Pub Date : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632926
Fabianaugie Jametoni, D. E. Saputra
The most basic capability of an autonomous drone is its positioning capability. There is various method available to calculate a drone position. To help any new researcher on autonomous drone to choose their option on drone positioning system, a proper categorization is needed. This work provides a taxonomy of drone positioning system. The taxonomy categorizes drone positioning system into two major methods: vision-based and non-vision-based. The taxonomy further divides each method into several sub-method based on the equipment and calculation method. The taxonomy also provides the advantage and disadvantage of each method.
{"title":"A Study on Autonomous Drone Positioning Method","authors":"Fabianaugie Jametoni, D. E. Saputra","doi":"10.1109/ICIC54025.2021.9632926","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632926","url":null,"abstract":"The most basic capability of an autonomous drone is its positioning capability. There is various method available to calculate a drone position. To help any new researcher on autonomous drone to choose their option on drone positioning system, a proper categorization is needed. This work provides a taxonomy of drone positioning system. The taxonomy categorizes drone positioning system into two major methods: vision-based and non-vision-based. The taxonomy further divides each method into several sub-method based on the equipment and calculation method. The taxonomy also provides the advantage and disadvantage of each method.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131215253","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632918
Ben Rahman, H. L. Hendric Spits Warnars, Boy Subirosa Sabarguna, W. Budiharto
Heart disease is a disease that needs to be watched out for and is of particular concern. Seeing to the WHO report, in 2018, as many as 17.9 million people died from heart disease, and especially in Indonesia, heart disease in 2020 became the highest cause of death. This study uses data mining techniques to pull out information from the data used. This research provides a scientific contribution, namely detecting heart disease as early as possible. In this case, the author uses the K-Nearest Neighbor Algorithm to classify the data based on the nearest neighbor data. The database is own in a reasonably high volume, so it should note that irrelevant attributes will be removed over or noise. If they are still used, data processing results will not be optimal, so data cleaning needs to be done carefully. The selection of the data used was 1243 records, and after being selected the data were taken in this study as many as 366 records, with parameters using 12 attributes, actual data from hospitals, data consisting of data from patients under surveillance for cardiac care, and data from patients who underwent surgery and Data from Medical Examination. Therefore, it is necessary to develop a decision support system that assists doctors in taking steps for early detection. Research conducted with the K-Nearest Neighbors algorithm accuracy up to 77% with a value of K = 7.
{"title":"Heart Disease Classification Model Using K-Nearest Neighbor Algorithm","authors":"Ben Rahman, H. L. Hendric Spits Warnars, Boy Subirosa Sabarguna, W. Budiharto","doi":"10.1109/ICIC54025.2021.9632918","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632918","url":null,"abstract":"Heart disease is a disease that needs to be watched out for and is of particular concern. Seeing to the WHO report, in 2018, as many as 17.9 million people died from heart disease, and especially in Indonesia, heart disease in 2020 became the highest cause of death. This study uses data mining techniques to pull out information from the data used. This research provides a scientific contribution, namely detecting heart disease as early as possible. In this case, the author uses the K-Nearest Neighbor Algorithm to classify the data based on the nearest neighbor data. The database is own in a reasonably high volume, so it should note that irrelevant attributes will be removed over or noise. If they are still used, data processing results will not be optimal, so data cleaning needs to be done carefully. The selection of the data used was 1243 records, and after being selected the data were taken in this study as many as 366 records, with parameters using 12 attributes, actual data from hospitals, data consisting of data from patients under surveillance for cardiac care, and data from patients who underwent surgery and Data from Medical Examination. Therefore, it is necessary to develop a decision support system that assists doctors in taking steps for early detection. Research conducted with the K-Nearest Neighbors algorithm accuracy up to 77% with a value of K = 7.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"395 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124347162","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632972
Abdul Rahman, Ermatita, D. Budianta, Abdiansah
The main problem in tidal land is high soil acidity, and the availability of nutrients in the soil is relatively low. Utilization of local resource vermicompost is used to improve soil conditions in tidal lands in order to increase crop yields. The parameter of paddy plant height has a very high correlation with paddy yields. This study aims to implement the ANFIS method to predict paddy plant height based on the treatment of vermicompost organic fertilizer. The dataset used for ANFIS training was taken directly from the observation data on the height of the paddy plant and the results of soil laboratory tests. The ANFIS process consists of 5 inputs consisting of fertilizer treatment, pH, N, P, K, and one output, namely paddy plant height. The results obtained from the training data process are that there are 486 rules and the error rate using MAPE is 3.53%, or the accuracy level of the prediction results is 96.47%.
