Pub Date : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979795
A. K. Nasution, S. Wijaya, W. Kusuma
Jamu is an Indonesian herbal medicine that has many benefits. Prediction of drug-target interactions on Jamu formula using a graph-based approach was carried out, but the results were unsatisfactory with the area under the precision-recall curve (AUPR) of 0.70. This study develops a prediction model of drug-target interactions with machine learning approach using Support Vector Machine (SVM) and Random Forest (RF). The dataset used in this study as the same as the dataset in the previous research, obtained from Indonesian Jamu Herbs (IJAH) Analytics. The dataset represents interactions of compounds and proteins, including labels to indicate those of interactions. Principal Component Analysis (PCA) is used as feature reduction in the pre-processing stage. The prediction models using SVM and RF combined with PCA obtain the best AUPR results of 0.99. These results indicate that the machine learning approach has better performance than those of the graph-based approach in predicting drug-target interactions on Jamu formulas.
{"title":"Prediction of Drug-Target Interaction on Jamu Formulas using Machine Learning Approaches","authors":"A. K. Nasution, S. Wijaya, W. Kusuma","doi":"10.1109/ICACSIS47736.2019.8979795","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979795","url":null,"abstract":"Jamu is an Indonesian herbal medicine that has many benefits. Prediction of drug-target interactions on Jamu formula using a graph-based approach was carried out, but the results were unsatisfactory with the area under the precision-recall curve (AUPR) of 0.70. This study develops a prediction model of drug-target interactions with machine learning approach using Support Vector Machine (SVM) and Random Forest (RF). The dataset used in this study as the same as the dataset in the previous research, obtained from Indonesian Jamu Herbs (IJAH) Analytics. The dataset represents interactions of compounds and proteins, including labels to indicate those of interactions. Principal Component Analysis (PCA) is used as feature reduction in the pre-processing stage. The prediction models using SVM and RF combined with PCA obtain the best AUPR results of 0.99. These results indicate that the machine learning approach has better performance than those of the graph-based approach in predicting drug-target interactions on Jamu formulas.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133057229","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 : 2019-10-01DOI: 10.1109/icacsis47736.2019.8979694
{"title":"ICACSIS 2019 Authors Index","authors":"","doi":"10.1109/icacsis47736.2019.8979694","DOIUrl":"https://doi.org/10.1109/icacsis47736.2019.8979694","url":null,"abstract":"","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133122896","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979672
Rahman Pujianto, Yohanes Gultom, A. Wibisono, Arry Yanuar, H. Suhartanto
This work evaluates usage feature selection methods to reduce the number of features required to predict docking results between Indonesian medicinal plant compounds and HIV protease. Two feature selection methods, Recursive Feature Elimination (RFE) and Wrapper Method (WM), are trained with a dataset of 7,330 samples and 667 features from PubChem Bioassay and DUD-E decoys. To evaluate the selected features, a dataset of 368 Indonesian herbal chemical compounds labeled by manually docking to PDB HIV-1 protease is used to benchmark the performance of linear SVM classifier using different sets of features. Our experiments show that a set of 471 features selected by RFE and 249 by WM achieve a reduction of classification time by 4.0 and 8.2 seconds respectively. Although the accuracy and sensitivity are also increased by 8% and 16%, no meaningful improvement observed for precision and specificity.
{"title":"The impact of feature selection methods on machine learning-based docking prediction of Indonesian medicinal plant compounds and HIV-1 protease","authors":"Rahman Pujianto, Yohanes Gultom, A. Wibisono, Arry Yanuar, H. Suhartanto","doi":"10.1109/ICACSIS47736.2019.8979672","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979672","url":null,"abstract":"This work evaluates usage feature selection methods to reduce the number of features required to predict docking results between Indonesian medicinal plant compounds and HIV protease. Two feature selection methods, Recursive Feature Elimination (RFE) and Wrapper Method (WM), are trained with a dataset of 7,330 samples and 667 features from PubChem Bioassay and DUD-E decoys. To evaluate the selected features, a dataset of 368 Indonesian herbal chemical compounds labeled by manually docking to PDB HIV-1 protease is used to benchmark the performance of linear SVM classifier using different sets of features. Our experiments show that a set of 471 features selected by RFE and 249 by WM achieve a reduction of classification time by 4.0 and 8.2 seconds respectively. Although the accuracy and sensitivity are also increased by 8% and 16%, no meaningful improvement observed for precision and specificity.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127855737","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979740
Toto Haryanto, H. Suhartanto, A. Murni, K. Kusmardi
Convolutional Neural Network (CNN) has been widely used in medical image processing. Histopathology is one of modality or images for a pathologist to analyze the status of cancer. The unstructured pattern of this image cause the problem, tend to miss identification or takes more time to analyze by the pathologist. Besides that, Deep learning training generally requires powerful hardware resources to improve performance during the training. Therefore, to address these problems, we propose two main activities in this study; to accelerate training time and to enhance the histopathology dataset. We train our CNN on three similar GPU specification (GTX-1080) as an alternative to become training time is faster. Mean-shift filter is one of the low-pass filter technique. We use this to handle unstructured pattern on histopathology images to enhance this dataset. The performance of all three GPUs is presented during the training process with 500 epochs measure by the speedup. Meanwhile, the performance of model testing is carried out with several batch-size selection scenarios from 32,64,128 and 256. The use of mean-shift can improve convergence during training in 128 batch-size become faster.
