Pub Date : 2018-05-01DOI: 10.1109/ICOICT.2018.8528794
Dony Arisandy Wiranata, M. Bijaksana, M. S. Mubarok
We conducted a semantic similarity study of semantic concepts in the context of the Holy Book Quran. Semantic similarity examines the degree of likeness and shared common properties of two concepts. For example, the Quranic concept of Allah and God will result in a high score of semantic similarity, whereas hell and paradise will yield in a low score because of its extremely different attributes and semantic features. Apart from that, we also delivered the Quranic concept semantic similarity standard dataset which consists of some pairs of Quranic concept along with its similarity score, which was manually annotated by human raters. This dataset resulted in the score of inter-annotator agreement 0.63, not far from the the ones yielded by some well-known datasets such as WordSim and Simlex. Furthermore, to measure the semantic similarity score, we chose the knowledge-based approach by utilizing lexical database properties such as the length and depth of a synonym set (synset). We then applied it to Yuhua Li equation, which has been considered to be the baseline among researchers within the problem of semantic similarity. In terms of the result, our system gained Pearson's correlation 0.33 and Spearman's 0.19. By considering inter-annotator agreement 0.63 that our Quranic standard dataset has as the upper bound score, there are still quite large room for improvement to better mimicking Muslim's intuition to measure the degree of similarity of concepts within the domain of Quran.
{"title":"Quranic Concepts Similarity Based on Lexical Database","authors":"Dony Arisandy Wiranata, M. Bijaksana, M. S. Mubarok","doi":"10.1109/ICOICT.2018.8528794","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528794","url":null,"abstract":"We conducted a semantic similarity study of semantic concepts in the context of the Holy Book Quran. Semantic similarity examines the degree of likeness and shared common properties of two concepts. For example, the Quranic concept of Allah and God will result in a high score of semantic similarity, whereas hell and paradise will yield in a low score because of its extremely different attributes and semantic features. Apart from that, we also delivered the Quranic concept semantic similarity standard dataset which consists of some pairs of Quranic concept along with its similarity score, which was manually annotated by human raters. This dataset resulted in the score of inter-annotator agreement 0.63, not far from the the ones yielded by some well-known datasets such as WordSim and Simlex. Furthermore, to measure the semantic similarity score, we chose the knowledge-based approach by utilizing lexical database properties such as the length and depth of a synonym set (synset). We then applied it to Yuhua Li equation, which has been considered to be the baseline among researchers within the problem of semantic similarity. In terms of the result, our system gained Pearson's correlation 0.33 and Spearman's 0.19. By considering inter-annotator agreement 0.63 that our Quranic standard dataset has as the upper bound score, there are still quite large room for improvement to better mimicking Muslim's intuition to measure the degree of similarity of concepts within the domain of Quran.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127658358","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528719
N. Wibowo, P. Destarianto, H. Y. Riskiawan, K. Agustianto, S. Kautsar
Water has always been an important part of human life, in the context of the global environment, water management and conservation is the focus because it impacts on human survival in a fundamental way. Current condition of the Kalibaru River Basin: (a) water use for agriculture, plantation and community needs is increasingly increasing, (b) unstable availability of water, (c) excessive utilization that does not pay attention to carrying capacity, (d) potentially for erosion and (e) high sources of water pollution that affect the quality of raw materials of drinking water, ecosystem, economy and human health and social security. This study aims to develop a system consisting of hardware and software capable of monitoring the quality of watersheds, in this research used Kalibaru's Watershed. This monitoring can be used by relevant agencies to conduct studies or even make policies related to quality watershed care.
