Pub Date : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.651
Okky Putra Barus, Kevil Lauwren, Jefri Junifer Pangaribuan, Romindo
Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.
{"title":"Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients","authors":"Okky Putra Barus, Kevil Lauwren, Jefri Junifer Pangaribuan, Romindo","doi":"10.34306/conferenceseries.v4i1.651","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.651","url":null,"abstract":"Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"22 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171281","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.625
Asriyanik, Agung Pambudi
Scholarship selection process has specific rules, but if the number of applicants exceeds the quota, a selection process is needed. Based on the observation of a university in Sukabumi, the selection for KIP scholarship has not yet had a standard method. Several methods can be used to assist the selection process, such as classification based on historical data of applicants. The algorithms used for classification include Decision Tree (DT) and Support Vector Machine (SVM). The research process uses SEMMA (Sample, Explore, Modify, Model, Assess) method. Dataset for KIP scholarship awardee from 2021-2022 consist of 519 samples with 16 attributes. From the exploration results, the most important features for model modeling are Status DTKS, Status P3KE, Father's income, mother's income, combined income, and performance. These attributes are converted into numerical data to facilitate model fitting. The K-Fold Cross-Validation results for the Decision Tree model in the case of KIP Scholarship classification yield an accuracy of 78.44% for the entire test dataset, a precision of 0.73107, indicating that 73.11% of the predictions are true, a recall (sensitivity) of 78.45%, and an F1 score of 73.20%. The results for the SVM model are an accuracy of 80.17%, a precision of 84.44%, and a recall of 80.17%.
{"title":"Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee","authors":"Asriyanik, Agung Pambudi","doi":"10.34306/conferenceseries.v4i1.625","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.625","url":null,"abstract":"Scholarship selection process has specific rules, but if the number of applicants exceeds the quota, a selection process is needed. Based on the observation of a university in Sukabumi, the selection for KIP scholarship has not yet had a standard method. Several methods can be used to assist the selection process, such as classification based on historical data of applicants. The algorithms used for classification include Decision Tree (DT) and Support Vector Machine (SVM). The research process uses SEMMA (Sample, Explore, Modify, Model, Assess) method. Dataset for KIP scholarship awardee from 2021-2022 consist of 519 samples with 16 attributes. From the exploration results, the most important features for model modeling are Status DTKS, Status P3KE, Father's income, mother's income, combined income, and performance. These attributes are converted into numerical data to facilitate model fitting. The K-Fold Cross-Validation results for the Decision Tree model in the case of KIP Scholarship classification yield an accuracy of 78.44% for the entire test dataset, a precision of 0.73107, indicating that 73.11% of the predictions are true, a recall (sensitivity) of 78.45%, and an F1 score of 73.20%. The results for the SVM model are an accuracy of 80.17%, a precision of 84.44%, and a recall of 80.17%.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"38 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171162","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}
Ngaji.AI is a mobile-based application that makes it possible to learn the recite very flexibly, wherever and whenever we can use it to learn the recite. This application is supported by artificial intelligence (AI) which provides direct and accurate assessments of how to recite Al-Quran verses properly and correctly and this application has been released on the Google Playstore platform and has been downloaded by more than 5 thousand. The Ngaji.AI application is faced with a crucial challenge, after direct observation of children and through the results of previous user input on Playstore, most of the input from users states that it needs to improve the User Interface (UI) design to make it easier to operate for children. The application of the Design Thinking method is an approach that prioritizes creativity and deep understanding of users and the problems they face and is indeed suitable for developing UI/UX of an application. Testing using the System Usability Scale (SUS) in the first test before the redesign got an average score of 50.25 and after the redesign got a significant score of 83.75. This reflects a significant increase in the level of satisfaction and ease for children in learning to recite the recite on the Ngaji.AI application.
