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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer EMITTER International Journal of Engineering Technology Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Syntax Literate : Jurnal Ilmiah Indonesia JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Indonesian Journal of Applied Informatics Charity : Jurnal Pengabdian Masyarakat I-Com : Indonesian Community Journal eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)

Febryanti Sthevanie

Universitas Telkom

Author-ID : 2490307

Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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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%.

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

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.

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.

Big Cats Classification Based on Body Covering Fernanda Januar Pratama; Wikky Fawwaz Al Maki; Febryanti Sthevanie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.

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.

Predicting Students’ Performance In Basic Algorithms Programming In an E-Learning Environment Using Decision Tree Approach Jonas de Deus Guterres; Kusuma Ayu Laksitowening; Febryanti Sthevanie
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : CV. Ridwan Publisher

Predicting the performance of students plays an important role in every institution to protect their students from failures and leverage their quality in higher education. Algorithm and Programming is a fundamental course for the students who start their studies in Informatics. Hence, the scope of this research is to identify the critical attributes which influence student performance in the E-learning Environment on Moodle LMS (Learning Management System) Platform and its accuracy. Data mining helps the process of preprocessing data in a dataset from raw data to quality data for advanced analysis. Dataset set is consisting of student academic performance such as grades of Quizzes, Mid exams, Final exams, and Final projects. Moreover, the dataset from LMS is considered as well in the process of modeling, in terms of constructing the decision tree, such as punctuality submission of Quizzes, Assignments, and Final Projects. Regarding the Basic Algorithm and Programming course, which is separated into two subjects in the first and second semester, thus the research will predict the student performance in the Basic Algorithm and programming course in the second semester based on the Introduction to programming course in the first semester. Decision Tree techniques are applied by using information gain in ID3 algorithm to get the important feature which is the PP index has the highest information gain with value 0.44, also the accuracy between ID3 and J48 algorithm that shows ID3 has the highest accuracy of modeling which is 84.80% compared to J48 82.34%.

Pengenalan Batik Indonesia Menggunakan Ciri Warna dan Tekstur Ema Rachmawati; Maula Ilma Ahgnia Dwi Anjani; Febryanti Sthevanie
IJAI (Indonesian Journal of Applied Informatics) Vol 4, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v4i2.41591

Upaya pelestarian budaya bangsa melalui pengenalan batik merupakan hal yang harus selalu ditingkatkan. Terlebih dengan diakuinya budaya batik Indonesia oleh UNESCO sebagai bagian dari warisan budaya tak berwujud (intangible). Hal inilah yang mendasari dilakukannya sejumlah penelitian terkait pengenalan batik. Hasil kinerja yang sangat baik telah dicapai oleh berbagai sistem pengenalan batik. Namun, berbagai penelitian yang dilakukan tersebut masih terbatas pada jumlah motif batik yang sedikit. Oleh karena itu, pada penelitian ini dibangun sistem pengenalan batik dengan menggunakan 114 motif batik dari 14 daerah di propinsi Jawa Barat. Ciri gabungan dibangun dengan mengkombinasikan ciri tekstur dan warna. Ciri tekstur didapatkan dari Gray Level Co-occurrence Matrix (GLCM) sedangkan ciri warna didapatkan dari Color Difference Histogram (CDH). Penulis juga menambahkan variasi dalam dataset yang berupa rotate dan flip untuk memperbesar variasi intra-class. Hasil utama dari kinerja sistem yang dibuat adalah akurasi sebesar 99,128 % dan F1-Score sebesar 98,9999% pada pengenalan batik berdasarkan daerah, sedangkan pada pengenalan batik berdasarkan motif didapatkan akurasi sebesar 98,2456% dan F1-Score sebesar 98,3208%.

