Journal of Technology Informatics and Engineering最新文献

{"title":"HYBRID MODEL MACHINE LEARNING FOR DETECTING HOAXES","authors":"Budi Hartono, Munifah, Sindhu Rakasiwi","doi":"10.51903/jtie.v1i1.142","DOIUrl":"https://doi.org/10.51903/jtie.v1i1.142","url":null,"abstract":"Unlimited availability of content provided by users on social media and websites facilitates aggregation around a broad range of people's interests, worldviews, and common narratives. However, over time, the internet, which is a source of information, has become a source of hoaxes. Since the public is commonly flooded with information, they occasionally find it difficult to distinguish misinformation disseminated on net platforms from true information. They may also rely massively on information providers or platform social media to collect information, but these providers usually do not verify their sources. \u0000The purpose of this research is to propose the use of machine learning techniques to establish hybrid models for detecting hoaxes. The research methodology used here is a feature extraction experiment, in which a series of features will be analyzed and grouped in an experiment to detect hoax news and hoax, especially in the political sphere by considering five modalities. \u0000The outcome of this research indicates that the relation between publisher Prejudice and the attitude of hyper-biased news sources makes them more possible than other sources to spread illusive articles, besides that the correlation between political Prejudice and news credibility is also very strong. This shows that the experiment using a hybrid model to detect hoaxes works. well. To achieve even better results in future research, it is highly recommended to analyze user-based features in terms of attitudes, topics, or credibility.","PeriodicalId":177576,"journal":{"name":"Journal of Technology Informatics and Engineering","volume":"94 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125008060","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}

{"title":"BLACK BOX APPROACH TO MONITORING CONTAINER MICROSERVICES IN FOG COMPUTING","authors":"Danang Danang, Nuris Dwi Setiawan, Indra Ava Adianta","doi":"10.51903/jtie.v1i1.141","DOIUrl":"https://doi.org/10.51903/jtie.v1i1.141","url":null,"abstract":"In recent years IoT has developed very rapidly. IoT devices are used to monitor and control physical objects to transform the physical world into intelligent spaces with computing and communication capabilities. Compared to cloud computing, fog computing is used to support latency-sensitive applications at the edge of the network which allows client requests to be processed faster. This study aims to propose a monitoring framework for containerized black box microservices in a fog computing environment to evaluate CPU overhead, as well as to determine the operating status, service characteristics, and dependencies of each container. \u0000This study proposes a monitoring framework to integrate computing resource usage and run-time information from service interactions using a black box approach that seeks to integrate service-level information and computing resource information into the same framework. The proposed framework is limited to observing information monitoring after the server receives a request. This study uses JMeter to simulate user actions, which send requests to the server, and this research assumes the user knows the IP address of the server. For container monitoring methods in fog computing, all are indirect monitoring methods. \u0000The results of this study indicate that the proposed framework can provide operational data for visualization that can help system administrators evaluate the status of running containers using a black box approach. System administrators do not need to understand and modify target microservices to gather service characteristics from containerized microservices. Regarding future research, it is suggested to expand the exploration of modified system information, and that part of the container management tool code can be pre-tried so that the framework proposed in this study can provide real-time quantitative indexes for the load balancing algorithm to help optimize the load balancing algorithm.","PeriodicalId":177576,"journal":{"name":"Journal of Technology Informatics and Engineering","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122013014","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}

