Pub Date : 2022-10-01DOI: 10.1109/icacsis56558.2022.9923531
{"title":"ICACSIS 2022 Program Schedule","authors":"","doi":"10.1109/icacsis56558.2022.9923531","DOIUrl":"https://doi.org/10.1109/icacsis56558.2022.9923531","url":null,"abstract":"","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127364394","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923514
Nadiza Lediwara, Hondor Saragih, R. Gultom, Emirul Mukmin, Gutri Rahmad Zuwa, Ray Hadi Fajri
The purpose of this reseacrh provids a design of the administrative selection system of new students in Universitas Pertahanan Republik Indonesia. This selection system aims to filter and find the best criteria students, so that they can be the best graduates. The result of this research is a design of a new student admissions of administration selection system by finite state automata. FSA has various advantages including a simple system that is easy to implement, light computing, and easy to transfer from abstract to program code. By the implementation of finite state automata on the new student admissions system, it can facilitate and help committee to determine students who can take written exams online.
{"title":"Finite State Automata On The Administrative Selection System Of New Student Admission in Universitas Pertahanan Republik Indonesia","authors":"Nadiza Lediwara, Hondor Saragih, R. Gultom, Emirul Mukmin, Gutri Rahmad Zuwa, Ray Hadi Fajri","doi":"10.1109/ICACSIS56558.2022.9923514","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923514","url":null,"abstract":"The purpose of this reseacrh provids a design of the administrative selection system of new students in Universitas Pertahanan Republik Indonesia. This selection system aims to filter and find the best criteria students, so that they can be the best graduates. The result of this research is a design of a new student admissions of administration selection system by finite state automata. FSA has various advantages including a simple system that is easy to implement, light computing, and easy to transfer from abstract to program code. By the implementation of finite state automata on the new student admissions system, it can facilitate and help committee to determine students who can take written exams online.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130404464","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923502
Mingyi Zhao, Yaling Wang, Y. Lepage
Abstract Meaning Representation (AMR) is a broad -coverage formalism for capturing the semantics of a given sentence. However, domain adaptation of AMR is limited by the shortage of annotated AMR graphs. In this paper, we explore and build a new large-scale dataset with 2.3 million AMRs in the domain of academic writing. Additionally, we prove that 30% of them are of similar quality as the annotated data in the downstream AMR-to-text task. Our results outperform previous graph-based approaches by over 11 BLEU points. We provide a pipeline that integrates automated generation and evaluation. This can help explore other AMR benchmarks.
{"title":"Large-scale AMR Corpus with Re-generated Sentences: Domain Adaptive Pre-training on ACL Anthology Corpus","authors":"Mingyi Zhao, Yaling Wang, Y. Lepage","doi":"10.1109/ICACSIS56558.2022.9923502","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923502","url":null,"abstract":"Abstract Meaning Representation (AMR) is a broad -coverage formalism for capturing the semantics of a given sentence. However, domain adaptation of AMR is limited by the shortage of annotated AMR graphs. In this paper, we explore and build a new large-scale dataset with 2.3 million AMRs in the domain of academic writing. Additionally, we prove that 30% of them are of similar quality as the annotated data in the downstream AMR-to-text task. Our results outperform previous graph-based approaches by over 11 BLEU points. We provide a pipeline that integrates automated generation and evaluation. This can help explore other AMR benchmarks.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130689624","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923426
Justin Edwards, M. El-Sharkawy
Convolutional Neural Networks have started making headway in solving the problem of semantic segmentation. The demand for increasingly lightweight neural networks has been driven by an abundance of cheap hardware capable of running such neural networks and utilization of such networks for real world applications. MobileNet’ s utilization of the depthwise separable convolution has been proven to be an efficient approach for reducing neural network size without incurring a high penalty in accuracy. In the realm of image segmentation, ICNet was a breakthrough in the ability for semantic segmentation networks to be deployed on commonly available hardware and run at close to real time. In this paper, ICNet is improved upon by utilizing lessons learned from MobileNet and applying these lessons to create a new lighter weight network, uICNet. uICNet achieves similar accuracy to ICNet while substantially improving model size.
