Reviewerly/exHarmony huggingface dataset:
Gender Representation in Literature: Analysis of Characters' Physical Descriptions

This study employs Natural Language Processing (NLP) techniques to quantitatively analyze the descriptions of male and female body parts in Portuguese literature. We investigate these descriptions' frequency, specificity, and objectification by examining a corpus of literary works. The results indicate distinct differences in how male and female bodies are portrayed, revealing evidence of gender bias in the choice of specific descriptors for body parts. This research contributes to the ongoing discourse surrounding gender representation in literature, shedding light on the potential biases in textual descriptions. Furthermore, it underscores the significance of NLP techniques in uncovering patterns within literary texts, providing valuable insights into data mining. Through this analysis, we deepen our understanding of gender dynamics within literary works and foster critical discussions on representation in literature.
Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition

Abstract Current skeleton‐based action recognition methods usually assume the input skeleton is complete and noise‐free. However, it is inevitable that the captured skeletons are incomplete due to occlusions or noisy due to changes in the environment. When dealing with these data, even State Of The Art (SOTA) recognition backbones experience significant degradation in recognition accuracy. Though a few methods have been proposed to address this issue, they still lack flexibility, efficiency and interpretability. In this work, an overcomplete Graph Convolutional Denoising Autoencoder (GCDAE) is proposed which can act as a flexible preprocessing module for pretrained recognition backbones and improve their robustness. Taking advantages of the overcomplete and fully graph convolutional structure, GCDAE is able to rectify noisy joints while keeping information of unspoiled details efficiently. On two large scale skeleton datasets NTU RGB+D 60 and 120, the introducing of GCDAE brings significant robustness improvements to SOTA backbones towards different types of noises.
Faculty Publishing Practices: Insights and Benefits for Liaison Librarianship

This study reports on the publishing preferences of engineering faculty at a research institution. Faculty publications indexed in Scopus database over a ten-year period (2012-2021) were analyzed to identify publication types, publisher preference, and changes in publisher preferences observed over the period of this study. The findings expand the liaison librarians' knowledge of the liaison area, offer ideas for future outreach and engagement activities, and provide additional data for collection development strategies.
Implementasi Digital Library untuk Meningkatkan Minat Baca Bagi Siswa SMPN 2 Babelan

Reading interest is an important aspect of middle school students' intellectual and academic development. However, in some schools, including SMPN 2 Babelan, students’ reading interests tend to decline. In addition, the development of information and communication technology (ICT) in the current era is accelerating significantly, with the use of IT as a solution to enhance students' interest in reading, especially Deploy digital library. This service activity aims to establish a digital library at SMPN 2 Babelan to enhance students' interest in reading. Digital library is an information technology-based platform that provides easy and quick access to various learning resources in digital form. The data collection methods used were through interviews, observations, and diaries. Research results show that the digital library has begun to increase students' interest in reading at SMP Negeri 2 Babelan, because it cannot be denied that students now use mobile phones more than books. Thanks to the digital library it will be easier for students to read it whenever and wherever they want. Therefore, it is hoped that using digital libraries to increase students' interest in reading will be more effective and efficient.
Natural Language Processing-based Method for Clustering and Analysis of Movie Reviews and Classification by Genre

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.
The Success Factors in Measuring the Millennial Generation’s Energy-Saving Behavior Toward the Smart Campus

The millennial generation has a pivotal role in leading the industrial digital revolution. Energy-saving behavior and millennials’ awareness of energy consumption for educational context become crucial in performing a smart campus. This study tries to identify the success factors in measuring the millennial generation’s energy-saving Behavior toward the smart campus. The measurement model considers two significant constructs, including energy-saving attitudes with energy-saving education (organizational saving climate); energy-saving education and environment knowledge (personal saving climate); and energy-saving information publicity as sub-indicators, and construct energy-saving Behavior viz sub-indicators Behavior regarding energy and behavior control. In order to determine the preference level of each indicator and sub-indicator, the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach was executed by disseminating the questionnaire to 100 respondents from energy practitioners, students, and academicians in Indonesia. The calculation reveals that the energy-saving behavior construct has a higher priority value (0.94) than the energy-saving attitude (0.06). Meanwhile, energy-saving education and environment knowledge (personal saving climate) have been analyzed at the cutting-edge sub-indicator, followed by energy-saving information publicity and education (organizational saving climate). In addition, the sub-indicator for behaviors regarding energy becomes more demanding compared to behavioral control. As a novelty, the priority analysis of this Model aids the management of the campus and government in developing smart campus policies and governance. This Model can be used as a guideline for the management level to execute the smart campus practices. Thus, the effectiveness and optimization of smart campus transformation can be cultivated and accelerated. Besides, the potential coming of risks can be avoidable.
Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning &amp; Transfer Learning Technique

