Pub Date : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274575
Ajeng Suci Ananda, E. C. Djamal, Fikri Nugraha
One instrument for stroke identification is Electroencephalogram (EEG). Previous studies often used the wave variables Delta, Theta, Mu, Alpha, and amplitude in stroke analysis. For this purpose, they are often using Wavelet and Fast Fourier Transform (FFT). Although the first is more appropriate for non-stationary signals such as EEG. Likewise, in this study. However, processing EEG signals also give complexity to the use of many channels. Therefore, in addition to wave extraction, it is necessary to reduce the information from multi-channel. This paper proposed using Principle Component Analysis (PCA) for extracted signals of multichannel, which are then identified against three classes using Recurrent Neural Networks (RNN). The experimental results showed that the use of PCA produced greater accuracy of 86% compared to without PCA, which only provides an accuracy of 60%. The choice of the number of components is also an essential configuration in PCA channel reduction. Experiments using six components of PCA, Delta-Theta-Alpha-Mu waves, and amplitude as features gave the best performance. The research showed that both Adam and SGD models carried the same accuracy. Nevertheless, Adam model faster and more stable compares to SGD Model.
{"title":"Post-Stroke Recognition Based on EEG Using PCA and Recurrent Neural Networks","authors":"Ajeng Suci Ananda, E. C. Djamal, Fikri Nugraha","doi":"10.1109/IC2IE50715.2020.9274575","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274575","url":null,"abstract":"One instrument for stroke identification is Electroencephalogram (EEG). Previous studies often used the wave variables Delta, Theta, Mu, Alpha, and amplitude in stroke analysis. For this purpose, they are often using Wavelet and Fast Fourier Transform (FFT). Although the first is more appropriate for non-stationary signals such as EEG. Likewise, in this study. However, processing EEG signals also give complexity to the use of many channels. Therefore, in addition to wave extraction, it is necessary to reduce the information from multi-channel. This paper proposed using Principle Component Analysis (PCA) for extracted signals of multichannel, which are then identified against three classes using Recurrent Neural Networks (RNN). The experimental results showed that the use of PCA produced greater accuracy of 86% compared to without PCA, which only provides an accuracy of 60%. The choice of the number of components is also an essential configuration in PCA channel reduction. Experiments using six components of PCA, Delta-Theta-Alpha-Mu waves, and amplitude as features gave the best performance. The research showed that both Adam and SGD models carried the same accuracy. Nevertheless, Adam model faster and more stable compares to SGD Model.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116088462","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274647
Hana Riana Yasin, E. C. Djamal, Fikri Nugraha
Stroke is a disease with the highest cause of disability in the world. Therefore, the post-stroke rehabilitation stage is crucial for patients to carry out daily activities as usual. Recording and processing Electroencephalogram (EEG) signals support to evaluate the development of post-stroke patients. EEG signal obtained from multi-channel is possible to become redundancy, which can affect processing time and computational time. The diminishing channel can reduce processing time, computational load, and the effects of overfitting due to excessive utilization of EEG channels. Some methods have been applied to cope with the problems. In this paper, the signal data used are those contained in the channel combination resulting from the channel optimization process. Wavelet transform is used for the extraction of EEG signals into Delta, Theta, Alpha, and Mu waves. The waves and amplitudes of each channel are extracted using a Genetic Algorithm (GA). GA reduced the channels from 14 channels to 12 channels. Then the channels optimized by GA are classified using Convolutional Neural Networks (CNN) into three classes, specifically “No Stroke”, “Minor Stroke”, and “Moderate Stroke”. The experiment showed that 12 channel combinations from GA output yield an accuracy of 93.33%, while classification using a complete channel produces an accuracy of 66.67%. The choice of optimization model also influences the accuracy where the study obtained SGD provides more accuracy in the long run (increased epoch). At the same time, Adam responds more quickly to improve accuracy at the beginning of training.
