Reviewerly/exHarmony huggingface dataset:
Eksperimen Layer Pooling menggunakan Standar Deviasi untuk Klasifikasi Dataset Citra Wajah dengan Metode CNN

Deep Learning, especially the Convolutional Neural Network (CNN) has proven to be reliable in processing data from various programming language platforms by utilizing deep learning. In this study, we modified it by calculating the statistical variance. The modifications made are replacing calculations on the Pooling Layer which generally use two formulas, namely max pooling and average pooling. We use the standard deviation to change the reduced image intensity value. With the research experiments built, it is expected to be able to perform facial recognition as an indicator for testing modifications. The Layer Pooling experiment uses the Standard Deviation for Classifying Face Image Datasets with the CNN Method, including the type of dataset used is the Aberdeen dataset https://pics.stir.ac.uk/2D_face_sets.htm. From the results of the experiments conducted, it was found that the highest value was using the Elu activation function and the Adagrad optimizer worth 77.844% for max pooling and 79.844% for pooling with a standard deviation. The Cellu activation function and the RMSprop optimizer are 77.986% for max pooling and 75.986% for pooling with a standard deviation. The highest score with the Softplus activation function and the Sgd optimizer is 77.844% for max pooling usage and 76.344% for pooling with standard deviation. The Tanh activation function and the Adadelta optimizer are 87.844% for max pooling and 85.844% for pooling with a standard deviation. The Elu activation function and the Adam optimizer are 87.853% for the use of max pooling and 85.285% for pooling with a standard deviation. By using the Elu activation function and the Adamax optimizer, the value is 87.842% for max pooling and 86.242% for pooling with a standard deviation. The highest score is using the Elu activation function and the Nadam optimizer with a value of 87.845% for max pooling usage and 86.345% for using standard deviation calculations as pixel pooling. From all experiments it was stated that the use of pooling with the highest value technique or max pooling still gave a better value than using the standard deviation calculation with the best tuning results using the Elu activation function and Adam's Optimiser, which was 87.853%.
IMPROVING THE QUALITY OF STUDENTS PARAGRAPH WRITING THROUGH PEER ASSESSMENT

The method used in this research is classroom action research design. The primary objectives of this research are to describe the teaching and learning process on paragraph writing through peer-assessment, to establish the amount of influence over the improvement of students ability in paragraph writing. The instruments employed to gain the data are through paragraph writing tests and observation. The tests are given before and after giving the treatment of peer assessment to appraise their own performance; furthermore, the observation is conducted to observe the teaching and learning writing process in the classroom using continuous assessment. The subjects of the research are the students of English Department who are at the fourth semester and join the subject of Writing III at Cirebon STIBA INVADA. The results of the students’ paragraph writing are then analyzed on the basis of the paragraph elements and the scoring procedure according to analytic scale for rating composition proposed by Brown and Bailey. The data are analyzed qualitatively through identification and classification; additionally, they are interpreted using comparative analysis. The results of the research is that the students ability increase substantially after participating in peer assessment. The average score for all paragraph elements is improving; the average score in pretest is 36.41, meanwhile the employing of peer assessment has given rise to the score in post test by 64.35; on the whole, the students’ ability on paragraph writing increases by 43.42%. Thus the evidence seems to be strong that peer assessment can do significantly for students’ achievement in writing.
Android-Based Online Bookstore System Design

Indonesia is one of the countries with low interest in reading, given the importance of reading to increase human knowledge, by utilizing technological developments, an application is made that provides books in digital form. The purpose of this research is to design an online bookstore system that can be accessed using an android device. This application serves as a platform for readers so that they can increase their interest in reading with various digital books that can be accessed for free or paid. The research that was conducted was by using a descriptive method of analysis with a qualitative approach. This application development process uses an object-oriented approach, namely system development life-cycle prototyping. The result of this research is to produce an online bookstore and an android-based audiobook that can be read and listened to for free or paid. In the application, there are various types of digital books with features that make it easier for readers. With this android-based online bookstore, users can read books anywhere and anytime without having to carry a lot of books. This research was conducted to make it easier for readers who want to read books effectively and efficiently with only one android device. In the end, this application is a reading medium to improve reading habits, especially the Indonesian people.
EFEKTIFITAS JOLLY PHONICS DAN PERMAINAN KARTU HURUF UNTUK MENINGKATKAN PENGETAHUAN TENTANG ABJAD DAN KESADARAN FONOLOGIS PADA ANAK TK B DENGAN KEMAMPUAN MEMBACA PERMULAAN YANG RENDAH

