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All Journal JSM (Jurnal SIFO Mikroskil) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Reswara: Jurnal Pengabdian Kepada Masyarakat Teknika Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Riset dan Inovasi Pembelajaran Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Armada Informatika Jurnal Mitra Pengabdian Farmasi
Wulan Sri Lestari
Universitas Mikroskil
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DETEKSI SERANGAN DDoS MENGGUNAKAN Q-LEARNING Wulan Sri Lestari
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 1 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i1.1473

Abstract

Distributed Denial of Service Attack (DDoS) is an attack by compiling multiple systems on the internet with infected zombies/agents and forming a network of botnets. DDoS attacks resulted in financial losses, lost productivity, brand damage, downgrades of credit and insurance ratings, and disrupted customer and supplier relationships. In addition, IoT technology is also vulnerable to large-scale DDoS attacks. To prevent DDOS attacks, a model that can detect DDoS attacks is needed. In this research, we propose Deep Q-Network (DQN) to detect DDoS attacks. DQN is a reinforcement learning algorithm that combines deep learning and q-learning. The application of DQN is used to improve the accuracy of attack detection on the dataset. In this paper, the dataset used to detect DDoS attacks or not is the CICDDoS2019 dataset provided by the Canadian Institute for Cybersecurity. Based on the comparison of the methods carried out, the results of the proposed DQN method can detect 11 DDoS attacks and benign/normal data with better accuracy values ​​compared to the LR and SVR methods. The results showed that the proposed model had an accuracy value of 96% and was better than LR and SCR methods
Deteksi Spoofing Wajah Menggunakan Faster R-CNN dengan Arsitektur Resnet50 pada Video Sunario Megawan; Wulan Sri Lestari
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 3: Agustus 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1114.222 KB) | DOI: 10.22146/.v9i3.231

Abstract

Face detection is a main and important process in the field of face recognition that has been widely studied. The purpose of face detection is to determine the presence and mark the position of faces, in both images and videos, called bounding boxes. One important problem in face detection is to differentiate between face spoof and non-spoof which is referred to as face spoofing detection. Face spoofing detection is an important task used to ensure the security of face-based authentication and facial analysis systems. Therefore, we need a model that can detect face spoofing. In this paper, the process to build a model that can be used to detect face spoofing on video is carried out using Faster R-CNN with Resnet50 architecture. Faster R-CNN is one of the superior algorithms in solving various object detection problems. The dataset used in this paper is a Replay-Attack Database, provided by Idiap Dataset Distribution Portal.The training phase used 360 videos, consisting of 300 spoof videos and 60 non-spoof videos. The average accuracy of the training stage is 97,07% with a total of 21 epochs. The test results show that the resulting model successfully determined bounding boxes and detected face spoof and non-spoof on the video effectively.
Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN Sunario Megawan; Wulan Sri Lestari; Apriyanto Halim
Journal of Information System Research (JOSH) Vol 3 No 3 (2022): April 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.976 KB) | DOI: 10.47065/josh.v3i3.1519

Abstract

Face non-spoofing detection is an important job used to ensure authentication security by performing an analysis of the captured faces. Face spoofing is the process of fake faces by other people to gain illegal access to the biometric system which can be done by displaying videos or images of someone's face on the monitor screen or using printed images. There are various forms of attacks that can be carried out on the face authentication system in the form of face sketches, face photos, face videos and 3D face masks. Such attacks can occur because photos and videos of faces from users of the facial authentication system are very easy to obtain via the internet or cameras. To solve this problem, in this research proposes a non-spoofing face detection model on video using Faster R-CNN. The results obtained in this study are the Faster R-CNN model that can detect non-spoof and spoof face in real time using the Raspberry Pi as a camera with a frame rate of 1 fps.
Optimasi Google Classroom dalam Membantu Pembelajaran Daring Siswa SMA Panglima Polem Rantauprapat Syanti Irviantina; Felix Felix; Wulan Sri Lestari
Dedikasi Sains dan Teknologi (DST) Vol. 2 No. 2 (2022): Dedikasi Sains dan Teknologi : Volume 2 Nomor 2, Nopember 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v2i2.1726

Abstract

Dampak pandemi covid 19 telah mengubah cara belajar dari offline menjadi online sehingga guru sebagai pemeran utama dalam menyelenggarakan pendidikan harus memiliki kemampuan untuk menguasai teknologi pembelajaran online yaitu aplikasi Google Classroom sebagai salah satu media pembelajaran online. Untuk memenuhi hal ini maka perlu dilakukan pelatihan kepada guru-guru di SMA Panglima Polem Rantauprapat agar dapat menguasai ketrampilan penggunaan aplikasi ini. Dari hasil pelatihan didapatkan bahwa para guru terbantu untuk menguasai Google Classroom sebagai media pembelajaran dan akan digunakan dalam proses belajar mengajar
Prediksi Kesuksesan Startup Menggunakan Deep Neural Network Wulan Sri Lestari; Apriyanto Halim
Jurnal SIFO Mikroskil Vol 23, No 2 (2022): JSM VOLUME 23 NOMOR 2 TAHUN 2022
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v23i2.885

