Indonesia has biodiversity which is very beneficial for human life. Existing applications for ethnomedicine have been developed using conventional methods that only utilized SPARQL Protocol and RDF Query Language (SPARQL), so they still have limitations in representing knowledge and its retrieval. Those conventional methods are which based of relational database and ontology that has not utilized inference in its query process. Therefore, this work proposed SPIN for Enthnomedicine Semantic Search (SESS), a framework of the semantic search for medicinal plants that were developed by using SPIN (SPARQL Inferencing Notation). SESS has two main parts, the ontology design included SPARQL Inferencing Notation (SPIN) library and query process. The experiments were assessed in terms of execution time, query variation and accuracy. The obtained results showed a ratio of precision at 1, recall at 0.98 and the average value of the f-measure was 0.99. Utilizing SPIN also decrease the time consuming to obtain the result by around .
{"title":"SESS: Utilization of SPIN for Ethnomedicine Semantic Search","authors":"Dewi Wardani, Mauluah Susmawati","doi":"10.1145/3575882.3575912","DOIUrl":"https://doi.org/10.1145/3575882.3575912","url":null,"abstract":"Indonesia has biodiversity which is very beneficial for human life. Existing applications for ethnomedicine have been developed using conventional methods that only utilized SPARQL Protocol and RDF Query Language (SPARQL), so they still have limitations in representing knowledge and its retrieval. Those conventional methods are which based of relational database and ontology that has not utilized inference in its query process. Therefore, this work proposed SPIN for Enthnomedicine Semantic Search (SESS), a framework of the semantic search for medicinal plants that were developed by using SPIN (SPARQL Inferencing Notation). SESS has two main parts, the ontology design included SPARQL Inferencing Notation (SPIN) library and query process. The experiments were assessed in terms of execution time, query variation and accuracy. The obtained results showed a ratio of precision at 1, recall at 0.98 and the average value of the f-measure was 0.99. Utilizing SPIN also decrease the time consuming to obtain the result by around .","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128200379","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}
R. S. Yuwana, Ruth Andini, H. Pardede, W. Sulandari, Endang Suryawati, Candra Ihsan, A. A. Supianto
The number of medical abbreviations in the world is due to the increasing number of diseases, technological advances in the medical field, research in the medical field, and the emergence of various drugs. A large number of medical abbreviations often have the same abbreviation but it has a different meaning. The similarity of these medical abbreviations often results in ambiguous abbreviations. The ambiguity of this abbreviation can be reduced by creating a system based on Artificial Intelligent (AI). In this paper, we have compared various models using Naive Bayes, LSTM, Logistic Regression, and SVM to get the best model for medical abbreviations disambiguation. The experimental results indicate that the highest model accuracy is obtained by LSTM model, which is at 97.21%.
{"title":"Evaluation of Machine Learning Models for Detecting Disambiguation on Medical Abbreviations","authors":"R. S. Yuwana, Ruth Andini, H. Pardede, W. Sulandari, Endang Suryawati, Candra Ihsan, A. A. Supianto","doi":"10.1145/3575882.3575907","DOIUrl":"https://doi.org/10.1145/3575882.3575907","url":null,"abstract":"The number of medical abbreviations in the world is due to the increasing number of diseases, technological advances in the medical field, research in the medical field, and the emergence of various drugs. A large number of medical abbreviations often have the same abbreviation but it has a different meaning. The similarity of these medical abbreviations often results in ambiguous abbreviations. The ambiguity of this abbreviation can be reduced by creating a system based on Artificial Intelligent (AI). In this paper, we have compared various models using Naive Bayes, LSTM, Logistic Regression, and SVM to get the best model for medical abbreviations disambiguation. The experimental results indicate that the highest model accuracy is obtained by LSTM model, which is at 97.21%.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131518033","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}
M. U. Siregar, Pahlevi Wahyu Hardjita, Farhan Armawan Asdin, Dewi Wardani, A. Wijayanto, Yessi Yunitasari, Muhammad Anshari
Predicting property prices provides a better service for customers to evaluate and estimate price movement before their purchases. Some features including OverallQual and GrLivArea, which were selected when applying GA, become important features that can influence property prices. This research proposes a hybrid Genetic algorithm combined with the Extreme Gradient Boosting algorithm to predict real estate housing prices. The proposed scheme is evaluated by Root Mean Square Error, processing time, and the number of deleted features. The proposed scheme has been compared with the sole Extreme Gradient Boosting. The experimental results show that the proposed scheme produces the smallest root mean square error value of 0.129 compared to 0.133 of the sole Extreme Gradient Boosting. Furthermore, the predicted time of the proposed scheme is much better than the sole method.