{"title":"Redesigning the User Interface in the Mobile-Based Ngaji.AI Application Using the Design Thinking Method","authors":"Aminudin, Aldiensyah, Gita Indah Marthasari, Ilyas Nuryasin, Saiful Amien, Galih Wasis Wicaksono, Didih Rizki Chandranegara, I'anatut Thoifah","doi":"10.34306/conferenceseries.v4i1.635","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.635","url":null,"abstract":"Ngaji.AI is a mobile-based application that makes it possible to learn the recite very flexibly, wherever and whenever we can use it to learn the recite. This application is supported by artificial intelligence (AI) which provides direct and accurate assessments of how to recite Al-Quran verses properly and correctly and this application has been released on the Google Playstore platform and has been downloaded by more than 5 thousand. The Ngaji.AI application is faced with a crucial challenge, after direct observation of children and through the results of previous user input on Playstore, most of the input from users states that it needs to improve the User Interface (UI) design to make it easier to operate for children. The application of the Design Thinking method is an approach that prioritizes creativity and deep understanding of users and the problems they face and is indeed suitable for developing UI/UX of an application. Testing using the System Usability Scale (SUS) in the first test before the redesign got an average score of 50.25 and after the redesign got a significant score of 83.75. This reflects a significant increase in the level of satisfaction and ease for children in learning to recite the recite on the Ngaji.AI application.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"365 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171838","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.621
Asril Adi Sunarto, Euis Kania Kurniawati
Indonesia is a country with a region that has disasters here. As a Regional Apparatus Organization which must distribute regional government food reserves to the community when natural disasters strike, Dinas Ketahanan Pangan, Peternakan dan Perikanan Kota Sukabumi took the initiative to develop an application that can speed up the distribution of aid to the community. This national food reserve policy can support national defense in emergency conditions. The hope is that the development of this application can speed up the administration of official correspondence, where the administration of this correspondence is an element that slows down actions in almost every department, resulting in the length of time that citizens receive assistance. There are many discussions and interviews with various users who need to adapt an environment that requires flexibility in changes to system development, so this system development uses the spiral method. As a result, based on the user requirement list, 100% of user needs can be completed on time. The result, almost nine (9) tons of rice have been distributed to residents spread across 22 of the 33 sub-districts in Sukabumi City.
印度尼西亚是一个灾害频发的国家。Dinas Ketahanan Pangan, Peternakan dan Perikanan Kota Sukabumi 作为一个地区机构，必须在自然灾害发生时向社区分发地区政府的粮食储备。这项国家粮食储备政策可以在紧急情况下支持国防。开发该应用程序的目的是加快公文管理，因为公文管理几乎是拖慢每个部门行动的因素，导致公民获得援助的时间延长。我们与不同的用户进行了多次讨论和访谈，这些用户需要适应一个需要灵活改变系统开发的环境，因此本系统的开发采用了螺旋式方法。因此，根据用户需求清单，100% 的用户需求都能按时完成。结果，近九（9）吨大米已分发给苏卡布米市 33 个分区中 22 个分区的居民。
{"title":"Agile Method in Developing Electronic Local Government Food Reserve Distribution Services (E-CPPD) in Sukabumi City","authors":"Asril Adi Sunarto, Euis Kania Kurniawati","doi":"10.34306/conferenceseries.v4i1.621","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.621","url":null,"abstract":"Indonesia is a country with a region that has disasters here. As a Regional Apparatus Organization which must distribute regional government food reserves to the community when natural disasters strike, Dinas Ketahanan Pangan, Peternakan dan Perikanan Kota Sukabumi took the initiative to develop an application that can speed up the distribution of aid to the community. This national food reserve policy can support national defense in emergency conditions. The hope is that the development of this application can speed up the administration of official correspondence, where the administration of this correspondence is an element that slows down actions in almost every department, resulting in the length of time that citizens receive assistance. There are many discussions and interviews with various users who need to adapt an environment that requires flexibility in changes to system development, so this system development uses the spiral method. As a result, based on the user requirement list, 100% of user needs can be completed on time. The result, almost nine (9) tons of rice have been distributed to residents spread across 22 of the 33 sub-districts in Sukabumi City.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"374 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171896","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 Myers-Briggs Type Indicator (MBTI) is a method for identifying an individual's personality type based on the psychological theory of Carl Gustav Jung. In the context of computer science students, they often face challenges in planning their academic journey and determining the direction of their career development during their studies, causing confusion when it comes to choosing a career path in the field of computer science in the future. To address these challenges, the researcher has developed a web-based expert system using the PHP programming language. This expert system is designed to make decisions based on a collection of user responses, which are processed using the forward chaining method, ultimately providing the user's personality type along with suitable career choices. The primary objective of the expert system is to assist students in making decisions regarding their studies and future careers. Through this research, the researcher has produced a functioning website capable of efficiently processing user responses and generating decisions regarding personality types and career options. Thus, this study provides a solution to aid computer science students in planning their academic and career paths.
