2016 4th International Conference on Information and Communication Technology (ICoICT), 2016
Product review in e-commerce can help prospective buyers to make decision whether the product tha... more Product review in e-commerce can help prospective buyers to make decision whether the product that they want to buy is good or bad and it can help the sellers to get their consumers feedback. But, there are two problems exist: the number of product review increases day by day and e-commerce allows their consumers to write positive negative opinion about some product features in one review, as known as “free format”. Therefore, there should be system which can extract product feature from review and classify their opinion automatically. Class Sequential Rule (CSR) method can be implemented in product feature extraction and Opinion Lexicon method can be implemented in feature opinion classification. The best F-score of feature extraction using CSR in free format review is 51.26% and the best f-score of opinion classification using Opinion Lexicon in free format review is 35.65%.
2021 9th International Conference on Information and Communication Technology (ICoICT), 2021
Humans are inseparable from emotions, emotions fill human life at all times. Emotions have an imp... more Humans are inseparable from emotions, emotions fill human life at all times. Emotions have an impact on social relationships, memory, and decision-making. In the era of this research, humans tended to express emotions through social media such as Twitter in the form of videos, images and text. Over time, Social media has to turn out to be a critical part of most people’s lives. Human emotion is a research area that is widely researched, especially in the field of linguistics. In this study, we classified emotions with Convolutional Neural Network. In addition, we compared the performance with three different word embedding methods, Glove, word2vec, and fastText in classifying the given dataset. The dataset that we used were 4403 tweets which will be classified into 5 classes, namely: love, joy, anger, sadness, and fear. F1-score is employed as an evaluation metric. The results of our experiments show that the combination of CNN and word2vec can achieve 72.06% of F1-score, which incr...
2018 International Conference on Information and Communications Technology (ICOIACT), 2018
Graph visualization is often used for a representation of interconnected relations of entities. I... more Graph visualization is often used for a representation of interconnected relations of entities. Individual entities such as cells, humans, computers, users, and other entities are represented as vertices while edges are used for entities' relations. With the visualization, people can get more information from the graph. There are already many tools for graph visualization of social media data. However, those tools need a specific file input format before generating a graph and visualizing it. In this research, we tried to do experiment and analysed data transformation from Twitter raw data to a JSON file format that can be used for most of the web-based network visualization tools. Using those techniques, we can decrease the memory usage by 1,526.1 MB and increase graph formation running time by 1,718.75 seconds. Those techniques can also be added with Text Mining modules for online context analysis of network visualization.
As the number of social media users rises, the probability of hate speech spread in social media ... more As the number of social media users rises, the probability of hate speech spread in social media also rises indirectly. Hate speech has become one of most common cases found on social media. The spread of hate speech can lead to a riot that might cause conflict, group extermination, and even human casualties. Some of the latest controversies in Indonesia related to hate speech was the hate speech uttered to the government that led to polemic and even demonstration in the country. Along with this, it is important to detect hate speech to avoid conflict to happen. As the spread of hate speech in social media increases, it requires significant human efforts and is costly to detect manually. Therefore, this experiment is built to detect hate speech detection in Indonesian twitter texts using several conventional machine learning and deep learning based, BiGRU, with various features. The machine learning approaches being used are SVM and RFDT, while deep learning based methods used are B...
Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people thr... more Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people through electronic devices and online social networking websites. Cyberbullying through the internet is more dangerous than traditional bullying, because it can potentially amplify the humiliation to an unlimited online audience. According to UNICEF and a survey by the Indonesian Ministry of Communication and Information, 58% of 435 adolescents do not understand about cyberbullying. Some of them might even have been the bullies, but since they did not understand about cyberbullying they could not recognise the negative effects of their bullying. The bullies may not recognise the harm of their actions, because they do not see immediate responses from their victims. Our study aimed to detect cyberbullying actors based on texts and the credibility analysis of users and notify them about the harm of cyberbullying. We collected data from Twitter. Since the data were unlabelled, we built a web-ba...