预测楼价为客户提供更好的服务，让他们在购买物业前评估和估计楼价的变动。在应用遗传算法时选择的一些特征，包括OverallQual和GrLivArea，成为可以影响房地产价格的重要特征。本研究提出一种结合极端梯度提升算法的混合遗传算法来预测房地产房价。采用均方根误差(Root Mean Square Error)、处理时间和删除的特征数量对该方法进行了评价。将该方法与单一的极限梯度增强方法进行了比较。实验结果表明，与单一的极限梯度增强方法的0.133相比，该方法的均方根误差最小，为0.129。此外，该方案的预测时间比单一的方法要好得多。
{"title":"Housing Price Prediction Using a Hybrid Genetic Algorithm with Extreme Gradient Boosting","authors":"M. U. Siregar, Pahlevi Wahyu Hardjita, Farhan Armawan Asdin, Dewi Wardani, A. Wijayanto, Yessi Yunitasari, Muhammad Anshari","doi":"10.1145/3575882.3575939","DOIUrl":"https://doi.org/10.1145/3575882.3575939","url":null,"abstract":"Predicting property prices provides a better service for customers to evaluate and estimate price movement before their purchases. Some features including OverallQual and GrLivArea, which were selected when applying GA, become important features that can influence property prices. This research proposes a hybrid Genetic algorithm combined with the Extreme Gradient Boosting algorithm to predict real estate housing prices. The proposed scheme is evaluated by Root Mean Square Error, processing time, and the number of deleted features. The proposed scheme has been compared with the sole Extreme Gradient Boosting. The experimental results show that the proposed scheme produces the smallest root mean square error value of 0.129 compared to 0.133 of the sole Extreme Gradient Boosting. Furthermore, the predicted time of the proposed scheme is much better than the sole method.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125667562","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}
Seven Siren, Rothna Pec, Vannak Ros, Kum Sithirith, S. Un, Sros Nhek
Data acquisition of electricity power is one of the cases for EDC (Electricity of Cambodia). Every month EDC must send staff to have a look at every electric meter. Automatic Data Acquisition of Electric Power Usage (ADAEPU) replaces the traditional energy meter with a digital energy meter which provides the EDC to know the usage of energy and collect those data correctly. All the data will transfer from the system to EDC wirelessly. Developing countries do not have the last mobile generation all over the place. Therefore, Global system for mobile communications (GSM) takes part here as a transmission medium. The result shown that the system could monitor the power used of each house perfectly. This saves a lot of time and money and keeps them away from electric shock.
{"title":"Automatic Data Acquisition of Electric Power Usage in Phnom Penh City","authors":"Seven Siren, Rothna Pec, Vannak Ros, Kum Sithirith, S. Un, Sros Nhek","doi":"10.1145/3575882.3575886","DOIUrl":"https://doi.org/10.1145/3575882.3575886","url":null,"abstract":"Data acquisition of electricity power is one of the cases for EDC (Electricity of Cambodia). Every month EDC must send staff to have a look at every electric meter. Automatic Data Acquisition of Electric Power Usage (ADAEPU) replaces the traditional energy meter with a digital energy meter which provides the EDC to know the usage of energy and collect those data correctly. All the data will transfer from the system to EDC wirelessly. Developing countries do not have the last mobile generation all over the place. Therefore, Global system for mobile communications (GSM) takes part here as a transmission medium. The result shown that the system could monitor the power used of each house perfectly. This saves a lot of time and money and keeps them away from electric shock.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131667590","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}
Khalisyahdini Khalisyahdini, M. Bijaksana, K. Lhaksmana
Identifiying part of speech of word is critical for Arabic language morphology aspects. Existing approaches either 1) predict morphological description from active voice Arabic words with neural based; or 2) predict morphological description from active and passive voice Arabic words with rule based. Both kinds of approaches have shortcomings. Therefore, we propose on adding some other Arabic type of word, which is passive voice word. Specifically, we convert the active voice to passive voice Arabic with computation and morphological description identification from that result. Experiments show that our system sucessfully to change active to passive voice automatically and achieves good performance on morphological description identification using neural based method.
