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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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Journal : Teknika

 1 
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.
Comparison of Deep Neural Networks and Random Forest Algorithms for Multiclass Stunting Prediction in Toddlers Lestari, Wulan Sri; Saragih, Yuni Marlina; Caroline
Teknika Vol. 13 No. 3 (2024): November 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.v13i3.1063

Abstract

Stunting in toddlers is a serious global health issue, with long-term impacts on physical growth and cognitive development. To address this problem more effectively, it is crucial not only to identify whether a child is stunted but also to predict the severity of the condition. Multiclass stunting prediction offers deeper insights into a child’s condition, enabling more precise and targeted interventions. This study aims to compare the performance of multiclass stunting prediction models using two machine learning algorithms: Deep Neural Networks and Random Forest. The research process involved data collection, preprocessing, as well as model development and testing. The results show that the Random Forest model achieved 100% accuracy in training and 99.92% accuracy in testing, while the Deep Neural Networks model achieved 93.49% accuracy in training and 93.21% in testing. Both models demonstrated strong performance in multiclass stunting prediction, with Random Forest proving superior in terms of accuracy compared to Deep Neural Networks.
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 Hanny Siagian Hernawati Gohzali Heru Kurniawan Karina Mannita Tarigan Kelvin Kelvin Mustika Ulina Naca Perangin-Angin 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 Winardi, Sunaryo Yuni Marlina Saragih Yusuf Chandra Nasution