{"title":"Strategies to Improve Performance of Convolutional Neural Network on Histopathological Images Classification","authors":"Toto Haryanto, H. Suhartanto, A. Murni, K. Kusmardi","doi":"10.1109/ICACSIS47736.2019.8979740","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979740","url":null,"abstract":"Convolutional Neural Network (CNN) has been widely used in medical image processing. Histopathology is one of modality or images for a pathologist to analyze the status of cancer. The unstructured pattern of this image cause the problem, tend to miss identification or takes more time to analyze by the pathologist. Besides that, Deep learning training generally requires powerful hardware resources to improve performance during the training. Therefore, to address these problems, we propose two main activities in this study; to accelerate training time and to enhance the histopathology dataset. We train our CNN on three similar GPU specification (GTX-1080) as an alternative to become training time is faster. Mean-shift filter is one of the low-pass filter technique. We use this to handle unstructured pattern on histopathology images to enhance this dataset. The performance of all three GPUs is presented during the training process with 500 epochs measure by the speedup. Meanwhile, the performance of model testing is carried out with several batch-size selection scenarios from 32,64,128 and 256. The use of mean-shift can improve convergence during training in 128 batch-size become faster.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124211595","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979864
Rashel Fam, Y. Lepage
Indonesian as an agglutinating language is known for its derivative morphological richness. Word forms are constructed by combining stem and affixes. In this paper, we study the influence of surface form and morphological information in analogical grids extracted from a set of word forms with varying sizes. Each word form is represented as a feature vector. In the experiment setting, we consider three features: characters, affixes, and morphosyntactic definition. The sizes and saturation are then observed to characterize the extracted grids.
{"title":"A study of analogical grids extracted using feature vectors on varying vocabulary sizes in Indonesian","authors":"Rashel Fam, Y. Lepage","doi":"10.1109/ICACSIS47736.2019.8979864","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979864","url":null,"abstract":"Indonesian as an agglutinating language is known for its derivative morphological richness. Word forms are constructed by combining stem and affixes. In this paper, we study the influence of surface form and morphological information in analogical grids extracted from a set of word forms with varying sizes. Each word form is represented as a feature vector. In the experiment setting, we consider three features: characters, affixes, and morphosyntactic definition. The sizes and saturation are then observed to characterize the extracted grids.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127107807","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979775
Maulana Ihsan, A. H. Saputro, W. Handayani
The total content of flavonoids in plants is generally measured using spectrophotometric analysis based on color absorption rates. The method could not inform the distribution of flavonoids in a leaf. In this study, the mapping system of flavonoid distribution of Velvet Apple (Diospyros discolor Willd.) leaf was introduced using a hyperspectral imaging technique combining spectral and spatial analysis. The proposed system consists of a measurement system and a mathematical model that converts each spatial pixel into a value that represents the number of flavonoids in velvet apple leaves. The measurement system consists of a hyperspectral camera, halogen lamp, slider, and measurement frame. A random forest (RF) method is used to calculate the transformation model between reflectance values and total flavonoids. The construction of the measurement system was carried out with 738 data containing spectral data and lab measurement data. The evaluation of the random forest model obtained a value of R2 of 0.94 and RMSE 15.87 mg/ml.