{"title":"Forward Chaining Algorithm on Informatics Graduate Job Recommendation System Based on MBTI Test","authors":"Jhonatan Laurensius Tjahjadi, Yulia Wahyuningsi, Padmavati Darma Putri Tanuwijaya, Ryan Putranda Kristianto","doi":"10.34306/conferenceseries.v4i1.641","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.641","url":null,"abstract":"The Myers-Briggs Type Indicator (MBTI) is a method for identifying an individual's personality type based on the psychological theory of Carl Gustav Jung. In the context of computer science students, they often face challenges in planning their academic journey and determining the direction of their career development during their studies, causing confusion when it comes to choosing a career path in the field of computer science in the future. To address these challenges, the researcher has developed a web-based expert system using the PHP programming language. This expert system is designed to make decisions based on a collection of user responses, which are processed using the forward chaining method, ultimately providing the user's personality type along with suitable career choices. The primary objective of the expert system is to assist students in making decisions regarding their studies and future careers. Through this research, the researcher has produced a functioning website capable of efficiently processing user responses and generating decisions regarding personality types and career options. Thus, this study provides a solution to aid computer science students in planning their academic and career paths.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"29 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172421","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.647
Elsa Julfiana, Natalis Ransi, Gusti Arviana Rahman
This research aims to analyze the information security culture at FMIPA Halu Oleo University. The results of the analysis show that exogenous latent variables, such as information security awareness, the role of faculty leaders, and information security policies, have a significant positive impact on information security culture. The research results show that the security awareness variable has a positive effect (0.221) on the Information Security Culture variable. Apart from that, the top management variable also has a positive effect (0.185) on the Information Security Culture variable. Likewise, the security policy variable has a significant positive influence (0.233) on the Information Security Culture variable. These findings provide an in-depth understanding of the factors that influence the culture of information security in the FMIPA Halu Oleo University environment, which can be the basis for recommending improvements in increasing information system security at the faculty.
{"title":"Analysis of Information Security Culture at FMIPA Halu Oleo University Using Partial Least Squares-Structural Equation Modeling Method","authors":"Elsa Julfiana, Natalis Ransi, Gusti Arviana Rahman","doi":"10.34306/conferenceseries.v4i1.647","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.647","url":null,"abstract":"This research aims to analyze the information security culture at FMIPA Halu Oleo University. The results of the analysis show that exogenous latent variables, such as information security awareness, the role of faculty leaders, and information security policies, have a significant positive impact on information security culture. The research results show that the security awareness variable has a positive effect (0.221) on the Information Security Culture variable. Apart from that, the top management variable also has a positive effect (0.185) on the Information Security Culture variable. Likewise, the security policy variable has a significant positive influence (0.233) on the Information Security Culture variable. These findings provide an in-depth understanding of the factors that influence the culture of information security in the FMIPA Halu Oleo University environment, which can be the basis for recommending improvements in increasing information system security at the faculty.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"34 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171188","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.645
L. Abednego, C. Nugraheni, Adelia Salsabina
Customer segmentation plays a crucial role in modern business strategies, enabling organizations to effectively target and personalize their marketing efforts and enhance customer relationships. Clustering algorithms have emerged as a powerful tool for segmenting customers based on their similarities and differences. We complement the data with an RFM model to support the clustering results. RFM, which stands for Recency, Frequency, and Monetary, is a model for segmenting customers based on their historical transaction data. This study aims to explore the concept of customer segmentation and the application of the RFM model combined with clustering algorithms in the real customer dataset of a company. It presents an overview of datasets, and introduces the RFM model and its components, emphasizing the significance of recency (how recently a customer made a purchase), frequency (how often a customer makes a purchase), and monetary value (the amount spent by a customer). It highlights the practicality of the RFM model in quantifying customer behavior and categorizing customers into distinct segments. It also explains popular clustering algorithms, analyzes experimental results, and concludes with future remarks on the potential of customer segmentation. We combine unsupervised (K-Means and DBSCAN clustering) and supervised machine learning methods to build customer clusters, label each cluster based on its characteristics, and propose a strategy for each cluster.