{"title":"uICNet: Lightweight Image Segmentation","authors":"Justin Edwards, M. El-Sharkawy","doi":"10.1109/ICACSIS56558.2022.9923426","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923426","url":null,"abstract":"Convolutional Neural Networks have started making headway in solving the problem of semantic segmentation. The demand for increasingly lightweight neural networks has been driven by an abundance of cheap hardware capable of running such neural networks and utilization of such networks for real world applications. MobileNet’ s utilization of the depthwise separable convolution has been proven to be an efficient approach for reducing neural network size without incurring a high penalty in accuracy. In the realm of image segmentation, ICNet was a breakthrough in the ability for semantic segmentation networks to be deployed on commonly available hardware and run at close to real time. In this paper, ICNet is improved upon by utilizing lessons learned from MobileNet and applying these lessons to create a new lighter weight network, uICNet. uICNet achieves similar accuracy to ICNet while substantially improving model size.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132530712","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9922962
Indiarto Aji Begawan, E. C. Djamal, Daswara Djajasasmita, Fatan Kasyidi, Fikri Nugraha
Sleep quality is essential to health, informed by the sleep stage. In other words, identifying the sleep stage can detect the possibility of sleep disorders. The standard carried out in medicine is Polysomnography (PSG) which consists of many devices. A simple one-channel Electroencephalogram (EEG) signal is one device that can identify sleep levels in humans. It means minimizing additional sleep disturbances. EEG captures electrical activity in the brain using electrodes. Identifying sleep levels is challenging as it usually uses a pair of channels. Many studies have discussed sleep disorders using several well-known methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This paper proposed parallel CNN-RNN methods that provide advantages in identifying EEG signals due to the characteristics of CNN, which processes features on the channel, and RNN, which processes sequence data. The parallel CNN-RNN method identified five sleep stages: Wake, non-REM 1 (N1), non-REM 2 (N2), non-REM 3 (N3), and Rapid Eye Movement (REM). Dataset recorded from the Sleep-EDF dataset with several EEG signal channels. The Wavelet feature was used to extract the features contained in the signal. The experimental results of the two EEG channels produced high accuracy values, which are 90.13 % for the Fpz-Cz channel. This proposed model using parallel CNN-RNN achieved higher performance based on single-channel EEG.)
{"title":"Sleep Stage Identification Based on EEG Signals Using Parallel Convolutional Neural Network and Recurrent Neural Network","authors":"Indiarto Aji Begawan, E. C. Djamal, Daswara Djajasasmita, Fatan Kasyidi, Fikri Nugraha","doi":"10.1109/ICACSIS56558.2022.9922962","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9922962","url":null,"abstract":"Sleep quality is essential to health, informed by the sleep stage. In other words, identifying the sleep stage can detect the possibility of sleep disorders. The standard carried out in medicine is Polysomnography (PSG) which consists of many devices. A simple one-channel Electroencephalogram (EEG) signal is one device that can identify sleep levels in humans. It means minimizing additional sleep disturbances. EEG captures electrical activity in the brain using electrodes. Identifying sleep levels is challenging as it usually uses a pair of channels. Many studies have discussed sleep disorders using several well-known methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This paper proposed parallel CNN-RNN methods that provide advantages in identifying EEG signals due to the characteristics of CNN, which processes features on the channel, and RNN, which processes sequence data. The parallel CNN-RNN method identified five sleep stages: Wake, non-REM 1 (N1), non-REM 2 (N2), non-REM 3 (N3), and Rapid Eye Movement (REM). Dataset recorded from the Sleep-EDF dataset with several EEG signal channels. The Wavelet feature was used to extract the features contained in the signal. The experimental results of the two EEG channels produced high accuracy values, which are 90.13 % for the Fpz-Cz channel. This proposed model using parallel CNN-RNN achieved higher performance based on single-channel EEG.)","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133180844","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923476
S. I. Kailaku, Taufik Djatna, M. Hakim, Afifah Nur Arfiana, Y. Arkeman, Y. Purwanto, F. Udin
The challenge of distributing climacteric fruit is quality assurance due to the long-distance and perishability nature of the fruit. While monitoring transportation conditions is common, little research has developed a prediction model of fruit quality affected by transportation conditions. The presented study designs a quality monitoring system for mango's long-distance supply chain by integrating the Internet of Things (IoT) and machine learning. The system modeling utilizes Business Process Model and Notation and a Use Case Diagram based on requirement analysis. The design of IoT architecture addresses the needs of the supply chain actors to monitor the transportation process and predict the final quality of mango upon arrival. Artificial Neural Network (ANN) predicts mango grade classification upon arrival. The dataset consists of initial (harvest) maturity level and transportation conditions as predictor variables and mango final grade as the target variable. The accuracy of the prediction model reaches more than 95%. The verification and validation of the system with traceability technique on the user's requirements confirm the fulfillment of each requirement's input, tasks, and output. This conceptual design presents IoT and machine learning as promising solutions to quality assurance problems in the global fresh produce supply chain.