{"title":"Development of Low-Cost Autonomous Surface Vehicles (ASV) for Watershed Quality Monitoring","authors":"N. Wibowo, P. Destarianto, H. Y. Riskiawan, K. Agustianto, S. Kautsar","doi":"10.1109/ICOICT.2018.8528719","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528719","url":null,"abstract":"Water has always been an important part of human life, in the context of the global environment, water management and conservation is the focus because it impacts on human survival in a fundamental way. Current condition of the Kalibaru River Basin: (a) water use for agriculture, plantation and community needs is increasingly increasing, (b) unstable availability of water, (c) excessive utilization that does not pay attention to carrying capacity, (d) potentially for erosion and (e) high sources of water pollution that affect the quality of raw materials of drinking water, ecosystem, economy and human health and social security. This study aims to develop a system consisting of hardware and software capable of monitoring the quality of watersheds, in this research used Kalibaru's Watershed. This monitoring can be used by relevant agencies to conduct studies or even make policies related to quality watershed care.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126635164","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528776
A. Alamsyah, Wirawan Rizkika, Ditya Dwi Adhi Nugroho, F. Renaldi, S. Saadah
Digital flows now exert a larger impact, the world is now more connected than ever, the amount of cross-border bandwidth that used has grown 45 times larger since 2005. With the massive amount of data spreading in the net, including social media, speed is one most essential factor in business. companies can take advantage of social media as a source to analyze and extract the customer's opinion, and therefore the company can have quick response towards the condition. The main purpose of this research is content analysis, to obtain the goal, we need to extract the information as well as summarize the topic inside it. However, in order to analyze the content quickly, there are varies choice of tools with its specific output that creates challenges in the process. We use Naïve Bayes Sentiment Analysis based on time-series, specifically on daily basis and topic modeling based on Latent Dirichlet Allocation (LDA) to evaluate the sentiment of the topic as well as the model of the topics discussed. This research may help both companies and individuals to map the public opinion towards certain topic by analyzing the sentiment of the text and create a topic model. Therefore, a real-time information for determining the consumer opinion become a crucial part. Twitter can serve the purpose as one source of realtime information from user-generated content. We pick Uber as the case study, viewed as one of the most favored transportation methods in most part of the world. Data collection period is from 10th February 2017 until 28th February 2017 with 1.048.576 tweets collected.
{"title":"Dynamic Large Scale Data on Twitter Using Sentiment Analysis and Topic Modeling","authors":"A. Alamsyah, Wirawan Rizkika, Ditya Dwi Adhi Nugroho, F. Renaldi, S. Saadah","doi":"10.1109/ICOICT.2018.8528776","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528776","url":null,"abstract":"Digital flows now exert a larger impact, the world is now more connected than ever, the amount of cross-border bandwidth that used has grown 45 times larger since 2005. With the massive amount of data spreading in the net, including social media, speed is one most essential factor in business. companies can take advantage of social media as a source to analyze and extract the customer's opinion, and therefore the company can have quick response towards the condition. The main purpose of this research is content analysis, to obtain the goal, we need to extract the information as well as summarize the topic inside it. However, in order to analyze the content quickly, there are varies choice of tools with its specific output that creates challenges in the process. We use Naïve Bayes Sentiment Analysis based on time-series, specifically on daily basis and topic modeling based on Latent Dirichlet Allocation (LDA) to evaluate the sentiment of the topic as well as the model of the topics discussed. This research may help both companies and individuals to map the public opinion towards certain topic by analyzing the sentiment of the text and create a topic model. Therefore, a real-time information for determining the consumer opinion become a crucial part. Twitter can serve the purpose as one source of realtime information from user-generated content. We pick Uber as the case study, viewed as one of the most favored transportation methods in most part of the world. Data collection period is from 10th February 2017 until 28th February 2017 with 1.048.576 tweets collected.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114528036","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528782
M. M. Muzakki, F. Nhita
Dengue Hemorrhagic Fever (DHF) is the health problem that exist in tropical country, includes Indonesia. Especially for the Bandung Regency, DHF sufferers fluctuated in the last three years. Through data by Health Department of Bandung Regency recorded from 2014 to 2016, in 2014 recorded as many as 524 cases, in 2015 as many as 1,017 cases, and then in 2016 as many as 3476 cases. Many factors that cause people become DHF sufferers in Bandung Regency are constantly increasing, some of them are high rainfall and also lack of awareness of the cleanness. In this research presents the research about the prediction of DHF in Bandung Regency using K-Means Clustering as preprocessing method and Support Vector Machine (SVM) algorithm as classification method according to historical data of DHF and weather data from BMKG (Meteorological, Climatological, and Geophysical Agency) in Bandung Regency from 2009 until 2016 using the dot and radial kernels on the SVM algorithm. The radial kernel obtains testing accuracy up to 93%, while the kernel dot obtains average of testing accuracy 62%.