{"title":"Prediction of Paddy Plant Height with Vermicompost Fertilizer Treatment on Tidal Land using ANFIS Method","authors":"Abdul Rahman, Ermatita, D. Budianta, Abdiansah","doi":"10.1109/ICIC54025.2021.9632972","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632972","url":null,"abstract":"The main problem in tidal land is high soil acidity, and the availability of nutrients in the soil is relatively low. Utilization of local resource vermicompost is used to improve soil conditions in tidal lands in order to increase crop yields. The parameter of paddy plant height has a very high correlation with paddy yields. This study aims to implement the ANFIS method to predict paddy plant height based on the treatment of vermicompost organic fertilizer. The dataset used for ANFIS training was taken directly from the observation data on the height of the paddy plant and the results of soil laboratory tests. The ANFIS process consists of 5 inputs consisting of fertilizer treatment, pH, N, P, K, and one output, namely paddy plant height. The results obtained from the training data process are that there are 486 rules and the error rate using MAPE is 3.53%, or the accuracy level of the prediction results is 96.47%.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657731","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632890
I. Irvanizam, Natasya Azzahra, Inayatur Nadhira, Z. Zulfan, M. Subianto, I. Syahrini
The office of social affairs has provided the productive economic endeavors (PEE) program that empowers increasing the income of micros, small and medium enterprises (MSMEs) to build harmonious social relationships among communities. However, in the selection process for this program recipient so far, an officer evaluated potential MSMEs based on requirement data conventionally so that it is very vulnerable to personal subjectivity problems. Therefore, we designed a Multiple Criteria Decision-Making (MCDM) model to apply to this decision-making process. The model integrated the AHP method with the VIKOR method. First, based on the professional decision-maker judgment in evaluating a pairwise criteria comparison, the AHP determined the acceptable criteria weights automatically, and the VIKOR then utilized them to rank alternatives based on the values of utility and regret measures. After checking the acceptability advantage and stability in decision-making, the results showed that alternative U5 and U8 were the compromise solutions representing the closeness to the ideal solution. Finally, this MCDM model is a feasible and suitable tool for dealing with this decision-making problem.
{"title":"Multiple Criteria Decision Making Based on VIKOR for Productive Economic Endeavors Distribution Problem","authors":"I. Irvanizam, Natasya Azzahra, Inayatur Nadhira, Z. Zulfan, M. Subianto, I. Syahrini","doi":"10.1109/ICIC54025.2021.9632890","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632890","url":null,"abstract":"The office of social affairs has provided the productive economic endeavors (PEE) program that empowers increasing the income of micros, small and medium enterprises (MSMEs) to build harmonious social relationships among communities. However, in the selection process for this program recipient so far, an officer evaluated potential MSMEs based on requirement data conventionally so that it is very vulnerable to personal subjectivity problems. Therefore, we designed a Multiple Criteria Decision-Making (MCDM) model to apply to this decision-making process. The model integrated the AHP method with the VIKOR method. First, based on the professional decision-maker judgment in evaluating a pairwise criteria comparison, the AHP determined the acceptable criteria weights automatically, and the VIKOR then utilized them to rank alternatives based on the values of utility and regret measures. After checking the acceptability advantage and stability in decision-making, the results showed that alternative U5 and U8 were the compromise solutions representing the closeness to the ideal solution. Finally, this MCDM model is a feasible and suitable tool for dealing with this decision-making problem.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128088234","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632987
Aa Zezen Zaenal Abidin, M. Othman, Aslinda Hassan, Yuli Murdianingsih, Usep Tatang Suryadi, Zulkiflee Muslim