2021 9th International Conference on Information and Communication Technology (ICoICT)
Cyberbullying is the act of threatening or endangering others by posting text or images that humi... more Cyberbullying is the act of threatening or endangering others by posting text or images that humiliate or harass people through the internet or other communication devices. According to a survey from Polling Indonesia and Asosiasi Penyelenggara Jasa Internet Indonesia (APJII) about cyberbullying, 49% of 5900 participants claimed they have been bullied. Therefore, this research was conducted with the intention to prevent cyberbullying acts, especially in Indonesia. We collected data from Twitter based on Twitter’s Trending keywords which correlated to cyberbully events. Then we combined it with the data from previous research. We obtained a total of 1425 tweets, consists of 393 data labeled as cyberbully and 1032 data labeled as non-cyberbully. Thereupon, we build a Doc2Vec model for features extraction, and a classifier model using the baseline classification method (SVM and RF) and CNN to detect cyberbully texts. The results show that the classifier using CNN and Doc2vec has the highest F1-score, 65.08%.
Product review in e-commerce can help prospective
buyers to make decision whether the product tha... more Product review in e-commerce can help prospective buyers to make decision whether the product that they want to buy is good or bad and it can help the sellers to get their consumers feedback. But, there are two problems exist: the number of product review increases day by day and e-commerce allows their consumers to write positive negative opinion about some product features in one review, as known as ”free format”. Therefore, there should be system which can extract product feature from review and classify their opinion automatically. Class Sequential Rule (CSR) method can be implemented in product feature extraction and Opinion Lexicon method can be implemented in feature opinion classification. The best F-score of feature extraction using CSR in free format review is 51.26% and the best f-score of opinion classification using Opinion Lexicon in free format review is 35.65%.
2016 4th International Conference on Information and Communication Technology (ICoICT), 2016
Product review in e-commerce can help prospective buyers to make decision whether the product tha... more Product review in e-commerce can help prospective buyers to make decision whether the product that they want to buy is good or bad and it can help the sellers to get their consumers feedback. But, there are two problems exist: the number of product review increases day by day and e-commerce allows their consumers to write positive negative opinion about some product features in one review, as known as “free format”. Therefore, there should be system which can extract product feature from review and classify their opinion automatically. Class Sequential Rule (CSR) method can be implemented in product feature extraction and Opinion Lexicon method can be implemented in feature opinion classification. The best F-score of feature extraction using CSR in free format review is 51.26% and the best f-score of opinion classification using Opinion Lexicon in free format review is 35.65%.
2021 9th International Conference on Information and Communication Technology (ICoICT), 2021
Humans are inseparable from emotions, emotions fill human life at all times. Emotions have an imp... more Humans are inseparable from emotions, emotions fill human life at all times. Emotions have an impact on social relationships, memory, and decision-making. In the era of this research, humans tended to express emotions through social media such as Twitter in the form of videos, images and text. Over time, Social media has to turn out to be a critical part of most people’s lives. Human emotion is a research area that is widely researched, especially in the field of linguistics. In this study, we classified emotions with Convolutional Neural Network. In addition, we compared the performance with three different word embedding methods, Glove, word2vec, and fastText in classifying the given dataset. The dataset that we used were 4403 tweets which will be classified into 5 classes, namely: love, joy, anger, sadness, and fear. F1-score is employed as an evaluation metric. The results of our experiments show that the combination of CNN and word2vec can achieve 72.06% of F1-score, which incr...
2018 International Conference on Information and Communications Technology (ICOIACT), 2018
Graph visualization is often used for a representation of interconnected relations of entities. I... more Graph visualization is often used for a representation of interconnected relations of entities. Individual entities such as cells, humans, computers, users, and other entities are represented as vertices while edges are used for entities' relations. With the visualization, people can get more information from the graph. There are already many tools for graph visualization of social media data. However, those tools need a specific file input format before generating a graph and visualizing it. In this research, we tried to do experiment and analysed data transformation from Twitter raw data to a JSON file format that can be used for most of the web-based network visualization tools. Using those techniques, we can decrease the memory usage by 1,526.1 MB and increase graph formation running time by 1,718.75 seconds. Those techniques can also be added with Text Mining modules for online context analysis of network visualization.