{"title":"Flavonoid Distribution Mapping System of Velvet Apple Leaf Based on Hyperspectral Imaging","authors":"Maulana Ihsan, A. H. Saputro, W. Handayani","doi":"10.1109/ICACSIS47736.2019.8979775","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979775","url":null,"abstract":"The total content of flavonoids in plants is generally measured using spectrophotometric analysis based on color absorption rates. The method could not inform the distribution of flavonoids in a leaf. In this study, the mapping system of flavonoid distribution of Velvet Apple (Diospyros discolor Willd.) leaf was introduced using a hyperspectral imaging technique combining spectral and spatial analysis. The proposed system consists of a measurement system and a mathematical model that converts each spatial pixel into a value that represents the number of flavonoids in velvet apple leaves. The measurement system consists of a hyperspectral camera, halogen lamp, slider, and measurement frame. A random forest (RF) method is used to calculate the transformation model between reflectance values and total flavonoids. The construction of the measurement system was carried out with 738 data containing spectral data and lab measurement data. The evaluation of the random forest model obtained a value of R2 of 0.94 and RMSE 15.87 mg/ml.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130813810","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979933
Nengah Widya Utami, I. N. Sukajaya, I. Candiasa, Eka Grana Aristyana Dewi
This research aimed to show the result of clustering students’ tuition (UKT) at Undiksha using algorithm FCM. The characteristics of each cluster, measurement of level implementing algorithm FCM accuracy in determining UKT. Students’ tuition data used in this research include students’ tuition from SBMPTN year 2017. The students’ data came from 30 students with 7 parameters, namely, parents’ occupation, parents’ income, number of dependents, assets, water payment, electronic voltage, and varieties of vehicles. The data of students’ tuition grouped into four groups, namely, UKT 1, UKT 2, UKT 3, and UKT 4. The data from grouping students’ tuition using FCM method in determining students’ tuition supported with Matlab Software 2017 a showed UKT 1 into 89 students, UKT 2 into 91 students, UKT 3 into 79 students, and UKT 4 into 46 students. The data characteristics of each student’s tuition were gathered from each parameter based on the result of the center vector (v) in the last iteration. Besides, the result showed an FCM method has high accuracy in 0.78. The result of factor analysis showed 3 factors determined students’ tuition from 7 parameters, namely, income factor, expulsion factor, and load factor. On the other hand, future research can be developed by grouping the 3 factors as computation variable in algorithm FCM and to use other methods, so that the results of clustering are more optimal.
{"title":"The Implementation of Data Mining to Show UKT (Students’ Tuition) Using Fuzzy C-Means Algorithm : (Case Study: Universitas Pendidikan Ganesha)","authors":"Nengah Widya Utami, I. N. Sukajaya, I. Candiasa, Eka Grana Aristyana Dewi","doi":"10.1109/ICACSIS47736.2019.8979933","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979933","url":null,"abstract":"This research aimed to show the result of clustering students’ tuition (UKT) at Undiksha using algorithm FCM. The characteristics of each cluster, measurement of level implementing algorithm FCM accuracy in determining UKT. Students’ tuition data used in this research include students’ tuition from SBMPTN year 2017. The students’ data came from 30 students with 7 parameters, namely, parents’ occupation, parents’ income, number of dependents, assets, water payment, electronic voltage, and varieties of vehicles. The data of students’ tuition grouped into four groups, namely, UKT 1, UKT 2, UKT 3, and UKT 4. The data from grouping students’ tuition using FCM method in determining students’ tuition supported with Matlab Software 2017 a showed UKT 1 into 89 students, UKT 2 into 91 students, UKT 3 into 79 students, and UKT 4 into 46 students. The data characteristics of each student’s tuition were gathered from each parameter based on the result of the center vector (v) in the last iteration. Besides, the result showed an FCM method has high accuracy in 0.78. The result of factor analysis showed 3 factors determined students’ tuition from 7 parameters, namely, income factor, expulsion factor, and load factor. On the other hand, future research can be developed by grouping the 3 factors as computation variable in algorithm FCM and to use other methods, so that the results of clustering are more optimal.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123347125","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979767
A. Indraprastha
We present study on the integration of augmented reality using Microsoft Hololens and architectural design documentation for cultural heritage application by practical evaluation method. Our goal is to understand the potential of AR implementation in architectural narration and documentation. Herein, we outlined our works: 1) Visualization of architectural forms; 2) Data visualization embedded in augmented environment; 3) Basic user interaction mechanism. Our focus of the study is on the methodology and workflows involved in the AR platform. The case study is traditional Balinese architectures that constitute issues of materiality, tectonics, aesthetics and embodied local and specific information, hence the cultural heritage. Our study found that AR and Hololens provide a promising tool for 3D visualization and experiences particularly in cultural heritage application where computer-generated objects are augmented into real and physical objects. Despite latency, limited visual field and interaction methods that are still in development, implementation of AR in the architectural field bring understanding architecture as a medium and interface where space, form, and information are combined
{"title":"An Interactive Augmented Reality Architectural Design Model : A Prototype for Digital Heritage Preservation","authors":"A. Indraprastha","doi":"10.1109/ICACSIS47736.2019.8979767","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979767","url":null,"abstract":"We present study on the integration of augmented reality using Microsoft Hololens and architectural design documentation for cultural heritage application by practical evaluation method. Our goal is to understand the potential of AR implementation in architectural narration and documentation. Herein, we outlined our works: 1) Visualization of architectural forms; 2) Data visualization embedded in augmented environment; 3) Basic user interaction mechanism. Our focus of the study is on the methodology and workflows involved in the AR platform. The case study is traditional Balinese architectures that constitute issues of materiality, tectonics, aesthetics and embodied local and specific information, hence the cultural heritage. Our study found that AR and Hololens provide a promising tool for 3D visualization and experiences particularly in cultural heritage application where computer-generated objects are augmented into real and physical objects. Despite latency, limited visual field and interaction methods that are still in development, implementation of AR in the architectural field bring understanding architecture as a medium and interface where space, form, and information are combined","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115637765","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979982
A. Hidayatullah, Anisa Miladya Hakim, Abdullah Aziz Sembada
In the last decade, social media networking sites have become an inseparable part of people’s life. However, not all of content in social media contain beneficial and necessary information. This can be seen from the existing of negative and harmful content in social media, such as adult or pornographic content. Therefore, this study aims to build a model for adult content classification by using Long Short Term Memory (LSTM) Neural Network to classify adult content and non-adult content. We also compared our LSTM methods with Multinomial Naive Bayes, Logistic Regression, and Support Vector Classification. According to our experiments, the best model was obtained from the LSTM model with two LSTM layers and dropout reached the accuracy of 98,39% and the loss value of 5,08&.
