Pub Date : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699374
N. Chamidah, M. M. Santoni, H. N. Irmanda, R. Astriratma, Lomo Mula Tua, Trihastuti Yuniati
Exams conducted in online learning to evaluate learning processes have many formats, including essay format. Essays are considered more proper to measure learning activity results. However, essays require longer to assess student answers and have consistency problems if the assessment is carried out by different teachers or done separately. This study investigates the influence of word expansion using synonyms in Indonesian thesaurus on short essay auto scoring. The first step, reference answers and student answer text data is preprocessed by case folding, stemming, stop word removal, tokenizing, and duplicate word removal. Second, Word expansion using synonyms in thesaurus is used to generate alternate words for reference answers. Third step, the scoring process by calculating similarity and matching words. The score from the similarity and matching results is then used to generate the final score. Performance evaluation shows that the Dice Coefficient similarity method achieved the highest correlation by a very good correlation, and the smallest MAE was achieved by the Cosine Coefficient similarity method.
{"title":"Word Expansion using Synonyms in Indonesian Short Essay Auto Scoring","authors":"N. Chamidah, M. M. Santoni, H. N. Irmanda, R. Astriratma, Lomo Mula Tua, Trihastuti Yuniati","doi":"10.1109/ICIMCIS53775.2021.9699374","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699374","url":null,"abstract":"Exams conducted in online learning to evaluate learning processes have many formats, including essay format. Essays are considered more proper to measure learning activity results. However, essays require longer to assess student answers and have consistency problems if the assessment is carried out by different teachers or done separately. This study investigates the influence of word expansion using synonyms in Indonesian thesaurus on short essay auto scoring. The first step, reference answers and student answer text data is preprocessed by case folding, stemming, stop word removal, tokenizing, and duplicate word removal. Second, Word expansion using synonyms in thesaurus is used to generate alternate words for reference answers. Third step, the scoring process by calculating similarity and matching words. The score from the similarity and matching results is then used to generate the final score. Performance evaluation shows that the Dice Coefficient similarity method achieved the highest correlation by a very good correlation, and the smallest MAE was achieved by the Cosine Coefficient similarity method.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114514397","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699207
Pradista Aprilia Winarno, Ermatita, S. Afrizal
Competition in providing internet services in Indonesia is getting tougher. Market demand that is increasingly complicated to predict makes companies have to work more to satisfy customers. The application of forecasting methods for client needs can be a solution. Machine Learning-based forecasting with the Long Short Term Memory (LSTM) method can be one way of making forecasts. The output of this research is the forecasting of the price of the service product which is expected to make the company take policies to take actions that can minimize losses for the client and the company. In this study, the author will use the Long Short Term Memory (LSTM) method to predict the price of internet services at the Hypernet Indodata company using time series data. The data used is internet service sales in 2016–2018 obtained from PT. Hypernet Indodata. The results obtained in this study resulted in a Root Mean Square Error (RMSE) value of 8.7463 and a Mean Absolute Percentage Error (MAPE) of 4.167% indicating that the LSTM model already has the right configuration and is successful in predicting service prices quite well.
