Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Random Forest and Double Random Forest Models from View Points of Their Interpretability

Authors:
raw_author_name: Adlina Khairunnisa
raw_affiliation_string: Faculty of Mathematics and Natural Sciences, Institut Pertanian Bogor, Indonesia
raw_author_name: Khairil Anwar Notodiputro
raw_affiliation_string: Faculty of Mathematics and Natural Sciences, Institut Pertanian Bogor, Indonesia
raw_author_name: Bagus Sartono
raw_affiliation_string: Faculty of Mathematics and Natural Sciences, Institut Pertanian Bogor, Indonesia

Abstract: Purpose: This study aims to compare the performance of ensemble trees such as Random Forest (RF) and Double Random Forest (DRF) from view points of interpretability of the models. Both models have strong predictive performance but the inner working of the models is not human understandable. Model interpretability is required to explain the relationship between the predictors and the response. We apply association rules to simplify the essence of the models.Methods: This study compares interpretability of RF and DRF using association rules. Each decision tree formed from each model is converted into if-then rules by following the path from root node to leaf nodes. The data was selected in such a way that they were underfit data. This is due to the fact that DRF has been shown by other researchers to overcome the underfitting problem faced by RF. A Simulation study has been conducted to evaluate the extracted rules from RF and DRF. The rules extracted from both models are compared in terms of model interpretability based on support and confidence values. Association rules may also be applied to identify the characteristics of poor people who are working in Yogyakarta.Result: The simulation results revealed that the interpretability of DRF outperformed RF especially in the case of modelling underfit data. On the other hand, using empirical data we have been able to characterize the profile of poor people who are working in Yogyakarta based on the most frequent rules.Novelty: Research on interpretable DRF is still rare, especially the interpretation model using association rules. Previous studies focused only on interpreting the random forest model using association rules. In this study, the rules extracted from the random forest and double random forest models are compared based on the quality of the rules extracted.
Knowledge Discovery from Confusion Matrix of Pruned CART in Imbalanced Microarray Data Ovarian Cancer Classification

Authors:
raw_author_name: Ni Kadek Emik Sapitri
raw_affiliation_string: Department of Mathematics, Universitas Brawijaya, Indonesia
raw_author_name: Umu Sa’adah
raw_affiliation_string: Department of Mathematics, Universitas Brawijaya, Indonesia
raw_author_name: Nur Shofianah
raw_affiliation_string: Department of Mathematics, Universitas Brawijaya, Indonesia

Abstract: Purpose: The results of microarray data analysis is important in cancer diagnosis, especially in early stages asymptomatic cancers like ovarian cancer. One of the challenges in analyzing microarray data is the problem of imbalanced data. Unfortunately, research that carries out cancer classification from microarray data often ignores this challenge, so that it doesn’t use appropriate evaluation metrics. It makes the results biased towards the majority class. This study uses a popular evaluation metric “accuracy” and an evaluation metric that is suitable for imbalanced data “balanced accuracy (BA)” to gain information from the confusion matrix regarding accuracy and BA values in case of ovarian cancer classification.Methods: This study use Classification and Regression Tree (CART) as the classifier. CART optimized by pruning. CART optimal is determined from the results of CART complexity analysis and confusion matrix.Results: The confusion matrix and CART interpretations in this research show that CART with low complexity is still able to predict majority class respondents well. However, when none of the data in the minority class was classified correctly, the accuracy value was still quite high, namely 86.97% and 88.03% respectively at the training and testing stages, while the BA value at both stages was only 50%.Novelty: It is very important to ensure that the evaluation metrics used match the characteristics of the data being processed. This research illustrate the difference between accuracy and BA. It concluded that that classification of an imbalanced dataset without doing resampling can use BA as evaluation metric, because based on the results, BA is more fairly to both classes.
YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Authors:
raw_author_name: Muhammad Ridho Dewanto
raw_affiliation_string: Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
raw_author_name: Mifta Nur Farid
raw_affiliation_string: Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
raw_author_name: Muhammad Abby Rafdi Syah
raw_affiliation_string: Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
raw_author_name: Aji Akbar Firdaus
raw_affiliation_string: Department of Engineering, Universitas Airlangga, Surabaya, Indonesia
raw_author_name: Hamzah Arof
raw_affiliation_string: Department of Electrical Engineering, University of Malaya, Malaysia