Abstract

Kesuksesan startup memiliki peran penting dalam pertumbuhan ekonomi dengan ide bisnis yang baru, inovatif dan dapat menciptakan lapangan pekerjaan. Pertumbuhan startup yang eksponensial dalam beberapa tahun terakhir membuat ketidakpastian dan tingkat kegagalan yang tinggi. Sehingga penting bagi investor untuk dapat memprediksi kesuksesan startup dalam menemukan perusahaan yang memiliki potensi kesuksesan yang lebih besar untuk didanai. Untuk membantu para investor, maka tujuan dari penelitian ini adalah membangun model untuk memprediksi apakah startup yang sedang beroperasi akan sukses atau gagal menggunakan Deep Neural Network (DNN). DNN mengkombinasikan keunggulan deep learning dan neural network untuk memecahkan masalah nonlinear. Proses yang dilakukan adalah menggunakan dataset startup success prediction, kemudian dataset tersebut di pre-processing untuk pengecekan missing value, data duplicate, data outlier serta penentuan atribut berdasarkan korelasi antar variabel. Kemudian, dataset yang sudah di pre-processing dibagi menjadi data training dan data testing. Selanjutnya ditentukan parameter DNN apa saja yang akan digunakan untuk membangun model prediksi menggunakan data training agar tidak mengalami overfitting ataupun underfitting. Model yang dibangun kemudian diuji mengunakan data testing. Hasil pengujian menunjukkan bahwa model prediksi yang diusulkan tidak overfitting ataupun underfitting dan memiliki nilai akurasi sebesar 83,93% dan nilai presisi sebesar 86,51% dalam memprediksi kesuksesan startup.
Implementasi Augmented Reality Menggunakan Metode Marker Based Pada Website Furniture Rumahan Dengan Konsep 3D Animation Yusuf Chandra Nasution; Ariani Pertiwi; Syanti Irviantina; Wulan Sri Lestari
Jurnal SIFO Mikroskil Vol 24, No 1 (2023): JSM VOLUME 24 NOMOR 1 TAHUN 2023
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v24i1.939

Abstract

Pesatnya teknologi yang berkembang, membuat pelaku usaha dibidang furniture harus berkontribusi dalam teknologi seperti membuat katalog 3D. Masyarakat masih mengalami kesulitan dalam melihat katalog 2D seperti, sulit untuk membayangkan bagaimana bentuk furniture tersebut. Penelitian ini bertujuan membuat katalog yang membantu masyarakat melihat furniture 3D berbasis website yang menerapkan augmented reality. Metodologi yang digunakan dalam pelaksanaan penelitian yaitu extreme programming, dan metode marker based pada augmented reality. Metode marker based dibutuhkan sebuah marker untuk menampilkan objek 3D diatas marker. Perancangan tampilan website menggunakan HTML5, JavaScript, CSS dan BootStrap, serta penggunaan ARJS untuk menerapan augmented reality pada websites dan framework AFrame untuk menampilkan desain 3D pada augmented reality. Pengujian dengan black box digunakan untuk menguji fungsionalitas sistem dan marker yang digunakan. Untuk menampilkan objek 3D yang baik dibutuhkan marker yang tidak rusak dan intensitas cahaya yang cukup untuk merekam marker. Jarak antara kamera dengan marker berada diantara 55 cm dan 295 cm, dengan sudut dapat diatur oleh penggunanya sampai marker terlihat jelas oleh kamera. Pengujian usability dengan menyebarkan kuesioner berupa pernyataan USE Questionnaire dan hasil yang didapat yaitu memiliki persentase kelayakan 84,56% menunjukkan bahwa sistem sangat layak diimplementasikan berdasarkan interpretasi kelayakan sistem pada skala likert.
PELATIHAN PEMROGRAMAN DASAR MENGGUNAKAN BAHASA PYTHON PADA SMK METHODIST TANJUNG MORAWA Sunario Megawan; Wulan Sri Lestari; Tanti Tanti
RESWARA: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 1 (2024)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v5i1.3648