{"title":"Implementation of Long Short Term Memory Model in Forecasting Internet Service Sales","authors":"Pradista Aprilia Winarno, Ermatita, S. Afrizal","doi":"10.1109/ICIMCIS53775.2021.9699207","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699207","url":null,"abstract":"Competition in providing internet services in Indonesia is getting tougher. Market demand that is increasingly complicated to predict makes companies have to work more to satisfy customers. The application of forecasting methods for client needs can be a solution. Machine Learning-based forecasting with the Long Short Term Memory (LSTM) method can be one way of making forecasts. The output of this research is the forecasting of the price of the service product which is expected to make the company take policies to take actions that can minimize losses for the client and the company. In this study, the author will use the Long Short Term Memory (LSTM) method to predict the price of internet services at the Hypernet Indodata company using time series data. The data used is internet service sales in 2016–2018 obtained from PT. Hypernet Indodata. The results obtained in this study resulted in a Root Mean Square Error (RMSE) value of 8.7463 and a Mean Absolute Percentage Error (MAPE) of 4.167% indicating that the LSTM model already has the right configuration and is successful in predicting service prices quite well.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127840839","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699180
Ray Novita Yasa, I. K. S. Buana, Girinoto, Hermawan Setiawan, R. B. Hadiprakoso
Privacy-Preserving Data Mining (PPDM) has become an exciting topic to discuss in recent decades due to the growing interest in big data and data mining. A technique of securing data but still preserving the privacy that is in it. This paper provides an alternative perturbation-based PPDM technique which is carried out by modifying the RNP algorithm. The novelty given in this paper are modifications of some steps method with a specific purpose. The modifications made are in the form of first narrowing the selection of the disturbance value. With the aim that the number of attributes that are replaced in each record line is only as many as the attributes in the original data, no more and no need to repeat; secondly, derive the perturbation function from the cumulative distribution function and use it to find the probability distribution function so that the selection of replacement data has a clear basis. The experiment results on twenty-five perturbed data show that the modified RNP algorithm balances data utility and security level by selecting the appropriate disturbance value and perturbation value. The level of security is measured using privacy metrics in the form of value difference, average transformation of data, and percentage of retains. The method presented in this paper is fascinating to be applied to actual data that requires privacy preservation.
{"title":"Modified RNP Privacy Protection Data Mining Method as Big Data Security","authors":"Ray Novita Yasa, I. K. S. Buana, Girinoto, Hermawan Setiawan, R. B. Hadiprakoso","doi":"10.1109/ICIMCIS53775.2021.9699180","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699180","url":null,"abstract":"Privacy-Preserving Data Mining (PPDM) has become an exciting topic to discuss in recent decades due to the growing interest in big data and data mining. A technique of securing data but still preserving the privacy that is in it. This paper provides an alternative perturbation-based PPDM technique which is carried out by modifying the RNP algorithm. The novelty given in this paper are modifications of some steps method with a specific purpose. The modifications made are in the form of first narrowing the selection of the disturbance value. With the aim that the number of attributes that are replaced in each record line is only as many as the attributes in the original data, no more and no need to repeat; secondly, derive the perturbation function from the cumulative distribution function and use it to find the probability distribution function so that the selection of replacement data has a clear basis. The experiment results on twenty-five perturbed data show that the modified RNP algorithm balances data utility and security level by selecting the appropriate disturbance value and perturbation value. The level of security is measured using privacy metrics in the form of value difference, average transformation of data, and percentage of retains. The method presented in this paper is fascinating to be applied to actual data that requires privacy preservation.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131092202","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699183
Derry Alamsyah, M. Fachrurrozi
Facial Expression Recognition (FER) of the image is one of the potential research fields. It remains some open problems to be solved such as various head positions, backgrounds, occlusion, face attribute etc., where the FER 2013 dataset give such conditions. In this research, the small balanced dataset used to recognize two common fundamental expression, happy and sad face image as our set conditions. Using SVM as classifier and HOG as feature expression method, this research shows best performance, that is 72% accuracy, in quadratic polynomial kernel with intercept constant $mathrm{b}=1$ and tolerance constant $mathrm{C}=0.1$. By using such conditions, minimized pose variant, a conventional approach in FER such SVM and HOG has shown fair performance in the FER 2013 dataset.
{"title":"Happy and Sad Classification using HOG Feature Descriptor in SVM Model Selection","authors":"Derry Alamsyah, M. Fachrurrozi","doi":"10.1109/ICIMCIS53775.2021.9699183","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699183","url":null,"abstract":"Facial Expression Recognition (FER) of the image is one of the potential research fields. It remains some open problems to be solved such as various head positions, backgrounds, occlusion, face attribute etc., where the FER 2013 dataset give such conditions. In this research, the small balanced dataset used to recognize two common fundamental expression, happy and sad face image as our set conditions. Using SVM as classifier and HOG as feature expression method, this research shows best performance, that is 72% accuracy, in quadratic polynomial kernel with intercept constant $mathrm{b}=1$ and tolerance constant $mathrm{C}=0.1$. By using such conditions, minimized pose variant, a conventional approach in FER such SVM and HOG has shown fair performance in the FER 2013 dataset.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116406471","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}
Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019–2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction; and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier.