{"title":"Optimization of Multi-Channel EEG Signal Using Genetic Algorithm in Post-Stroke Classification","authors":"Hana Riana Yasin, E. C. Djamal, Fikri Nugraha","doi":"10.1109/IC2IE50715.2020.9274647","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274647","url":null,"abstract":"Stroke is a disease with the highest cause of disability in the world. Therefore, the post-stroke rehabilitation stage is crucial for patients to carry out daily activities as usual. Recording and processing Electroencephalogram (EEG) signals support to evaluate the development of post-stroke patients. EEG signal obtained from multi-channel is possible to become redundancy, which can affect processing time and computational time. The diminishing channel can reduce processing time, computational load, and the effects of overfitting due to excessive utilization of EEG channels. Some methods have been applied to cope with the problems. In this paper, the signal data used are those contained in the channel combination resulting from the channel optimization process. Wavelet transform is used for the extraction of EEG signals into Delta, Theta, Alpha, and Mu waves. The waves and amplitudes of each channel are extracted using a Genetic Algorithm (GA). GA reduced the channels from 14 channels to 12 channels. Then the channels optimized by GA are classified using Convolutional Neural Networks (CNN) into three classes, specifically “No Stroke”, “Minor Stroke”, and “Moderate Stroke”. The experiment showed that 12 channel combinations from GA output yield an accuracy of 93.33%, while classification using a complete channel produces an accuracy of 66.67%. The choice of optimization model also influences the accuracy where the study obtained SGD provides more accuracy in the long run (increased epoch). At the same time, Adam responds more quickly to improve accuracy at the beginning of training.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118041","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274601
Hafizh Rafizal Adnan, L. M. Hasani, D. I. Sensuse
Knowledge management is a universal need for all organizations, and ministerial military defense organization is no exception. Some studies have described the KM implementation in various types of organizations. However, the study that comprehensively explores the KM implementation in military organizations is still deemed lacking. Intriguingly, different cultures in a military defense organization that consisted of both military and civilian personnel may affect the perception and the implementation of KM in a unique way. This study investigated the current state of KM implementation in a ministerial defense research and development agency and its personnel perceptions regarding the definition and the impacts of KM. Semi-structured interviews across commanding officers, military personnel, and civilian cohorts were conducted to gain some insights about the KM implementation. The results showed that KM practices had been implemented in a limited way, with a heavy emphasis on knowledge internalization and sharing. Finally, some recommendations were proposed based on the findings to cater to the needs of the organization.
{"title":"Exploring Knowledge Management Practices in Military RnD Agency: An Indonesian Case Study","authors":"Hafizh Rafizal Adnan, L. M. Hasani, D. I. Sensuse","doi":"10.1109/IC2IE50715.2020.9274601","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274601","url":null,"abstract":"Knowledge management is a universal need for all organizations, and ministerial military defense organization is no exception. Some studies have described the KM implementation in various types of organizations. However, the study that comprehensively explores the KM implementation in military organizations is still deemed lacking. Intriguingly, different cultures in a military defense organization that consisted of both military and civilian personnel may affect the perception and the implementation of KM in a unique way. This study investigated the current state of KM implementation in a ministerial defense research and development agency and its personnel perceptions regarding the definition and the impacts of KM. Semi-structured interviews across commanding officers, military personnel, and civilian cohorts were conducted to gain some insights about the KM implementation. The results showed that KM practices had been implemented in a limited way, with a heavy emphasis on knowledge internalization and sharing. Finally, some recommendations were proposed based on the findings to cater to the needs of the organization.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123387576","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274669
Nur Aini Rakhmawati, Karima Mufidah
The 2019 general election in Indonesia aims to elect the President and Vice President, Legislative Assembly (DPR and DPRD), and Regional Representative Council (DPD). The candidates must fill out their personal data in the general election official website at the time of registration. We perform the Social Network Analysis (SNA) over legislative candidate data to determine the pattern of relationships between candidates based on these data. The community detection algorithms in SNA can map and illustrate the pattern of relationship that is owned by the candidates in the 2019 elections. The output of this analysis will be visualized in graphs to illustrate the pattern of relationships based on the results of the SNA algorithm calculation. Based on the calculation of community detection algorithms, 60 communities were consisting of two to four candidates.