Pengembangan Situs Web Sebagai Pembaharu Media Informasi Taman Kanak-Kanak Islam Terpadu (TKIT) Luqmanul Hakim Bandung Gia Septiana Wulandari; Febryanti Sthevanie; Mahmud Dwi Sulistiyo
I-Com: Indonesian Community Journal Vol 2 No 2 (2022): I-Com: Indonesian Community Journal (Agustus 2022)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

TKIT Luqmanul Hakim merupakan taman kanak-kanak di kota Bandung yang berada di bawah naungan Yayasan Pendidikan Luqmanul Hakim. Di masa pandemi yang melanda dunia sejak dua tahun lalu, kebutuhan akan informasi yang tersedia secara daring semakin meningkat, baik bagi pihak sekolah maupun orangtua. Kebutuhan tersebut dirasakan pula oleh orang tua calon siswa yang sedang mencari sekolah untuk anaknya, sebagai bahan pertimbangan pemilihan sekolah bagi anaknya. Akan tetapi, TKIT Luqmanul Hakim Bandung sampai sekitar pertengahan tahun 2022 masih belum memiliki situs web yang dapat diakses orang tua/wali siswa/calon siswa. Oleh karena itu, untuk memperluas persebaran informasi dari pihak sekolah, Tim Abdimas dari Fakultas Informatika, Universitas Telkom menerapkan metode hibah teknologi tepat guna dengan cara membangun sebuah situs web resmi untuk TKIT Luqmanul Hakim dengan menggunakan framework Laravel. Untuk keberlanjutan pengelolaan situs web ini, disediakan pula halaman untuk Administrator dan dilaksanakan pelatihan pengelolaan situs web untuk para guru TKIT Luqmanul Hakim. Kegiatan ini mendapat sambutan positif dari berbagai pihak dan sedang dirasakan manfaatnya secara nyata, serta terus diupayakan pengelolaannya sehingga informasi yang disajikan senantiasa aktual.

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).

Co-Authors 1Faizal Bima Prayudha Adam Geraldy Katab Adhika Widya Prastomo Al Faraby, Said Alda Putri Utami Amalina, Neneng Nur Anang Kurniawan Anditya Arifanto Anditya Arifianto Anditya Arifiyanto Andri Arindiah Arida Kartika Bedy Purnama Brilian Aringga Prabowo Danu Hary Prakoso Diah Ajeng Dwi Yuniasih Dian Masmawati Dindin Dhino Alamsyah Dwi Prasetya Sujoko Ema Rachmawati Enki Probo Sidhi Farid Hidayat Fernanda Januar Pratama Fikri Firdaus Gamma Kosala Ghali Marzan Ghilman Muhammad Zaki Gia Septiana Wulandari Grandhys Setyo Utomo Gugy Lucky Khamdani Hafidh Fikri Rasyid Hizas Sabilal Rasyad I Putu Indra Aristya Imamul Akhyar Indra Bayu Kusuma Jonas de Deus Guterres Ketut Sudyatmika Putra Kurniawan Nur R Kurniawan Nur Ramadhani Kurniawan Nur Ramdhani Laksitowening, Kusuma Ayu Mahmud Dwi Sulistiyo Maula Ilma Ahgnia Dwi Anjani Maulana Nur Hidayah Mochamad Rakha Luthfi Fahsya Muhammad Afif Amanullah Fawwaz Muhammad Jendro Yuwono Muhammad Jendro Yuwono Muhammad Rizki Atria Salim Muhammad Salman Farhan Muhammad Zulfiqar Shafar Naufal Luthfi Saputra Neneng Nur Amalina Nur Azizah Agustina Nur Indah Puspa Idham Nurul Halimatul Azizah Rachmi Azanisa Putri Retno Novi Retno Novi Reza Dwi Ansari Rimba Whidiana Ciptasari Rita Rismala Rivan Ardyanto Sutoyo Sakinah Indriyani Shabran Fauzan Ahmad Tito Prihambodo Tjokorda Agung Budi W Tjokorda Agung Budi Wiharja Tjokorda Agung Budi Wirayuda Untari Novia Wisesty Wikky Fawwaz Al Maki Zeyhan Aliyah

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