Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315527
Muhammad Rizal Fanani, I. Sudiharto, I. Ferdiansyah
Implementation of Solar thermal energy as a source of renewable electricity is currently being developed. The main problem with photovoltaic systems is the result of power efficiency is low. The maximum power point tracking (MPPT) method can increase the efficiency of photovoltaic output power. This research will use the MPPT method with an artificial bee colony (ABC) algorithm. MPPT design will be simulated using Power Simulation (PSIM) software. Simulation results will be compared with no MPPT and MPPT human psychology optimization (HPO) algorithm. The results show MPPT ABC gets the best average accuracy from the average accuracy without MPPT and MPPT HPO, which is 99.95%. And the MPPT ABC has a response time of MPP tracking faster than MPPT HPO, during irradiation 800 W/m2, 900 W/m2, 1000 W/m2.
{"title":"Implementation of Maximum Power Point Tracking on PV System using Artificial Bee Colony Algorithm","authors":"Muhammad Rizal Fanani, I. Sudiharto, I. Ferdiansyah","doi":"10.1109/ISRITI51436.2020.9315527","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315527","url":null,"abstract":"Implementation of Solar thermal energy as a source of renewable electricity is currently being developed. The main problem with photovoltaic systems is the result of power efficiency is low. The maximum power point tracking (MPPT) method can increase the efficiency of photovoltaic output power. This research will use the MPPT method with an artificial bee colony (ABC) algorithm. MPPT design will be simulated using Power Simulation (PSIM) software. Simulation results will be compared with no MPPT and MPPT human psychology optimization (HPO) algorithm. The results show MPPT ABC gets the best average accuracy from the average accuracy without MPPT and MPPT HPO, which is 99.95%. And the MPPT ABC has a response time of MPP tracking faster than MPPT HPO, during irradiation 800 W/m2, 900 W/m2, 1000 W/m2.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126519074","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}
Recommendation systems have become a staple feature for any e-commerce sites. The ability to predict whether a customer likes an unseen product forms the very foundation of a recommendation system. In this paper, we concern the issue of explicit rating prediction over cosmetic products. Given a dataset of cosmetic product ratings, we analyze the characteristics of the dataset and implement a wide range of algorithms, such as KNN and matrix factorization, to predict such ratings. We evaluate the performance of these algorithms using MAE and RMSE measures, and discuss factors that may contribute to their performance results. Our experiments have shown that the SVD++ technique performs the best among all with an MAE of 0.7699 and an RMSE of 0.9696. We hope that our paper can shed new light on the selection of explicit rating prediction algorithms not only in the domain of beauty products, but also in wider scenarios.
{"title":"Benchmarking Explicit Rating Prediction Algorithms for Cosmetic Products","authors":"Raditya Nurfadillah, Fariz Darari, Radityo Eko Prasojo, Yasmin Amalia","doi":"10.1109/isriti51436.2020.9315512","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315512","url":null,"abstract":"Recommendation systems have become a staple feature for any e-commerce sites. The ability to predict whether a customer likes an unseen product forms the very foundation of a recommendation system. In this paper, we concern the issue of explicit rating prediction over cosmetic products. Given a dataset of cosmetic product ratings, we analyze the characteristics of the dataset and implement a wide range of algorithms, such as KNN and matrix factorization, to predict such ratings. We evaluate the performance of these algorithms using MAE and RMSE measures, and discuss factors that may contribute to their performance results. Our experiments have shown that the SVD++ technique performs the best among all with an MAE of 0.7699 and an RMSE of 0.9696. We hope that our paper can shed new light on the selection of explicit rating prediction algorithms not only in the domain of beauty products, but also in wider scenarios.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117214354","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315435
A. Lagunov, A. Ladvishchenko
The Arctic attracts the attention of many countries around the world because it is rich in hydrocarbons. To conduct exploration for hydrocarbons, researchers need electricity. Traditionally, diesel or gasoline generators are used to generate electricity in the circumpolar region. Fuel delivery is costly, and environmental pollution occurs during the operation of electric generators. Wind generators and solar power plants can be used as alternative sources of electricity. In adverse conditions in the Arctic, wind turbines quickly fail. This work is devoted to choosing the type of solar cells that can operate efficiently at low temperatures.
