Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Public Responses on Indonesia Government Using Naïve Bayes and Support Vector Machine

Many people are interested in knowing how the public views President Joko Widodo's administration. Text Mining analysis can be one way to collect and analyze text data about Joko Widodo's administration and extract relevant information from the data. Data was obtained by collecting tweet data about Joko Widodo's government in 2022 on Twitter using Netlyitic. Then the Text Mining analysis of Joko Widodo's government was carried out using the Navie Bayes (NVB) classification and Support Vector Machine (SVM). This classification can be used to predict sentiment or public views of the government based on the tweets collected. Based on a case study of the classification results of President Joko Widodo using Naive Bayesian classification, we obtained a precision value of 79%, a recall value of 91% and a precision value of 82%. And by using SVM, we get 85% precision, 95% recall, and 83% precision. Due to the high accuracy, recall, and precision, it can be said that SVM classification is more accurate than NVB.
Sentiment analysis of sarcasm detection in social media

When people talk or write more especially on social media, they tend to sometimes, flip the polarity of their expressions. Oftentimes, this change of mood affects the accuracy of the sentiment analysis. Sarcasm is a sophisticated form of irony widely used in social networks and micro blogging websites and is usually a type of sentiment where person speaks the contradictory of what individual word means, expressing gloomy feelings applying positive words. However, it is hard even for humans to recognize. Hence, sarcastic expressions play a vital role in the outcome of automatic sentiment detection in sentiment analysis. To improve on the performance of automatic detection of sentiments in social media networks, there is need for sarcastic statements to be recognised and classify correctly. Sentiment Analysis is a method for identifying people’s opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. This paper proposed an ensemble classification method having base classifiers as Random Forest, K-Nearest Neighbour and Naive Bayes for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. The same ensemble method and Support Vector Machines (SVM) were used to identify the sarcasm types so as to diagnose the mood of the users. We report the results and present a comparative evaluation of the ensemble method and SVM classifiers for sarcasm and sarcasm type detection indicating their suitability for the task. The results obtained from the set of experiments conducted indicate the reliability of our approach.
Improving Software Requirements Prioritization through the Lens of Constraint Satisfaction

Requirements prioritization is a critical activity during the early software development process, which produces a set of key requirements to implement.The prioritization process offers a parity among the requirements based on multiple characteristics, including end-users' preferences, cost to implement, and technical dependencies.This paper presents an interactive method to requirements prioritization that leverages the pairwise comparisons and a constraint solver.Our method employs an interactive accumulation of knowledge from the requirements analyst when the relative priority among the requirements cannot be determined based on the existing knowledge from the requirements documents.The final ranking of the requirements is produced via the constraint solver and interactive pairwise comparisons.We evaluate the proposed method using the requirements from a real healthcare project.The proposed prioritization method relying on a constraint solver outperforms state-of-the-art interactive prioritization methods in terms of effectiveness and robustness to analyst's errors.
An Empirical Analysis on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data

In this data age, connected devices are continuously generating petabytes of images, text, and internet of things (IoT) sensor data.One approach to efficiently store this massive data is to extract the relevant and representative features and store only those features instead of the continuous streaming data.However, it raises a question as to the amount of information content we can retain from the data and if we can reconstruct the pseudo-original data when needed.By facilitating relevant and representative feature extraction, storage and reconstruction of near original pattern, we aim to address some of the challenges faced by the explosion of the streaming data.We present a preliminary study, where we explored multiple autoencoders for the concise feature extraction and reconstruction for human activity recognition (HAR) sensor data.Our Multi-Layer Perceptron (MLP) deep autoencoder achieved a storage reduction of 90.18%, where as convolutional autoencoder achieved 11.18%.For Long-Short Term Memory (LSTM) autoencoder the reduction was 91.47% and for convolutional LSTM autoencoder it was 72.35%.The storage reduction depended on the size and dimension of the concise representation.For higher dimensions of the representation, the storage reduction was low.But relevant information retention was high which was validated by the classification performed on the reconstructed data.
Towards an Automated Framework of Root Cause Analysis in the Canadian Telecom Industry

The telecommunication industry today is essential in our everyday life, and providing consistent and stable internet service to the population is the number one priority of telecom companies.That said, it is also important for companies to keep up to date with latest technologies to ensure customers are being offered the best services, whether it's related to speed, reliability etc.As telecom companies introduce new services and technologies, it is important to ensure a seamless delivery and no impact to customers.When that occurs, telecom companies launch investigative processes to examine and analyze the root causes.This process is lengthy and time consuming.In this paper, I propose automating the analysis process by using association rule mining to fast track the investigative process towards identifying root causes.
Kohesi Gramatikal dalam Karangan Teks Eksposisi Siswa Kelas VIII SMP Negeri 16 Padang

