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[The Descriptive Analysis of Perceptions of ITTP Data Science Students regarding Face-to-Face Learning Plans ]]>
<![CDATA[Wahyu Nouval Aghniya]]>;
<![CDATA[Lutfhi Rakan Nabila]]>;
<![CDATA[Rizky Ananda Putra]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i2.1028
<![CDATA[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.]]>
<![CDATA[Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm ]]>
<![CDATA[Alvina Tahta Indal Karim]]>;
<![CDATA[Sudianto Sudianto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i2.1040
<![CDATA[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%.]]>
<![CDATA[Minimalist DCT-based Depthwise Separable Convolutional Neural Network Approach for Tangut Script ]]>
<![CDATA[Agi Prasetiadi]]>;
<![CDATA[Julian Saputra]]>;
<![CDATA[Imada Ramadhanti]]>;
<![CDATA[Asti Dwi Sripamuji]]>;
<![CDATA[Risa Riski Amalia]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i2.1106
<![CDATA[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.]]>
<![CDATA[Classification of Drug Types using Decision Tree Algorithm ]]>
<![CDATA[Alissiyah Putri]]>;
<![CDATA[Dani Azka Faz]]>;
<![CDATA[Felis Tigris Hafizhulloh]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i2.1203
<![CDATA[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.]]>
<![CDATA[Classification of Sleep Disorders Using Random Forest on Sleep Health and Lifestyle Dataset ]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v3i2.1215
<![CDATA[This study aims to classify sleep disorders using 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 split quality 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 could accurately predict sleep disorders. Analysis of class distributions, correlation relationships between features,and visualization by gender provided insights into the factors that influence sleep disorders. This research can potentially contribute to the field of health and medicine, especially in the recognition and diagnosis of sleepdisorders.]]>
