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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Business Intelligence (IJUBI) Journal of Applied Data Sciences Jurnal Abdimas
Fauziyyah, Anni Karimatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Social Sciences

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Analyzing Image Malware with OSINTs after Steganography using Symmetric Key Algorithm Fauziyyah, Anni Karimatul; Adrian, Ronald; Alam, Sahirul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12266

Abstract

Steganography is the practice of hiding a message or information within another file, such as an image (Singh & Singla, 2022). OSINT (Open Source Intelligence) involves using publicly available information for intelligence gathering purposes. In this research, the asymmetric key algorithm will be applied to the steganography method, using 10 images with different sizes and dimensions. Images tested for steganography are in tiff, gif, png, jpg, and bmp format. A combination of steganography and OSINT could involve analyzing and decoding images found on publicly available platforms, such as social media, to uncover hidden messages. On the other hand, steganography within OSINT can also be used to protect sensitive information from prying eyes. Overall, the combination of Symmetric Key Algorithm steganography and OSINT can be a powerful tool for both intelligence gathering and secure communication. Here in this work, malware is developed, and using that malware the victim’s machine is exploited. Later, an analysis is done via freely available OSINTs to find out which is the best OSINT that gives the best results. OSINTs have been very helpful in identifying whether the URLs and files are malicious or not. But how binding an image with the malware makes it difficult for OSINTs to identify they are malicious or not is being analyzed in this work. The analysis shows that the best OSINT is VirusTotal which has a greater number of engines that could detect the malware whereas others don’t have a variety of engines to detect the malware. Also, when it comes to malware afore binding it with an image is easier to detect whereas for an OSINT it was difficult to identify and detect the malware after binding with an image
Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders Dzakiyullah, Nur Rachman; Pramuntadi, Andri; Fauziyyah, Anni Karimatul
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.16

Abstract

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.
Empowering Vocational Educators in Kulon Progo: IoT and Digital Transformation Training for Enhanced Teaching Skills Fauziyyah, Anni Karimatul; Subardono, Alif; Rosyid, Nur Rohman; Adrian, Ronald; Oktiawati, Unan Yusmaniar; Putri, Tika Erna; Santoso, Ardhi Wicaksono; Saputra, Yuris Mulya
Jurnal Abdimas Vol. 28 No. 2 (2024): December 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/yb0ejk04

Abstract

The IoT (Internet of Things) and Digital Transformation - Skill For All (SFA) training for vocational school teachers in Kulon Progo is an information technology training program. The SFA training addresses the skills gap by offering knowledge and skills necessary to work with IoT devices using IoT Kits from the Network Technology and Application lab, which is evolving in the digital transformation era. This training provides a solid foundation for managing IoT devices, which are highly relevant to today's industry needs. Configuring, managing, and programming IoT devices is essential in the digital age. By participating in this training, vocational school teachers in Kulon Progo can acquire practical skills directly applicable to their curriculum, helping students understand IoT concepts more deeply and practically. The IoT (Internet of Things) and Digital Transformation - Skill For All (SFA) training is designed to provide a comprehensive understanding of networking basics and how to configure and manage IoT Kits devices
MARKET BASE ANALYSIS IN DROPSHIP BUSINESS WITH APRIORI ALGORITHM IN DETERMINING R-BASED PRODUCT BUNDLING Fauziyyah, Anni Karimatul
Indonesian Journal of Business Intelligence (IJUBI) Vol 2 No 1 (2019): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v2i1.967

Abstract

The association method will associate data using a priori (experience) rules that meet the minimum support requirements, namely the combination of each item in the database and the minimum confidence requirement, namely the strength of the relationship between items in the association rules. In this study, the sales transaction data for three months will be analyzed from the dropshipper in the field of sales of beauty and fashion products using the association method with a priori algorithm to look for product bundling data patterns. The minimum value of support is 10% and 20% confidence, and with % ain function where Ordinary Shampoo items become the main products in product bundling, obtained 15 packages of the strongest products with lift values more than 1.1.
Web-based geothermal drilling stuck pipe prediction using decision tree algorithm Muhtadlor, Rosyihan; Rosyid, Nur Rohman; Fauziyyah, Anni Karimatul; Setiawan, Lalu Hendra Permana; Saputra, Irfan; Stasa, Pavel; Benes, Filip; Syafrudin, Muhammad; Alfian, Ganjar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp604-614

Abstract

In geothermal drilling operations, data from rig-mounted sensors play a crucial role in maintaining operational efficiency and preventing drilling failures. However, sensor uncertainties and complex subsurface conditions can lead to stuck pipe incidents, causing significant non-productive time and financial losses. This study proposes web-based drilling monitoring system integrated with machine learning (ML) to predict stuck pipe occurrences in geothermal drilling. Several ML algorithms—decision tree (DT), random forest (RF), naïve Bayes (NB), multilayer perceptron (MLP), and support vector machine (SVM)—were evaluated using geothermal drilling data from an Indonesian geothermal project conducted in 2023. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied to the training dataset. Feature selection was performed using the correlation coefficient method, and predictions were generated using a 5 minute sliding window. Among the evaluated models, the DT consistently demonstrated superior performance across multiple prediction horizons (PH), achieving an accuracy of 97.4%, precision of 98.6%, recall of 72.9%, and a ROC-AUC of 0.729 using the top five selected features. The trained model was integrated into web-based monitoring platform that provides visualization and predictive alerts. This system enables early detection and better decision-making, helping improve drilling efficiency, reduce stuck pipe risks, and enhance operational safety.
Co-Authors Alam, Sahirul Alfian, Ganjar Alif Subardono, Alif Benes, Filip Dzakiyullah, Nur Rachman Irfan Saputra Muhammad Syafrudin Muhtadlor, Rosyihan Oktiawati, Unan Yusmaniar Pramuntadi, Andri Ronald Adrian, Ronald Rosyid, Nur Rohman Santoso, Ardhi Wicaksono Saputra, Yuris Mulya Setiawan, Lalu Hendra Permana Stasa, Pavel Tika Erna Putri