Pub Date : 2023-08-07DOI: 10.20895/dinda.v3i2.1215
Idfian Azhar Hidayat
This study aims to classify sleep disorders using the Random Forest method on the Sleep Health and Lifestyledataset. This dataset contains information about sleep, lifestyle, and relevant health factors. In this study, thedataset was processed and divided into training and testing subsets. The Random Forest model was trained usingthe training subset with sleep and health related features. The quality of the split in each decision tree wasmeasured using the Gini Index. The model was evaluated using the testing subset to measure its accuracy andclassification performance. The evaluation results showed that the Random Forest model was able to predictsleep disorders with good accuracy. Analysis of class distributions, correlation relationships between features,and visualization by gender provided insights into the factors that influence sleep disorders. This research has thepotential to contribute to the field of health and medicine, especially in the recognition and diagnosis of sleepdisorders.
{"title":"Classification of Sleep Disorders Using Random Forest on Sleep Health and Lifestyle Dataset","authors":"Idfian Azhar Hidayat","doi":"10.20895/dinda.v3i2.1215","DOIUrl":"https://doi.org/10.20895/dinda.v3i2.1215","url":null,"abstract":"This study aims to classify sleep disorders using the Random Forest method on the Sleep Health and Lifestyledataset. This dataset contains information about sleep, lifestyle, and relevant health factors. In this study, thedataset was processed and divided into training and testing subsets. The Random Forest model was trained usingthe training subset with sleep and health related features. The quality of the split in each decision tree wasmeasured using the Gini Index. The model was evaluated using the testing subset to measure its accuracy andclassification performance. The evaluation results showed that the Random Forest model was able to predictsleep disorders with good accuracy. Analysis of class distributions, correlation relationships between features,and visualization by gender provided insights into the factors that influence sleep disorders. This research has thepotential to contribute to the field of health and medicine, especially in the recognition and diagnosis of sleepdisorders.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123110873","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 accurate classification of drugs plays a crucial role in various areas of pharmaceutical research and development. In recent years, machine learning techniques have emerged as powerful tools for drug classification tasks. This paper presents a study on drug classification using machine learning techniques implemented in Python. The objective of this research is to explore the effectiveness of different machine learning algorithms in accurately classifying drugs based on their molecular properties and characteristics. The dataset used in this study consists of a diverse collection of drug compounds with annotated class labels. Several popular machine learning algorithms, including decision trees are implemented and evaluated using Python's extensive libraries such as scikit-learn. The dataset is pre-processed to handle missing values, normalize features, and reduce dimensionality using appropriate techniques. Experimental results demonstrate the performance of each algorithm in terms of accuracy, precision, recall, and F1-score. The findings of this study highlight the potential of machine learning techniques in accurately classifying drugs and provide valuable insights into the selection and optimization of algorithms for drug classification tasks. The Python implementation serves as a practical guide for researchers and practitioners interested in applying machine learning for drug classification purposes.
{"title":"Classification of Drug Types using Decision Tree Algorithm","authors":"Alissiyah Putri, Dani Azka Faz, Felis Tigris Hafizhulloh","doi":"10.20895/dinda.v3i2.1203","DOIUrl":"https://doi.org/10.20895/dinda.v3i2.1203","url":null,"abstract":"The accurate classification of drugs plays a crucial role in various areas of pharmaceutical research and development. In recent years, machine learning techniques have emerged as powerful tools for drug classification tasks. This paper presents a study on drug classification using machine learning techniques implemented in Python. The objective of this research is to explore the effectiveness of different machine learning algorithms in accurately classifying drugs based on their molecular properties and characteristics. The dataset used in this study consists of a diverse collection of drug compounds with annotated class labels. Several popular machine learning algorithms, including decision trees are implemented and evaluated using Python's extensive libraries such as scikit-learn. The dataset is pre-processed to handle missing values, normalize features, and reduce dimensionality using appropriate techniques. Experimental results demonstrate the performance of each algorithm in terms of accuracy, precision, recall, and F1-score. The findings of this study highlight the potential of machine learning techniques in accurately classifying drugs and provide valuable insights into the selection and optimization of algorithms for drug classification tasks. The Python implementation serves as a practical guide for researchers and practitioners interested in applying machine learning for drug classification purposes.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129302899","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 : 2023-07-31DOI: 10.20895/dinda.v3i2.1028
Wahyu Nouval Aghniya, Lutfhi Rakan Nabila, Rizky Ananda Putra
The case of Covid-19 which has been going up and down has forced educational units to think about what learning methods will be applied in the future and also have to pay attention to the responses that students will say. Remember, some students have various arguments, including students who can think maturely in assessing something related to their future interests. This research was conducted with the aim of knowing student perceptions regarding face-to-face learning plans during the pandemic at IT Telkom Purwokerto. In knowing each student's perception, there are several variables that can influence the results of their perception. For the population in this study, all undergraduate students of the IT Telkom Purwokerto Faculty of Informatics in 2021 with judgment/expert sampling as the sampling technique. The instrument used is a questionnaire or questionnaire. The data analysis method used in this research is descriptive quantitative analysis method. Based on the research that has been done, the results show that there were 37 answers (56.1%) who strongly agreed with the question regarding facilities & infrastructure, for Regarding service quality, there were 45 answers (68.2%) who strongly agreed, then for questions regarding student perceptions, there were 17 answers (25.8%) who felt strongly agreed. And obtained results of less than 15% and even up to 0% in each variable for answers that do not agree. So, most students agree with face-to-face learning and attending lectures. Likewise with the parents of each student who agreed to the plan.