{"title":"Real- Time Quality Monitoring and Prediction System for Logistics 4.0 of Mango Agroindustry","authors":"S. I. Kailaku, Taufik Djatna, M. Hakim, Afifah Nur Arfiana, Y. Arkeman, Y. Purwanto, F. Udin","doi":"10.1109/ICACSIS56558.2022.9923476","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923476","url":null,"abstract":"The challenge of distributing climacteric fruit is quality assurance due to the long-distance and perishability nature of the fruit. While monitoring transportation conditions is common, little research has developed a prediction model of fruit quality affected by transportation conditions. The presented study designs a quality monitoring system for mango's long-distance supply chain by integrating the Internet of Things (IoT) and machine learning. The system modeling utilizes Business Process Model and Notation and a Use Case Diagram based on requirement analysis. The design of IoT architecture addresses the needs of the supply chain actors to monitor the transportation process and predict the final quality of mango upon arrival. Artificial Neural Network (ANN) predicts mango grade classification upon arrival. The dataset consists of initial (harvest) maturity level and transportation conditions as predictor variables and mango final grade as the target variable. The accuracy of the prediction model reaches more than 95%. The verification and validation of the system with traceability technique on the user's requirements confirm the fulfillment of each requirement's input, tasks, and output. This conceptual design presents IoT and machine learning as promising solutions to quality assurance problems in the global fresh produce supply chain.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128446919","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923429
Mary Joy P. Canon, Christian Y. Sy, T. Palaoag, R. Roxas, Lany L. Maceda
Pre-trained language models (PLMs) have gained significant attention in NLP because of its effectiveness in improving the performance of several downstream tasks. Pre-training these PLMs requires benchmark datasets to create universal language representation and to generate robust models. This paper established the first linguistic resource for Philippine English language to help future researchers in language modeling and other NLP tasks. We used NLP approach to prepare and build our data and transformers paradigm to generate small PLMs. The PHEnText corpus is composed of multi-domain Philippine English text data in formal language scraped from different sources. Tokenization process was performed using BPE and WordPiece tokenizer algorithms. Using a subset of the PHEnText, we generated four small versions of transformer-based language models. Cross-validation during the pre-training reported that a RoBERTa-base model outperformed all other variants in terms of training loss, evaluation loss and accuracy. This work introduced the PHEnText benchmark corpus composed of 2.6B tokens primarily intended for pre-training objective. The corpus provides starting point and opportunities for current and future NLP researches and once trained, can be used more efficiently via fine-tuning. Additionally, the dataset was prepared to be pre-training compatible with different transformer models. Furthermore, the generated PLMs using a subset of PHEnText rendered notable results in terms of minimal loss and nearly acceptable accuracy. Next step for this undertaking is to train PLMs using the entire PHEnText dataset and to test the models' effectiveness by fine-tuning them to NLP downstream tasks.