{"title":"ADVANCED MALICIOUS SOFTWARE DETECTION USING DNN","authors":"Sulartopo Sulartopo, Dani Sasmoko, Zaenal Mustofa, Arsito Ari Kuncoro","doi":"10.51903/jtie.v1i1.144","DOIUrl":"https://doi.org/10.51903/jtie.v1i1.144","url":null,"abstract":"The special component of malicious software analysis is advanced malicious software analysis which implicates interested the main framework of malicious software that can be executed after executing it and aggressive malicious software investigation depend on inquisitive of the practice of malicious software after running it in a composed habitat. Advanced malicious software analysis is usually performed by contemporary anti-malicious software operating systems using signature-based analysis. \u0000The purpose of this research is to propose also decide a DNN for the progressive identification of portable files to study the features of portable executable malicious software to minimize the occurrence of distorted likeness when aware of advanced malicious software. The model proposed in this study is a NN with a Dropout model contrary to a resolution tree model to examine how well it performs in detecting real malicious PE files. Setup-skeptic methods are used to extract features from files. The dataset is used to train the proposed approach and measure outcomes by alternative common malicious software datasets. \u0000The results from this study illustrate that the use of simple DNNs to study PE vector elements is not only efficient but more fewer system comprehensive than the traditional interested disclosure approach. The model proposed in this study achieves an A-UC of ninety-nine point eight with ninety accurate specifics at one percent inaccurate specific on the R-OC curve. For shows that this model has the potential to complement or replace conventional anti-malicious software operating systems so for future research, it is proposed to implement this model practically.","PeriodicalId":177576,"journal":{"name":"Journal of Technology Informatics and Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125755774","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}

{"title":"APPLICATION OF SOLAR ENERGY TO MEASURE PHOTOVOLTAIC CAPACITY AND BATTERY OPTIMIZATION","authors":"Unang Achlison, Iman Saufik Suasana, Dendy Kurniawan","doi":"10.51903/jtie.v1i1.145","DOIUrl":"https://doi.org/10.51903/jtie.v1i1.145","url":null,"abstract":"This study uses the Markov Decision Model (MDP) to implement battery degradation and optimize battery use in Photovoltaic and the battery system model created. The battery optimization scheme for home loads uses the application of solar energy to optimally measure photovoltaic and battery capacity against each other. The different qualities of the standard used in this study are described starting from system characteristics and charge settings to an analysis of MDP and battery degeneration. Various systems undergo a list of analyses to implement awareness reasoning although developing battery volume and photovoltaic for the current system. The parametric span of cosmic and battery central tariff, the tariff of power worn taken away the framework, tariff of battery degeneration, time of year, photovoltaic generator size, battery size, and Health Status (SoH) of batteries were carried out to determine the optimal volume estimate and analyze the trade-offs essential in a mix scheme. This is then used to treasure trove the minimum amount of fee of the scheme with photovoltaic and battery application. \u0000This study support decision of the essential sizing deliberation for photovoltaic and battery-managed home loads linked to the services grid. Insightful that the battery can be used more destructively, also it can be formed lower and run at a greater C speed. This study analyzes actual fog computing research tools and storage composition algorithms for fog computing and develops a fog computing monitoring framework to provide data for fog computing storage composition algorithms. The framework proposed in this study provides granular container virtual hardware resource information and black box monitoring of service layer information associated with microservices. Framework usefulness on Raspberry Pis and CPU overhead of framework tested. \u0000The results of this study present the framework proposed could be used on single-chip microcomputers with relatively inadequate computational performance. In addition, a minimal effect on the battery degeneration system on the MDP decision due to the low system C-rate limit for the battery and interesting behavior of total fee and demand is also found. For future research, testing different maximum C levels should be considered to determine the photovoltaic size and battery system affected. Various battery optimization systems can be proved to check the benefit and disbenefits in the microgrid system case study. Lastly, collecting a scheme for actual-time reproduction to know how nice the operation is performing is the next stage of implementing MDP for battery management and system development.","PeriodicalId":177576,"journal":{"name":"Journal of Technology Informatics and Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133268187","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}

{"title":"MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION","authors":"Fujiama Diapoldo Silalahi, Toni Wijanarko Adi Putra, Edy Siswanto","doi":"10.51903/jtie.v1i1.143","DOIUrl":"https://doi.org/10.51903/jtie.v1i1.143","url":null,"abstract":"Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. \u0000The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global  f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. \u0000Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.","PeriodicalId":177576,"journal":{"name":"Journal of Technology Informatics and Engineering","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121603999","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}