Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)最新文献_第8页

Remote Penetration Testing with Telegram Bot 远程渗透测试与电报机器人

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-03 DOI: 10.29207/resti.v7i3.4870

Naufal Hafiz, O. Briliyant, Dimas Febriyan Priambodo, Muhammad Hasbi, Sri Siswanti

{"title":"Remote Penetration Testing with Telegram Bot","authors":"Naufal Hafiz, O. Briliyant, Dimas Febriyan Priambodo, Muhammad Hasbi, Sri Siswanti","doi":"10.29207/resti.v7i3.4870","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4870","url":null,"abstract":"The widespread of websites and web applications makes them the main target of cyber attacks. One way to increase security is to perform a penetration test. This test is carried out using the attacker's point of view to find out vulnerabilities on a website or web application and then exploit these vulnerabilities. The results of the penetration test can be used as recommendations to close the gaps that have been known through testing. Because penetration testing requires special resources such as tools and operating systems, a solution is needed to make penetration testing possible with low resources. Telegram bots that are open source offer a solution to overcome these problems. Using the SDLC waterfall approach, this bot was built to provide penetration testing services by connecting the Kali Linux server as a tools provider and the Telegram bot as an interface to users. As a result, users can access penetration testing tools anywhere and anytime via the Telegram bot. To ensure that the bot can run well, testing is carried out through black box testing and load testing. Telegram bot is a solution for integrated compact automatic mobile penetration tester with low resources. Based on load testing, the maximum limit of users who can access Telegram bots simultaneously is 35 users with the highest load average of 5.4. Based on the results of the User Acceptance Test, the Telegram bot has an acceptance rate score of 88,457 % and a questionnaire score of 774 which is an agreed area. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130630098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Detection of Face Mask Using Convolutional Neural Network 基于卷积神经网络的面罩实时检测

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-03 DOI: 10.29207/resti.v7i3.5036

Imam Husni Al Amin, Deva Ega Marinda, Edy Winarno, Dewi Handayani U.N, Veronica Lusiana

{"title":"Real-Time Detection of Face Mask Using Convolutional Neural Network","authors":"Imam Husni Al Amin, Deva Ega Marinda, Edy Winarno, Dewi Handayani U.N, Veronica Lusiana","doi":"10.29207/resti.v7i3.5036","DOIUrl":"https://doi.org/10.29207/resti.v7i3.5036","url":null,"abstract":"Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127796905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter LSTM和IndoBERT方法在Twitter上识别恶作剧的比较

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4830

Muhammad Ikram Kaer Sinapoy, Yuliant sibaroni, Sri Suryani Prasetyowati

{"title":"Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter","authors":"Muhammad Ikram Kaer Sinapoy, Yuliant sibaroni, Sri Suryani Prasetyowati","doi":"10.29207/resti.v7i3.4830","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4830","url":null,"abstract":"In recent years, social media users have been increasing significantly, in January 2022 social media users in Indonesia reached 191 million people which has an increase of 12.35% from the previous year as many as 170 million people, With this massive increase every year, more and more people tend to seek and consume information through social media. Despite the many advantages provided by social media, However, the quality of information on social media is lower than in traditional news media there is a lot of hoax information spreading. With many disadvantages felt by hoax information, it has led to many research to detect hoax information on social media, especially information that is widely spread on Twitter. There are several previous researches that use various models using machine learning and also using deep learning to detect hoax. deep learning is very well used to perform several text classification tasks, especially in detecting hoax. The aim of this paper is to compare the LSTM and IndoBERT methods in detecting hoax using datasets taken from Twitter. In this study, two experiments work are conducted, LSTM and IndoBERT methods. The experimental results is average value obtained from experiments using 10-fold cross-validation. The IndoBERT model shows good performance with an average accuracy value of 92.07%, and the LSTM model provides an average accuracy value of 87.54%. The IndoBERT model can show good performance in hoax detection tasks and is shown to outperform the LSTM model which can provide the best average accuracy results in this study.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points 基于随机森林算法的植物健康水平农业无人机目标点定位

