Journal of Dinda : Data Science, Information Technology, and Data Analytics
Journal of Dinda : Data Science, Information Technology, and Data Analytics as a publication media for research results in the fields of Data Science, Information Technology, and Data Analytics, but not implicitly limited. Published 2 times a year in February and August. The journal is managed by the Data Engineering Research Group, Faculty of Informatics, Telkom Purwokerto Institute of Technology. Journal of Dinda is a medium for scientific studies resulting from research, thinking, and critical-analytic studies regarding Data Science, Informatics, and Information Technology. This journal is expected to be a place to foster enthusiasm in education, research, and community service which continues to develop into supporting references for academics. FOCUS AND SCOPE Journal of Dinda : Data Science, Information Technology, and Data Analytics receive scientific articles with the scope of research on: Machine Learning, Deep Learning, Artificial Intelligence, Databases, Statistics, Optimization, Natural Language Processing, Big Data and Cloud Computing, Bioinformatics, Computer Vision, Speech Processing, Information Theory and Models, Data Mining, Mathematical, Probabilistic and Statical Theories, Machine Learning Theories, Models and Systems, Social Science, Information Technology
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<![CDATA[Comparison Analysis of Native Database Design with Object Oriented Design ]]>
<![CDATA[Muhamad Fernandy]]>;
<![CDATA[Khevien Rizkhi Darmawan]]>;
<![CDATA[Daniel Yeri Kristiyanto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.707
<![CDATA[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.]]>
<![CDATA[Prediction of Covid-19 Cases in Central Java using the Autoregressive (AR) Method ]]>
<![CDATA[Tangguh Widodo]]>;
<![CDATA[Siti Maghfiroh]]>;
<![CDATA[Surya Haganta Brema Ginting]]>;
<![CDATA[Alif Aryaputra]]>;
<![CDATA[Sudianto Sudianto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.740
<![CDATA[Since the beginning of the Covid-19 case in Indonesia in March 2020, more than 6 million confirmed cases had been confirmed. The rapid development of this case can be accessed through the covid19.go.id page. In Central Java province, confirmed cases as of July 6, 2022, reached 628,393 people, with the number of recovered patients reaching 594,783 people and the number of patients dying as many as 33,215 people. With this data, a prediction is needed to help the government anticipate an increase in Covid-19 cases in Central Java Province. This study aims to create a forecasting model using the Autoregressive (AR) method by optimizing the function parameters. Then Mean Squared Error (MSE) to analyze the results of forecasting data errors. The results are the best parameter functions on AR (30) with the smallest MSE. Furthermore, predictions are made from July 1 to August 30, 2022, showing an increase in cases]]>
<![CDATA[Diabetes Diagnostic Expert System using Website-Based Forward Chaining Method ]]>
<![CDATA[Tiara Khumaira Putri]]>;
<![CDATA[Mahda Laina Arnumukti]]>;
<![CDATA[Khusnul Khatimah]]>;
<![CDATA[Egidya Zalsabila]]>;
<![CDATA[Sudianto Sudianto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.752
<![CDATA[Diabetes is a chronic disease. The World Health Organization predicts that Indonesia's number of diabetic patients will continue to increase significantly to 16.7 million in 2045. As early prevention, early diagnosis is needed to anticipate more severe diabetes. This study aims to build an expert system for detecting diabetes using a web-based forward chaining method. The expert system is built by collecting indications from experts by collecting facts using the forward chaining method. Furthermore, judging by the unhealthy lifestyle of many people who consult with hospitals or health workers. From the results obtained, the system can work well based on knowledge from experts]]>
<![CDATA[An Expert System for Diagnosing the Impact of Traffic Accidents using the Forward Chaining Method ]]>
<![CDATA[Akbar Maulana Yusuf]]>;
<![CDATA[Jonathan Indra Chelidivano]]>;
<![CDATA[Tavany Amalia Rizky]]>;
<![CDATA[Yanuar Sabikhi]]>;
<![CDATA[Sudianto Sudianto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.767
<![CDATA[Unexpected events that we often hear about are traffic accidents caused by many factors. Accidents also cause impacts in terms of health. This study aims to provide information regarding the effects of traffic accidents in terms of health based on some visible symptoms that emerged from the victim's body at the scene using an expert system. The Expert System is designed on a website-based application. The forward chaining method is used to get a conclusion based on the facts. The results of this research users gain knowledge about the impact of traffic accidents and the diagnosis on the victim's body that is close to the knowledge of experts with accuracy 87.5%. The website is designed to be used as a guide for users to be able to provide appropriate first aid to accident victims.]]>
<![CDATA[Design and Creation of Online Attendance Systems in Web-Based Higher Education Institutions ]]>
<![CDATA[Heldiansyah Heldiansyah]]>;
<![CDATA[Muchtar Salim]]>;
<![CDATA[Rustaniah Rustaniah]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.771
<![CDATA[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.]]>
<![CDATA[Utilization of Google Trends in Knowing Public Attention to Diabetes in Indonesia in 2018 ]]>
<![CDATA[Guruh Dewa Prataba]]>;
<![CDATA[Aida Devanty Putri]]>;
<![CDATA[Lalu Moh. Arsal Fadila]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.765
<![CDATA[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]]>
<![CDATA[Cluster Analysis of Covid-19 in Indonesia Using K-means Method ]]>
<![CDATA[Claudia Larasvaty]]>;
<![CDATA[Siti Khomsah]]>;
<![CDATA[Rona Nisa SA]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.822
<![CDATA[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.]]>
<![CDATA[Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program) ]]>
<![CDATA[Disty Dikriani]]>;
<![CDATA[Alvina Tahta Indal Karim]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i1.782
<![CDATA[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.]]>
