Article Per Year (5 Year)
p-Index From 2021 - 2026
1.383
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Abdi Masyarakat Teknika Jurnal Pengabdian Kepada Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Algoritma
Vessa Rizky Oktavia
Universitas Telkom
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Deteksi Botnet pada Jaringan DNS secara Virtual menggunakan Decision Tree Kharisma Monika Dian Pertiwi; Vessa Rizky Oktavia; Rizky Fenaldo Maulana
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 3 (2023): Volume 9 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i3.70193

Abstract

Internet adalah aspek yang paling penting dan krusial dalam kehidupan di dunia. DNS Server bertugas menerjemahkan atau mengarahkan alamat IP ke alamat domain aplikasi yang diminta oleh client. DNS Server merupakan komponen krusial yang rentan terhadap serangan. Serangan paa DNS Server dapat berupa phising, penyebaran malware dan DDoS. Dampak dari serangan tersebut dapat menyebabkan layanan DNS mati hingga pencurian data pribadi. Serangan tersebut tidak hanya dilakukan oleh individual, namun juga dapat dilakukan oleh robot atau program komputer yang biasa disebut dengan botnet. Botnet merupakan sistem komputer yang telah terinfeksi program yang dapat dikendalikan jarak jauh. Untuk mencegah serangan botnet pada jaringan DNS, diperlukan sebuah metode yang mampu mendeteksi serangan dengan cepat. Penelitian ini bertujuan untuk mengembangkan metode untuk mendeteksi serangan botnet pada jaringan DNS menggunakan algoritma pembelajaran mesin. Penelitian ini kami lakukan secara simulasi menggunakan mesin virtual untuk mendapatkan data lalu lintas DNS. Penelitian ini menghasilkan pemahaman atau perspektif baru mengenai metode deteksi serangan botnet berdasarkan lalu lintas jaringan DNS Server yang disimulasikan secara virtual. Metode pembelajaran mesin untuk deteksi serangan botnet yang diimplementasikan adalah decision tree. Hasil pengujian menunjukkan bahwa model mampu mendeteksi dengan baik. Model dapat mendeteksi botnet dengan akurasi 95%. Model memiliki rata-rata nilai precision 97%, recall 92.6% dan F1-score 95%.
Detection of Motorcycle Headlights Using YOLOv5 and HSV Vessa Rizky Oktavia; Ahmad Wali Satria Bahari Johan; Whisnumurty Galih Ananta; Fahril Refiandi; Muhammad Khuluqil Karim
Teknika Vol 12 No 3 (2023): November 2023
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v12i3.682

Abstract

"Electronic Traffic Law Enforcement" (ETLE) denotes a mechanism that employs electronic technologies to implement traffic regulations. This commonly entails utilizing a range of electronic apparatuses like cameras, sensors, and automated setups to oversee and uphold traffic protocols, administer fines, and enhance road security. ETLE systems are frequently utilized for identifying and sanctioning infractions like exceeding speed limits, disregarding red lights, and turning off the headlights. In Indonesia, there is currently no dedicated system designed to detect traffic violation, especially regarding vehicle headlights. Therefore, this research was conducted to detect vehicle headlights using digital images. With the results of this study, it will be possible to develop a system capable of classifying whether vehicle headlights are on or off. This research employed the deep learning method in the form of the YOLOv5 model, which achieved an accuracy of 94.12% in detecting vehicle images. Furthermore, the white color extraction method was performed by projecting the RGB space to HSV to detect the Region of Interest (ROI) of the vehicle headlights, achieving an accuracy of 73.76%. The results of this vehicle headlight detection are influenced by factors such as lighting, image capture angle, and vehicle type.
Social Media Analysis Training for Digital Talent Development in Indonesia Rochmah, Wachda Yuniar; Oktavia, Vessa Rizky; Rausanfita, Alqis; Hakim, Maulana Naufal; Deudoena, Dara Ilma; Sayoga, Dhiki Sidik
Abdi Masyarakat Vol 5, No 2 (2023): Abdi Masyarakat
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58258/abdi.v5i2.6219