Verifying a set of most frequent problems is essential before introducing practical solutions using new technology, processes, and practices. This study proposes a way to verify these problem sets. The main contribution of this paper is a method to verify a set of most frequent problems in waste disposal practices previously identified through a survey questionnaire, using Google Earth visualization and the Apriori algorithm. Google Earth is used to pinpoint the geographical locations of existing waste bins, illegal landfills, and people's houses. The distance between the waste bins and the residents' houses, sites of waste disposal by burning, and sites of waste disposal by dumping are then analyzed as a combination of the problems of waste disposal practices. Support, Confidence, multiplication between Support and Confidence, and lift ratio values are then calculated to obtain a combination of the most frequent problems sets. Next, the support value in the Apriori algorithm is compared with the FP-Growth method using Rapidminer. Results obtain support and thus verify data previously obtained from the survey. For a 2-itemset problem and a minimum support value of 0.1, 33% accuracy is obtained, while a 3-itemset problem returns 99% accuracy. We show that our method is useful in verifying data previously obtained from other sources.
{"title":"Verifying Waste Disposal Practice Problems of Rural Areas In Indonesia Using the Apriori Algorithm","authors":"Aa Zezen Zaenal Abidin, M. Othman, Aslinda Hassan, Yuli Murdianingsih, Usep Tatang Suryadi, Zulkiflee Muslim","doi":"10.1109/ICIC54025.2021.9632987","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632987","url":null,"abstract":"Verifying a set of most frequent problems is essential before introducing practical solutions using new technology, processes, and practices. This study proposes a way to verify these problem sets. The main contribution of this paper is a method to verify a set of most frequent problems in waste disposal practices previously identified through a survey questionnaire, using Google Earth visualization and the Apriori algorithm. Google Earth is used to pinpoint the geographical locations of existing waste bins, illegal landfills, and people's houses. The distance between the waste bins and the residents' houses, sites of waste disposal by burning, and sites of waste disposal by dumping are then analyzed as a combination of the problems of waste disposal practices. Support, Confidence, multiplication between Support and Confidence, and lift ratio values are then calculated to obtain a combination of the most frequent problems sets. Next, the support value in the Apriori algorithm is compared with the FP-Growth method using Rapidminer. Results obtain support and thus verify data previously obtained from the survey. For a 2-itemset problem and a minimum support value of 0.1, 33% accuracy is obtained, while a 3-itemset problem returns 99% accuracy. We show that our method is useful in verifying data previously obtained from other sources.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122622088","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632914
Anis Fitri Nur Masruriyah, H. Basri, H. H. Handayani, Ahmad Fauzi, Ayu Ratna Juwita, Deden Wahiddin
COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy.
{"title":"The Rise Efficiency of Coronavirus Disease Classification Employing Feature Extraction","authors":"Anis Fitri Nur Masruriyah, H. Basri, H. H. Handayani, Ahmad Fauzi, Ayu Ratna Juwita, Deden Wahiddin","doi":"10.1109/ICIC54025.2021.9632914","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632914","url":null,"abstract":"COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004132","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9633010
Arief Hidayat, K. Adi, B. Surarso
The student learning process is influenced by several factors, one of which is student learning styles. Learning style is one of the most important factors in the E-learning environment because it can help the system to effectively personalize the learning process of students according to their learning style. Previously, to detect student learning styles by asking students to fill out questionnaires. However, there are problems with this static technique. One of these problems is the lack of students' self-awareness of their learning preferences. In addition, almost all students feel bored when asked to fill out a questionnaire. This research determined the learning style based on the Felder and Silverman Learning Style. This determination process is carried out using student activity data on a pure Moodle learning management system (LMS). The process begins with processing based on the literature to get a vector combination of learning styles. Student activity data is processed to produce data that only contains activities that are included in the selected features. The results of both are combined as input to the clustering process. This research applies the modified K-Means Clustering algorithm. Modifications were made using the learning style combination vector as the initial centroid. The k value used in this study was 8 which came from 8 combinations of learning styles from 3 dimensions used in this study. This is different from the combination of learning styles in FSLSM which has 16 combinations of learning styles originating from 4 dimensions of learning styles. This difference is caused by student activity data that only supports 3 dimensions of learning style.