As the number of social media users rises, the probability of hate speech spread in social media ... more As the number of social media users rises, the probability of hate speech spread in social media also rises indirectly. Hate speech has become one of most common cases found on social media. The spread of hate speech can lead to a riot that might cause conflict, group extermination, and even human casualties. Some of the latest controversies in Indonesia related to hate speech was the hate speech uttered to the government that led to polemic and even demonstration in the country. Along with this, it is important to detect hate speech to avoid conflict to happen. As the spread of hate speech in social media increases, it requires significant human efforts and is costly to detect manually. Therefore, this experiment is built to detect hate speech detection in Indonesian twitter texts using several conventional machine learning and deep learning based, BiGRU, with various features. The machine learning approaches being used are SVM and RFDT, while deep learning based methods used are B...
Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people thr... more Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people through electronic devices and online social networking websites. Cyberbullying through the internet is more dangerous than traditional bullying, because it can potentially amplify the humiliation to an unlimited online audience. According to UNICEF and a survey by the Indonesian Ministry of Communication and Information, 58% of 435 adolescents do not understand about cyberbullying. Some of them might even have been the bullies, but since they did not understand about cyberbullying they could not recognise the negative effects of their bullying. The bullies may not recognise the harm of their actions, because they do not see immediate responses from their victims. Our study aimed to detect cyberbullying actors based on texts and the credibility analysis of users and notify them about the harm of cyberbullying. We collected data from Twitter. Since the data were unlabelled, we built a web-ba...
2021 9th International Conference on Information and Communication Technology (ICoICT)
Cyberbullying is the act of threatening or endangering others by posting text or images that humi... more Cyberbullying is the act of threatening or endangering others by posting text or images that humiliate or harass people through the internet or other communication devices. According to a survey from Polling Indonesia and Asosiasi Penyelenggara Jasa Internet Indonesia (APJII) about cyberbullying, 49% of 5900 participants claimed they have been bullied. Therefore, this research was conducted with the intention to prevent cyberbullying acts, especially in Indonesia. We collected data from Twitter based on Twitter’s Trending keywords which correlated to cyberbully events. Then we combined it with the data from previous research. We obtained a total of 1425 tweets, consists of 393 data labeled as cyberbully and 1032 data labeled as non-cyberbully. Thereupon, we build a Doc2Vec model for features extraction, and a classifier model using the baseline classification method (SVM and RF) and CNN to detect cyberbully texts. The results show that the classifier using CNN and Doc2vec has the highest F1-score, 65.08%.
Product review in e-commerce can help prospective
buyers to make decision whether the product tha... more Product review in e-commerce can help prospective buyers to make decision whether the product that they want to buy is good or bad and it can help the sellers to get their consumers feedback. But, there are two problems exist: the number of product review increases day by day and e-commerce allows their consumers to write positive negative opinion about some product features in one review, as known as ”free format”. Therefore, there should be system which can extract product feature from review and classify their opinion automatically. Class Sequential Rule (CSR) method can be implemented in product feature extraction and Opinion Lexicon method can be implemented in feature opinion classification. The best F-score of feature extraction using CSR in free format review is 51.26% and the best f-score of opinion classification using Opinion Lexicon in free format review is 35.65%.
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Papers by Hani Nurrahmi
buyers to make decision whether the product that they want
to buy is good or bad and it can help the sellers to get their
consumers feedback. But, there are two problems exist: the
number of product review increases day by day and e-commerce
allows their consumers to write positive negative opinion about
some product features in one review, as known as ”free format”.
Therefore, there should be system which can extract product
feature from review and classify their opinion automatically.
Class Sequential Rule (CSR) method can be implemented in
product feature extraction and Opinion Lexicon method can be
implemented in feature opinion classification. The best F-score of
feature extraction using CSR in free format review is 51.26% and
the best f-score of opinion classification using Opinion Lexicon
in free format review is 35.65%.
buyers to make decision whether the product that they want
to buy is good or bad and it can help the sellers to get their
consumers feedback. But, there are two problems exist: the
number of product review increases day by day and e-commerce
allows their consumers to write positive negative opinion about
some product features in one review, as known as ”free format”.
Therefore, there should be system which can extract product
feature from review and classify their opinion automatically.
Class Sequential Rule (CSR) method can be implemented in
product feature extraction and Opinion Lexicon method can be
implemented in feature opinion classification. The best F-score of
feature extraction using CSR in free format review is 51.26% and
the best f-score of opinion classification using Opinion Lexicon
in free format review is 35.65%.