{"title":"The Spreading Prediction of Dengue Hemorrhagic Fever (DHF) in Bandung Regency Using K-Means Clustering and Support Vector Machine Algorithm","authors":"M. M. Muzakki, F. Nhita","doi":"10.1109/ICOICT.2018.8528782","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528782","url":null,"abstract":"Dengue Hemorrhagic Fever (DHF) is the health problem that exist in tropical country, includes Indonesia. Especially for the Bandung Regency, DHF sufferers fluctuated in the last three years. Through data by Health Department of Bandung Regency recorded from 2014 to 2016, in 2014 recorded as many as 524 cases, in 2015 as many as 1,017 cases, and then in 2016 as many as 3476 cases. Many factors that cause people become DHF sufferers in Bandung Regency are constantly increasing, some of them are high rainfall and also lack of awareness of the cleanness. In this research presents the research about the prediction of DHF in Bandung Regency using K-Means Clustering as preprocessing method and Support Vector Machine (SVM) algorithm as classification method according to historical data of DHF and weather data from BMKG (Meteorological, Climatological, and Geophysical Agency) in Bandung Regency from 2009 until 2016 using the dot and radial kernels on the SVM algorithm. The radial kernel obtains testing accuracy up to 93%, while the kernel dot obtains average of testing accuracy 62%.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116073554","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528802
Al Mira Khonsa Izzaty, M. S. Mubarok, Nanang Saiful Huda, Adiwijaya
Quran is an eternal miracle for depicting its linguistic perfection, truth, and validating of the latest scientific research. Every Muslims must conceive and implement the commandments, also avoid the prohibitions mentioned in the Quran. Each verse of the Quran has a different meaning, and one verse in the Quran can depict one or more topics of class that can be studied. To ease learning and to understand the verses of Quran, each of them needs to be classified appropriately on its different topics. In this research, the model of classification was built that is able to identify the topics classes of each verse of Quran by multi-label classification approach. The model was built using Tree Augmented Naïve Bayes (TAN). In order to improve performance, Mutual Information (MI) is employed to select dependent variables. The results show that the classification model built using TAN with MI obtained best performance with average Hamming Loss of 0.1121, while the model built using TAN without MI obtained average Hamming Loss of 0.1208.
{"title":"A Multi-Label Classification on Topics of Quranic Verses in English Translation Using Tree Augmented Naïve Bayes","authors":"Al Mira Khonsa Izzaty, M. S. Mubarok, Nanang Saiful Huda, Adiwijaya","doi":"10.1109/ICOICT.2018.8528802","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528802","url":null,"abstract":"Quran is an eternal miracle for depicting its linguistic perfection, truth, and validating of the latest scientific research. Every Muslims must conceive and implement the commandments, also avoid the prohibitions mentioned in the Quran. Each verse of the Quran has a different meaning, and one verse in the Quran can depict one or more topics of class that can be studied. To ease learning and to understand the verses of Quran, each of them needs to be classified appropriately on its different topics. In this research, the model of classification was built that is able to identify the topics classes of each verse of Quran by multi-label classification approach. The model was built using Tree Augmented Naïve Bayes (TAN). In order to improve performance, Mutual Information (MI) is employed to select dependent variables. The results show that the classification model built using TAN with MI obtained best performance with average Hamming Loss of 0.1121, while the model built using TAN without MI obtained average Hamming Loss of 0.1208.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124975660","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528795
Muhammad Ikhsan Sani, Simon Siregar, Marlindia Ike Sari, L. Mardiana
Research activity in underwater wireless sensor and video transmission for marine and aquatic environment monitoring has increased rapidly. Data acquisition system is essential component for Remotely Operated Vehicle (ROV) especially for marine or other aquatic environment surveillance. ROV's data acquisition is typically transmitted using tether cable connected to ground control station. The use of tether may cause a problem since the cable can hinder the movement of ROV especially for shallow water area. A mobile ROV with wireless data acquisition solution play a key role in underwater environment monitoring. This paper presented an alternative method of data acquisition using 2.4 GHz Wi-Fi communication module on existing ROV platform of Telkom University - FIToplankton. This study aims to explore the possibility of Wi-Fi communication device as underwater IMU sensor's data and video transmission system. The prototype of the system has been implemented and evaluated to confirm the functionality of the proposed approach. The results indicate sensor and video data can still transmitted up to 20 cm water depth.
