Reviewerly/exHarmony huggingface dataset:
Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review

Purpose: This study aims to monitor the service quality of JMO applications from time to time by classifying JMO user reviews into the class of positive, neutral, and negative sentiments.Design/methodology/approach : The method used in this study is the random forest classification method. Data processing in this study uses feature extraction, TF-IDF and labeling with the lexicon-based method.Findings/result: Based on the research results, it was found that the highest frequency of classification was the positive class with 17571 reviews compared to the neutral class with 8701 reviews and the negative class with 3876 reviews with an accuracy evaluation value of 93%, precision 88%, recall 93%, and f1-score 90%.Originality/value/state of the art:This study uses 150737 reviews that have been pre-processed using the random forest method and TF-IDF and lexicon-based feature extraction.
Bundle Expending with Customer Embedding: A Case Study

Today, in addition to the purchased products, the correct recommendations of the by-products that can be sold with these products are a very important source of income for companies. To achieve this, decision-makers frequently apply association analysis methods. At this point, the traditional apriori algorithm comes to the fore. In this study, a recommendation system is created with the apriori algorithm for two different data sets. In addition, customers who interact with products have been embedded with the word embedding mechanism, which is a natural language processing method that has been frequently used in recommendation systems in recent years. Applying that, similar customer products determined by embedding have been added to the product bundles. It has been observed that expanding bundles via customer embedding is useful in both data sets for some cases, and it gives promising results for future studies.
Contrastive Correlation Preserving Replay for Online Continual Learning

Online Continual Learning (OCL), as a core step towards achieving human-level intelligence, aims to incrementally learn and accumulate novel concepts from streaming data that can be seen only once, while alleviating catastrophic forgetting on previously acquired knowledge. Under this mode, the model needs to learn new classes or tasks in an online manner, and the data distribution may change over time. Moreover, task boundaries and identities are not available during training and evaluation. To balance the stability and plasticity of networks, in this work, we propose a replay-based framework for OCL, named Contrastive Correlation Preserving Replay (CCPR), which focuses on not only instances but also correlations between multiple instances. Specifically, besides the previous raw samples, the corresponding representations are stored in the memory and used to construct correlations for the past and the current model. To better capture correlation and higher-order dependencies, we maximize the low bound of mutual information between the past correlation and the current correlation by leveraging contrastive objectives. Furthermore, to improve the performance, we propose a new memory update strategy, which simultaneously encourages the balance and diversity of samples within the memory. With limited memory slots, it allows less redundant and more representative samples for later replay. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms the state-of-the-art methods and can effectively consolidate knowledge to alleviate forgetting.
Intelligent Compression of Data on Cloud Storage

Cloud computing is one of the most used technologies today. The main advantage of cloud computing is that the project developer need not buy the physical devices that will be used for the development of the project. They just need to rent the needed physical devices that are required for the project. The rented devices can easily be scaled up if the requirement of the project is increased. The major problem in cloud computing is the duplication of files that are stored in the cloud server. This will increase the memory usage of the cloud storage. In order to reduce the memory of the cloud storage, we need the application that will check the duplicate files in the cloud server before uploading the files in the main memory. If the duplicate of files is available on the cloud server, the application needs to map the original files to the current files that is uploaded so that the user will not get affected by the mapping but on the backend no duplicate files will be created but the files get mapped by the application. In this way, the duplication files on the cloud server will be avoided thereby reducing the storage space requirement on the cloud server.
Relaxed Differential Evolution Algorithm

This article presents a new variant of the Differential Evolution Algorithm (DEA) without the sequential condition of the individuals' selections in the Mutation process. Unlike other variants, where researchers focus on changes: in the formulation of the Mutation equation; the Crossover process; and/or Control parameters, this variant focuses on relaxing the selection conditions of the individuals involved in the creation of the mutation vector. Therefore, the main motivation of this proposal is to eliminate the condition that dictates that the selected individuals are different from each other. By relaxing this constraint, on the one hand, it can be expected that the execution time of the algorithm is lower, and on the other hand, the algorithm convergence could be slower, when the relaxed and original versions are compared at the same number of iterations at sequential computational processing. To analyze the performance, a comparative analysis of the proposed algorithm versus simple DEA is present by using as a case study a set of mathematical functions.
Study on the Effect of Chaotic Maps in the Formation of New Universes in the Multiverse Optimizer Algorithm