White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction.
Child assistance training: Effects of training on parents’ knowledge in children’s language and numeracy development

Preschool-age children show rapid development in various aspects of their development, including numeracy and language development. Various and appropriate stimulations are expected to optimize their development. Parents as the closest support system for preschool children have a very significant role, especially in helping children optimize their numeracy and language skills. Parents can play an optimal role if they understand children's numeracy and language development. In this community service program, parents are given training on "Optimizing the Learning Environment for Numeracy and Language in Preschool Children" which aims to increase parents' understanding of children's numeracy and language development and efforts to optimize it. The participants were 31 parents and Posyandu cadres in Jatimukti, Sumedang, West Java. Parents' understanding was measured through a pre-test questionnaire administered at the beginning of the training and a post-test questionnaire at the end of the training. The results showed a positive effect of the training on parents' understanding of children's numeracy and language development. Therefore, the results of this training recommend further trainings aimed at improving parents' understanding of language and numeracy development in preschool childrens.
Pengaruh Model Pembelajaran MIKIR Berbasis Storytelling terhadap Keterampilan Berkolaborasi Siswa Kelas V

This study aims to determine the effect of using storytelling-based experience, interaction, communication, and reflection (thinking) learning models on the collaboration skills of fifth grade students in the Learning Studio, North Gombak, Malaysia. The category of this research is Pre-Experimental research using the One group Pretest design. t-Posttest Design. The sample of this study was the fifth grade students of the North Gombak Learning Studio, Malaysia, totaling 8 students. The data were collected using tests and observations. Meanwhile, the research data were analyzed descriptively and inferentially with the t test, based on the average posttest results obtained. the posttest average results of students' collaborative skills after the implementation of the storytelling-based thinking learning model was greater than the average pretest result before the implementation of the storytelling-based thinking learning model, the average pretest obtained before the implementation of the storytelling-based thinking learning model was 63.75 after being given activities with the treatment of the storytelling-based Thinking learning model so that the posttest average score was 85.00. It can be concluded that the form of storytelling-based Thinking learning that is applied to Indonesian language learning has an effect on the collaboration skills of class V students at the Learning Studio, North Gombak, Malaysia
Pengaruh Kualitas Pelayanan Perpustakaan Digital terhadap Peningkatan Minat Baca Mahasiswa pada Dinas Perpustakaan Daerah Kota Medan

This research was aimed to examine empirically the effect of service quality digital library on Reading interest. The analysis used an independent variable (Quality Service Digital Library) and dependent variable (Reading Interest). The sample was conducted by purposive sampling method. This study location at perpustakaan PEMKO Medan Jalan Iskandar Muda Collecting data was conducted by a questionnaire distributed directly to responden and visitor as much 30 questionnaires. The statistical method used was multiplied analysis linear regression, with hypotheses testing of statistic t-tests. The result of this research showed that There is a significant positive influence between the quality of digital library services on students' reading interest in the regional library of Medan City (Library and Archives Service). Obtained based on the significance value that by partial test the significance value of t is 0.001 &lt; than 0.05 this value shows the t value is smaller than the significance value of 0.05.
DESIGNING INTERFACE OF MARKETPLACE BUILD ID MERCHANT ARCHITECT USING TASK-CENTERED SYSTEM DESIGN