Early reading skills is an important ability for early childhood learner, considering that these skills are the foundation for children to overcome the diffiulties on the next level of education. One of the main predictors of early reading ability are letter knowledge and phonological awareness. Early intervention is known to be very necessary on improving children's low early reading skills. Interventions using the jolly phonics method and picture card games are known to be effective in improving early reading skills, especially in terms of improving alphabet knowledge and phonological awareness, moreover these methods have been known as a fun and interesting methods for improving early reading skills for young children. In this study jolly phonics and alphabet card games were used to improve the early reading skills of children aged 6 years 5 months with low early reading skills. The subject is known to just recognize and know the letters c, o, and s and still lacking on distinguishing letter sounds. This study uses qualitative research design with a single case design. The intervention was carried out for 60 to 90 minutes for 12 sessions. The results show that subject could recognize, mention, and distinguish the sounds of vowels such as (a, i, u, e, o) and consonants such as (b, c, d, m, s, n, k, l, p, t). In conclusion the uses of jolly phonics and alphabet card games increased early reading skills, especially for improving alphabet knowledge and phonological awareness.
Web-Based Inventory Information System Design of Research and Community Service

This study aims to produce a website-based inventory information system that is appropriate for user needs and to become a supporting tool for the Institute for Research and Community Service (LP2M) in a practical inventory data collection process. The research method used in this research is Research and Development using the Prototype development model with the stages of Requirements analysis, Prototype Design Making, Prototype Evaluation, System Coding, System Evaluation and System Use. This research was conducted through a trial phase, the first stage was to test the validity of the instrument, the results of the validity test from two experts were 97.5% with a very valid category. The results showed that the Web-Based Goods Inventory information system for the Institute for Research and Community Service (LP2M) Makassar State University was valid and feasible to use after being tested using the ISO 25010 standard. In the test results obtained: a) functionality suitability is worth 100% which means that all functions of the system are running well; b) reliability was carried out using the WAPT 10.1 tool in testing successful sessions, successful pages, successful hits, a success percentage of 99.62% were categorized as 'good'; c) usability with the number of respondents 7 obtaining an average percentage of effectiveness of 95% which is categorized as very good; d) performance efficiency with success presentation at point A or very good; and e) portability by accessing the system on a mobile device or in several web browsers on a desktop and can run properly.Keywords: Inventory Information System, ISO/IEC 25010, Web, Prototype
Cinematography Design of Integrated Service Video Documentation

This study aims to find out how the scene is in the integrated service video documentation, to know the cinematographic techniques used in the integrated service video documentation, and to find out the responses of respondents to the integrated research video documentation. This study used descriptive qualitative method. The method of data collection is by means of observation, documentation, and questionnaires. Data analysis used is data reduction, and the feasibility percentage scale. The results of this study indicate that: (1) The scan applied to the 2022 community service video media is the initial scan showing the Phinisi tower, the next scan showing departure to the service location, the next scan showing the servant at the service location, and the closing scan showing the logo animation LP2M. (2) The cinematographic technique that is often used in this community service video media uses an objective camera angel and a subjective camera angel to capture every scene in the community service video media that is made. Low angel level, Eye level angle, High level camera, Extreme long shot, Very long shot, Medium long shot, Close up, and Medium close up. The composition in the dedication video media "Cinematographic Design on Video Documentation of the Results of Community Service LP2M UNM" also varies, but uses more informal compositions in shooting. (3) Service Lecturers provide positive feedback on the resulting media on a number of video criteria that are made, including from taking pictures, video stages, and explanations on videos that are displayed clearly.Keywords :Cinematography, Video Documentation, Results of Service, LP2M.
Architectural Design of Business Processes For Community Empowerment and Flexibility Based on Presidential Regulation Number 132 of 2022