In this work, we study the effect of using chaotic maps to replace random values in the formation of new solutions over the Multi-Verse Optimizer algorithm (MVO). We present new variations of the MVO algorithm, where we use Fuzzy Logic in combination with chaotic maps, to achieve a better improvement on the universes generated by the algorithm with respect to only using Fuzzy Logic. The use of chaotic maps substitutes a random parameter in the generation of universes, so, in this work, we are analyzing the effect of this parameter over the generation of better solutions for the problems that are being applied. For testing, we use 13 mathematical functions, to do a comparison with the original MVO and other variations such as Fuzzy-MVO and Chaotic-MVO; we use 10 chaotic maps to do our main tests for the comparison between the variants. The objective of this work is, to study the effect of chaotic maps in the MVO algorithm, by generating new universes when using the chaotic map in a specific parameter of the algorithm, and then, go through further testing on more complex problems, like optimizing fuzzy controllers.
Comparison of the Effect of Parameter Adaptation in Bio-inspired CS Algorithm Using Type-2 Fuzzy Logic

In the present paper, we will carry out the comparison of the effect that the parameter adjustment has on the Cuckoo Search algorithm via Levy Flight using type- 2 Fuzzy logic, the results of the comparison will be able to demonstrate through the benchmark functions that the algorithm with an adjustment that the tests are promising when selecting the ideal parameter to achieve the proper balance of exploration and exploitation. The variables that can be configured in the CS algorithm are Pa, Alpha, and Beta, so far, the Alpha algorithm has represented the best results to find the equilibrium in the algorithm, this parameter is related to the scale factor of the problem.
Android-based Learning Media Development for Computer and Basic Networking at State Vocational High School 1 Bandung Tulungagung

This development aims to design and develop android-based learning media for Computer and Networking subjects in Class X of Computer and Network Engineering of State Vocational High School 1 Bandung Tulungagung. The development method applied in this development uses the ADDIE development model. This model has five steps, namely (1) analysis, (2) design, (3) development, (4) implementation, (5) evaluation. This android-based learning media was then tested on experts and 30 students of class XI of the Computer and Network Engineering Expertise Program at State Vocational High School 1 Bandung Tulungagung. The product produced in this development is an android-based learning media. After analyzing the data, the proportion of learning media from expert I was 82.50 percent, expert II was 83.33 percent, small group trial was 86.50 percent, field trial was 88.58 percent. The overall average percentage of expert I, expert II, small group trial, and field trial was 86.38 percent
HealthE: Recognizing Health Advice &amp; Entities in Online Health Communities

The task of extracting and classifying entities is at the core of important Health-NLP systems such as misinformation detection, medical dialogue modeling, and patient-centric information tools. Granular knowledge of textual entities allows these systems to utilize knowledge bases, retrieve relevant information, and build graphical representations of texts. Unfortunately, most existing works on health entity recognition are trained on clinical notes, which are both lexically and semantically different from public health information found in online health resources or social media. In other words, existing health entity recognizers vastly under-represent the entities relevant to public health data, such as those provided by sites like WebMD. It is crucial that future Health-NLP systems be able to model such information, as people rely on online health advice for personal health management and clinically relevant decision making. In this work, we release a new annotated dataset, HealthE, which facilitates the large-scale analysis of online textual health advice. HealthE consists of 3,400 health advice statements with token-level entity annotations. Additionally, we release 2,256 health statements which are not health advice to facilitate health advice mining. HealthE is the first dataset with an entity-recognition label space designed for the modeling of online health advice. We motivate the need for HealthE by demonstrating the limitations of five widely-used health entity recognizers on HealthE, such as those offered by Google and Amazon. We additionally benchmark three pre-trained language models on our dataset as reference for future research. All data is made publicly available.
Evolutionary Computing Model for Finding Breast Cancer Masses using Image Enhancement Procedures with Artificial Intelligent Algorithms

In this research, Particle Swarm Optimization (PSO) based image equalization is projected to enhance the contrast of different breast cancer images. Breast cancer is the highest and another important root of tumor disease in females worldwide. Mass and microcalcification clusters are a significant early signs of breast cancer. The mortality rate can effectively be decreased by early diagnosis and treatment. Most practical approach for the early detection and identification of breast cancer diseases is mammography. Mammographic images contaminated by noise usually involve image enhancement techniques to aid interpretation. Contrast enhancement is divided into two categories: development of direct contrast and enhancement of indirect contrast. Indirect contrast improvement is used in the image histogram update. Histogram Equalization (HE) is the modest enhancement of the indirect contrast approach usually used for contrast enhancement. The proposed method's average entropy is 5.3251 with the highest structural similarity index 0.99725. The best contrast improvement of this method is 1.0404 and PSNR is 46.3803. The MSE value is 2157.08. This paper recommends an innovative method of enhancing digital mammogram image contrast based on different histogram equalization approaches. The performance of the projected method has been related to other prevailing techniques using the parameters, namely, discrete entropy, contrast improvement index, structural similarity index measure, mean square error, and peak signal-to-noise ratio. Investigational findings indicate that the projected strategy is efficient and robust and shows better results than others.
RekBen Tube with AR 3D for Electrical Flow among Vocational College Students