{"title":"Social Network Analysis Of Legislative Candidates in Indonesia General Election 2019 Using Community Detection","authors":"Nur Aini Rakhmawati, Karima Mufidah","doi":"10.1109/IC2IE50715.2020.9274669","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274669","url":null,"abstract":"The 2019 general election in Indonesia aims to elect the President and Vice President, Legislative Assembly (DPR and DPRD), and Regional Representative Council (DPD). The candidates must fill out their personal data in the general election official website at the time of registration. We perform the Social Network Analysis (SNA) over legislative candidate data to determine the pattern of relationships between candidates based on these data. The community detection algorithms in SNA can map and illustrate the pattern of relationship that is owned by the candidates in the 2019 elections. The output of this analysis will be visualized in graphs to illustrate the pattern of relationships based on the results of the SNA algorithm calculation. Based on the calculation of community detection algorithms, 60 communities were consisting of two to four candidates.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125721334","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274632
Y. A. Sari, Luthfi Maulana, Yusuf Gladiesnyah Bihanda, J. M. Maligan, Nabila Nur’aini, Dhea Rahma Widyadhana
Some people tend to leave their food when eating caused by their changing lifestyle during the time. Leaving food means wasting its nutritional content acquired in people’s bodies. By understanding the number of nutrient loss, the factors that influence of leftovers food is found, so that, it can prevent the number of food waste. In this paper, we present a method of leftovers nutrition estimation from food images in a single tray box employing an image processing approach. This feature is also embedded in our prototype named as Smart Nutrition Box (SNB). We apply the Automatic Food Leftover Estimation (AFLE) algorithm, which is suitable to predict the weight of food images placed in the tray box. The information on food weight is then utilized for calculating nutrition inside food leftovers. By using Root Mean Square Error (RMSE), the experimental result achieves 1.35 of error. It shows that the proposed method is able to project the leftover nutrition of food.
{"title":"Leftovers Nutrition Prediction for Augmenting Smart Nutrition Box Prototype Feature Using Image Processing Approach and AFLE Algorithm","authors":"Y. A. Sari, Luthfi Maulana, Yusuf Gladiesnyah Bihanda, J. M. Maligan, Nabila Nur’aini, Dhea Rahma Widyadhana","doi":"10.1109/IC2IE50715.2020.9274632","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274632","url":null,"abstract":"Some people tend to leave their food when eating caused by their changing lifestyle during the time. Leaving food means wasting its nutritional content acquired in people’s bodies. By understanding the number of nutrient loss, the factors that influence of leftovers food is found, so that, it can prevent the number of food waste. In this paper, we present a method of leftovers nutrition estimation from food images in a single tray box employing an image processing approach. This feature is also embedded in our prototype named as Smart Nutrition Box (SNB). We apply the Automatic Food Leftover Estimation (AFLE) algorithm, which is suitable to predict the weight of food images placed in the tray box. The information on food weight is then utilized for calculating nutrition inside food leftovers. By using Root Mean Square Error (RMSE), the experimental result achieves 1.35 of error. It shows that the proposed method is able to project the leftover nutrition of food.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132544750","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274680
D. I. Sensuse, Andy Syahrizal
Amidst the rapid development of information technology and communication, there is a demand for fast and reliable information that results in increasingly competitive competition. To the extent of carrying out and fulfillment of public policy and governmental output, public e-services are essential. In return, public e-service becomes a matter of obtaining access, not only to the IT artifact or the service process but also to governments and governmental output in general. The main goal of this study is intended to design an initial framework for measuring the quality of e-services services. The results of the research conducted produced an initial framework for measuring the service quality of e-services. The initial framework resulting from the stages of this study consists of five main aspects: security and trust, quality of service, information quality, system quality, system support. Twenty-two factors are obtained from the results of a literature review. This study is a part of our research in designing the e-Service quality framework. Hopefully, this research can add knowledge within the field of measuring e-Services service quality and collude with other existing frameworks to enhance the quality of e-services evaluations.
