2021 International Conference on ICT for Smart Society (ICISS)最新文献

{"title":"ICISS 2021 Cover Page","authors":"","doi":"10.1109/iciss53185.2021.9533258","DOIUrl":"https://doi.org/10.1109/iciss53185.2021.9533258","url":null,"abstract":"","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114109100","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":"When Homecoming is not Coming: 2021 Homecoming Ban Sentiment Analysis on Twitter Data Using Support Vector Machine Algorithm","authors":"Lidia Sandra, Ford Lumbangaol","doi":"10.1109/ICISS53185.2021.9533255","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533255","url":null,"abstract":"Homecoming, more traditionally known as Mudik, has become a trending topic on several social media platforms as soon as the 11-day homecoming ritual ban was announced on 7 April 2021. Opinions, varying from those in favor of and against the ban, start to rapidly appear. Twitter, a social media platform which is now considered to be an extension of oneself and often used to express ones’ opinion, has become flooded with comments on the homecoming ritual ban. The swarm of opinions in the form of tweets were then used as a dataset for sentiment analysis in order to understand how people perceive the ban. The algorithm used in this research is the classification algorithm using the Support Vector Machine method. The dataset was classified into three sentiments: positive, negative, and neutral. The use of the Support Vector Machine algorithm yielded a 62% accuracy with this dataset. The sentiment analysis showed that the keyword \"mudik\" had a neutral sentiment for the most part. Meanwhile, results of engagement analysis show that the largest forms of engagements were retweets and liking tweets that had a neutral sentiment. When the neutral sentiment was removed, we found that the largest sentiment on the homecoming ritual ban was negative. This is likely due to the release of an addendum to the Covid-19 Handling Task Force Circular Number 13 of 2021 on 22 April 2021 that imposes more restrictions on and extends the effective dates of the restrictions related to the homecoming ritual ban; exactly one day before the data scraping of 5000 datasets on tweets from 23 April 2021 was carried out. The researcher had already sampled the tweets with the most engagements (those with the most retweets and likes). It was found that some tweets had a negative sentiment, but the model classified it as having a neutral sentiment. This may be affected by inaccuracies of dataset training as some of the tweets were in Malay rather than Indonesian. A challenge that needs to be overcome is the limited number of datasets for NLP training or sentiment analysis for the Indonesian language in comparison to that of the English language. On the other hand, this has become an opportunity for the researcher to develop a more appropriate training model.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122630860","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":"Detection of Railroad Anomalies using Machine Learning Approach","authors":"Ade Chandra Nugraha, S. Supangkat, I. B. Nugraha, Harno Trimadi, Awan Hermawan Purwadinata, Sumarni, Santi Sundari","doi":"10.1109/ICISS53185.2021.9533226","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533226","url":null,"abstract":"Maintenance of assets owned by an organization or company is an activity that will never stop. From the time of implementation, maintenance will be more optimal if it is carried out before the asset is in a damaged condition or cannot operate. Pro-active repair model is proven to reduce 15-60% of operational costs. The existence of technology and computing models currently supports big data processing, both in the form of transactional data, historical data and statistical data. The asset maintenance cycle transformed into an autonomous and integrated system, will assist in the decision-making process. A machine learning approach that is supported by big data analysis is one solution that can realize the predictive maintenance process. To accurately predict the condition of critical components, it can be started with data collection, followed by detecting normal and abnormal behavior, and continued by training algorithms to make predictions. Detection of railroad anomalies is used as the initial process in the predictive maintenance of railroads. The process of detecting railroad anomalies can be done by comparing the lateral, longitudinal and vertical acceleration from the sensing results through the accelerometers on both sides of the train wheels. Differences will pay attention to the data acceleration draft rail geometry either angkatan or listringan. The results of rail anomaly detection will indicate the rail maintenance process that can be carried out immediately.