{"title":"Customer Segmentation: Transformation from Data to Marketing Strategy","authors":"L. Abednego, C. Nugraheni, Adelia Salsabina","doi":"10.34306/conferenceseries.v4i1.645","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.645","url":null,"abstract":"Customer segmentation plays a crucial role in modern business strategies, enabling organizations to effectively target and personalize their marketing efforts and enhance customer relationships. Clustering algorithms have emerged as a powerful tool for segmenting customers based on their similarities and differences. We complement the data with an RFM model to support the clustering results. RFM, which stands for Recency, Frequency, and Monetary, is a model for segmenting customers based on their historical transaction data. This study aims to explore the concept of customer segmentation and the application of the RFM model combined with clustering algorithms in the real customer dataset of a company. It presents an overview of datasets, and introduces the RFM model and its components, emphasizing the significance of recency (how recently a customer made a purchase), frequency (how often a customer makes a purchase), and monetary value (the amount spent by a customer). It highlights the practicality of the RFM model in quantifying customer behavior and categorizing customers into distinct segments. It also explains popular clustering algorithms, analyzes experimental results, and concludes with future remarks on the potential of customer segmentation. We combine unsupervised (K-Means and DBSCAN clustering) and supervised machine learning methods to build customer clusters, label each cluster based on its characteristics, and propose a strategy for each cluster.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"115 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172020","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.637
Titus Kristanto, Riza Akhsani Setyo Prayoga, Muhammad Nasrullah, Mustafa Kamal, Wahyuddin S
In the current digital era, especially in the world of education, the use of information and communication technology (ICT) is growing rapidly to meet needs. Universities rely on information systems, especially in managing new student admissions. The new student admission selection information system contains sensitive and dangerous prospective student data, as well as the risks that arise in the information system, limited to data processing during the new student admission process and the administration process, thus causing problems. The New Student Registration Information System is one of the services provided by the university as part of the new student registration process. Therefore, risk management is needed to minimize the impact of risks on maintaining data integrity, confidentiality, and availability. The aim of the research is to identify, analyze, and evaluate risks when using information systems for new student admission procedures. The approach used in risk management is Octave Allegro, and Octave Allegro is used to help evaluate information assets. The method used is data collection by conducting interviews with related sources. Based on the findings on the New Student Admissions site, there are 5 risk areas; 9 IT risks were identified as a result of potential risk analysis; and 4 IT risks were mitigated based on recommendations.
{"title":"Risk Management for New Student Admission Information Systems at Higher Education using the Octave Allegro Approach","authors":"Titus Kristanto, Riza Akhsani Setyo Prayoga, Muhammad Nasrullah, Mustafa Kamal, Wahyuddin S","doi":"10.34306/conferenceseries.v4i1.637","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.637","url":null,"abstract":"In the current digital era, especially in the world of education, the use of information and communication technology (ICT) is growing rapidly to meet needs. Universities rely on information systems, especially in managing new student admissions. The new student admission selection information system contains sensitive and dangerous prospective student data, as well as the risks that arise in the information system, limited to data processing during the new student admission process and the administration process, thus causing problems. The New Student Registration Information System is one of the services provided by the university as part of the new student registration process. Therefore, risk management is needed to minimize the impact of risks on maintaining data integrity, confidentiality, and availability. The aim of the research is to identify, analyze, and evaluate risks when using information systems for new student admission procedures. The approach used in risk management is Octave Allegro, and Octave Allegro is used to help evaluate information assets. The method used is data collection by conducting interviews with related sources. Based on the findings on the New Student Admissions site, there are 5 risk areas; 9 IT risks were identified as a result of potential risk analysis; and 4 IT risks were mitigated based on recommendations.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"127 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171563","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.617
Hozairi, Buhari, Rofiudin, Syariful Alim
User experience describes the experience a user gets when using a software product. This research aims to measure the user experience when using the Bakamla Messenger application. Measurements were carried out using the User Experience Questionnaire (UEQ) method. The research was carried out by distributing online questionnaires to users of the Bakamla Messenger application, with a total of 117 respondents. The measurement results for the attractiveness aspect of 2.26, clarity of 2.30, efficiency of 2.24, accuracy of 2.27, and stimulation of 2.28 have a positive impression value and are included in the excellent criteria. However, the novelty aspect gets a value of 0.02, meaning it has a negative impression value and is included in the bad criteria, so the innovation of the product needs to be increased. Thus, we recommend that Bakamla messenger application developers focus on improving aspects of the novelty value of the application, such as the level of security of confidential data and the messenger system being able to provide new features beyond messenger in general.