Abstract: Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.
Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data

Authors:
raw_author_name: Aji Hamim Wigena
raw_affiliation_string: Faculty of Mathematics and Natural Sciences, Institut Pertanian Bogor, Indonesia
raw_author_name: Kusman Sadik
raw_affiliation_string: Faculty of Mathematics and Natural Sciences, Institut Pertanian Bogor, Indonesia
raw_author_name: Efriwati Efriwati
raw_affiliation_string: National Research and Innovation Agency, Indonesia

Abstract: Purpose: Numerous factors can affect the duration of COVID-19 recovery. One method involves utilizing natural herbal medication. This study seeks to determine the variables influencing the duration of COVID-19 recovery and to compare discriminant analysis and support vector machine models using COVID-19 patient data from West Sumatra.Methods: Two data mining methods, Discriminant Analysis and Support Vector Machine with different types of kernels (linear, polynomial, and radial basis function), were employed to categorize the time of COVID-19 recovery in this work. The study utilized 428 data points, with 75% allocated for training data and 25% for testing data. The independent factors were evaluated by determining the selection variables' information value (IV) to gauge their influence on the dependent variable. Data resampling techniques were employed to tackle the problem of data imbalance. This study employs data resampling techniques, including undersampling, oversampling, and SMOTE. The balancing accuracy of Discriminant Analysis and Support Vector Machine was examined.Result: The Discriminant Analysis with SMOTE achieved a balanced accuracy of 66.50%, outperforming the linear kernel Support Vector Machine with SMOTE, which had a balanced accuracy of 63.20% in this dataset.Novelty: This study assessed the novelty, originality, and value by comparing Discriminant Analysis and SVM algorithms with categorical and continuous independent variables. This research explores techniques for managing imbalanced data using undersampling, oversampling, and SMOTE, with variable selection based on information value assessment.
Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree

Authors:
raw_author_name: Vinna Rahmayanti Setyaning Nastiti
raw_affiliation_string: Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
raw_author_name: Christian Sri Kusuma Aditya
raw_affiliation_string: Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia

Abstract: Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.
The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time

Authors:
raw_author_name: Aaqila Dhiyaanisafa Goenawan
raw_affiliation_string: Department of Computer Science, STMIK ESQ, Indonesia

Abstract: Purpose: Comparing 2 models or prototype programs which can recognize Indonesian Sign Language System or Sistem Isyarat Bahasa Indonesia (SIBI) fonts from hand gesture and translate it’s into writing Messages in real-time.Methods: After selecting datasets and reprocessed by the researcher into 1 dataset, which are a combination of several sign image datasets of the SIBI letters images available on the Kaggle website, the dataset is converted into landmarks. The landmarks are divided into 26 sign classes and preprocessed to a total of 19,826 rows of data, and then divided into 67% training data and 33% test data. Next, both K-NN and Random Forest algorithm are implemented into different program and get tested into 2 different tests, model evaluation and real-time. At the end, the result is compared to see the increase of accuracy level of both K-Nearest Neighbors (K-NN) and Random Forest algorithm.Result: The constructed and trained model is then evaluated and the results of Precision, Recall, Accuracy, and F1-Score are 99.88% using the Random Forest algorithm. The results of real-time program testing with the K-Nearest Neighbors algorithm get higher results, where the average accuracy value reaches 99%.Novelty: From the result shows that the model built with the Random Forest algorithm is superior, but the K-Nearest Neighbors algorithm is better in real-time testing. Therefore, image data and its diversity should be increased, in order to improve recognition accuracy. The program could be enhanced by adding a function where the program can recognize hand gesture, not only one or two hands but also can recognize a hand gesture with movements so the program can recognize static and dynamic letter (required hands movement).
Analysis Impact of Rapid Application Development Method on Development Cycle and User Satisfaction: A Case Study on Web-Based Registration Service