{"title":"2.4 GHz Wireless Data Acquisition System for FIToplankton ROV","authors":"Muhammad Ikhsan Sani, Simon Siregar, Marlindia Ike Sari, L. Mardiana","doi":"10.1109/ICOICT.2018.8528795","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528795","url":null,"abstract":"Research activity in underwater wireless sensor and video transmission for marine and aquatic environment monitoring has increased rapidly. Data acquisition system is essential component for Remotely Operated Vehicle (ROV) especially for marine or other aquatic environment surveillance. ROV's data acquisition is typically transmitted using tether cable connected to ground control station. The use of tether may cause a problem since the cable can hinder the movement of ROV especially for shallow water area. A mobile ROV with wireless data acquisition solution play a key role in underwater environment monitoring. This paper presented an alternative method of data acquisition using 2.4 GHz Wi-Fi communication module on existing ROV platform of Telkom University - FIToplankton. This study aims to explore the possibility of Wi-Fi communication device as underwater IMU sensor's data and video transmission system. The prototype of the system has been implemented and evaluated to confirm the functionality of the proposed approach. The results indicate sensor and video data can still transmitted up to 20 cm water depth.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123186608","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528723
Yadhi Aditya Permana, D. S. Kusumo, Dade Nurjanah
Intrinsic motivation is essential in learning programming. Gamification can be used to increase the intrinsic motivation of students in the learning process. Gamification is a technique of applying game-elements into learning activities. A drawback of gamification is that it is one-size-fit-all, which means that the same gamification element is applied to all students. Hence, it is necessary to apply adaptation that consider the characteristics of students, so that students can get learning materials that suit their needs and abilities. In our research, we have combined adaptive navigation, in the forms of direct guidance, link hiding, link annotation and link generation, and gamification for learning basic algorithm. The results indicate that our approach can increase the intrinsic motivation of students.
{"title":"Gamification for Learning Basic Algorithm","authors":"Yadhi Aditya Permana, D. S. Kusumo, Dade Nurjanah","doi":"10.1109/ICOICT.2018.8528723","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528723","url":null,"abstract":"Intrinsic motivation is essential in learning programming. Gamification can be used to increase the intrinsic motivation of students in the learning process. Gamification is a technique of applying game-elements into learning activities. A drawback of gamification is that it is one-size-fit-all, which means that the same gamification element is applied to all students. Hence, it is necessary to apply adaptation that consider the characteristics of students, so that students can get learning materials that suit their needs and abilities. In our research, we have combined adaptive navigation, in the forms of direct guidance, link hiding, link annotation and link generation, and gamification for learning basic algorithm. The results indicate that our approach can increase the intrinsic motivation of students.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129619329","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528733
Ridho Alif Utama, Parman Sukarno, E. Jadied
This paper considers danger level classification of Android applications based on permissions and vulnerabilities by using Naive Bayes (NB) algorithm in order to assist and inform users whether an application is safe to use or not. With the increasing development and use of Android, unfortunately, malicious software (malware) and malicious applications are also beginning to increase. Many methods have been proposed to protect Android, however, they are only able to detect or classify Android applications against malware based on permission. This kind of approach is still considered less effective, because there is no information in classifying the danger level of an Android application, be it malware or goodware. To overcome the problem, this research classifies the danger level into three categories namely, safe, suspicious, and dangerous. The accuracy obtained from this research is 97.2%. To our knowledge, this is the first and only work to use danger level classification of Android applications based on permissions and vulnerabilities.
{"title":"Analysis and Classification of Danger Level in Android Applications Using Naive Bayes Algorithm","authors":"Ridho Alif Utama, Parman Sukarno, E. Jadied","doi":"10.1109/ICOICT.2018.8528733","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528733","url":null,"abstract":"This paper considers danger level classification of Android applications based on permissions and vulnerabilities by using Naive Bayes (NB) algorithm in order to assist and inform users whether an application is safe to use or not. With the increasing development and use of Android, unfortunately, malicious software (malware) and malicious applications are also beginning to increase. Many methods have been proposed to protect Android, however, they are only able to detect or classify Android applications against malware based on permission. This kind of approach is still considered less effective, because there is no information in classifying the danger level of an Android application, be it malware or goodware. To overcome the problem, this research classifies the danger level into three categories namely, safe, suspicious, and dangerous. The accuracy obtained from this research is 97.2%. To our knowledge, this is the first and only work to use danger level classification of Android applications based on permissions and vulnerabilities.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131039906","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528724
A. Arifianto, Qhansa Di'ayu Putri Bayu, M. D. Sulistiyo, N. I. Wendianto, Naufal Dzaky Anwari, Muhammad Adhi Satria, D. N. G. A. M. Eka, Admining Hastuti, Isma Dewi Liana, P. Safitri, Rachmi Azanisa Putri
In the fierce competition of product sale, in recent years product owners have used the services of Artists and Celebrities to co-promote the products they sell called endorsements. One way the celebrities promotes the products they endorse is through various online social media like Facebook, Twitter, or Instagram. However, there are many cases where product owners merely choose any famous celebrities to promote their products based on the fame of the artist without a proper analysis. We think that it is important to consider the chance of sales success viewed from a match between promoted items and the tastes or likeness of the artist's fans. In this paper, we propose a method to automatically recommend what types of product to endorse for an Instagram user based on his/her followers' profiling. We use Instagram to perform a user profiling to determine what the follower users like based on annotation tags of the images they upload. By using a clustering method to group the followers, we were able to provide an endorsement recommendation to a user that matched with the followers' preferences.