The development of information technology in daily life is very rapid so that it can increase the productivity of an individual or organization. The Government Sector also utilizes information technology in an effort to increase the provision of effective and efficient public services. Likewise with the Government of Indonesia, in 2018 issued Presidential Regulation No. 95 of 2018 concerning Electronic-Based Government Systems (SPBE) as a guideline for governance that utilizes information and communication technology to provide quality public services. SPBE evaluation and monitoring is carried out by the Ministry of State Apparatus Empowerment and Bureaucratic Reform (KemenPANRB) in every government agency in Indonesia. In 2020 the results of the SPBE evaluation at the Tangerang City Government obtained a score of 3.44 with a good index. For this reason, this research focuses on improving SPBE governance which includes the standardization and integration of SPBE business processes for the Tangerang City Government. This research provides a solution in the form of an SPBE enterprise architecture design in the business process domain according to the SPBE architecture. Several phases are used, namely the preliminary phase, architecture vision, and business process architecture. The main phase of this design is the business process architecture which produces level 0 business process maps, level 1, 2, 3, 4, 5 business process maps and cross-functional maps as standardization of SPBE business processes according to PermenPANRB No. 19 of 2018.
Impacts of Gamification Learning Approach on Student’s Performance and Perception During Covid 19 Post Pandemic 2021 In Indonesia New Normal Learning Setting

This study aims to determine the impact of student performance and perceptions of those taught with a gamification learning approach compared to students in rote classes on their academic reading comprehension during COVID 19 Post-Pandemic 2021 in Indonesia's New Normal learning setting. A quasi-experimental study design with pre-test and post-test control groups was used. Students are allocated to one of two versions of the program's learning environment at random: the gamified version or the original non-gamified version. The results of the pre-post test through the Independent Sample T-Test found a significant value which proved that the Gamification Learning Technique was also more effective than the rote learning method. Questionnaires were used to collect data regarding opinions related to gamification elements and it was found that most students chose "strongly agree" to "agree" with positive statements about research. In conclusion, gamified learning is recommended to make learning more enjoyable, even without fully solving some of the motivational challenges students face.
A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

Machine Learning (ML) algorithms are widely used in solving classification problems.The biggest challenge of classification lies in the robustness of the ML algorithm in various dataset characteristics.Quadratic Interpolation Flower Pollination Neural Network (QIFPNN) is categorised into ML algorithm.The new QIFPNN's extraordinary capabilities are measured on binary-type datasets.This research ensures that the remarkable ability of QIFPNN also applies to non-binary datasets with balanced and unbalanced data class characteristics.Flower Pollination Neural Network (FPNN), Particle Swarm Optimisation Neural Network (PSONN), and Bat Neural Network (BANN) were used as comparisons.The QIFPNN, FPNN, PSONN, and BANN were used to train Multi-Layer-Perceptron (MLP).The test results on five datasets show that QIFPNN obtains an average classification accuracy higher than its comparison in three datasets with balanced and unbalanced data class characteristics.The three datasets are Iris, Wine, and Glass.The highest classification accuracy obtained by QIFPNN in the three datasets is 97.1462%, 98.6551%, and 73.1979%, respectively.Based on the F1-score test from QIFPNN, it is higher than all the comparisons in four datasets: Iris, Wine, Vertebral column, and Glass.Sequentially, 96.4599%, 98.7155%, 90.7517%, and 60.2843%.It proves that QIFPNN can also classify datasets with non-binary data types with balanced and unbalanced data class characteristics because they are more consistently tested on various datasets and are not susceptible to the influence of variations in dataset characteristics so that they can be applied to various types of data or cases.
A New Model for Blood Cancer Classification Based on Deep Learning Techniques

Artificial intelligence and deep learning algorithms have become essential fields in medical science.These algorithms help doctors detect diseases early, reduce the incidence of errors, and decrease the time required for disease diagnosis, thereby saving human lives.Deep learning models are widely used in Computer-Aided Diagnosis Systems (CAD) for the classification of various diseases, including blood cancer.Early diagnosis of blood cancer is crucial for effective treatment and saving patients' lives.Therefore, this study developed two distinct models to classify eight types of blood cancer.These types include follicular lymphoma (FL), mantle cell lymphoma (MCL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and the subtypes of acute lymphoblastic leukemia (ALL) known as early pre-B, pre-B, pro-B ALL, and benign.AML and ALL are specific classifications for human leukemia cancer, while FL, MCL, and CLL are specific classifications for lymphoma.Both models consist of different phases, including data collection, preprocessing, feature extraction techniques, and the classification process.The techniques applied in these phases are the same in both proposed models, except for the classification phase.The first model utilizes the VGG16 architecture, while the second model utilizes DenseNet-121.The results indicated that DenseNet-121 achieved a lower accuracy compared to VGG16.VGG16 exhibited excellent results, achieving an accuracy of 98.2% when classifying the eight classes.This outcome suggests that VGG16 is the most effective classifier for the utilized dataset.
The Application of Intelligent Evaluation Method with Deep Learning in Calligraphy Teaching

Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality.At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect.Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality.First, based on relevant research, the CTQE indicator system is constructed.Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors.Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting.Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality.The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953.The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967.These values are superior to the most advanced teaching QE model available.The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE.This is of positive significance to the improvement of teaching quality and students' calligraphy level.
Deep Learning for Personal Activity Recognition Under More Complex and Different Placement Positions of Smart Phone

Personal Activity Recognition (PAR) is an indispensable research area as it is widely used in applications such as security, healthcare, gaming, surveillance and remote patient monitoring.With sensors introduced in smart phones, data collection for PAR made easy.However, PAR is non-trivial and difficult task due to bulk of data to be processed, complexity and sensor placement positions.Deep learning is found to be scalable and efficient in processing such data.However, the main problem with existing solutions is that, they could recognize up to 6 or 8 actions only.Besides, they suffer from accurate recognition of other actions and also deal with complexity and different placement positions of smart phone.To address this problem, in this paper, we proposed a framework named Robust Deep Personal Action Recognition Framework (RDPARF) which is based on enhanced Convolutional Neural Network (CNN) model which is trained to recognize 12 actions.RDPARF is realized with our proposed algorithm known as Enhanced CNN for Robust Personal Activity Recognition (ECNN-RPAR).This algorithm has provision for early stopping checkpoint to optimize resource consumption and faster convergence.Experiments are made with MHealth benchmark dataset collected from UCI repository.Our empirical results revealed that ECNN-RPAR could recognize 12 actions under more complex and different placement positions of smart phone besides outperforming the state of the art exhibiting highest accuracy with 96.25%.
Image Processing on Agricultural Dataset Using Parallel Processing Based on Python

Parallel processing divides data to be processed in each core on the CPU. One programming language that is widely used for machine learning applications and can accommodate the use of parallel processing is Python. Multiprocessing, mpi4py and CuPy are examples of Python libraries that can process data in parallel. In this research, we will compare the use of these libraries to process huge amounts of data. This research takes agricultural data for data image enhancement through color conversion from RGB to grayscale. The results showed that multiprocessing, mpi4py, and CuPy can increase the image enhancement speed three times faster than its singlecore execution. Then, using a combination of multiprocessing with CuPy, a 1.7 times performance improvement is achieved compared with multiprocessing-only. Also, using a combination of mpi4py with CuPy achieved 2.5 times performance improvement compared to mpi4py-only.
Hate Speech Detection in Social Media Using the Ensemble Learning Technique

Our lives have become intertwined with social media platforms such as Twitter, Facebook, LinkedIn, etc. They provide us with a platform to express our opinions and share our thoughts with the world. However, some individuals abuse the freedom of expression afforded to them by these platforms and utilize them to disseminate content that is derogatory and promotes hate speech. This has become a significant problem today, and detecting such content is a challenging task. In this research paper, we propose a solution for hate speech detection in social media using natural language processing techniques. We use a publicly available dataset provided by CrowdFlower and perform text pre-processing to clean the dataset. We then conduct feature engineering to extract key features that can be used in machine learning classification algorithms. We compare the performance of various algorithms about each feature set and conduct an in-depth analysis of the results obtained.
Bidirectional Long-Short-Term Memory with Attention Mechanism for Emotion Analysis in Textual Content

Emotion analysis in textual content plays a crucial role in various applications, including sentiment analysis, customer feedback monitoring, and mental health assessment.Traditional machine learning and deep learning techniques have been employed to analyze emotions; however, these methods often fail to capture complex and long-range dependencies in text.To overcome these limitations, this paper proposes a novel bidirectional long-short-term memory (Bi-LSTM) model for emotion analysis in textual content.The proposed Bi-LSTM model leverages the power of recurrent neural networks (RNNs) to capture both the past and future context of text, providing a more comprehensive understanding of the emotional content.By integrating the forward and backward LSTM layers, the model effectively learns the semantic representations of words and their dependencies in a sentence.Additionally, we introduce an attention mechanism to weigh the importance of different words in the sentence, further improving the model's interpretability and performance.To evaluate the effectiveness of our Bi-LSTM model, we conduct extensive experiments on Kaggle Emotion detection dataset.The results demonstrate that our proposed model outperforms several state-of-the-art baseline methods, including traditional machine learning algorithms, such as support vector machines and naive Bayes, as well as other deep learning approaches, like CNNs and vanilla LSTMs.