{"title":"Upsurging of Quality: Antencendent Framework for EServices Quality Measurement","authors":"D. I. Sensuse, Andy Syahrizal","doi":"10.1109/IC2IE50715.2020.9274680","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274680","url":null,"abstract":"Amidst the rapid development of information technology and communication, there is a demand for fast and reliable information that results in increasingly competitive competition. To the extent of carrying out and fulfillment of public policy and governmental output, public e-services are essential. In return, public e-service becomes a matter of obtaining access, not only to the IT artifact or the service process but also to governments and governmental output in general. The main goal of this study is intended to design an initial framework for measuring the quality of e-services services. The results of the research conducted produced an initial framework for measuring the service quality of e-services. The initial framework resulting from the stages of this study consists of five main aspects: security and trust, quality of service, information quality, system quality, system support. Twenty-two factors are obtained from the results of a literature review. This study is a part of our research in designing the e-Service quality framework. Hopefully, this research can add knowledge within the field of measuring e-Services service quality and collude with other existing frameworks to enhance the quality of e-services evaluations.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112812","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274650
Irene Irawaty, R. Andreswari, Dita Pramesti
Youtube is a popular social media used by several companies to market their products, both in the form of advertisements and videos. Nokia is one company that uses Youtube as social media to advertise and market its products until now. Nokia was a cellphone company that had fallen in 2013 due to the company's unwillingness to follow the operating system trend at the time. Nokia continues to rise and launch new products that are increasingly sophisticated. In seeing and summarizing public opinion towards the revival of the Nokia company, this research will classify the sentiment given by the public towards latest Nokia products through comments on the videos of Nokia products on Youtube. This research using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) algorithm to classify, with comparing performance of three vectorizers, namely CountVectorizer, TFIDFVectorizer and HashingVectorizer. Compared to other algorithms and vectorizers, SVM with TFIDFVectorizer has the highest accuracy with score of 97.5%. The best vectorizer in this research is TFIDFVectorizer because there are almost no errors in predicting negative values, and also has many positive predictive values compared to other vectorizers. So, the best way to do classification is using SVM algorithm with TFIDFVectorizer.
{"title":"Vectorizer Comparison for Sentiment Analysis on Social Media Youtube: A Case Study","authors":"Irene Irawaty, R. Andreswari, Dita Pramesti","doi":"10.1109/IC2IE50715.2020.9274650","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274650","url":null,"abstract":"Youtube is a popular social media used by several companies to market their products, both in the form of advertisements and videos. Nokia is one company that uses Youtube as social media to advertise and market its products until now. Nokia was a cellphone company that had fallen in 2013 due to the company's unwillingness to follow the operating system trend at the time. Nokia continues to rise and launch new products that are increasingly sophisticated. In seeing and summarizing public opinion towards the revival of the Nokia company, this research will classify the sentiment given by the public towards latest Nokia products through comments on the videos of Nokia products on Youtube. This research using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) algorithm to classify, with comparing performance of three vectorizers, namely CountVectorizer, TFIDFVectorizer and HashingVectorizer. Compared to other algorithms and vectorizers, SVM with TFIDFVectorizer has the highest accuracy with score of 97.5%. The best vectorizer in this research is TFIDFVectorizer because there are almost no errors in predicting negative values, and also has many positive predictive values compared to other vectorizers. So, the best way to do classification is using SVM algorithm with TFIDFVectorizer.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128945078","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274609
Heru Suroso, I. Budi, A. Santoso, P. K. Putra
The "Homecoming Tradition Restriction" was one of the government's policies to terminate and limit the spread of the Covid-19 Virus. Apart from being a media for socializing government policies, Twitter can be utilized by the public to convey responses, opinions, and criticisms towards government policies. This study aims were to determine public sentiment towards the "Homecoming Tradition Restriction" policy. This study uses a data mining approach to classify public sentiments delivered via Twitter. Sentiment classification models are built using two algorithms, Support Vector Machine (SVM) and Naïve Bayes. Naïve Bayes produces the highest performance measurement with a recall of 80% and an F-measure of 71.32%. This study shows that the majority of people support this government policy as indicated by the majority of sentiments that have been collected is positive, and also backed by the fact that the total number of homecoming vehicles during Eid Holiday was decreased by 62% from the previous year. This shows that social media data is relevant enough to be used in the assessment of public responses to government policies.