Authors:
raw_author_name: Imam Riadi
raw_affiliation_string: Department of Information System, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
raw_author_name: Anton Yudhana
raw_affiliation_string: Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

Abstract: Purpose: This research was conducted to respond to obstacles and inefficiencies in the new student registration system at RA Plus Rabbani. Currently, the conventional method of using physical documents for registration is vulnerable to damage and data loss. Therefore, the proposed solution is implementing a website-based online registration system using the Rapid Application Development (RAD) method. This aims to simplify the process, increase accessibility for prospective students, and reduce the costs and time required.Methods: This research commenced by identifying constraints within the conventional student registration system at RA Plus Rabbani through observations and interviews. The development, following the RAD methodology, involved testing with PHPUnit and Blackbox Testing to ensure the functionality of the system aligned with specifications. In addition, usability evaluation was conducted based on the ISO 9126 standard.Result: The research results show that testing on MVC indicated a 100% success rate for each architectural feature. Referring to expectations with a “valid” conclusion on functionality using Blackbox testing, based on ISO 9126 percentage displayed, it is known that the criterion with the most significant value is the understandability characteristic with a value of 83%. Novelty: This research makes a significant contribution by improving student registration services at RA Plus Rabbani through the implementation of various testing techniques, following the research flow offered by RAD. The study also provides substantial references for further research in web-based system development.
Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF

Authors:
raw_author_name: Khurotul Aeni
raw_affiliation_string: Department of Informatics, Faculty of Science and Technology, Universitas Peradaban, Indonesia
raw_author_name: Nurul Mega Saraswati
raw_affiliation_string: Department of Informatics, Faculty of Science and Technology, Universitas Peradaban, Indonesia

Abstract: Purpose: Freedom in social media gives rise to the possibility of disturbing users through the sentences they send, which is limited by the Electronic Information and Transactions Law (UU ITE). This research aims to find an effective method for classifying hate speech text data, especially in Indonesian, with many categories expected to minimize this case.Methods: This study used 1.000 data from Twitter with five labels, including religion, race, physical, gender and other (invective or slander). The process started with several steps of preprocessing, data transformation using TF-IDF-ICSρF term weighting and data mining using an Improved KNN algorithm. Then, the results were compared with the TF-IDF and KNN methods to evaluate the differences.Result: Using TF-IDF-ICSρF and Improved KNN algorithms gets an average accuracy value of 88.11%, 17.81% higher compared with the same data and parameters to the K-Nearest Neighbor and TF-IDF algorithms, which get results of 70.30%.Novelty: Based on the comparison results, TF-IDF-ICSρF and Improved KNN methods can effectively classify hate speech sentences that have many labels with fairly good accuracy.
Enhancing Mathematical Disposition in Geometry Instruction : The Role of E-Comics in Realistic Mathematics Education

Authors:
raw_author_name: Trisna Nugraha
raw_affiliation_string: IKIP Siliwangi
raw_author_name: Asep Samsudin
raw_affiliation_string: IKIP Siliwangi

Abstract: &lt;p dir="ltr"&gt;&lt;span&gt;Success in learning mathematics can be measured through five aspects called the five strands of mathematical proficiency. These five aspects need to be taught fairly, resulting in a change in students' mathematical attitudes where this aspect is still minimally looked at and researched. Therefore, the research aims to analyze changes in students' mathematical dispositions by reviewing the role of e-comics in Realistic Mathematics Education (RME). This context-based mathematics learning approach is assumed to provide meaningful learning for students. This mixed-methods research with a sequential explanatory research design was carried out on 30 elementary school students by combining quantitative data collection and analysis techniques with qualitative data support. The research showed that using e-comics in RME learning significantly improved students' mathematical dispositions with a large effect size (Cohen’s effect size = 0,98). In a qualitative review involving data triangulation, evidence was found that using e-comics in RME learning has a role in facilitating the development of each student indicator, as evidenced in the high students’ enthusiasm or response in learning both in observations, questionnaires, and documentation studies. In this way, it is hoped that this innovation can be researched and developed further on a more diverse scale and content. In addition, this research provides further opportunities for research and teacher training related to developing RME learning designs and e-comics in learning.&lt;/span&gt;&lt;/p&gt;&lt;div&gt;&lt;span&gt;&lt;br /&gt;&lt;/span&gt;&lt;/div&gt;
Empowering Future Teachers: The Role of Student Organizations in Enhancing Public Speaking and Teaching Skills