Purpose: The electric and electronic subject is one of the important subjects in vocational colleges. Admittedly, it is difficult to teach the concept of electrical flow based on the conventional method that relies on explanation using printed materials. The lack of technology-based materials has made it difficult for students to effectively learn such a concept. Against this background, this study was carried out to design, develop, and test a learning application based on Augmented Reality (AR) called RekBen Tube to help technical and vocational school students in learning the concept of electrical flow.&#x0D; Design: The methods used throughout the production of RekBen Tube application technology education learning tools are the ADDIE Model and the TAM Model.&#x0D; Findings: The actual image depiction of the electric current through the circuit is shown to clearly explain the movement of the electric current.&#x0D; Research limitations: This study has developed an application of 10 flip cards for scanning by RekBen Tube app to generate 3D images on each of the different picture cards.&#x0D; Practical implications: Much of the software used to produce this learning tool has been able to attract students to learn the basics of electricity.&#x0D; Paper type: Research Paper.
The effective skyline quantify-utility patterns mining algorithm with pruning strategies

Frequent itemset mining and high-utility itemset mining have been widely applied to the extraction of useful information from databases. However, with the proliferation of the Internet of Things, smart devices are generating vast amounts of data daily, and studies focusing on individual dimensions are increasingly unable to support decision-making. Hence, the concept of a skyline query considering frequency and utility (which returns a set of points that are not dominated by other points) was introduced. However, in most cases, firms are concerned about not only the frequency of purchases but also quantities. The skyline quantity-utility pattern (SQUP) considers both the quantity and utility of items. This paper proposes two algorithms, FSKYQUP-Miner and FSKYQUP, to efficiently mine SQUPs. The algorithms are based on the utility-quantity list structure and include an effective pruning strategy which calculates the minimum utility of SQUPs after one scan of the database and prunes undesired items in advance, which greatly reduces the number of concatenation operations. Furthermore, this paper proposes an array structure superior to utilmax for storing the maximum utility of quantities, which further improves the efficiency of pruning. Extensive comparison experiments on different datasets show that the proposed algorithms find all SQUPs accurately and efficiently.
Game-Based Learning Media Development with Role-Playing Game Mechanism in Basic Programming

Learning activities for students in the current era, especially in productive subjects, must be interesting, innovative, and fun. In order to realize this, one of the efforts that can be done is to use appropriate media. The results of observations made by the author at SMK Negeri 1 Kepanjen showed that students claimed to lack understanding of productive materials, especially in basic programming subjects. At the time of delivery of material, educators often use media that are less interactive, so students are quickly bored. Therefore, to overcome the problems that have been explained previously, the author develops a game based learning (educational game) that uses the Role Playing Games (RPG) mechanism as a learning medium. This study aims to determine the feasibility of the educational game developed by the author. The author develops an educational game that uses the Role Playing Games (RPG) mechanism in basic programming subjects. This research uses the Addie research model according to what Branch (2009). The trial subjects of this study include material experts, media experts, and also students of class X RPL and X TKJ at SMK Negeri 1 Kepanjen. Educational game learning media developed have been through validation from media experts and material experts, and a trial has been tested on small group users and large groups. From various validation and testing results, this learning media received a percentage of 87.3 percent of 93.7 percent media experts from mater experts, 94.3 percent of small group trials, and 90.1 percent of large group trials. The percentage results obtained through validation and trials as a whole are classified in the very valid and very feasible category. So it can be concluded that the media based learning using the Role Playing Games (RPG) mechanism is very valid and feasible to be used in learning activities
Improving the Quality of Blended Learning through Mobile Learning Applications in Basic Programming Learning for Vocational School Students