{"title":"Active-to-Passive Arabic Word Conversion and MSD Identification using RNN","authors":"Khalisyahdini Khalisyahdini, M. Bijaksana, K. Lhaksmana","doi":"10.1145/3575882.3575901","DOIUrl":"https://doi.org/10.1145/3575882.3575901","url":null,"abstract":"Identifiying part of speech of word is critical for Arabic language morphology aspects. Existing approaches either 1) predict morphological description from active voice Arabic words with neural based; or 2) predict morphological description from active and passive voice Arabic words with rule based. Both kinds of approaches have shortcomings. Therefore, we propose on adding some other Arabic type of word, which is passive voice word. Specifically, we convert the active voice to passive voice Arabic with computation and morphological description identification from that result. Experiments show that our system sucessfully to change active to passive voice automatically and achieves good performance on morphological description identification using neural based method.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130004887","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}
The act of plagiarism in a thesis can decrease the quality of a student’s thesis. The plagiarism is perhaps unintentional. One form of this plagiarism in the thesis is the similarity of ideas in terms of topics, methods and cases used. Detecting the similarity of ideas has its difficulties because it must be able to understand the context of a document. This research implements a similar idea detection framework with a semantic similarity approach. Calculating the similarity value between the thesis proposal and the crawled data is carried out by considering the cluster distance and the hierarchical structure in the knowledge base. Comparative data were obtained from open portal publications. The evaluation results return accuracy is over 100 data testing.
{"title":"SSTI: Semantic Similarity to detect Novelty of Thesis Ideas","authors":"D. Wardani, Chairul Achmad","doi":"10.1145/3575882.3575955","DOIUrl":"https://doi.org/10.1145/3575882.3575955","url":null,"abstract":"The act of plagiarism in a thesis can decrease the quality of a student’s thesis. The plagiarism is perhaps unintentional. One form of this plagiarism in the thesis is the similarity of ideas in terms of topics, methods and cases used. Detecting the similarity of ideas has its difficulties because it must be able to understand the context of a document. This research implements a similar idea detection framework with a semantic similarity approach. Calculating the similarity value between the thesis proposal and the crawled data is carried out by considering the cluster distance and the hierarchical structure in the knowledge base. Comparative data were obtained from open portal publications. The evaluation results return accuracy is over 100 data testing.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133060645","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}
A. Heryana, Dikdik Krisnandi, H. Pardede, Galih Nugraha Nurkahfi, M. Dinata, A. Rozie, Rendra Firmansyah
Real-time video streaming with low latency is essential for autonomous vehicles’ teleoperation. Studies show that the latency for self-driving teleoperation should not exceed 50 milliseconds. Latency could be caused by hardware, software, or network factors. Here, we focus on latency reduction due to network factors. We applied two data protocols: user datagram protocols (UDP) and real-time messaging protocol (RTMP), and afterward tuned the encoder along with data compression. The glass-to-glass method was applied to measure the network performance and video latency. The measurement of video latency achieves 300 milliseconds, which implements a direct connection (without a broadcaster) with the UDP data protocol. While this may still exceed the requirements, the study could be seen as a preliminary effort to deal with data latency for teleoperation driving.
{"title":"Realtime Video Latency Reduction for Autonomous Vehicle Teleoperation Using RTMP Over UDP Protocols","authors":"A. Heryana, Dikdik Krisnandi, H. Pardede, Galih Nugraha Nurkahfi, M. Dinata, A. Rozie, Rendra Firmansyah","doi":"10.1145/3575882.3575891","DOIUrl":"https://doi.org/10.1145/3575882.3575891","url":null,"abstract":"Real-time video streaming with low latency is essential for autonomous vehicles’ teleoperation. Studies show that the latency for self-driving teleoperation should not exceed 50 milliseconds. Latency could be caused by hardware, software, or network factors. Here, we focus on latency reduction due to network factors. We applied two data protocols: user datagram protocols (UDP) and real-time messaging protocol (RTMP), and afterward tuned the encoder along with data compression. The glass-to-glass method was applied to measure the network performance and video latency. The measurement of video latency achieves 300 milliseconds, which implements a direct connection (without a broadcaster) with the UDP data protocol. While this may still exceed the requirements, the study could be seen as a preliminary effort to deal with data latency for teleoperation driving.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132056978","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}
Machine learning is a superior tool that is unbiased and moderately comparable to the medical expert in making medical diagnostics if trained with correct supervision. In this paper we developed a supervised learning algorithm employing plantar pressure data to detect the anomaly called hallux valgus (HV) on a number of subject. Support vector machine (SVM) and its variants such as kernel SVM and ensemble SVM were evaluated on a plantar pressure open dataset. Results show that SVMs in general have the average classification rate of above 90 percent.