{"title":"Endorsement Recommendation Using Instagram Follower Profiling","authors":"A. Arifianto, Qhansa Di'ayu Putri Bayu, M. D. Sulistiyo, N. I. Wendianto, Naufal Dzaky Anwari, Muhammad Adhi Satria, D. N. G. A. M. Eka, Admining Hastuti, Isma Dewi Liana, P. Safitri, Rachmi Azanisa Putri","doi":"10.1109/ICOICT.2018.8528724","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528724","url":null,"abstract":"In the fierce competition of product sale, in recent years product owners have used the services of Artists and Celebrities to co-promote the products they sell called endorsements. One way the celebrities promotes the products they endorse is through various online social media like Facebook, Twitter, or Instagram. However, there are many cases where product owners merely choose any famous celebrities to promote their products based on the fame of the artist without a proper analysis. We think that it is important to consider the chance of sales success viewed from a match between promoted items and the tastes or likeness of the artist's fans. In this paper, we propose a method to automatically recommend what types of product to endorse for an Instagram user based on his/her followers' profiling. We use Instagram to perform a user profiling to determine what the follower users like based on annotation tags of the images they upload. By using a clustering method to group the followers, we were able to provide an endorsement recommendation to a user that matched with the followers' preferences.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134372114","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528732
Eko Darwiyanto, M. Bijaksana
Quran is holy book for Moslems. Reading it, understanding its meaning, even memorizing it is very useful. But memorizing 6236 of its verses is not an easy task, even short juz ‘amma chapters. Several memorizing methods have been known. In panipati, Turkey, Mauritanian, Singapore method, students memorize Quran page by page, from first juz or last juz. In Sudan, students memorize verses with writing its out. In mnemonic learning, verses are linked with the association. Photographic memory is used to recall an image of verses in any page. From computing theory, especially artificial intelligence, Breadth First Search algorithm can be hoped to support memorizing Quran. Memorize its chapter title, what the main topic, memorize verses that tell it, then expand to previously or next verses. Another method is using statistic, using Term Frequency (TF) to get list verses in each chapter of Juz ‘Amma that its weight of term at least is eighty percent of chapter weight of term. With minimum verses, student has memorized most important verses in each chapter.
{"title":"Searching Quran Chapters Verses Weight with TF and Pareto Principle to Support Memorizing (Case Study Juz ‘Amma)","authors":"Eko Darwiyanto, M. Bijaksana","doi":"10.1109/ICOICT.2018.8528732","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528732","url":null,"abstract":"Quran is holy book for Moslems. Reading it, understanding its meaning, even memorizing it is very useful. But memorizing 6236 of its verses is not an easy task, even short juz ‘amma chapters. Several memorizing methods have been known. In panipati, Turkey, Mauritanian, Singapore method, students memorize Quran page by page, from first juz or last juz. In Sudan, students memorize verses with writing its out. In mnemonic learning, verses are linked with the association. Photographic memory is used to recall an image of verses in any page. From computing theory, especially artificial intelligence, Breadth First Search algorithm can be hoped to support memorizing Quran. Memorize its chapter title, what the main topic, memorize verses that tell it, then expand to previously or next verses. Another method is using statistic, using Term Frequency (TF) to get list verses in each chapter of Juz ‘Amma that its weight of term at least is eighty percent of chapter weight of term. With minimum verses, student has memorized most important verses in each chapter.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115617304","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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