{"title":"Features of the Use of Solar Panels at Low Temperatures in the Arctic","authors":"A. Lagunov, A. Ladvishchenko","doi":"10.1109/ISRITI51436.2020.9315435","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315435","url":null,"abstract":"The Arctic attracts the attention of many countries around the world because it is rich in hydrocarbons. To conduct exploration for hydrocarbons, researchers need electricity. Traditionally, diesel or gasoline generators are used to generate electricity in the circumpolar region. Fuel delivery is costly, and environmental pollution occurs during the operation of electric generators. Wind generators and solar power plants can be used as alternative sources of electricity. In adverse conditions in the Arctic, wind turbines quickly fail. This work is devoted to choosing the type of solar cells that can operate efficiently at low temperatures.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115831756","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}
Pub Date : 2020-12-10DOI: 10.1109/isriti51436.2020.9315491
{"title":"Welcome Speech from the Chairman of Stmik Akakom Yogyakarta","authors":"","doi":"10.1109/isriti51436.2020.9315491","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315491","url":null,"abstract":"","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127987761","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315332
Yenni Astuti, Risanuri Hidayat, Agus Bejo
This paper compares the performance of speaker identification systems based on feature extraction methods. Fast Fourier Transform (FFT), Mel-Frequency Cepstral Coefficient (MFCC) and Discrete Wavelet Transform (DWT) are three of chosen feature extraction techniques used to test. These methods are applied to identify speakers by a word spoken. The system used Dynamic Time Warping (DTW) as classifier. Programming is done on MATLAB for training and testing. In this experiment, the combination of DWT and DTW gives better accuracy result than the other methods.
{"title":"Comparison of Feature Extraction for Speaker Identification System","authors":"Yenni Astuti, Risanuri Hidayat, Agus Bejo","doi":"10.1109/ISRITI51436.2020.9315332","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315332","url":null,"abstract":"This paper compares the performance of speaker identification systems based on feature extraction methods. Fast Fourier Transform (FFT), Mel-Frequency Cepstral Coefficient (MFCC) and Discrete Wavelet Transform (DWT) are three of chosen feature extraction techniques used to test. These methods are applied to identify speakers by a word spoken. The system used Dynamic Time Warping (DTW) as classifier. Programming is done on MATLAB for training and testing. In this experiment, the combination of DWT and DTW gives better accuracy result than the other methods.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130265640","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315364
T. Hermawan, Y. Suryanto, Fahdiaz Alief, Linda Roselina
This research discusses mobile phone forensics on the unsend message feature of social media. It assists investigators forensic or law enforcers in Indonesia to get digital evidence of cybercrime problems such as hoaxes, cyberbullying, illegal transactions, online protection, or other crimes on social media. This research uses Universal Forensic Extraction Device (UFED) and MOBILedit tools to get digital evidence. The selected social media that will be investigated by investigator forensic are Instagram, Line, Whatsapp, Facebook Messenger, Skype, Snapchat, Viber, and Telegram. Based on the results obtained, artifacts can only be found by UFED on social media such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram, whereas digital evidence can not be found on social media such as Line and Snapchat.
{"title":"Android Forensic Tools Analysis for Unsend Chat on Social Media","authors":"T. Hermawan, Y. Suryanto, Fahdiaz Alief, Linda Roselina","doi":"10.1109/ISRITI51436.2020.9315364","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315364","url":null,"abstract":"This research discusses mobile phone forensics on the unsend message feature of social media. It assists investigators forensic or law enforcers in Indonesia to get digital evidence of cybercrime problems such as hoaxes, cyberbullying, illegal transactions, online protection, or other crimes on social media. This research uses Universal Forensic Extraction Device (UFED) and MOBILedit tools to get digital evidence. The selected social media that will be investigated by investigator forensic are Instagram, Line, Whatsapp, Facebook Messenger, Skype, Snapchat, Viber, and Telegram. Based on the results obtained, artifacts can only be found by UFED on social media such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram, whereas digital evidence can not be found on social media such as Line and Snapchat.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131323248","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315397
V. J. L. Engel, Firhat Hidayat, Richard Dwiputra
The selection of features plays a big role in improving the results of a computer network attack detection system. This research used a model of feature selection to find the best combination of network traffic features to identify network attacks while retaining power explanations. This research also used filter-based feature selection, namely Information Gain (IG) and Gain Ratio (GR). Training and testing can be carried out after sigma value of SVM parameter has been determined. From sigma value testing, we chose sigma value of 5000. After SVM training, it is found that Gain Ratio with 30 features perform best for most measurement and classes. Nevertheless, full 41 features outperform IG and GR for probe class. Also, model that integrating feature selection has possibility to converge faster. It is recommended that further analysis and examination is needed to understand features combination result. Additionally, further research is needed to determine the effectiveness of features combinations to improve model performance and to try different approaches besides the filter-based method.