{"title":"A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence","authors":"Vina Ayumi, Ermatita Ermatita, Abdiansah Abdiansah, Handrie Noprisson, Mariana Purba, Marissa Utami","doi":"10.1109/ICIMCIS53775.2021.9699363","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699363","url":null,"abstract":"Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019–2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction; and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128913890","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699352
Dominic Okeke, S. Musa
Different concepts of condition and energy monitoring systems in manufacturing facilities have been studied extensively, in relation to the improvement and enhancement of the decision-making processes in industries. Internet of Things (IoT) communication networks has also provided more integrated machine connectivity for real time data, and so its application in industrial processes has enabled effective energy usage and condition monitoring for sustainable management. In this paper, the operational status of the machines categorically ascertained within a short time interval and maintenance is predicted by the system in response on user interface application Node-RED dashboards and Python Shell environment. Furthermore, a portable and scalable wireless sensor network using the IEEE 802.15.4e protocol has been integrated with Machine Learning (ML) algorithm to analyze the anomaly detection in the condition and energy monitoring sensor datasets. As a result, the 99.16% accuracy of this supervised learning model is observed.
{"title":"Energy Management and Anomaly Detection in Condition Monitoring for Industrial Internet of Things Using Machine Learning","authors":"Dominic Okeke, S. Musa","doi":"10.1109/ICIMCIS53775.2021.9699352","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699352","url":null,"abstract":"Different concepts of condition and energy monitoring systems in manufacturing facilities have been studied extensively, in relation to the improvement and enhancement of the decision-making processes in industries. Internet of Things (IoT) communication networks has also provided more integrated machine connectivity for real time data, and so its application in industrial processes has enabled effective energy usage and condition monitoring for sustainable management. In this paper, the operational status of the machines categorically ascertained within a short time interval and maintenance is predicted by the system in response on user interface application Node-RED dashboards and Python Shell environment. Furthermore, a portable and scalable wireless sensor network using the IEEE 802.15.4e protocol has been integrated with Machine Learning (ML) algorithm to analyze the anomaly detection in the condition and energy monitoring sensor datasets. As a result, the 99.16% accuracy of this supervised learning model is observed.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121592558","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699233
Debby Debora Hutajulu, M. E. Simaremare, Yessi Sovranita Pangaribuan, Angelia Regina Ginting
Quality codes reflects the quality of the one who is behind the keyboard. Acknowledging quality codes is useful for either companies or learning institutions in finding prospect employee or assessing the students' learning process. In this paper, we propose an approach to find quality programmers from their contributions in the crowdsourcing projects (Git-based). This approach measures the complexity level of every contribution committed contributors (or programmers) from the beginning of the project to date. This will help us to find quality programmers and see when they start improving. We use cyclomatic complexity (CC) to decide the complexity level of a contribution. In practice, we could use this approach to assess the quality of a programmer based on his/her previous contributions.
