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International Conference of computer and Informatics Engineering (IC2IE)

作者： Agung Nugroho Teddy Mantoro Cyber Defense Engineering Study Program Sains and Defense Technology Faculty The Republic of Indonesia Defense University Bogor Indonesia Departement of Computer Science Media-Tech Lab Sampoerna University Jakarta Indonesia

Using MD5 hashing of passwords in web logins is highly vulnerable to dictionary attacks. One online application for cracking hashed passwords using a dictionary attack that is free and easy to use is ***. Even so, the use of hashing passwords using MD5 which has a low level of security is still commonly found to secure web login passwords. This research presents an increase in security through a combination of salt hash passwords when using MD5. The methodology of this study uses a quantitative experimental method to apply hash salt passwords using the MD5 combination. The password cracking test was carried out using the *** online hash cracking application on weak passwords such as “admin” with the addition of a password hash salt using the MD5 combination. The results showed that Salt Password using the MD5 combination could not be cracked by *** using a dictionary attack.

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An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

An Evaluation of Number of Pixels Change Rate (NPCR) in Syme...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Rizky Naufal Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Folasade Olubusola Isinkaye Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria School of Information Technologies and Engineering ADA University Baku Azerbaijan

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR.

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Automation Snort Rule for XSS Detection with Honeypot

Automation Snort Rule for XSS Detection with Honeypot

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International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Syaifuddin Syaifuddin Diah Risqiwati Hanugra Aulia Sidharta Faculty of Engineering School of Information Technology University of Muhammadiyah Malang Malang Indonesia Faculty of Technology and Design Computer Science Program Study Bina Nusantara Institute of Creative Technology Malang Malang Indonesia

ISBN: (纸本)9781538684030

In modern era, data has become precious and important, data leak can lead to high damage to business, impact on losing profit. To avoiding those problems, organization need equip with updated security tools to protecting data and network. As computer network growing rapidly, currently security become important to prevent from attack, both from outside and inside the network. In line with that, new vulnerability grown on everyday basis and resulting increment number of intrusion and attack. Unfortunately, each intrusion and attack have a different pattern and unique behavior, continuous development and research on intrusion detection is mandatory. By statistic, in 2017 attack associated with XSS attack is grew 39% compared with 2015.

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第3期50卷 858页

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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Greybody factor for massive fermion emitted by a black hole in de Rham-Gabadadze-Tolley massive gravity theory

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Physical Review D 2021年第8期104卷 084054-084054页

作者： P. Boonserm C. H. Chen T. Ngampitipan P. Wongjun Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation Bangkok 10400 Thailand The institute for fundamental study Naresuan University Phitsanulok 65000 Thailand Physics Program Faculty of Science Chandrakasem Rajabhat University Bangkok 10900 Thailand

The greybody factor of the massive Dirac field around the black hole in the dRGT massive gravity theory is investigated using the rigorous bound and the WKB approximation methods. In both methods, the greybody factor significantly depends on the shape of the potential. If the potential is smaller, there is more probability for the Dirac field to transmit through the black hole, therefore, the greybody factor is higher. Moreover, there exists a critical mass of the Dirac field such that the greybody factor is maximum. By comparing the results from these two methods, we argue that it is useful to use the rigorous bound method for the low potential cases, while using the WKB approximation method for the high potential cases.

关键词： Alternative gravity theories Classical black holes Quantum aspects of black holes

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Classification Of Aedes Mosquito Larva Using Convolutional Neural Networks And Extreme Learning Machine 7

Classification Of Aedes Mosquito Larva Using Convolutional N...

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7th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2023

作者： Nainggolan, Pauzi Ibrahim Efendi, Syahril Budiman, Mohammad Andri Lydia, Maya Silvi Joshua, Ivan Bukit, Dhani Syahputra Rahmat, Romi Fadillah Sinaga, Ledy Afrida Subroto, Edi Universitas Sumatera Utara Department of Computer Science Medan20155 Indonesia Universitas Sumatera Utara Department of Information Technology Medan20155 Indonesia Universitas Sumatera Utara Department of Public Health Medan20155 Indonesia Environmental Health and Diseases Control Ministry of Health Medan20153 Indonesia STIKes Mitra Husada Department of Midwifery Study Program Sumatera Utara Medan20142 Indonesia