{"title":"Anomaly Detection of Hallux Valgus using Plantar Pressure Data","authors":"Latif Rozaqi, Yukhi Mustaqim Kusuma Sya'Bana, Asep Nugroho, Nugrahaning Sani Dewi, Kadek Heri Sanjaya","doi":"10.1145/3575882.3575952","DOIUrl":"https://doi.org/10.1145/3575882.3575952","url":null,"abstract":"Machine learning is a superior tool that is unbiased and moderately comparable to the medical expert in making medical diagnostics if trained with correct supervision. In this paper we developed a supervised learning algorithm employing plantar pressure data to detect the anomaly called hallux valgus (HV) on a number of subject. Support vector machine (SVM) and its variants such as kernel SVM and ensemble SVM were evaluated on a plantar pressure open dataset. Results show that SVMs in general have the average classification rate of above 90 percent.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116536502","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}
Elpri Eka Permadi, S. Kusumaningrum, Donny Ramadhan, Sjaikhurrizal El Muttaqien, A. Supriyono
Atherosclerosis is one of the causes of cardiovascular disease (CVD). The high level of cholesterol which is controlled by 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase plays an essential role in the pathogenesis of atherosclerosis. By inhibiting the activity of HMG-CoA reductase, the biosynthesis of cholesterol may be limited and therefore contribute to the reduction of blood cholesterol. This research aims to identify the hit compounds of HMG-CoA reductase inhibitors from the natural compounds database of West Bali National Park from the Internal PRBBOT BRIN database (Indonesia's natural compounds data base). We conducted a virtual screening workflow using a quantitative structure-activity relationship (QSAR) strategy based on artificial intelligence approach to allow faster screening of HMG-CoA reductase inhibitor from 2608 compounds. Eight classifications and five regressions in machine learning algorithms were applied to build a virtual screening workflow using the 1173 compounds dataset from the ChEMBL database. The classification QSAR model used the Random Forest and Fuzzy Rule algorithm with a tied score of accuracy were 0.972 and the regression QSAR model used the Tree Ensemble algorithm with the R2 pred = 0.88. Virtual screening results identified three hit compounds as HMG-CoA reductase inhibitors from Calophyllum inophyllum L., including Inocalophyllin B, Brasiliensic acid, and Inophylloidic acid. These results indicated the benefit of the machine learning approaches for potential screening compounds as an inhibitor for the HMG-CoA reductase enzyme, and it may be useful to screen various drug candidates for other target diseases.