Abstract: The advancement of information technology is progressively exerting significant impact across diverse aspects of life, including business. PT Semen Baturaja Tbk is developing a marketplace platform called Build Id to meet community's needs for high-quality construction materials and services, also aims to enhance company's product sales and brand recognition. A system with quality provides ease of use in order to achieve customer satisfaction. Build Id merchant for architects website requires user-friendly design recommendations to increase the user experience when it gets distributed to the market. To achieve this goal, Task-Centered System Design (TCSD) method is being used to focused the design process. TCSD is a method in Human Computer Interaction (HCI) used to identify task and user requirements. The method consists of 4 stages, namely, identification, user-centered requirements analysis, design through scenarios, and walkthrough evaluation. In this study, identification was carried out by conducting system observations and interviews with Build Id team at Digital Marketing Unit of PT Semen Baturaja Tbk and architect as the prospective system users. The interface design results were evaluated using System Usability Scale (SUS) and received final score of 69.615, indicating that the interface design made using TCSD method is feasible for users to use.&#x0D; &#x0D; Keywords: Interface; System Usability Scale; Task-Centered System Design&#x0D; &#x0D; &#x0D; Abstrak: Perkembangan teknologi informasi kian berpengaruh besar dalam berbagai bidang kehidupan, termasuk di dunia bisnis. PT Semen Baturaja Tbk mengembangkan suatu sistem dalam bentuk marketplace dengan nama Build Id, dengan tujuan untuk memenuhi kebutuhan bahan bangunan dan jasa konstruksi yang berkualitas bagi masyarakat, serta memperluas penjualan produk dan branding merk dari PT Semen Baturaja Tbk ini sendiri. Sistem yang berkualitas memberikan kemudahan dan kenyamanan dalam penggunaannya demi mewujudkan kepuasan pelanggan. Build Id merchant untuk arsitek memerlukan rekomendasi desain atau prototype user interface yang user friendly demi meningkatkan pengalaman pengguna ketika didistribusikan ke pasaran nantinya. Metode Task-Centered System Design (TCSD) digunakan untuk membantu proses perancangan lebih terarah. TCSD adalah metode dalam Human Computer Interaction (HCI) yang digunakan untuk mengidentifikasi kebutuhan task dan pengguna. Metode TCSD terdiri dari 4 tahap yaitu, identification, user-centered requirements analysis, design through scenario, dan walkthrough evaluation. Identifikasi dalam penelitian ini dilakukan dengan observasi dan wawancara bersama tim Build Id dan calon pengguna sistem yaitu arsitek. Hasil dari rancangan interface kemudian dievaluasi menggunakan System Usability Scale (SUS) dengan hasil skor akhir 69,615, menunjukkan rancangan interface yang dibuat dengan metode TCSD ini layak untuk digunakan pengguna.&#x0D; &#x0D; Kata kunci: Interface; System Usability Scale; Task-Centered System Design
Efficient Medical Image Compression Based on Wavelet Transform and Modified Gray Wolf Optimization

The use of medical images in diagnostic procedures is increasing, leadning to a significant rise in the memory and bandwidth requirements for preserving and transmitting these images. To address this issue, image compression techniques have garnered significant attention. These techniques are capable of reducing the data size necessary to represent an image, allowing for more efficient utilization of storage space and communication bandwidth by eliminating unnecessary information. Numerous research directions have focused on compressing medical images, but past approaches have been time-consuming and risked information loss. To trounce these limitations, this paper introduces an effiective method for reducing the size of medical images in telemedicine applications. The method utilizes Integer Wavelet Transform (IWT) and sophisticated algorithm. Primarily, input images undergo pre-processing with a circular median filter to eliminate noise and improve image quality. Subsequently, the pre-processed images are divided into multiple sub bands using IWT.Then, these sub bands are furhter divided into n X n non-overlapping matrices, and optimal coefficients are chosen by employing a modified grey wolf optimizer algorithm. Finally, the selected coefficients are encoded using Huffman coding for transmission. During decompression, the reverse process of image compression is applied. The introduced method is tested on various medical images, and the findings demonstrate its superior performance compared to previous methods, generating visually similar images with a smaller data size.
COMPARISON OF NBC, SVM, KNN CLASSIFICATION RESULTS IN SENTIMENT ANALYSIS OF MOBILE JKN