{"title":"Language Resource Construction of Multi-Domain Philippine English Text for Pre-training Objective","authors":"Mary Joy P. Canon, Christian Y. Sy, T. Palaoag, R. Roxas, Lany L. Maceda","doi":"10.1109/ICACSIS56558.2022.9923429","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923429","url":null,"abstract":"Pre-trained language models (PLMs) have gained significant attention in NLP because of its effectiveness in improving the performance of several downstream tasks. Pre-training these PLMs requires benchmark datasets to create universal language representation and to generate robust models. This paper established the first linguistic resource for Philippine English language to help future researchers in language modeling and other NLP tasks. We used NLP approach to prepare and build our data and transformers paradigm to generate small PLMs. The PHEnText corpus is composed of multi-domain Philippine English text data in formal language scraped from different sources. Tokenization process was performed using BPE and WordPiece tokenizer algorithms. Using a subset of the PHEnText, we generated four small versions of transformer-based language models. Cross-validation during the pre-training reported that a RoBERTa-base model outperformed all other variants in terms of training loss, evaluation loss and accuracy. This work introduced the PHEnText benchmark corpus composed of 2.6B tokens primarily intended for pre-training objective. The corpus provides starting point and opportunities for current and future NLP researches and once trained, can be used more efficiently via fine-tuning. Additionally, the dataset was prepared to be pre-training compatible with different transformer models. Furthermore, the generated PLMs using a subset of PHEnText rendered notable results in terms of minimal loss and nearly acceptable accuracy. Next step for this undertaking is to train PLMs using the entire PHEnText dataset and to test the models' effectiveness by fine-tuning them to NLP downstream tasks.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128458692","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923436
Urbano B. Patayon, Renato V. Crisostomo
Changes in enrolment would result to many problems such as shortage in human resource and infrastructure. Using Prophet to forecast future student numbers will aid administrators in effectively allocating resources and making future decisions. The data used in this study is the entire population of college students enrolled in Jose Rizal Memorial State University - Tampilisan Campus from 2000–2022. Data shows a fluctuation in enrolment data but significant increase is observable in A.Y. 2013–2014 up to A.Y. 2015–2016 and A.Y. 2018–2019 up to 2021–2022, respectively. Likewise, data shows a seasonal decrease of number of enrolees in the second semester in comparison to first semester in every academic year. Further, results during the training phase in terms of root mean square error (RMSE) and coefficient of determination (R2) of the different forecasting models trained using different enrolment data and Prophet shows that model trained using BS Business Administration (BSBA), BS Agriculture (BSA), and BS Criminology (BSCrim) dataset attains the top three (3) smallest RMSE result of 15.51 and 17, and the top three (3) highest R2 value of 0.97 and 0.95, respectively. On the other hand, model trained using consolidated enrolment data attains an RMSE of 36.7 and a R2 score of 0.87. Based on the findings, different models attain varied results; however, there are models which attain higher degree of accuracy as depicted in the RMSE and R2. This indicates that predicting enrolment data using those models with higher accuracy is similar to real data thus it is viable in predicting future values. The researcher assumes that this study may be implemented and incorporated into current school and university information systems. Further, other mathematical models may be incorporated into the current model to improve forecast accuracy.
{"title":"Time Series Analysis on Enrolment Data: A case in a State University in Zamboanga del Norte, Philippines","authors":"Urbano B. Patayon, Renato V. Crisostomo","doi":"10.1109/ICACSIS56558.2022.9923436","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923436","url":null,"abstract":"Changes in enrolment would result to many problems such as shortage in human resource and infrastructure. Using Prophet to forecast future student numbers will aid administrators in effectively allocating resources and making future decisions. The data used in this study is the entire population of college students enrolled in Jose Rizal Memorial State University - Tampilisan Campus from 2000–2022. Data shows a fluctuation in enrolment data but significant increase is observable in A.Y. 2013–2014 up to A.Y. 2015–2016 and A.Y. 2018–2019 up to 2021–2022, respectively. Likewise, data shows a seasonal decrease of number of enrolees in the second semester in comparison to first semester in every academic year. Further, results during the training phase in terms of root mean square error (RMSE) and coefficient of determination (R2) of the different forecasting models trained using different enrolment data and Prophet shows that model trained using BS Business Administration (BSBA), BS Agriculture (BSA), and BS Criminology (BSCrim) dataset attains the top three (3) smallest RMSE result of 15.51 and 17, and the top three (3) highest R2 value of 0.97 and 0.95, respectively. On the other hand, model trained using consolidated enrolment data attains an RMSE of 36.7 and a R2 score of 0.87. Based on the findings, different models attain varied results; however, there are models which attain higher degree of accuracy as depicted in the RMSE and R2. This indicates that predicting enrolment data using those models with higher accuracy is similar to real data thus it is viable in predicting future values. The researcher assumes that this study may be implemented and incorporated into current school and university information systems. Further, other mathematical models may be incorporated into the current model to improve forecast accuracy.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102831","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923460
Serra Charisma Viontita, Mahendrawathi Er, Ika Nurkasanah, A. I. Sonhaji
Organizations often face difficulty in the evaluation and prioritization of their business processes. Many performance measurement indicators are defined at the aggregate and not at the process level. Surabaya City Office for Population Administration & Civil Registration (COP ACR) is a local government agency which face these challenges. This paper attempts to solve COP ACR challenges by applying BPM approach. First, process performance measurement guideline is developed. Next, the business process selection stage is carried out. The last stage is composing performance measurement indicators. From the business process selection stage, the priorities for process improvement initiatives are application processes for Birth Certificate, Biodata Change on Family Cards with the KLAMPID Application, and (3) Indonesian Citizens Transfer Certificate of Inter-City/Regency/Province processes certificate. In addition, the development of performance measurement indicators results in 29 performance measurement indicators related to Birth Certificate business processes.