{"title":"Adult Content Classification on Indonesian Tweets using LSTM Neural Network","authors":"A. Hidayatullah, Anisa Miladya Hakim, Abdullah Aziz Sembada","doi":"10.1109/ICACSIS47736.2019.8979982","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979982","url":null,"abstract":"In the last decade, social media networking sites have become an inseparable part of people’s life. However, not all of content in social media contain beneficial and necessary information. This can be seen from the existing of negative and harmful content in social media, such as adult or pornographic content. Therefore, this study aims to build a model for adult content classification by using Long Short Term Memory (LSTM) Neural Network to classify adult content and non-adult content. We also compared our LSTM methods with Multinomial Naive Bayes, Logistic Regression, and Support Vector Classification. According to our experiments, the best model was obtained from the LSTM model with two LSTM layers and dropout reached the accuracy of 98,39% and the loss value of 5,08&.","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"9 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113990438","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 : 2019-10-01DOI: 10.1109/ICACSIS47736.2019.8979844
Dimas Agung Saputra, I. Alif, R. Wijaya, Y. G. Sucahyo, M. K. Hammi
In the organization, IT should be sustained and extended from the organization’s strategy and objectives of business. Good IT governance can increase the effectiveness of IT utilization by aligning business and IT strategy. Organization also need to know what the role of IT to give understanding about the IT utilization consequences for business impact. The purpose of this study is to analyse the role of IT in IT governance practices maturity perspective which implemented in the organization. This study compose relevant maturity model based on theory of IT governance practices that have relation to two key dimensions of IT Strategic Impact Grid. There are five case studies to verify the proposed maturity model, a case from Indonesian Telecommunication Company and four cases from the study of related IT governance practices. From the case studies result, IT governance practices can provide an understanding related to the utilization of IT roles in an organization. The maturity of business/IT alignment is consistent with the two key dimensions of IT Strategic Grid maturity but it possible to get the lower or higher maturity measurement result comparison with business/IT alignment maturity because of the different weight effectiveness. Details on IT governance practices could help the Board or CIO to take further action to achieve target maturity that accordance with the desired role of IT in the organization
{"title":"Role of IT in IT Governance Practices Maturity Perspective","authors":"Dimas Agung Saputra, I. Alif, R. Wijaya, Y. G. Sucahyo, M. K. Hammi","doi":"10.1109/ICACSIS47736.2019.8979844","DOIUrl":"https://doi.org/10.1109/ICACSIS47736.2019.8979844","url":null,"abstract":"In the organization, IT should be sustained and extended from the organization’s strategy and objectives of business. Good IT governance can increase the effectiveness of IT utilization by aligning business and IT strategy. Organization also need to know what the role of IT to give understanding about the IT utilization consequences for business impact. The purpose of this study is to analyse the role of IT in IT governance practices maturity perspective which implemented in the organization. This study compose relevant maturity model based on theory of IT governance practices that have relation to two key dimensions of IT Strategic Impact Grid. There are five case studies to verify the proposed maturity model, a case from Indonesian Telecommunication Company and four cases from the study of related IT governance practices. From the case studies result, IT governance practices can provide an understanding related to the utilization of IT roles in an organization. The maturity of business/IT alignment is consistent with the two key dimensions of IT Strategic Grid maturity but it possible to get the lower or higher maturity measurement result comparison with business/IT alignment maturity because of the different weight effectiveness. Details on IT governance practices could help the Board or CIO to take further action to achieve target maturity that accordance with the desired role of IT in the organization","PeriodicalId":165090,"journal":{"name":"2019 International Conference on Advanced Computer Science and information Systems (ICACSIS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122846585","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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