Abstract

SMK Swasta Methodist Tanjung Morawa merupakan salah satu sekolah swasta di bawah naungan Yayasan Methodist Kasih Imanuel Indonesia yang berdiri sejak tahun 2008. Salah satu jurusan yang ada di SMK Swasta Methodist Tanjung Morawa adalah Teknik Komputer dan Jaringan (TKJ). Sesuai dengan kurikulum yang digunakan di jurusan TKJ, pemrograman merupakan salah satu pelajaran yang harus diberikan kepada para siswa untuk mencapai dasar bidang keahlian dan dasar program keahlian. Namun, saat ini bahasa pemrograman yang sudah diberikan masih terbatas pada HTML dan Javascript saja dimana keduanya merupakan mata pelajaran pemrograman web yang hanya mencapai dasar program keahlian saja. Sedangkan untuk mencapai dasar bidang keahlian dibutuhkan pemahaman tentang pemrograman dasar lainnya, sehingga para siswa memiliki kompetensi yang lebih baik. Oleh karena itu, Fakultas Informatika Universitas Mikroskil menawarkan solusi berupa pelatihan pemrograman dasar menggunakan bahasa Python yang bertujuan untuk membantu para siswa meningkatkan kemampuan pemrograman mereka. Kegiatan pelatihan ini berlangsung selama 2 hari dan dilaksanakan di Laboratorium komputer Universitas Mikroskil dengan metode workshop/praktek langsung. Berdasarkan hasil evaluasi kegiatan pelatihan, diperoleh 86,3% siswa merasa Python mudah dipahami dan 95,5% merasa materi pelatihan yang diberikan ini bermanfaat. Selain itu, berdasarkan hasil pre-test dan post-test diketahui bahwa pengetahuan para siswa secara umum meningkat setelah mengikuti pelatihan
Optimizing Deep Neural Networks Using ANOVA for Web Phishing Detection Wulan Sri Lestari; Mustika Ulina
Teknika Vol 13 No 1 (2024): Maret 2024
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.v13i1.758

Abstract

Phishing attacks are crimes committed by sending spoofed Web URLs that appear to come from a legitimate organization in order to obtain another party's sensitive information, such as usernames, passwords, and other confidential data. The stolen information is then used to commit fraud, such as identity theft and financial fraud, and can cause reputational damage to the party that is the victim of the phishing attack. This can cause great harm to the victimized individual or organization. To overcome these problems, this research uses feature selection using ANOVA and Deep Neural Networks (DNN) to detect web phishing attacks. Feature selection is used to optimize the performance of the DNN model to achieve more accurate results. Based on the results of feature selection using ANOVA, there are 52 attributes that have a significant impact on web phishing attack detection. The next step is to implement DNN to build a web phishing attack detection model. The results of testing the web phishing detection model show that in the training phase, the accuracy value increased by 17.51% for the 80:20 dataset and 18.39% for the 70:30 dataset. During the testing phase, the accuracy value increased by 17.8% for the 80:20 dataset and 18.58% for the 70:30 dataset. The resulting recognition model shows consistent and reliable results not only during training, but also during testing in situations closer to real-world conditions. Conclusively, the use of ANOVA proves effective in mitigating less relevant features and contributing to the optimization of web phishing detection models.
Object Detection in E-Commerce Using YOLO in Real Time Frans Mikael Sinaga; Gunawan; Sunaryo Winardi; Heru Kurniawan; Wulan Sri Lestari; Karina Mannita Tarigan
Teknika Vol 13 No 1 (2024): Maret 2024
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.v13i1.773

Abstract

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.
Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT Sinaga, Frans Mikael; Purba, Ronsen; Pipin, Sio Jurnalis; Lestari, Wulan Sri; Winardi, Sunaryo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7861

Abstract

This research is grounded in the emergence of ChatGPT technology, supported by prior and similar studies. The urgency of the issue is highlighted by previous research indicating non-convergent classification outcomes in LSTM (Long Short-Term Memory) methods due to suboptimal hyperparameter settings and limitations in understanding text data within Big Data. The presence of ChatGPT technology brings both benefits and potential misuse, such as copyright infringement, unauthorized news extraction, and violations of accountability principles. Understanding public sentiment towards the presence of ChatGPT technology is crucial. The research aims to implement the BERT (Bidirectional Encoder Representations from Transformers) method to achieve accurate and convergent sentiment analysis classification. This study involves data preprocessing stages using Natural Language Processing (NLP) techniques. Text data, already vectorized, is classified using BERT to determine public sentiment (positive, negative, neutral) towards ChatGPT technology, ensuring greater accuracy, convergence, and contextual relevance. Performance testing of the BERT model is conducted using a Confusion Matrix. With parameters set to Max Sequence Length = 128 and Batch Size = 16, the highest classification accuracy achieved is 93.4%.
Co-Authors Apriyanto Halim Ariani Pertiwi Carles Juliandy Caroline Caroline Caroline Caroline Caroline Felix Frans Mikael Sinaga Gaol, Manto Lumban Gunawan Gunawan Gunawan Gunawan Halim, Marwa Hanes Hanes Hanes Hanes, Hanes Hanny Siagian Hernawati Gohzali Heru Kurniawan Karina Mannita Tarigan Kelvin Kelvin Mustika Ulina Naca Perangin-Angin Paulus Paulus Paulus Paulus Pipin, Sio Jurnalis Ronsen Purba Saragih, Yuni Marlina Sinaga, Frans Mikael Sunario Megawan Sunario Megawan Sunario Megawan Sunaryo Winardi Syanti Irviantina Tanti Tanti Tanti Ulina, Mustika William William William William Winardi, Sunaryo Yuni Marlina Saragih Yusuf Chandra Nasution