Abstract

The development of digital technology has allowed people to share opinions on social media, send emails, make purchases online, to make phone calls every day. As a result, the amount of data continues to grow rapidly day by day. Most of the data available today is public and accessible to anyone, such as social media data, blogs, news, discussion forums, public government data, and others. With the immense value of this abundant source of social media data, there is an opportunity to extract knowledge or insights from this unstructured social media data, especially to understand opinions, current trends, or influential actors on information spread on the internet. As part of Telkom Surabaya's IT Community Service team that supports student development in SMA/SMK/MA, we propose solutions to the main problems faced today, namely in the field of data analysis. The solutions we offer are also in line with the government's program to increase Digital Talent in Indonesia. In the midst of increasing demand for Digital Talent, there is still a gap between the need for digital talent and job opportunities in Indonesia. The program we will create is Social Media Analysis Training, which will provide an understanding of the benefits of open social media data in general, how to take insights from social media data, and solve problems in various fields.  
Implementasi Digitalisasi Pembuatan Rapor untuk TPQ Al-Mubaarok Surabaya dalam Mendukung Evaluasi Santri Vessa Rizky Oktavia; Rausanfita, Alqis; Safitri, Pima Hani; Satria Bahari Johan, Ahmad Wali
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 1 (2025): Mei 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v5i1.4102

Abstract

Taman Pendidikan Al-Quran (TPQ) is a non-formal educational institution. Often, TPQ prioritizes the quality of educational materials so that it slightly ignores the administrative aspects. A TPQ ​​usually contains asatidz (ustadz and ustadzah) who have expertise in the field of religion, but do not have staff who are experts in administrative matters. Not a few TPQs still use manual recording such as using paper to make report cards. As a result, there are still often errors in recording grades on report cards and asatidz who find it difficult to manage report cards. The problems faced by asatidz become more complicated when the number of students increases. Digitalization is a solution that can solve the problems faced by TPQ. With the digitalization of report cards, asatidz will easily manage student grades and print report cards massively in a short time. Application creation is carried out by analyzing needs, designing interfaces, implementing, and training. This activity was carried out by a community service team from Telkom University and partners of TPQ Al-Mubaarok Surabaya. The result is an application that has reliability in managing student grade data. This application can be accessed from anywhere by TPQ, making it easier for TPQ to manage student report cards. Positive impacts felt include reducing errors when entering data and ease in storing digital data.
Rancang Bangun Prototipe Sistem Deteksi Dini Retinopathic Diabetic Berbasis Website Muhajir, Daud; Mustaqim, Tanzilal; Safitri, Pima Hani; Oktavia, Vessa Rizky
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2255

Abstract

Diabetic Retinopathic (DR) is one of the retinal disorders caused by high blood sugar levels. There are fewer ophthalmologists available, and treating DR patients manually is a time-consuming process. Therefore, there is a need for an automatic DR early detection method using Deep Learning. The purpose of this research is to build a web-based DR early detection prototype with retinal image classification using the DenseNet121 Deep Learning model and the Stochastic Gradient Descent (SGD) optimizer to improve the accessibility and efficiency of screening. The software development method used in this research is waterfall which consists of analysis phase, design phase, implementation phase, and testing phase. To ensure the prototype runs as planned, black-box testing is carried out on each of its features to ensure system functionality in accordance with predetermined specifications. This research produces a RD early detection prototype that has been tested with all 16 test cases and has a suitable status. Future research can be carried out further system development by involving real users such as ophthalmologists and can be applied in hospitals.
SISTEM PENGELOLAAN DATA BERBASIS WEB DAN PELATIHAN BAGI PENGURUS TPQ AL-MUBAAROK SURABAYA rausanfita, alqis; Vessa Rizky Oktavia; Ahmad Wali Satria Bahari Johan; Moch. Andi Divangga Pratama; Fendi Virgiansyah; Muhammad Hanafi Choirulloh
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): APTEKMAS Volume 7 Nomor 2 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v7i2.8561