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124853181","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":"Schizophrenia Detection Based on Electroencephalogram Using Support Vector Machine","authors":"Ivan Kurnia Laksono, E. Imah","doi":"10.1109/ICISS53185.2021.9533200","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533200","url":null,"abstract":"Schizophrenia is a mental disorder caused by genetic factors and brain chemical factors. This disease requires early treatment. One way to detect schizophrenia is to use an electroencephalogram (EEG). An EEG is a device used to record signals generated by the brain’s electrical activity. This study was conducted on detecting Schizophrenia brain disorders based on EEG signals using the Alexnet Convolutional Neural Network (CNN) algorithm with SVM. CNN is a popular algorithm and state-of-the-art in machine learning, and SVM is still the baseline for comparing the proposed new methods. The dataset used in the study was taken from 32 normal subjects and 49 schizophrenic subjects. The data consisted of 3072 features. The test results show SVM has better performance than CNN, with a maximum accuracy of SVM 0.792 in comparison with CNN accuracy is 0.76. The fastest training time is SVM 0.5 seconds while CNN is 88 seconds, CNN training time is longer because CNN performs convolution calculations on five layers.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128661508","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":"Developing AI Bots with Minimax Algorithm for Surakarta Board Game","authors":"Radisa H. Rachmadi, Rainamira Azzahra, Rayhan A. Darmawan, P. A. Nigo, N. N. Qomariyah","doi":"10.1109/ICISS53185.2021.9533206","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533206","url":null,"abstract":"Surakarta Board Game is a traditional Indonesian board game that has been forgotten all over the world, especially in Indonesia where it originally came from. This game is still in its early stages of development, even though this game has been published through a book by Sid Sackson since 1970. Due to this we wanted to create an AI for this game in order to raise awareness of this beautiful Indonesian board game. Therefore, we were able to develop and create a simple Surakarta Board Game AI player implementing the Minimax algorithm. Minimax is a well-known decision-making and game-theory technique for finding the best move for a player, given that the opponent likewise plays optimally. Additionally, in this paper, we investigated the performance of our developed AI player including the chance of winning and the time taken for each game. Several experiments were carried out by opposing AI with a random player. Through developing this game, we will be able to help the survival of Indonesian culture.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121295155","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":"Blended Learning Platform: A Requirement Analysis","authors":"Sabam Parjuangan, Meliyanti","doi":"10.1109/ICISS53185.2021.9533257","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533257","url":null,"abstract":"Learning during the pandemic and during the application of new habits in the context of implementing learning that implements the protocol for preventing the spread of Covid-19 has unique and uncertain needs. Unique means that the learning process has special characteristics from pre-pandemic learning. Meanwhile, the meaning of “nonpermanent” is a learning process that changes in learning planning, implementation, and evaluation. This causes the need for new innovations in learning platforms that are able to accommodate this unique and variable learning. This article provides an analysis of the requirements of a blended learning platform. Where changes in dynamic forms of learning give rise to various needs in learning. The method used is the survey, and analysis of responses of respondents using user story quality (USQ). The results found in this study are presented in the form of a table containing the item requirements for developing a blended learning platform.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116567110","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":"The Smart Mobility Insight of Bus Rapid Transit (BRT) Trans Jateng Purwokerto-Purbalingga Ridership","authors":"D. Kusumawardani, Yudha Saintika, Fauzan Romadlon","doi":"10.1109/ICISS53185.2021.9533253","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533253","url":null,"abstract":"The rapid development of technology presents a new paradigm, one of which is in the transportation sector. The concept of smart mobility is one of the components in the realization of a smart city, which is closely related to the transportation sector. The Purwokerto-Purbalingga Bus Rapid Transit (BRT) is the government's effort to encourage the transportation sector towards smart mobility. It requires three crucial categories: accessibility, sustainability, and communication and information technology. This study proves and explains that these three categories can be fulfilled by the Purwokerto-Purbalingga Bus Rapid Transit, especially from women's perception, indicated by an average accessibility score of 4.04, sustainability of 4.22, and Information and Communication Technology of 3.80. Efforts that can be taken by the government and Bus Rapid Transit managers include monitoring and evaluating bus stops and bus arrival frequencies as well as developing mobile-based applications that provide real-time information related to bus rapid transit.