特征的选择对提高计算机网络攻击检测系统的检测效果起着至关重要的作用。本研究使用特征选择模型来寻找网络流量特征的最佳组合，以识别网络攻击，同时保留功率解释。本研究还采用基于滤波器的特征选择，即信息增益(Information Gain, IG)和增益比(Gain Ratio, GR)。确定支持向量机参数的sigma值后，即可进行训练和测试。从sigma值检验中，我们选择sigma值为5000。经过SVM训练，发现增益比为30个特征时，对大多数测量和类的效果最好。然而，对于探针类，41个特性的性能优于IG和GR。此外，集成特征选择的模型有可能收敛得更快。建议进一步分析和检查，以了解特征组合结果。此外，还需要进一步的研究来确定特征组合对提高模型性能的有效性，并尝试除基于滤波器的方法之外的不同方法。
{"title":"Network Attack Detection System Using Filter-based Feature Selection and SVM","authors":"V. J. L. Engel, Firhat Hidayat, Richard Dwiputra","doi":"10.1109/ISRITI51436.2020.9315397","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315397","url":null,"abstract":"The selection of features plays a big role in improving the results of a computer network attack detection system. This research used a model of feature selection to find the best combination of network traffic features to identify network attacks while retaining power explanations. This research also used filter-based feature selection, namely Information Gain (IG) and Gain Ratio (GR). Training and testing can be carried out after sigma value of SVM parameter has been determined. From sigma value testing, we chose sigma value of 5000. After SVM training, it is found that Gain Ratio with 30 features perform best for most measurement and classes. Nevertheless, full 41 features outperform IG and GR for probe class. Also, model that integrating feature selection has possibility to converge faster. It is recommended that further analysis and examination is needed to understand features combination result. Additionally, further research is needed to determine the effectiveness of features combinations to improve model performance and to try different approaches besides the filter-based method.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129610008","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}
Pub Date : 2020-12-10DOI: 10.1109/isriti51436.2020.9315355
{"title":"Index","authors":"","doi":"10.1109/isriti51436.2020.9315355","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315355","url":null,"abstract":"","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501104","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315351
Rinda Mardhotillah, B. Dirgantoro, C. Setianingsih
Speaker Recognition is included in pattern recognition, where one of the most critical parts is the process of data classification. In the classification, the built system must estimate the classification of data into a classroom dimension closest to the training set. The speaker's introduction aims to identify evidence of speech recording by a handheld telephone that involves comparing one or more unidentified sound samples with one or more known sound samples. In this research, the data used in the form of evidence of recording conversation by telephone and recording of comparison of some unexpected. The part that is done is to classify speaker recognition with the Support Vector Machine (SVM) classification method to recognize the speaker. Using the SVM method, the accuracy of classifying the speaker's introduction is excellent. From the test results, the SVM method's use resulted in an accuracy rate of 86.67% for the test with the same sentence and up to 67% for different sentences to recognize the speaker with the values of C 0.01 and $boldsymbol{gamma}$ (Gamma) 0.0001.
{"title":"Speaker Recognition for Digital Forensic Audio Analysis using Support Vector Machine","authors":"Rinda Mardhotillah, B. Dirgantoro, C. Setianingsih","doi":"10.1109/ISRITI51436.2020.9315351","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315351","url":null,"abstract":"Speaker Recognition is included in pattern recognition, where one of the most critical parts is the process of data classification. In the classification, the built system must estimate the classification of data into a classroom dimension closest to the training set. The speaker's introduction aims to identify evidence of speech recording by a handheld telephone that involves comparing one or more unidentified sound samples with one or more known sound samples. In this research, the data used in the form of evidence of recording conversation by telephone and recording of comparison of some unexpected. The part that is done is to classify speaker recognition with the Support Vector Machine (SVM) classification method to recognize the speaker. Using the SVM method, the accuracy of classifying the speaker's introduction is excellent. From the test results, the SVM method's use resulted in an accuracy rate of 86.67% for the test with the same sentence and up to 67% for different sentences to recognize the speaker with the values of C 0.01 and $boldsymbol{gamma}$ (Gamma) 0.0001.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124855866","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}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315414
Manlika Ratchagit, B. Wiwatanapataphee, D. Nur
This paper aims to present how to estimate a model parameter, namely the fixed rate of the investment return in the stochastic delay difference equation in financial time series using the two m-delay autoregressive coefficients. The autoregressive coefficients (ARC) algorithm is proposed and compares with the classical differential evolution (DE) algorithm. For a Monte-Carlo simulation tool, the results obtained from the model with the estimated parameter are validated with historical financial data of IBEX 35, JPM and GOOG from Thomson Reuters database in the period between 2008 and 2010. The numerical results confirm that the two $m$-delay autoregressive coefficients perform well to estimate the fixed rate of the investment return and reduce the computation time for the matching process.
{"title":"On Parameter Estimation of Stochastic Delay Difference Equation using the Two $m$-delay Autoregressive Coefficients","authors":"Manlika Ratchagit, B. Wiwatanapataphee, D. Nur","doi":"10.1109/ISRITI51436.2020.9315414","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315414","url":null,"abstract":"This paper aims to present how to estimate a model parameter, namely the fixed rate of the investment return in the stochastic delay difference equation in financial time series using the two m-delay autoregressive coefficients. The autoregressive coefficients (ARC) algorithm is proposed and compares with the classical differential evolution (DE) algorithm. For a Monte-Carlo simulation tool, the results obtained from the model with the estimated parameter are validated with historical financial data of IBEX 35, JPM and GOOG from Thomson Reuters database in the period between 2008 and 2010. The numerical results confirm that the two $m$-delay autoregressive coefficients perform well to estimate the fixed rate of the investment return and reduce the computation time for the matching process.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125216606","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}
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