Abstract

TPQ Al-Mubaarok, an institution for Quranic learning located in Surabaya, is currently facing challenges in managing student data. Presently, information regarding the students is manually recorded in logbooks, posing a hindrance for the administrators of TPQ Al-Mubaarok to efficiently manage and analyze data. In order to address this issue, we aim to develop a web-based system that facilitates the digital recapitulation process of data. This endeavor begins with conducting field surveys to understand user needs. Subsequently, we proceed to the system design phase and interface design to cater to these needs. System implementation follows the design phase, where we integrate all planned features into a functional system. Next, we conduct system trials involving the authors and the main users of the system, the ustadz/ustadzah. These trials are conducted to ensure that the system operates smoothly and meets user requirements. Finally, we evaluate user satisfaction with the system, particularly from the perspective of the ustadz/ustadzah. The implementation of this system is expected to provide significant benefits, including expediting the registration process for new students, facilitating access to information regarding donations, and providing facilities for managing and summarizing student data more efficiently
An Extreme Gradient Boosting for Blood Disease Classification Using Hematological Parameters: A Comparative Evaluation with Ensemble and Non-Ensemble Models Saputra, Dimas Chaerul Ekty; Oktavia, Vessa Rizky; Futri, Irianna; Pertiwi, Affifah Mutiara
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31659

Abstract

The early detection of hematological disorders remains challenging because many conditions share similar clinical characteristics and show substantial variation in laboratory measurements. Existing machine learning systems often struggle to maintain consistent accuracy in multi-class settings with imbalanced data. The research contribution is a multi-class diagnostic framework that identifies nine hematological disease categories using only routine laboratory parameters, supported by a leakage-free evaluation protocol and a comprehensive comparison across baseline classifiers. The proposed solution uses an extreme gradient boosting model as the primary classifier and evaluates it against support vector machine, random forest, and extra trees. The method includes data cleaning and numerical standardization, and class balancing with the Synthetic Minority Oversampling Technique applied only to the training subset within each fold of ten-fold cross-validation to prevent optimistic bias. Model performance is assessed using accuracy, precision, recall, and F1-score, together with computational efficiency measured through processing time and memory usage. The results show that the extreme gradient boosting model achieves the best overall performance, with an average accuracy of 98.67%, precision of 98.80%, recall of 98.67%, and an F1-score of 98.66%. It also demonstrates efficient memory usage and shorter processing time compared with the other tested methods. The competing models perform adequately but exhibit higher variability and weaker recognition for minority classes. In conclusion, these findings indicate that extreme gradient boosting provides an accurate and efficient approach for hematology-based multi-class disease classification when evaluated under a strict, leakage-free resampling protocol.
Peningkatan Sensitivitas Deteksi Diabetic Retinopathy melalui Mekanisme Hierarchical Self-Attention pada Swin Transformer Mustaqim, Tanzilal; Safitri, Pima Hani; Oktavia, Vessa Rizky
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2986

Abstract

Diabetic Retinopathy (DR) is a complication of diabetes that can cause blindness if not detected early. CNN has limitations in capturing scattered lesions due to its narrow receptive field, while Vision Transformers are generally less computationally efficient. The objective of this study is to develop an approach that can capture long-range spatial dependencies while maintaining computational efficiency for resource-limited clinical applications. The Swin Transformer-Tiny was implemented with a shifted window-based hierarchical self-attention mechanism on the APTOS 2019 dataset (3,663 retinal images), with pre-processing (CLAHE, gamma correction, Gaussian filtering) and data augmentation. The model was trained using SGD with CosineAnnealingLR and evaluated based on accuracy, precision, recall, and F1-score with a focus on minimizing false negatives. Swin Transformer-Tiny achieved an accuracy of 84.99%, precision of 84.89%, and recall of 84.99%, surpassing EfficientNet-B0 by 1.32% in F1-score and outperforming ResNet50 by 5.60%. The attention mechanism reduces false negatives by 1.28% compared to conventional CNNs while maintaining linear computational complexity. This research contributes to showing that hierarchical self-attention in Swin Transformer effectively improves DR detection sensitivity by overcoming the limitations of CNN receptive fields, while maintaining computational efficiency for clinical implementation.
Co-Authors Ahmad Wali Satria Bahari Johan Ahmad Wali Satria Bahari Johan Alqis Rausanfita Deudoena, Dara Ilma Dimas Chaerul Ekty Saputra Fahril Refiandi Fendi Virgiansyah Futri, Irianna Hakim, Maulana Naufal Moch. Andi Divangga Pratama Muhajir, Daud Muhammad Hanafi Choirulloh Muhammad Khuluqil Karim Pertiwi, Affifah Mutiara Pertiwi, Kharisma Monika Dian Rizky Fenaldo Maulana Safitri, Pima Hani Sayoga, Dhiki Sidik Tanzilal Mustaqim Wachda Yuniar Rochmah Whisnumurty Galih Ananta