动脉粥样硬化是心血管疾病(CVD)的病因之一。3-羟基-3-甲基戊二酰辅酶A (HMG-CoA)还原酶控制的高胆固醇水平在动脉粥样硬化的发病机制中起重要作用。通过抑制HMG-CoA还原酶的活性，可以限制胆固醇的生物合成，从而有助于降低血胆固醇。本研究旨在从印尼内部PRBBOT BRIN数据库(印尼天然化合物数据库)中鉴定西巴厘岛国家公园天然化合物数据库中HMG-CoA还原酶抑制剂的命中化合物。我们使用基于人工智能方法的定量构效关系(QSAR)策略进行了虚拟筛选工作流程，以便从2608种化合物中更快地筛选HMG-CoA还原酶抑制剂。利用ChEMBL数据库中的1173种化合物数据集，采用机器学习算法中的8种分类和5种回归构建虚拟筛选工作流。分类QSAR模型采用随机森林和模糊规则算法，准确率为0.972，回归QSAR模型采用树集成算法，R2 pred = 0.88。虚拟筛选结果确定了3个从卡罗勒叶中提取的HMG-CoA还原酶抑制剂，分别为Inocalophyllin B、brasilienensis acid和Inophylloidic acid。这些结果表明，机器学习方法可用于筛选HMG-CoA还原酶抑制剂的潜在化合物，并可用于筛选其他靶标疾病的各种候选药物。
{"title":"Virtual Screening of HMG-CoA reductase inhibitors of West Bali National Park natural compounds database using machine learning","authors":"Elpri Eka Permadi, S. Kusumaningrum, Donny Ramadhan, Sjaikhurrizal El Muttaqien, A. Supriyono","doi":"10.1145/3575882.3575915","DOIUrl":"https://doi.org/10.1145/3575882.3575915","url":null,"abstract":"Atherosclerosis is one of the causes of cardiovascular disease (CVD). The high level of cholesterol which is controlled by 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase plays an essential role in the pathogenesis of atherosclerosis. By inhibiting the activity of HMG-CoA reductase, the biosynthesis of cholesterol may be limited and therefore contribute to the reduction of blood cholesterol. This research aims to identify the hit compounds of HMG-CoA reductase inhibitors from the natural compounds database of West Bali National Park from the Internal PRBBOT BRIN database (Indonesia's natural compounds data base). We conducted a virtual screening workflow using a quantitative structure-activity relationship (QSAR) strategy based on artificial intelligence approach to allow faster screening of HMG-CoA reductase inhibitor from 2608 compounds. Eight classifications and five regressions in machine learning algorithms were applied to build a virtual screening workflow using the 1173 compounds dataset from the ChEMBL database. The classification QSAR model used the Random Forest and Fuzzy Rule algorithm with a tied score of accuracy were 0.972 and the regression QSAR model used the Tree Ensemble algorithm with the R2 pred = 0.88. Virtual screening results identified three hit compounds as HMG-CoA reductase inhibitors from Calophyllum inophyllum L., including Inocalophyllin B, Brasiliensic acid, and Inophylloidic acid. These results indicated the benefit of the machine learning approaches for potential screening compounds as an inhibitor for the HMG-CoA reductase enzyme, and it may be useful to screen various drug candidates for other target diseases.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125187263","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}
The objective of this study is to find out the continuance adoption and use of a Geographic Information System (GIS) for scholarship recipient distribution in Central Sulawesi. For the data gathering instrument, we employed an online structured questionnaire. One hundred fifty scholarship administrators were selected from fifteen state and private higher education institutions in Palu Central Sulawesi. They were assigned a five-scale survey. All completed questionnaires were analyzed using AMOS. The findings show that the factors of perceived usefulness, perceived ease of use, information quality, system quality, and change management have significantly influenced the continuance adoption and use of the geographic information system by university scholarship administrators. The results highlighted that when a system was developed based on those criteria, the sustainable adoption and use of the geographic information system can be consistently maintained to improve universities' scholarship management and distribution. Our study contributes to the body of knowledge in geographic information system continuance adoption and use within education institutions and to practices that support universities for better scholarship management and distribution.
{"title":"Geographic Information System Continuance Adoption and Use to Determine Bidikmisi Scholarship Recipients Distribution","authors":"N. Nurdin, Muhammad Agam, Adawiyah Adawiyah","doi":"10.1145/3575882.3575953","DOIUrl":"https://doi.org/10.1145/3575882.3575953","url":null,"abstract":"The objective of this study is to find out the continuance adoption and use of a Geographic Information System (GIS) for scholarship recipient distribution in Central Sulawesi. For the data gathering instrument, we employed an online structured questionnaire. One hundred fifty scholarship administrators were selected from fifteen state and private higher education institutions in Palu Central Sulawesi. They were assigned a five-scale survey. All completed questionnaires were analyzed using AMOS. The findings show that the factors of perceived usefulness, perceived ease of use, information quality, system quality, and change management have significantly influenced the continuance adoption and use of the geographic information system by university scholarship administrators. The results highlighted that when a system was developed based on those criteria, the sustainable adoption and use of the geographic information system can be consistently maintained to improve universities' scholarship management and distribution. Our study contributes to the body of knowledge in geographic information system continuance adoption and use within education institutions and to practices that support universities for better scholarship management and distribution.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129075547","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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