Authors:
raw_author_name: Nur Amalia
raw_affiliation_string: Universitas Muhammadiyah Surakarta

Abstract: &lt;span id="docs-internal-guid-8c3491fc-7fff-0197-f738-be37e25a0884"&gt;&lt;span&gt;Recognizing the pivotal role of teachers in shaping education, extensive research has been conducted to assess their effectiveness. Among the prominent challenges faced by many educators is mastering public speaking, a crucial skill for effective classroom instruction. This study delves into whether participation in student groups can augment aspiring elementary school teachers' public speaking and teaching competencies while emphasizing the significance of public speaking skills within the teaching profession. Employing descriptive qualitative methods such as interviews, observations, and document analysis, the research elucidates the profound impact of public speaking proficiency on teaching effectiveness. The study encompasses four key stages: preparation, implementation, analysis, and reporting. Twelve students participated as respondents, focusing on those actively engaged in group activities. Additionally, six lecturers from the Primary School Teacher Education Study Program, Muhammadiyah University of Surakarta, knowledgeable about the fundamental skill requirements for aspiring teachers and the program director, served as supplementary subjects. The findings suggest that engagement in student groups can significantly enhance aspiring teachers' public speaking skills, thereby better equipping them for successful careers in education.&lt;/span&gt;&lt;/span&gt;
The Relationship Between Training Methods And Student Activity in Extracurricular Arts SMKN 5 Padang

Authors:
raw_author_name: Jamaris Jamaris
raw_affiliation_string: Universitas Negeri Padang

Abstract: This research was hampered by the low student activity on extracurricular arts, which was allegedly due to the inadequacy of the practice methods used. The aim is to look at the methods of practice, student activity and see the relationship between the method of practice and student activity on extracurricular arts. The research method uses a quantitative approach to the type of correlational research. The population of this research is all that follows the activity with sampling using the Cluster Random Sampling technique. Data gathering techniques using lifting. Data analysis techniques using percentage formula and Product Moment formula.The results of the study showed that; the methods of training used by trainers in extracurricular still belong to the less appropriate category; student activity also belonged to low; and there was a significant relationship between the method of training and student activity in the extra-curricular arts.Since the extracurricular activities of the arts are very beneficial to the students, there is a need for the efforts of the school to pay attention and enhance the construction of such extracursual activities. It is recommended to the extra-curricular art trainers, to increase and maximize the use of methods of training in extracural activities so as to a high level of activity.
The Relationship Between Parenting Styles And Student Learning Discipline At MDTA Nurul Amal, Sungai Aur District, West Pasaman Regency

Authors:
raw_author_name: Solfema Solfema
raw_affiliation_string: Universitas Negeri Padang

Abstract: The inspiration behind this study stems from the insufficient level of discipline observed in students' learning. The primary objective is to assess the implementation of democratic parenting patterns among the parents of students, evaluate the extent of students' learning discipline, and investigate the correlation between parents' parenting styles and students' learning discipline at MDTA Nurul Amal Muara Tapus, Sungai Aur District, West Pasaman Regency. Employing a quantitative approach with a correlational research design, the study encompasses a population of 47 individuals from classes III and IV enrolled as students at MDTA Nurul Amal. A sample representing 80% of the population is selected using the Stratified Random Sampling technique. Data is collected through the administration of questionnaires, and for analysis, the percentage and product-moment formulas are employed. The findings indicate a low level of democratic parenting among the parents of students, a low level of students' learning discipline, and a significant correlation between parents' democratic parenting style and students' learning discipline of santri at MDTA Nurul Amal Muara Tapus, Sungai Aur District, West Pasaman Regency. The researcher's suggestions in this study to parents are expected to increase the application of democratic parenting patterns to support children in carrying out high learning disciplineKeywords: Parenting style, Democratic, Discipline