani Arif M Barmawi Arifudin Achmad Cherry Nasa Putra Diah Ajeng Dwi Yuniasih Dimitri Mahayana Dwiki Lazzaro Ema Rachmawati Febriyanti Sthevanie Febryanti Sthevanie Fikri Firdaus Fitri Amalia Hadi Friendly Halomoan Sipayung Gamma Kosala Hafidh Fikri Rasyid I Putu Indra Aristya Ignasius Indra Kusuma Wijaya Indra Bayu Kusuma Jondri Jondri Ketut Sudyatmika Putra Ledya Novamizanti M. Adhi Satria M. Ardi Firmansyah M.Syahrul Mubarok Maruanaya, Farhan Bary Mochamad Rakha Luthfi Fahsya Mohamad Syahrul Mubarok Mohamad Syahrul Mubarok Muh Aswan Abidin1 Muhammad Afif Amanullah Fawwaz Muhammad Hariz Arasy Muhammad Salman Farhan Naufal Ihsan Kusumayadhi Neneng Nur Amalina Ni Gusti Ayu Mirah Eka Darmayanti Parjuangan, Sabam Pima Hani Safitri Prasti Eko Yunanto Rini Shintawati Riski Novanda Rita Rismala Sakinah Indriyani Saputra, Naufal Luthfi Sthevanie, Febryanty Suyanto Suyanto Tjokorda Agung Budi W Tjokorda Agung Budi Wirayuda YUYUN SITI ROHMAH

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