ISBN: (纸本)9798350385526

The spread of diseases transmitted by Aedes mosquitoes, such as Dengue fever, Chikungunya (CHIKV), and Zika, poses a severe health problem in many countries. Aedes mosquitoes can be found in tropical and subtropical areas. One method to control the diseases is by controlling the population of Aedes mosquitoes, and one way to control the population of Aedes mosquito is by exterminating Aedes mosquito larvae. Identifying mosquito larvae, however, demands a considerable amount of effort, time, and specialized knowledge about the morphology and anatomy of mosquito larvae to accurately classify the various types. Therefore, a system is needed to classify Aedes and non-Aedes mosquito larvae. In this study, classification was carried out using larvae images taken using a digital microscope. The methods used to classify mosquito larvae images are Convolutional Neural Networks (CNN) to extract features from the larvae image and Extreme Learning Machine (ELM) to classify the larvae image. The result obtained from evaluating the CNN-ELM model is an accuracy of 98%, f1-score of 99% for Aedes larvae, and fl-score of 96% for non-Aedes larvae. © 2023 IEEE.

关键词： Image classification

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Defect Detection of Agricultural Commodities using Image Processing and Artificial Neural Networks

Defect Detection of Agricultural Commodities using Image Pro...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Suprijono, Herwin Kartikadarma, Etika Yusianto, Rindra Marimin, Marimin Dian Nuswantoro University Faculty of Engineering Department of Electrical Engineering Semarang53192 Indonesia Dian Nuswantoro University Study Program of Information Technology Faculty of Computer Science Semarang53192 Indonesia Dian Nuswantoro University Faculty of Engineering Department of Industrial Engineering Semarang53192 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology West Java Bogor16002 Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Production losses of agricultural commodities on agricultural land due to product defects depend on the level of pest and disease attacks. Defects cause the product not to be harvested or rejected by the market. Data from the Ministry of Agriculture of the Republic of Indonesia in 2021 shows the percentage of defective products for this commodity is 5%. This study aims to detect defects in agricultural products using image processing and artificial neural networks. We used the screening threshold method with potato research samples from Dieng, Indonesia. The study results for spot defects showed that from 100 training data, there were five incorrect identification data, so the accuracy of the training process was 95%. At the same time, the hole defect accuracy is better, which is 97%. It shows that defects in agricultural commodities can be detected using this method. For further research, the development of agricultural product sorting can combine sorting techniques with this method. © 2022 IEEE.

关键词： Defects

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Inappropriate Benefits and Identification of ChatGPT Misuse in programming Tests: A Controlled Experiment

arXiv

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arXiv 2023年

作者： Toba, Hapnes Karnalim, Oscar Johan, Meliana Christianti Tada, Terutoshi Djajalaksana, Yenni Merlin Vivaldy, Tristan Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Media Culture Department Faculty of Information Sciences and Arts Toyo University Kawagoe Japan EC-Council TampaFL United States

While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people’s work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT. © 2023, CC BY.

关键词： Students

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Comparison Performance of Decision Tree Classification Model for Spam Filtering with or without the Recursive Feature Elimination (RFE) Approach

Comparison Performance of Decision Tree Classification Model...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ahmad Fikri Zulfikar Dede Supriyadi Yaya Heryadi Lukas Faculty of Technique Program Study of Informatics University of Pamulang South Tangerang Indonesian Program Study of Informatics STMIK Eresha South Tangerang Indonesian Dept. of Computer Science Bina Nusantara University West Jakarta Indonesian Cognitive Enginnering Research Group (CERG) Faculty of Enginnering University of Katolik Indonesia Atma Jaya Jakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

Spam filters have become an important tool for ISP(Internet service Provider) to address the growing spam in cyberspace. Spam filtering is a program used to detect spam, prevent it and enter users' mailboxes. With the rapid growth of Internet technology, email communication is becoming increasingly important in people's daily lives. In addition, the amount of spam contained in emails has increased considerably in recent years. In this study, the authors will compare the performance of the decision tree classification models using or not using Recursive Feature Elimination (RFE) in spam filtering. The results of this test will be compared with the results of level accuracy, recall, precision, and F 1 Score using or not using the RFE method. where in the results of this level measurement, the model used can be a reference or solution to filter spam correctly, especially through the use of supervised learning data.

关键词： Unsolicited e-mail Decision trees Machine learning Classification algorithms Information filtering

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