{"title":"The Descriptive Analysis of Perceptions of ITTP Data Science Students regarding Face-to-Face Learning Plans","authors":"Wahyu Nouval Aghniya, Lutfhi Rakan Nabila, Rizky Ananda Putra","doi":"10.20895/dinda.v3i2.1028","DOIUrl":"https://doi.org/10.20895/dinda.v3i2.1028","url":null,"abstract":"The case of Covid-19 which has been going up and down has forced educational units to think about what learning methods will be applied in the future and also have to pay attention to the responses that students will say. Remember, some students have various arguments, including students who can think maturely in assessing something related to their future interests. This research was conducted with the aim of knowing student perceptions regarding face-to-face learning plans during the pandemic at IT Telkom Purwokerto. In knowing each student's perception, there are several variables that can influence the results of their perception. For the population in this study, all undergraduate students of the IT Telkom Purwokerto Faculty of Informatics in 2021 with judgment/expert sampling as the sampling technique. The instrument used is a questionnaire or questionnaire. The data analysis method used in this research is descriptive quantitative analysis method. Based on the research that has been done, the results show that there were 37 answers (56.1%) who strongly agreed with the question regarding facilities & infrastructure, for Regarding service quality, there were 45 answers (68.2%) who strongly agreed, then for questions regarding student perceptions, there were 17 answers (25.8%) who felt strongly agreed. And obtained results of less than 15% and even up to 0% in each variable for answers that do not agree. So, most students agree with face-to-face learning and attending lectures. Likewise with the parents of each student who agreed to the plan.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132276605","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 : 2023-07-31DOI: 10.20895/dinda.v3i2.1040
Alvina Tahta Indal Karim, Sudianto Sudianto
Graduating on time is one of the indicators in the achievement and ranking of educational institutions. The achievement of graduating on time in educational institutions is essential to balance incoming and graduating students. The problem that occurs, the attributes for graduating on time have varying weightings, so the determinants of the attributes for passing on time need to be known so that the anticipation of achieving graduation on time can be met. The purpose of this study is to find out the dominant attributes in the prediction of graduating on time for students. The attributes used are credit scores (Semester Credit Units), GPA scores (Grade Point Average), and English scores (TOEFL). The method used is the C4.5 Algorithm which is one of the classification methods in data mining. The data used was 262 data, split randomly with a composition of training and testing data of 80:20. Data is processed using the data mining process by creating decision trees. The decision tree results using the C4.5 Algorithm show that the GPA value is the most influential attribute in predicting a student's graduation time. In addition, predictions based on the decision tree of the C4.5 Algorithm with criterion = 'gini' and max_depth = 5 showed an accuracy result of 77%.