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122520569","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":"Online Learning Effect on Student Learning Effectiveness","authors":"Nathania Joyce Irene, Hadista Azzahra, Ryan Christianto Giri, Yoshe Ariel, Christopher Darren, Tanty Oktavia, F. Gaol, Takaaki Hosoda","doi":"10.1109/ICISS53185.2021.9533205","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533205","url":null,"abstract":"Pandemic COVID-19 has been giving the impact that large to the entire community in Indonesia because the pandemic of this, many activities are obstructed, and regulations protocol Health implemented by the government make its people must adjust themselves to abide by the rules that exist. Likewise, in the education sector, this pandemic make learning should be done in online. Learning process would be not as same when face-to- face and each university also has a method of learning that is different to improve the quality of learning that is given to the students. Higher education as learning environment also wanted to know the level of effectiveness of learning online are given and the attitude / behavior of students during follow online learning procedure. This study aims to determine the effect that significant to the effectiveness of the learning of students who do study via online (E-Learning). The sample was determined using the questionnaire collection method and the results of the questionnaire were collected as research data. Then the data is processed and tested (by testing the validity, reliability, etc.). The results show that internal factors have significant correlation to the effectiveness of e-learning.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115794067","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":"Interconnection System Simulation Analysis of Transient Micro-grid Stability in Indonesia","authors":"A. Ramelan, Chico Hermanu Brillianto A., F. Adriyanto, M. Ibrahim, Gilang Satria Ajie, Ayu Latifah","doi":"10.1109/ICISS53185.2021.9533244","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533244","url":null,"abstract":"Global economic development causes higher energy demand. The superiority of technology in distributed power generation from renewable energy sources is a solution to these problems. The writers designed a system modeling in ETAP software to design a micro-grid interconnection system that utilized the potential sources of solar and wind energy in Indonesia. This design is combined with models in the ETAP software such as wind turbine, photovoltaic, inverter, energy storage, generator, user-side loads, industrial system loads, transmission-distribution, transformers, and conventional power grid. The simulation of the micro-grid interconnection system is designed in operating conditions in order to analyze the load flow and transient stability analysis when a three-phase fault occurs on the main bus. This system is modeled in graphical form to determine the effect of errors on stress recovery to return to normal operating conditions. The use of wind turbine in a micro-grid system causes the transient stability conditions in the main bus voltage to fluctuate. To maintain calm on the main bus, a variable picth control setting is required on the wind turbine generator. The effect of energy storage on the micro-grid interconnection system in the event of an error can reduce the voltage drop on the main bus.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130680107","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":"Comparison of SMOTE Sampling Based Algorithm on Imbalanced Data for Classification of New Student Admissions","authors":"Yoga Handoko Agustin, Fitri Nuraeni, D. Kurniadi, Y. Septiana, A. Mulyani, W. Baswardono","doi":"10.1109/ICISS53185.2021.9533243","DOIUrl":"https://doi.org/10.1109/ICISS53185.2021.9533243","url":null,"abstract":"One of the efforts to get quality students is through selection. The selection process must be balanced with a strategy so that the selected students are truly qualified. Classification techniques can be used to see the history of new student admissions who are accepted with the student’s lecture history. There are many classification algorithms that can be used, so comparisons need to be made to see the best performance of the algorithm. The classification algorithm used is Decision Tree C4.5, K-Nearest Neighbor, Naïve Bayes and Neural Network. The data used are 546 records in the imbalanced data category. So we need the Smote algorithm to make the data balanced so as not to result in misclassification. The classification results were tested using the Confusion Matrix, ROC and Geometric Mean (G-Mean) as well as a T-Test. The comparison results show that the best performance is on the K-Nearest Neighbor algorithm with an accuracy value of 84.99%, AUC of 0.700, G-Mean 62.95% and the T-test produces a significant different from other algorithms.","PeriodicalId":220371,"journal":{"name":"2021 International Conference on ICT for Smart Society (ICISS)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130837298","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}