{"title":"User Experience Analysis on Bakamla Messenger Applications Using User Experiences Questionnaire (UEQ)","authors":"Hozairi, Buhari, Rofiudin, Syariful Alim","doi":"10.34306/conferenceseries.v4i1.617","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.617","url":null,"abstract":"User experience describes the experience a user gets when using a software product. This research aims to measure the user experience when using the Bakamla Messenger application. Measurements were carried out using the User Experience Questionnaire (UEQ) method. The research was carried out by distributing online questionnaires to users of the Bakamla Messenger application, with a total of 117 respondents. The measurement results for the attractiveness aspect of 2.26, clarity of 2.30, efficiency of 2.24, accuracy of 2.27, and stimulation of 2.28 have a positive impression value and are included in the excellent criteria. However, the novelty aspect gets a value of 0.02, meaning it has a negative impression value and is included in the bad criteria, so the innovation of the product needs to be increased. Thus, we recommend that Bakamla messenger application developers focus on improving aspects of the novelty value of the application, such as the level of security of confidential data and the messenger system being able to provide new features beyond messenger in general.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"534 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171831","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.639
Arie Satia Dharma, Evi Rosalina Silaban, Hana Maria Siahaan
Program Keluarga Harapan (PKH) is a conditional social assistance program as an effort to alleviate poverty which is allocated to poor vulnerable households. The determination of candidates for the Program Keluarga Harapan assistance recipients is still carried out in village meetings, so it takes quite a long time and there is potential for subjectivity in the assessment carried out by Village Government officials which can lead to differences of opinion between deliberation participants in assessing the eligibility of residents as PKH recipients. For this reason, this research will use an optimization method, namely Particle Swarm Optimization (PSO) to select the most optimal attribute out of 39 attributes. After that, a classification algorithm, namely the Support Vector Machine (SVM), was chosen to form a classification model for Candidates for Social Assistance for the Program Keluarga Harapan (PKH). The classification of Candidates for Social Assistance Recipients of the Program Keluarga Harapan (PKH) was carried out in 2 experiments, namely before and after optimization. Experiments before optimization give an accuracy value of 92.44%. While the Support Vector Machine accuracy value after optimization gives an accuracy value of 92.51%. Based on the experimental results, it can be concluded that the Particle Swarm Optimization method can increase the accuracy of the Support Vector Machine algorithm by 0.07%. And the best model is the Support Vector Machine after optimizing Particle Swarm Optimization by using the 17 most optimized attributes in determining class targets.
民望计划（Program Keluarga Harapan，PKH）是一项有条件的社会援助计划，旨在向贫困弱势家庭提供扶贫援助。目前，民望计划援助对象候选人的确定工作仍在村级会议上进行，因此耗时较长，而且村级政府官员在进行评估时有可能存在主观性，这可能导致议事参与者在评估居民是否有资格成为民望计划援助对象时出现意见分歧。因此，本研究将采用优化方法，即粒子群优化法（PSO），从 39 个属性中选出最优属性。然后，选择一种分类算法，即支持向量机（SVM），为民望计划（PKH）的社会援助候选人建立一个分类模型。对民望计划（PKH）社会援助受助者候选人的分类进行了两次实验，即优化前和优化后。优化前的实验准确率为 92.44%。优化后的支持向量机准确率为 92.51%。根据实验结果，可以得出结论：粒子群优化方法可以将支持向量机算法的准确率提高 0.07%。而支持向量机是经过粒子群优化后的最佳模型，它在确定类目标时使用了 17 个最优化程度最高的属性。
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