{"title":"Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm","authors":"Alvina Tahta Indal Karim, Sudianto Sudianto","doi":"10.20895/dinda.v3i2.1040","DOIUrl":"https://doi.org/10.20895/dinda.v3i2.1040","url":null,"abstract":"Graduating on time is one of the indicators in the achievement and ranking of educational institutions. The achievement of graduating on time in educational institutions is essential to balance incoming and graduating students. The problem that occurs, the attributes for graduating on time have varying weightings, so the determinants of the attributes for passing on time need to be known so that the anticipation of achieving graduation on time can be met. The purpose of this study is to find out the dominant attributes in the prediction of graduating on time for students. The attributes used are credit scores (Semester Credit Units), GPA scores (Grade Point Average), and English scores (TOEFL). The method used is the C4.5 Algorithm which is one of the classification methods in data mining. The data used was 262 data, split randomly with a composition of training and testing data of 80:20. Data is processed using the data mining process by creating decision trees. The decision tree results using the C4.5 Algorithm show that the GPA value is the most influential attribute in predicting a student's graduation time. In addition, predictions based on the decision tree of the C4.5 Algorithm with criterion = 'gini' and max_depth = 5 showed an accuracy result of 77%.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347955","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 Tangut script, a lesser-explored dead script comprising numerous characters, has received limited attention in deep learning research, particularly in the field of optical character recognition (OCR). Existing OCR studies primarily focus on widely-used characters like Chinese characters and employ deep convolutional neural networks (CNNs) or combinations with recurrent neural networks (RNNs) to enhance accuracy in character recognition. In contrast, this study takes a counterintuitive approach to develop an OCR model specifically for the Tangut script. We utilize shorter layers with slimmer filters using a depthwise separable convolutional neural network (DSCNN) architecture. Furthermore, we preprocess the dataset using a frequency-based transformation, namely the Discrete Cosine Transform (DCT). The results demonstrate successful training of the model, showcasing faster convergence and higher accuracy compared to traditional deep neural networks commonly used in OCR applications.
{"title":"Minimalist DCT-based Depthwise Separable Convolutional Neural Network Approach for Tangut Script","authors":"Agi Prasetiadi, Julian Saputra, Imada Ramadhanti, Asti Dwi Sripamuji, Risa Riski Amalia","doi":"10.20895/dinda.v3i2.1106","DOIUrl":"https://doi.org/10.20895/dinda.v3i2.1106","url":null,"abstract":"The Tangut script, a lesser-explored dead script comprising numerous characters, has received limited attention in deep learning research, particularly in the field of optical character recognition (OCR). Existing OCR studies primarily focus on widely-used characters like Chinese characters and employ deep convolutional neural networks (CNNs) or combinations with recurrent neural networks (RNNs) to enhance accuracy in character recognition. In contrast, this study takes a counterintuitive approach to develop an OCR model specifically for the Tangut script. We utilize shorter layers with slimmer filters using a depthwise separable convolutional neural network (DSCNN) architecture. Furthermore, we preprocess the dataset using a frequency-based transformation, namely the Discrete Cosine Transform (DCT). The results demonstrate successful training of the model, showcasing faster convergence and higher accuracy compared to traditional deep neural networks commonly used in OCR applications.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125903605","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}
In tertiary institutions, students become one of the important parameters in the evaluation of study program organizers. Prediction of student graduation is a special concern to know, early identification for students is needed as an important action. Information processing to predict student graduation is by implementing data mining. The implementation of data mining can be applied if a university, especially a study program, does not yet have an early classification in achieving student graduation on time. The ITTP Information System study program is one of the study programs that does not have an early identification of student graduation on time. Determination of graduation for SI ITTP Study Program students includes GPA, TOEFL scores, and total credits. The purpose of this research is to find out which attributes have the most influence in predicting graduation of ITTP IS Study Program students. The method used in this prediction is by using the classification of the C4.5 Algorithm and Naïve Bayes. The classification is used to determine which attributes have an effect on predicting student graduation on time and to compare the two classification methods. The results obtained are the training set size 70% which has the best accuracy when compared to other training set sizes. Comparing the accuracy between the two methods, it is known that the C4.5 algorithm has good accuracy when training set size is 70% and Naïve Bayes has higher accuracy when training set size is 75%. Decision tree C4.5 interprets that the most influential attribute is the GPA as the root of the decision tree to predict student graduation on time. The research is expected to be used as a reference for the ITTP IS Study Program in formulating student graduation policies on time and as a reference for further researchers in predicting in the same field.