{"title":"Sentiment Analysis on “Homecoming Tradition Restriction” Policy on Twitter","authors":"Heru Suroso, I. Budi, A. Santoso, P. K. Putra","doi":"10.1109/IC2IE50715.2020.9274609","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274609","url":null,"abstract":"The \"Homecoming Tradition Restriction\" was one of the government's policies to terminate and limit the spread of the Covid-19 Virus. Apart from being a media for socializing government policies, Twitter can be utilized by the public to convey responses, opinions, and criticisms towards government policies. This study aims were to determine public sentiment towards the \"Homecoming Tradition Restriction\" policy. This study uses a data mining approach to classify public sentiments delivered via Twitter. Sentiment classification models are built using two algorithms, Support Vector Machine (SVM) and Naïve Bayes. Naïve Bayes produces the highest performance measurement with a recall of 80% and an F-measure of 71.32%. This study shows that the majority of people support this government policy as indicated by the majority of sentiments that have been collected is positive, and also backed by the fact that the total number of homecoming vehicles during Eid Holiday was decreased by 62% from the previous year. This shows that social media data is relevant enough to be used in the assessment of public responses to government policies.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132987928","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274641
Athar Sheikh, A. B.S.
Decomposition is one of the most mind-boggling assignments during the movement from legacy systems to microservices, by and large performed physically, in light of the experience of the product designers. In this paper we define a 6-step process that will help to reduce the complexity of determining when to introduce a new service into the project. This decomposition technique will help to ease the architecture of the project and determine a decomposition option that the product architect had not considered.
{"title":"Decomposing Monolithic Systems to Microservices","authors":"Athar Sheikh, A. B.S.","doi":"10.1109/IC2IE50715.2020.9274641","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274641","url":null,"abstract":"Decomposition is one of the most mind-boggling assignments during the movement from legacy systems to microservices, by and large performed physically, in light of the experience of the product designers. In this paper we define a 6-step process that will help to reduce the complexity of determining when to introduce a new service into the project. This decomposition technique will help to ease the architecture of the project and determine a decomposition option that the product architect had not considered.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133011625","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274598
G. Komala, A. Agus
Ice cream might not be the most favorable snacks for the general public in Indonesia, however the past five years has shown otherwise. This occurrence has encouraged one of the biggest ice cream manufacturing in the country to formulate and implement the new business model for the low-end market – stressing on the efficient and effective outbound logistics of goods. The existence of information systems as well as information technology (IT) systems, are undeniably significant to support the business processes and the actors involved. This research is an implementation of case study and design-based, in which business process modeling is formulated to reach a better understanding of the business processes as well as the actors and IT systems involved. The result shows that every actor has their own specialized IT system to support their activities. Intensive data communication between the systems, in the same time is limited to certain extent, are found to be the important key for the overall business process to be executed efficiently.
{"title":"Information Technology Systems in Supporting Outbound Logistics: Study of Low-End Ice Cream Distribution Business in Indonesia","authors":"G. Komala, A. Agus","doi":"10.1109/IC2IE50715.2020.9274598","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274598","url":null,"abstract":"Ice cream might not be the most favorable snacks for the general public in Indonesia, however the past five years has shown otherwise. This occurrence has encouraged one of the biggest ice cream manufacturing in the country to formulate and implement the new business model for the low-end market – stressing on the efficient and effective outbound logistics of goods. The existence of information systems as well as information technology (IT) systems, are undeniably significant to support the business processes and the actors involved. This research is an implementation of case study and design-based, in which business process modeling is formulated to reach a better understanding of the business processes as well as the actors and IT systems involved. The result shows that every actor has their own specialized IT system to support their activities. Intensive data communication between the systems, in the same time is limited to certain extent, are found to be the important key for the overall business process to be executed efficiently.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127647407","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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