{"title":"Determining Business Process Improvement Priorities at Surabaya City Office for Population Administration & Civil Registration","authors":"Serra Charisma Viontita, Mahendrawathi Er, Ika Nurkasanah, A. I. Sonhaji","doi":"10.1109/ICACSIS56558.2022.9923460","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923460","url":null,"abstract":"Organizations often face difficulty in the evaluation and prioritization of their business processes. Many performance measurement indicators are defined at the aggregate and not at the process level. Surabaya City Office for Population Administration & Civil Registration (COP ACR) is a local government agency which face these challenges. This paper attempts to solve COP ACR challenges by applying BPM approach. First, process performance measurement guideline is developed. Next, the business process selection stage is carried out. The last stage is composing performance measurement indicators. From the business process selection stage, the priorities for process improvement initiatives are application processes for Birth Certificate, Biodata Change on Family Cards with the KLAMPID Application, and (3) Indonesian Citizens Transfer Certificate of Inter-City/Regency/Province processes certificate. In addition, the development of performance measurement indicators results in 29 performance measurement indicators related to Birth Certificate business processes.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116787605","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 : 2022-10-01DOI: 10.1109/ICACSIS56558.2022.9923513
Hendrana Tjahjadi, Hery Sudaryanto, Agung Budi Rahmanto, Azka V Lesmana, Ahmad Ilham Irianto, Oczha Alifian
This paper discusses several cutting-edge non-invasive techniques for measuring blood glucose levels (BGL) using photoplethysmography (PPG) signals. These methods can be efficiently and precisely carried out using artificial intelligence algorithms (AI). The most important parameter for identifying the presence of health issues in a person's body is blood glucose. The state of blood circulation is reflected in the PPG signal. PPG-based BGL measurement utilizing AI is a non-invasive measurement approach because BGL measurement is still currently invasive. This study examines the development of this technology using data collected between 2009 and 2022. The future of non-invasive BGL employing PPG signals with artificial intelligence technology looks promising. Further studies may use the findings of the methodological mapping in this review as a guidance when deciding which BGL measuring methodology to use.
{"title":"A Review of Non-Invasive Monitoring of Blood Glucose Levels Based on Photoplethysmography Signals Using Artificial Intelligence","authors":"Hendrana Tjahjadi, Hery Sudaryanto, Agung Budi Rahmanto, Azka V Lesmana, Ahmad Ilham Irianto, Oczha Alifian","doi":"10.1109/ICACSIS56558.2022.9923513","DOIUrl":"https://doi.org/10.1109/ICACSIS56558.2022.9923513","url":null,"abstract":"This paper discusses several cutting-edge non-invasive techniques for measuring blood glucose levels (BGL) using photoplethysmography (PPG) signals. These methods can be efficiently and precisely carried out using artificial intelligence algorithms (AI). The most important parameter for identifying the presence of health issues in a person's body is blood glucose. The state of blood circulation is reflected in the PPG signal. PPG-based BGL measurement utilizing AI is a non-invasive measurement approach because BGL measurement is still currently invasive. This study examines the development of this technology using data collected between 2009 and 2022. The future of non-invasive BGL employing PPG signals with artificial intelligence technology looks promising. Further studies may use the findings of the methodological mapping in this review as a guidance when deciding which BGL measuring methodology to use.","PeriodicalId":165728,"journal":{"name":"2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161622","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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