{"title":"Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program)","authors":"Disty Dikriani, Alvina Tahta Indal Karim","doi":"10.20895/dinda.v3i1.782","DOIUrl":"https://doi.org/10.20895/dinda.v3i1.782","url":null,"abstract":"In tertiary institutions, students become one of the important parameters in the evaluation of study program organizers. Prediction of student graduation is a special concern to know, early identification for students is needed as an important action. Information processing to predict student graduation is by implementing data mining. The implementation of data mining can be applied if a university, especially a study program, does not yet have an early classification in achieving student graduation on time. The ITTP Information System study program is one of the study programs that does not have an early identification of student graduation on time. Determination of graduation for SI ITTP Study Program students includes GPA, TOEFL scores, and total credits. The purpose of this research is to find out which attributes have the most influence in predicting graduation of ITTP IS Study Program students. The method used in this prediction is by using the classification of the C4.5 Algorithm and Naïve Bayes. The classification is used to determine which attributes have an effect on predicting student graduation on time and to compare the two classification methods. The results obtained are the training set size 70% which has the best accuracy when compared to other training set sizes. Comparing the accuracy between the two methods, it is known that the C4.5 algorithm has good accuracy when training set size is 70% and Naïve Bayes has higher accuracy when training set size is 75%. Decision tree C4.5 interprets that the most influential attribute is the GPA as the root of the decision tree to predict student graduation on time. The research is expected to be used as a reference for the ITTP IS Study Program in formulating student graduation policies on time and as a reference for further researchers in predicting in the same field.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130391980","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}
These days technology are rapidly increasing and developing in various fields, especially data storage. The information that has been stored in a database usually called a dataset. Covid-19 is a new type of respiratory disease that attacks the respiratory system with rapid transmission, followed by the increasing number of Covid-19 cases that continues to increase every day in all provinces in Indonesia. This study aims to cluster the spread of Covid-19 in every province in Indonesia by using the data that obtained from the website named kaggle with many data variables. The method used in this research is K-Means. From many variables in the data, for this study only 3 variables were taken, which are: Number of Recovery, Number of Deaths, and Number of total Cases in Covid-19 in Indonesia. These 3 variables then will be applied using the K-Means method and formed 3 provincial groups. By using the clustering method and the K-means algorithm, this research can be carried out to find the characteristics of the distribution in each province in Indonesia by looking at the best clusters.
{"title":"Cluster Analysis of Covid-19 in Indonesia Using K-means Method","authors":"Claudia Larasvaty, S. Khomsah, R. Sa","doi":"10.20895/dinda.v3i1.822","DOIUrl":"https://doi.org/10.20895/dinda.v3i1.822","url":null,"abstract":"These days technology are rapidly increasing and developing in various fields, especially data storage. The information that has been stored in a database usually called a dataset. Covid-19 is a new type of respiratory disease that attacks the respiratory system with rapid transmission, followed by the increasing number of Covid-19 cases that continues to increase every day in all provinces in Indonesia. This study aims to cluster the spread of Covid-19 in every province in Indonesia by using the data that obtained from the website named kaggle with many data variables. The method used in this research is K-Means. From many variables in the data, for this study only 3 variables were taken, which are: Number of Recovery, Number of Deaths, and Number of total Cases in Covid-19 in Indonesia. These 3 variables then will be applied using the K-Means method and formed 3 provincial groups. By using the clustering method and the K-means algorithm, this research can be carried out to find the characteristics of the distribution in each province in Indonesia by looking at the best clusters.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134119836","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}
Guruh Dewa Prataba, Aida Devanty Putri, Lalu Moh. Arsal Fadila
Diabetes is one of the four non-communicable diseases that are prioritized because of the sufferer’s number and the increasing prevalence rate. The results of the 2018 Basic Health Research shows an iceberg phenomenon where there are far more people living with diabetes who have not been diagnosed than those who live with diabetes and know their condition. The public's desire to find out in advance the disease that may be suffered on Google opens up opportunities of research in public concern about diabetes. This research with descriptive analysis aims to describe the public's attention to diabetes based on Google Trends data. The results show that the development of public attention in 2018 tends to fluctuate with the highest index on World Diabetes Day. Then there are provinces that need attention with high diabetes prevalence values but still have a low volume of diabetes-related searches. Most topics related to diabetes are about the drugs, causes, and symptoms of diabetes. So it is necessary to socialize diabetes literacy, especially in areas with low public attention