{"title":"Determine Felder Silverman Learning Style Model using Literature Based and K-Means Clustering","authors":"Arief Hidayat, K. Adi, B. Surarso","doi":"10.1109/ICIC54025.2021.9633010","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9633010","url":null,"abstract":"The student learning process is influenced by several factors, one of which is student learning styles. Learning style is one of the most important factors in the E-learning environment because it can help the system to effectively personalize the learning process of students according to their learning style. Previously, to detect student learning styles by asking students to fill out questionnaires. However, there are problems with this static technique. One of these problems is the lack of students' self-awareness of their learning preferences. In addition, almost all students feel bored when asked to fill out a questionnaire. This research determined the learning style based on the Felder and Silverman Learning Style. This determination process is carried out using student activity data on a pure Moodle learning management system (LMS). The process begins with processing based on the literature to get a vector combination of learning styles. Student activity data is processed to produce data that only contains activities that are included in the selected features. The results of both are combined as input to the clustering process. This research applies the modified K-Means Clustering algorithm. Modifications were made using the learning style combination vector as the initial centroid. The k value used in this study was 8 which came from 8 combinations of learning styles from 3 dimensions used in this study. This is different from the combination of learning styles in FSLSM which has 16 combinations of learning styles originating from 4 dimensions of learning styles. This difference is caused by student activity data that only supports 3 dimensions of learning style.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121225069","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632912
Gede A. Pradipta, Retantyo Wardoyo, Aina Musdholifah, I. Sanjaya, Muhammad Ismail
Imbalanced class data distribution occurs when the number of examples representing one class is much lower than others. This conditioning affects the prediction accuracy degraded on minority data. To overcome this problem, Synthetic Minority Oversampling Technique (SMOTE) is a pioneer oversampling method in the research community for imbalanced classification. The basic idea of SMOTE is oversampled by creating a synthetic instance in feature space formed by the instance and its K-nearest neighbors due to the ability to avoid overfitting and assist the classifier in finding decision boundaries between classes. In this paper, we review current issue and problem occurs in classification with imbalanced data, performance evaluation in imbalanced data, a survey on an extension of SMOTE in recent years, and finally identify current challenges and future work in learning with imbalanced data.
{"title":"SMOTE for Handling Imbalanced Data Problem : A Review","authors":"Gede A. Pradipta, Retantyo Wardoyo, Aina Musdholifah, I. Sanjaya, Muhammad Ismail","doi":"10.1109/ICIC54025.2021.9632912","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632912","url":null,"abstract":"Imbalanced class data distribution occurs when the number of examples representing one class is much lower than others. This conditioning affects the prediction accuracy degraded on minority data. To overcome this problem, Synthetic Minority Oversampling Technique (SMOTE) is a pioneer oversampling method in the research community for imbalanced classification. The basic idea of SMOTE is oversampled by creating a synthetic instance in feature space formed by the instance and its K-nearest neighbors due to the ability to avoid overfitting and assist the classifier in finding decision boundaries between classes. In this paper, we review current issue and problem occurs in classification with imbalanced data, performance evaluation in imbalanced data, a survey on an extension of SMOTE in recent years, and finally identify current challenges and future work in learning with imbalanced data.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450693","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632934
Muhammad Yoma Putra Perdana, Arini, Andrew Fiade, Iik Muhamad Malik Matin
Financing is very important in running a company's business processes. However, in the process, financing is often an obstacle when an organization handles many projects simultaneously. XYZ company is a construction finance company that handles four different projects. Some of the projects are supervised by the same supervisor, making the supervisory function not run optimally. In addition, the budget plan is still made using the manual method. This makes it difficult for decision holders to determine the right budget allocation. As a result, the budget allocation for each existing project is not optimal. This problem can be solved if XYZ company has a decision support system. In this paper, we develop a decision support system based on Fuzzy Multi-Criteria Decision Making (FMCDM). FMCDM is a method of decision-making by determining the best alternative from existing alternatives based on certain criteria. We use 4 decision alternatives with 8 decision consideration criteria. The organization inputs the budget then the system calculates the value of the degree of optimism. Through experiments conducted, it is known that FMCDM is proven to be able to help companies identify conditions in each project so that the best projects can be prioritized to share financing with projects.