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4959

Try Kusuma Wardana, Y. Arkeman, K. Priandana, F. Kurniawan

{"title":"Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points","authors":"Try Kusuma Wardana, Y. Arkeman, K. Priandana, F. Kurniawan","doi":"10.29207/resti.v7i3.4959","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4959","url":null,"abstract":"Chemical residues from the use of pesticides in agriculture can impact human health through environmental and food pollution. To lessen the negative effects of excessive pesticide use, pesticides must be applied to plants by dose. The dose of pesticide application can be based on a plant health level, which is the result of drone Normalized Difference Vegetation Index (NDVI) image analysis. Drones can also be used for spraying pesticides. Analysis of plant health levels was carried out using the Random Forest (RF) algorithm. The results of the classification plant health levels will be used to design spray drone flight routes. The objective of this research is to classify plant health levels of rice based on NDVI imagery using the RF algorithm and to compile a database of spray drone target points. The results of this study indicate that the classification of plant health levels using the RF algorithm produces an accuracy value of 98% and a Kappa value of 0.96. As a result, the model developed and the algorithm employed is quite effective at classifying the level of plant health. Furthermore, spray drone target points based on plant health levels can be generated. Optimally the spray distance between rows is 2 m. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116040742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Analysis of Support Vector Machine and Perceptron In The Classification of Subsidized Fuel Receipts 支持向量机与感知机在补贴燃料收入分类中的比较分析

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4731

Jaka Tirta Samudra, Rika Rosnelly, Z. Situmorang

{"title":"Comparative Analysis of Support Vector Machine and Perceptron In The Classification of Subsidized Fuel Receipts","authors":"Jaka Tirta Samudra, Rika Rosnelly, Z. Situmorang","doi":"10.29207/resti.v7i3.4731","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4731","url":null,"abstract":"Currently, fuel oil is one of the important factors for the community and even a country on this earth to utilize this natural gas fuel for daily use as the main use and also by increasing the community's need for fuel oil. But there are several factors that cause this fuel problem, there is a factor of time and usage time, which is certain that one day it will expire and its capacity in a country, even if the country runs out of fuel, will make requests to other countries and also obstacles to supplying this fuel oil to the public. which is the main fuel from the Pertamina government agency which has begun to limit purchases for this fuel oil to certain circles by marking the types of subsidies or not subsidies that must be controlled by the government in limiting purchases for the public. In dealing with solving problems from the perspective of ownership or even utilization, there are limits to owning fuel, and not everyone has to have a lot or even too much. In solving the problem of dividing fuel revenue, which is good for filling revenue, it can be solved by using machine learning, namely data mining itself can help in completing subsidized fuel receipts without being excessive for the community so that they can be controlled and managed for their purchases. In building a fuel oil reception design, it can be grouped into a classification model that uses SVM and perceptron which uses the activation function of the sigmoid to get the final result of accuracy where getting the average value of 5-fold, 10-fold, 20-fold is accuracy. is 90.0%, the F1 value is 85.6%, the precision value is 87.6%, and the recall value is 90.0%.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122147159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Game Design for Mobile App-Based IoT Introduction Education in STEM Learning 基于移动应用程序的物联网游戏设计

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.5007

Indra Puja Laksana, Evi Dwi Wahyuni, Christian Sri Kusuma Aditya

{"title":"Game Design for Mobile App-Based IoT Introduction Education in STEM Learning","authors":"Indra Puja Laksana, Evi Dwi Wahyuni, Christian Sri Kusuma Aditya","doi":"10.29207/resti.v7i3.5007","DOIUrl":"https://doi.org/10.29207/resti.v7i3.5007","url":null,"abstract":"STEM education has received considerable attention in recent years. However, developing valid and reliable assessments in interdisciplinary learning in STEM has been a challenge. Therefore, many students ranging from junior high school to university students are only familiar with the Internet of Things (IoT) from social media but do not know its concept and function in STEM learning. This is also supported by the absence of educational applications about IoT. This research aims to introduce IoT by using mobile applications. This research refers to the multimedia development method according. The data collection method in this study was carried out by means of observation and interviews randomly to high school students to university students. This data collection was carried out using the experimental method of application testing to analyze user needs from several aspects such as features, images, and fonts. This research is also supported by the existence of literature studies derived from several journals. The results show that the functions in the application can operate as expected. Based on the survey results of the application, 75.37% of respondents rated this application in the very good category and gave positive responses so that this application could be well received by users \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115522933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Supermarket Product Purchase Transactions With the Association Data Mining Method 基于关联数据挖掘方法的超市商品采购交易分析

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4844

Norbertus Tri Suswanto Saptadi, Phie Chyan, Eremias Mathias Leda

引用次数: 0

Analysis of User Readiness Using the TRI Model for Smart School Implementation in the City of Pekanbaru 使用TRI模型分析北干巴鲁市智能学校实施的用户准备情况