{"title":"Utilization of Google Trends in Knowing Public Attention to Diabetes in Indonesia in 2018","authors":"Guruh Dewa Prataba, Aida Devanty Putri, Lalu Moh. Arsal Fadila","doi":"10.20895/dinda.v3i1.765","DOIUrl":"https://doi.org/10.20895/dinda.v3i1.765","url":null,"abstract":"Diabetes is one of the four non-communicable diseases that are prioritized because of the sufferer’s number and the increasing prevalence rate. The results of the 2018 Basic Health Research shows an iceberg phenomenon where there are far more people living with diabetes who have not been diagnosed than those who live with diabetes and know their condition. The public's desire to find out in advance the disease that may be suffered on Google opens up opportunities of research in public concern about diabetes. This research with descriptive analysis aims to describe the public's attention to diabetes based on Google Trends data. The results show that the development of public attention in 2018 tends to fluctuate with the highest index on World Diabetes Day. Then there are provinces that need attention with high diabetes prevalence values but still have a low volume of diabetes-related searches. Most topics related to diabetes are about the drugs, causes, and symptoms of diabetes. So it is necessary to socialize diabetes literacy, especially in areas with low public attention","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128338477","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}
Muhamad Fernandy, Khevien Rizkhi Darmawan, Daniel Kristiyanto
Database design requires a structured database design, because the database contains data or information. The design method of the database design determines the structure of the designed design. Database design have two methods, either native or object-oriented method. Native database design has two stages, it is Data Flow Dia-gram and Entity Relationship Diagram, where as if it is object-oriented design using use case diagrams. It is ac-companied by class diagrams. Native designs tend to be more unstructured than object-oriented design. Native design focuses more on entity flow while object-oriented design focuses on database design entities. Another ad-vantage of using object-oriented design is the ease of explaining the database design to the client because of the simple design so that it can be easily understood. The method used in this research is prototype and relational algebra. The prototyping method is a technique to collect certain information about the user's information needs appropriately. This research focuses on comparing the native and object-oriented design.
{"title":"Comparison Analysis of Native Database Design with Object Oriented Design","authors":"Muhamad Fernandy, Khevien Rizkhi Darmawan, Daniel Kristiyanto","doi":"10.20895/dinda.v3i1.707","DOIUrl":"https://doi.org/10.20895/dinda.v3i1.707","url":null,"abstract":"Database design requires a structured database design, because the database contains data or information. The design method of the database design determines the structure of the designed design. Database design have two methods, either native or object-oriented method. Native database design has two stages, it is Data Flow Dia-gram and Entity Relationship Diagram, where as if it is object-oriented design using use case diagrams. It is ac-companied by class diagrams. Native designs tend to be more unstructured than object-oriented design. Native design focuses more on entity flow while object-oriented design focuses on database design entities. Another ad-vantage of using object-oriented design is the ease of explaining the database design to the client because of the simple design so that it can be easily understood. The method used in this research is prototype and relational algebra. The prototyping method is a technique to collect certain information about the user's information needs appropriately. This research focuses on comparing the native and object-oriented design.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134645316","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}
Kedisiplinan dan kinerja merupakan faktor penting pada institusi pendidikan. Penilaian disiplin dan kinerja pegawai tersebut dapat dinilai melalui kehadiran. Pada masa pandemi COVID-19 dimana seluruh pegawai diharuskan bekerja dari rumah, namun data kehadiran tetap harus dicatat dengan baik tanpa datang secara fisik ke kampus. Hal ini dapat dilakukan dengan memanfaatkan teknologi komputer dan internet berupa sistem presensi online. Penelitian ini merancang dan membuat prototype sistem presensi online berbasis web bagi pegawai institusi pendidikan untuk memberikan solusi terhadap kendala yang dihadapi membantu melakukan pencatatan kehadiran dari mana saja.
{"title":"Design and Creation of Online Attendance Systems in Web-Based Higher Education Institutions","authors":"Heldiansyah Heldiansyah, Muchtar Salim, Rustaniah Rustaniah","doi":"10.20895/dinda.v3i1.771","DOIUrl":"https://doi.org/10.20895/dinda.v3i1.771","url":null,"abstract":"\u0000Kedisiplinan dan kinerja merupakan faktor penting pada institusi pendidikan. Penilaian disiplin dan kinerja pegawai tersebut dapat dinilai melalui kehadiran. Pada masa pandemi COVID-19 dimana seluruh pegawai diharuskan bekerja dari rumah, namun data kehadiran tetap harus dicatat dengan baik tanpa datang secara fisik ke kampus. Hal ini dapat dilakukan dengan memanfaatkan teknologi komputer dan internet berupa sistem presensi online. Penelitian ini merancang dan membuat prototype sistem presensi online berbasis web bagi pegawai institusi pendidikan untuk memberikan solusi terhadap kendala yang dihadapi membantu melakukan pencatatan kehadiran dari mana saja. \u0000","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121738261","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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