{"title":"Measuring Programmer Quality from Complexity Point of View","authors":"Debby Debora Hutajulu, M. E. Simaremare, Yessi Sovranita Pangaribuan, Angelia Regina Ginting","doi":"10.1109/ICIMCIS53775.2021.9699233","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699233","url":null,"abstract":"Quality codes reflects the quality of the one who is behind the keyboard. Acknowledging quality codes is useful for either companies or learning institutions in finding prospect employee or assessing the students' learning process. In this paper, we propose an approach to find quality programmers from their contributions in the crowdsourcing projects (Git-based). This approach measures the complexity level of every contribution committed contributors (or programmers) from the beginning of the project to date. This will help us to find quality programmers and see when they start improving. We use cyclomatic complexity (CC) to decide the complexity level of a contribution. In practice, we could use this approach to assess the quality of a programmer based on his/her previous contributions.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133930602","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699319
C. Victoria, Jaimee Tumewa Diets, Vania Kalyana, P. A. Manaf
Modernization causes customers to be inseparable from the need for online shopping or e-commerce. E-commerce uses various strategies to attract customers to buy their products, especially during a pandemic due to the restriction of offline shopping by the government. The purpose of this research is to gain a more profound knowledge of the influence of sales promotion, self-control, and hedonism on impulsive buying in e-commerce platforms, especially during the Pandemic. Data were collected through a questionnaire from 205 respondents of e-commerce users who purchased during the COVID-19 Pandemic. A judgemental sampling technique is applied in this study. The data analysis method is a regression model and processed using Statistical Package for the Social Science (SPSS). The result of this study indicates that the hypothesis made by the authors is supported. The study's most important finding is that when self-control is low, impulsive purchasing occurs.
现代化使得顾客离不开网上购物或电子商务的需求。电子商务使用各种策略来吸引顾客购买他们的产品，特别是在疫情期间，由于政府限制线下购物。本研究的目的是更深入地了解促销、自我控制和享乐主义对电子商务平台尤其是疫情期间冲动购买的影响。通过问卷调查收集了205名在COVID-19大流行期间购物的电子商务用户的数据。本研究采用了判断抽样技术。数据分析方法是回归模型，并使用SPSS (Statistical Package for Social Science)进行处理。本研究的结果表明，作者的假设是支持的。该研究最重要的发现是，当自制力较低时，冲动购物就会发生。
{"title":"The Effect of Sales Promotion, Self-Control, And Hedonism on Impulsive Buying In E-Commerce Platform During The Covid-19 Pandemic","authors":"C. Victoria, Jaimee Tumewa Diets, Vania Kalyana, P. A. Manaf","doi":"10.1109/ICIMCIS53775.2021.9699319","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699319","url":null,"abstract":"Modernization causes customers to be inseparable from the need for online shopping or e-commerce. E-commerce uses various strategies to attract customers to buy their products, especially during a pandemic due to the restriction of offline shopping by the government. The purpose of this research is to gain a more profound knowledge of the influence of sales promotion, self-control, and hedonism on impulsive buying in e-commerce platforms, especially during the Pandemic. Data were collected through a questionnaire from 205 respondents of e-commerce users who purchased during the COVID-19 Pandemic. A judgemental sampling technique is applied in this study. The data analysis method is a regression model and processed using Statistical Package for the Social Science (SPSS). The result of this study indicates that the hypothesis made by the authors is supported. The study's most important finding is that when self-control is low, impulsive purchasing occurs.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134039771","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699206
Uus Rusdiana, Iin Ernawati, Noor Falih, A. Arista
Distance metrics are often used in a similarity-based algorithm like clustering to improve the performance when deciding to group data based on similarities. It has a crucial role when building machine learning models. Therefore, this research would like to examine the optimal distance metrics method in the clustering algorithm. The algorithm that will be used in this research is Fuzzy C-Means clustering by applying several data distance measurement methods (Euclidean Distance, Manhattan Distance, Chebyshev Distance, and Minkowski Distance). Then, the resulting cluster will be evaluated using a validity index including partition coefficient index (PC), modified partition coefficient index (MPC), and RMSE. The results represent that the most optimal distance of the 2 clusters dataset was obtained using Manhattan Distance measurement methods. The most optimal distance of the 3 clusters dataset was obtained using Minkowski Distance measurement methods. From a series of conducted experiments of the dataset, the Manhattan and Minkowski measurement methods represented the optimal results for the FCM algorithm.