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4772

M. Khairul Anam, Indra Prayogo, Susandri, Yoyon Efendi, Nurjayadi

{"title":"Analysis of User Readiness Using the TRI Model for Smart School Implementation in the City of Pekanbaru","authors":"M. Khairul Anam, Indra Prayogo, Susandri, Yoyon Efendi, Nurjayadi","doi":"10.29207/resti.v7i3.4772","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4772","url":null,"abstract":"Currently, Smart Schools have been widely applied in several schools, within the scope of education and services as they are being encouraged to support Smart City. Smart Schools is a school concept utilizing information technology used in the teaching and learning process in the class and school administration. One of the schools in Pekanbaru City that will implement smart schools is Junior High School 17 Pekanbaru. The aspect of building smart schools themselves is not only adequate infrastructure such as servers, labor, and integrated systems but also the readiness on the part of schools and students in implementing Smart Schools in the future. Therefore, to find out the level of readiness of prospective users of the Smart Schools concept, the technology readiness index (TRI) method with four personality variables; optimism, innovativeness, discomfort, and insecurity was used. The purpose of this research was to find out the readiness index of prospective users in the implementation of Smart Schools and see what factors need to be improved from the readiness of prospective users. The results show that teachers and students are ready to apply new technologies in an effort to implement smart schools at Junior High School 17 Pekanbaru. This can be seen from the results obtained, namely the optimism and innovation variables received medium to high ratings. for the discomfort and insecurity to be completely low. However, the student guardians are still unsure because all variables get medium scores. From these results it was stated that Junior High School 17 Pekanbaru was ready to apply new technology for implementing smart schools. In addition, this research can also serve as a guideline for other junior high schools in analyzing new technology users, so that the applied technology can run well. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131289857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Alter Ego Accounts using Social Media Mining 使用社交媒体挖掘检测另一个自我账户

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4919

Deyana Kusuma Wardani, Iwan Syarif, Tessy Badriyah

{"title":"Detecting Alter Ego Accounts using Social Media Mining","authors":"Deyana Kusuma Wardani, Iwan Syarif, Tessy Badriyah","doi":"10.29207/resti.v7i3.4919","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4919","url":null,"abstract":"Alter ego is a condition of someone who creates a new character with a conscious state. Original character role play is a game to create new imaginary characters that is used as research material for identification alter ego accounts. The negative effects of playing alter ego are stress, depression, and multiple personalities. Current research only focuses on the phenomenon and impacts of a role-playing game. We propose a new method to detect accounts of alter ego players in social media, especially Twitter. We develop an application to analyze the characteristics of alter ego accounts. Psychologists can use this application to discover the characteristics of alter ego accounts that are useful for analyzing personality so that the results can be used to appropriately handle alter ego players. Most user profiles, tweets, and platforms are used to detect account Twitter. This research proposes a new method using bio features as input data. We crawled and collected 565 bios from Twitter for one month. We observe the data to search for unique words and collect them into a classification dictionary. In this research, we use the cosine similarity method because this method is popular for detecting text and has a good performance in many cases. This research could identify alter ego accounts and other types of Twitter accounts. From the detection results of alter ego accounts, it is possible to analyze the characteristics of Twitter accounts. We use a sampling technique that takes 30% of the data as testing data. According to the results of the experiment cosine similarity obtained an accuracy of 0.95. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"593 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123417080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction 计算机恶意软件预测中各种集成算法的比较分析

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-02 DOI: 10.29207/resti.v7i3.4492

Yusuf Bayu Wicaksono, Christina Juliane

{"title":"Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction","authors":"Yusuf Bayu Wicaksono, Christina Juliane","doi":"10.29207/resti.v7i3.4492","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4492","url":null,"abstract":"By 2022 it is estimated that 29 billion devices have been connected to the internet so that cybercrime will become a major threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system (Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has infected a user's device. This study uses the opposite approach, which is to predict the potential for malware infection on the user's device before the infection occurs. Similar studies still use single algorithms, while this study uses ensemble algorithms that are more resistant to bias-variance trade-off. This study builds models from data on computer features that affect the possibility of malware infection on computer devices with Microsoft Windows operating system using ensemble algoritms, such as Bagging Classifier, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting Machine, Category Boosting, and Stacking Classifier. The best model is Stacking Classifier, which is a combination of Light Gradient Boosting Machine and Category Boosting Classifier, with training and test results of 0.70665 and 0.64694. Important features have also been identified as a reference for taking policies to protect user devices from malware infections.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123206057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0