{"title":"Fuzzy Multi-Criteria Decision Making for Optimization of Housing Construction Financing","authors":"Muhammad Yoma Putra Perdana, Arini, Andrew Fiade, Iik Muhamad Malik Matin","doi":"10.1109/ICIC54025.2021.9632934","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632934","url":null,"abstract":"Financing is very important in running a company's business processes. However, in the process, financing is often an obstacle when an organization handles many projects simultaneously. XYZ company is a construction finance company that handles four different projects. Some of the projects are supervised by the same supervisor, making the supervisory function not run optimally. In addition, the budget plan is still made using the manual method. This makes it difficult for decision holders to determine the right budget allocation. As a result, the budget allocation for each existing project is not optimal. This problem can be solved if XYZ company has a decision support system. In this paper, we develop a decision support system based on Fuzzy Multi-Criteria Decision Making (FMCDM). FMCDM is a method of decision-making by determining the best alternative from existing alternatives based on certain criteria. We use 4 decision alternatives with 8 decision consideration criteria. The organization inputs the budget then the system calculates the value of the degree of optimism. Through experiments conducted, it is known that FMCDM is proven to be able to help companies identify conditions in each project so that the best projects can be prioritized to share financing with projects.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129439240","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632953
Siska Puspitaningsih, Suryono, Farikhin
There are many diseases and disorders in pregnancy that can lead to an emergency pregnancy. Delays in recognizing and making a diagnosis, delays in making decisions and delays in making referrals are three causes of higher maternal and infant mortality' rates. Several problems with delays in the diagnosis and referral process have become very important discussions and solutions must be sought. Because good referral management is the key to reducing maternal and infant mortality. Likewise, establishing the right pregnancy diagnosis when it is not too late is one of the efforts to prevent pregnancy emergencies. This study aims to design and implement a rule-based expert system forward chaining method for emergency pregnancy referrals and to measure the level of system accuracy based on the results of validation tests. The input of this research is symptom data which is then processed using a rule-based expert system forward chaining and produces output information on the type of disease and the place of reference. The results of the validation test, the probability- value of the system accuracy is 78.4% and the system inaccuracy is 21.6% so that this reference application can be declared to be running well.
{"title":"Design and Implementation of an Emergency Pregnancy Referral System Using Rule-Based Expert System Forward Chaining Method","authors":"Siska Puspitaningsih, Suryono, Farikhin","doi":"10.1109/ICIC54025.2021.9632953","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632953","url":null,"abstract":"There are many diseases and disorders in pregnancy that can lead to an emergency pregnancy. Delays in recognizing and making a diagnosis, delays in making decisions and delays in making referrals are three causes of higher maternal and infant mortality' rates. Several problems with delays in the diagnosis and referral process have become very important discussions and solutions must be sought. Because good referral management is the key to reducing maternal and infant mortality. Likewise, establishing the right pregnancy diagnosis when it is not too late is one of the efforts to prevent pregnancy emergencies. This study aims to design and implement a rule-based expert system forward chaining method for emergency pregnancy referrals and to measure the level of system accuracy based on the results of validation tests. The input of this research is symptom data which is then processed using a rule-based expert system forward chaining and produces output information on the type of disease and the place of reference. The results of the validation test, the probability- value of the system accuracy is 78.4% and the system inaccuracy is 21.6% so that this reference application can be declared to be running well.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123566900","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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