Abstract: The JKN Mobile application is a mobile application created to facilitate healthcare administration in Indonesia since 2017. The application has been downloaded by over 10 million users and has received 484,000 diverse reviews, including positive, negative, and neutral feedback. The average rating given by users is 4.5 out of 5 stars. This research aims to perform sentiment analysis on user reviews found in the Google Play Store review column. The methods used for sentiment analysis are Naive Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM). The test results show that with a 10% test data and 90% training data proportion, the SVM method achieves the highest accuracy of 95%. Naive Bayes follows with an accuracy of 87%, and K-NN with an accuracy of 75%. Keywords: JKN mobile application, sentiment analysis, naive bayes, k-nearest neighbor (K-NN), support vector machine (SVM). Abstrak: Aplikasi Mobile JKN adalah sebuah aplikasi yang dibuat untuk mempermudah administrasi kesehatan di Indonesia sejak tahun 2017. Aplikasi ini telah diunduh lebih dari 10 juta pengguna dengan 484 ribu ulasan beragam positif, negatif, dan netral. Rata-rata rating yang diberikan pengguna adalah 4,5 bintang dari 5 bintang. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan pengguna yang terdapat di kolom review Google Play Store. Metode yang digunakan untuk analisis sentimen adalah Naive Bayes, K-Nearest Neighbor (K-NN), dan Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa dengan menggunakan proporsi data uji sebesar 10% dan data training sebesar 90%, metode SVM mencapai akurasi tertinggi sebesar 95%. Diikuti oleh Naive Bayes dengan akurasi 87%, dan K-NN dengan akurasi 75%. Kata kunci: JKN mobile, analisis sentimen, naïve bayes, k-nearest neighbor (K-NN), support vector machine (SVM).
Pengaruh Story Reading (Buku Bilingual) terhadap Perkembangan Kecerdasan Linguistik Anak Usia Dini 

Linguistic intelligence is the ability to organize thoughts clearly and be able to use them competently through words to express thoughts in speaking, reading and writing. This study aims to find out the effect of story-reading on the development of linguistic intelligence in early childhood, especially in Ra Al-WasliyahJl. Asahan Km. 17 woke up. This research was conducted at RA Al - Wasliyah jl. Asahan km. 17 Wake up. On March 6 - March 8, 2023. This research uses the type of Classroom Action Research (Teacher PTK) which is research conducted in the classroom using an action to improve the quality of the teaching and learning process in order to obtain better results than before. From the results of the study it was found that story-reading had several positive influences on increasing linguistic intelligence, namely: (1) early childhood knew and understood new vocabulary (2) understood the plot of a story (3) arranged words to convey the implied message of a story. In conclusion, that storyreading can have an influence on the development of linguistic intelligence in early childhood.&#x0D; Keywords: Linguistics, Early Childhood, Story-Reading.
Managing the Free-Rider Effect in a Top-Down Security Analysis Course

As an example of managing the free-rider problem, we report our experiences related to integrating group work associated with the top-down approach to fundamental analysis. Using group projects and sharing information with other groups and students is an effective method to reduce the work load for each individual student, while maintaining the scope of the top-down approach.By changing the composition of the groups at each level of the top-down approach, we are able to reduce the free-rider effect inherent in group projects. Overall, our methodology accomplishes the following three objectives: (1) reducing student workload, (2) sharing information, information sources, and analysis, and (3) reducing the free-rider problem. We find that 88% of the students agreed or strongly agreed that the top-down approach “had more educational value as a group project than as an individual project.” We also find that 82% of the students agreed or strongly agreed that changing the group composition helped reduce the free-rider problem.
The Prediction Of Product Sales Level Using K-Nearest Neighbor and Naive Bayes Algorithms (Case Study : PT Kotamas Bali)

PT Kotamas Bali is a company that operates in tableware and kitchenware, where every sale is sold at various counters. Product sales are permanently printed and entered in sales reports, and there are problems such as the fact that the product is sold very much and it is difficult to see the rate of sale of most products, lots, and not lots. Then, to see the level of product sales, it is necessary to use data mining techniques with the method of Knowledge Discovery in Databases to predict the purchase rate of products using the two algorithms K-Nearest Neighbor and Naïve Bayes. The purpose of this research is so that PT Kotamas Bali can see the sales rate of each product sold so that there is no accumulation of goods and more focus on the most marketed products. These two algorithms result in different accuracy on the 90:10 data split, where the K-Nearest Neighbor algorithm successfully predicted the sales rate of the product with a 99% accuracy rate and was categorized as an excellent classification. The Naïve Bayes algorithm failed to make predictions with an accuracy of only 54% and was classified as a failure classification. ROC performance results on the K-Nearest Neighbor algorithm with an AUC value of 99% and the Naïve Bayes algorithm with an AUC of 74%. K-Nearest Neighbor managed to obtain the highest accuracy, while the Naïve Bayes algorithm failed to conduct classification.