The development of science and technology demands a more effective and innovative learning process that gives rise to various types of technology-based educational innovations. The results of observations and informal interviews at SMKN 1 Kepanjen found that basic programming is a subject that is considered difficult, the time allocation given is also lacking, the methods and media used by teachers have not helped students learn the material, and the use of mobile devices in learning is still lacking. This development research followed ADDIE's steps which consisted of five stages, namely analysis, design, development, implementation, and evaluation. The results of this development research are (1) to produce a mobile learning media product called coding; (2) learning media is stated to be very valid to be used as learning media in terms of the media expert's average score of 96.64 percent in the very valid category, the mean material expert's validity score is 87.94 percent in the very valid category, the small group's average score is 85.15 percent in the very decent category, and the mean score of the large group was 81.90 percent in the very decent category; and (3) the usefulness of the product is proven through learning independence which is obtained from the student's response of 79.28 percent in the effective category.
Synchronization of fractional-order reaction–diffusion neural networks with Markov parameter jumping: Asynchronous boundary quantization control

This paper tries to study the synchronization problem of a kind of fractional-order reaction–diffusion neural networks (FRDNNs) with Markov parameter jumping. Considering the spatial and parametric characteristics of the proposed Markov FRDNNs, asynchronous boundary quantization control, is applied to achieve the driven-response synchronization of the systems. In this control scheme, one actuator is placed at the spatial boundary, and two types of quantizers is used to further reduce the control energy consumption, which is economical and easier to implement than the distributed control strategies. Besides, the parameter jumping rules of the controller and system are subject to two different Markov chains, which is more general and practical. Moreover, for fractional-order Markovian systems, the continuous frequency distributed model of fractional integrator is introduced to deal with synchronization issue of the considered systems, the corresponding synchronization criteria are obtained with the help of indirect Lyapunov functionals method. Last but not least, a numerical simulation is carried out to support the proposed control methods and theoretical results.
Role of Blockchain Technology in ERP Systems for a Transparent Supply Chain: A Systematic Review of Literature

This study examines blockchain technology's role in ERP systems for a transparent supply chain. Furthermore, this study investigates the application of blockchain-driven ERP systems, benefits, and challenges facing the supply chain to achieve transparency and authenticity. Over the past few decades, ERP systems have evolved with the integration of new modules with improved capabilities that include new technological improvements like blockchain and cloud-based ERP systems. Such improvements have resulted in ERP systems for firms with expanded business functions, resulting in more streamlined, efficient supply chains and transparent business processes than ever before. Due to significant advancements in the last few years, blockchain applications for operations and supply chain management (OSCM) are still in their infancy. Little is known about the function of blockchain in operational traceability in supply chain functions and other domains. Thus, the study aims to enhance the knowledge of blockchain-driven ERP applications in operation and supply chain management and how companies create and capture business value with blockchain technology. This study gives a well-articulated and in-depth discussion of the role of blockchain-driven ERP in creating value in the supply chain area. An in-depth review of the literature was done to acquire the existing knowledge. The findings of the study, combining current ERP with blockchain technology, would improve optimization, strengthen transaction security, regulate transparency, and build trust in a trustless environment.
Development Of Interactive Media With Contextual Teaching Learning To Enhance Vocational Schools Student Creativity In Malang

Basic Graphic Design is one of the compulsory subjects that must be taken by students of class X Multimedia Department at SMK Negeri 7 Malang. This research and development aim to produce products in the form of interactive media containing Contextual Teaching Learning (CTL) Basic Graphic Design subjects to foster creativity. This type of research and analysis is included in mixed or quantitative and qualitative research. The Research and Development model used is the ADDIE model. The development procedure is (1) Analyze; (2) Design; (3) Development; (4) Implementation; (5) Evaluation. The results of the validation by material experts were 88.75 percent, media experts 95 percent, small group trials 83.18 percent, large group trials 87.69 percent. Therefore, it can be concluded that the basic interactive media of Graphic Design with CTL is suitable for use as a student learning medium. Based on the results of the study as long as students used interactive media, it was found that 19 students (63 percent) had high creativity, 10 students (33 percent) had moderate creativity, and 1 student (3 percent) had low creativity. This indicates that the use of interactive media has been able to foster student learning creativity in general
Review of the Book: “Analytical Techniques for Trace Elements in Geochemical Exploration” by Dr. Raghaw Saran

Geochemical explorations are of paramount importance in optimally using the resources, thereby strengthening the economy. Trace element’s analysis requires application of state-of-the-art techniques so as to detect and / or quantify them with utmost sensitivity and selectivity. The book Analytical Techniques for Trace Elements in Geochemical Exploration provides a comprehensive treatise of hitherto reported analytical techniques that could be used for geochemical analysis of multitude of samples.