{"title":"Comparison of Distance Metrics on Fuzzy C-Means Algorithm Through Customer Segmentation","authors":"Uus Rusdiana, Iin Ernawati, Noor Falih, A. Arista","doi":"10.1109/ICIMCIS53775.2021.9699206","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699206","url":null,"abstract":"Distance metrics are often used in a similarity-based algorithm like clustering to improve the performance when deciding to group data based on similarities. It has a crucial role when building machine learning models. Therefore, this research would like to examine the optimal distance metrics method in the clustering algorithm. The algorithm that will be used in this research is Fuzzy C-Means clustering by applying several data distance measurement methods (Euclidean Distance, Manhattan Distance, Chebyshev Distance, and Minkowski Distance). Then, the resulting cluster will be evaluated using a validity index including partition coefficient index (PC), modified partition coefficient index (MPC), and RMSE. The results represent that the most optimal distance of the 2 clusters dataset was obtained using Manhattan Distance measurement methods. The most optimal distance of the 3 clusters dataset was obtained using Minkowski Distance measurement methods. From a series of conducted experiments of the dataset, the Manhattan and Minkowski measurement methods represented the optimal results for the FCM algorithm.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124580921","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 : 2021-10-28DOI: 10.1109/ICIMCIS53775.2021.9699247
Abi Hanindito, T. Raharjo, B. Hardian, Agus Suhanto
Many organizations have adopted agile methodology in their project process and management to take advantage of benefits to organizations. The benefits are quicker good software quality, return of Investment, and customer satisfaction. For some organizations that have mature software development methodology using traditional approaches, they face some difficulties in adopting agile methodology. Instead of being successful in IT project implementation, the organization has failed in IT project, bad quality software, and running out of budget. The Organization conducted an agile readiness assessment using an agile adoption framework called SAMI (Sidky Agile Measurement Index) to know their agility level. Based on this assessment framework, there is a questionnaire which assesses the process for agile practices adoption in project and organization. Organization involved customer manager, IT manager, IT Section head and IT developers as respondents to fill in the questionnaire related to their experience in IT project. The assessment result mentioned that this organization has passed the criterias to continue the adoption, and agile adoption of project level is at level 3 (Effective), and organizational readiness level is at level 1 (Collaborative). To increase agility level up to maximum level at level 5 (Encompassing), Organization can choose to improve failed assessment items through follow up the recommendation or lower the expectation through reducing the project level into level 1, which is similar level to organization readiness. Knowing agility level in organization through assessment and proceed the recommendation, can increase agile level and ease agile adoption especially on software development in organization.
{"title":"Agile Readiness Measurement in Organizations using Agile Adoption Framework : A Case study on Indonesian Automotive Company","authors":"Abi Hanindito, T. Raharjo, B. Hardian, Agus Suhanto","doi":"10.1109/ICIMCIS53775.2021.9699247","DOIUrl":"https://doi.org/10.1109/ICIMCIS53775.2021.9699247","url":null,"abstract":"Many organizations have adopted agile methodology in their project process and management to take advantage of benefits to organizations. The benefits are quicker good software quality, return of Investment, and customer satisfaction. For some organizations that have mature software development methodology using traditional approaches, they face some difficulties in adopting agile methodology. Instead of being successful in IT project implementation, the organization has failed in IT project, bad quality software, and running out of budget. The Organization conducted an agile readiness assessment using an agile adoption framework called SAMI (Sidky Agile Measurement Index) to know their agility level. Based on this assessment framework, there is a questionnaire which assesses the process for agile practices adoption in project and organization. Organization involved customer manager, IT manager, IT Section head and IT developers as respondents to fill in the questionnaire related to their experience in IT project. The assessment result mentioned that this organization has passed the criterias to continue the adoption, and agile adoption of project level is at level 3 (Effective), and organizational readiness level is at level 1 (Collaborative). To increase agility level up to maximum level at level 5 (Encompassing), Organization can choose to improve failed assessment items through follow up the recommendation or lower the expectation through reducing the project level into level 1, which is similar level to organization readiness. Knowing agility level in organization through assessment and proceed the recommendation, can increase agile level and ease agile adoption especially on software development in organization.","PeriodicalId":250460,"journal":{"name":"2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125343551","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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