This type of research is a qualitative research with descriptive method. This research was conducted at SMP Negeri 16 Padang. The data of this study is the use of grammatical cohesion in expository text essays. The source of the data in this study was written exposition texts by class VIII students of SMP Negeri 16 Padang. Based on the results of research on the use of grammatical cohesion in exposition text essays for class VIII students of SMP Negeri 16 Padang, there is the use of grammatical cohesion, namely references, substitutions, ellipsis, and conjunctions. Based on the data analysis that has been done, the most dominant use of grammatical cohesion used in exposition texts for class VIII students of SMP Negeri 16 Padang is the use of conjunctions and the least use of grammatical cohesion found is the use of ellipsis. From the quantitative data, it can be categorized as an error in the use of grammatical cohesion in an exposition text essay for class VIII students of SMP Negeri 16 Padang, which is included in the high category.
RANCANG BANGUN ALAT FIKSASI PEMERIKSAAN RADIOGRAFI ABDOMEN PROYEKSI LEFT LATERAL DECUBITUS

Background: Radiologic fixation device is a tool made of specially designed iron plate that is used by radiology officers to assist in treating patients. As in the LLD (Left Lateral Decubitus) projection abdominal radiography, non-cooperative patients need an assistive device to facilitate the examination. LLD projection abdominal fixation device This can be used to place cassettes of various sizes, limit patient movement, ensure that the cassette and grid remain attached to the posterior part of the patient without being touched by the patient's family, make the left side of the patient's abdomen not cut, and provide radiation safety to the patient, patient's family, and staff. This study aims to design a fixation tool for projection abdominal radiography examinations LLD and test tools.&#x0D; Methods: This research is an applied type of research by designing a fixation device for LLD. projection abdominal radiography examination as well as testing these tools through questionnaires and documentation studies. The research was conducted at the Radiology Laboratory of Awal Bros University from May to June.&#x0D; Results: The LLD projection abdominal examination fixation device, this tool composed of 5 components: patient seat, metal support, cassette holder, foam pad and strap.&#x0D; Conclusions: From the results of data collection using a questionnaire, it was found that the LLD projection abdominal examination fixation tool has met the performance test in accordance with the specifications of the tool and has good value in terms of the components of the tool, durability, efficiency of the tool and the resulting radiograph.
Motion detection using a Raspberry Pi 4 and OpenCV

Currently, one of the most important parts of the cloud and computing platforms are the data centers, since they house the equipment where the applications and information processing are executed.The vitalkey areas of data centers are the equipment sites.In addition to the fact that access is controlled and restricted to these areas, continuous monitoring of the presence of people in these areas is critical.The data centers have closed circuit television systems that allow this activity to be carried out; however, these systems do not send alert messages that allow timely reaction to any threat from unauthorized persons who have accessed the equipment site.Similarly, presence detectors of different technologies are used, which present disadvantages compared to the system presented here.For these reasons, in this work a motion detection system was developed based on the processing of video images captured with a camera integrated into a Raspberry Pi 4 card and programmed with Python and OpenCV functions.When the system detects movement, it sends a WhatsApp alert message to a mobile phone.The tests carried out showed that the system is accurate to 98.08%.
A morpho-semantic analysis of some Nigerian internet-based slangs

One of the distinguishing features of the Internet language is slang formation and usage among youths in some Internet domains. Such slangs are formed following morphological processes and they are used to convey in-group identity and meaning. In the light of the above, this paper undertakes a morphological analysis of some Internet-based slangs with a view to uncover the morphological processes of slang formation on the Internet and the semantic implication of such slangs. Twenty slangs were sourced from some Internet domains for this study, and it adopts Bauer’s (2004) Morphological Productivity with insights from Booij’s (2010) Construction Morphology as its theoretical framework. The findings reveal that Internet slangs as a means of forming in-group identity follow varied morphological processes with varied semantic implications which reflect the social context and the topic of interaction within the group.
Prediction of Student Decisions in Choosing the Type of Bank Using Support Vector Machine (SVM)

A bank is an intermediate financial institution authorized to take deposits, lend money, and issue promissory notes or banknotes. In the present day, every adult must have at least one bank account. Additionally, bank services range from regular and hajj savings to large-scale loans. Students, one of the bank’s customers, usually utilize services confined to savings to preserve pocket money received from their parents and ordinary transactions like transfers and payments. Several factors, including the atmosphere, administrative fees, and the accessibility of ATMs and bank branch offices, impact students’ decisions about where to save money. It prevents the bank from predicting which services must be enhanced to encourage customers, particularly students, to select the bank. Therefore, prediction is required to ascertain the students’ choice of bank. This study employed data mining and the Support Vector Machine (LibSVM) algorithm. The quantity of data impacted the outcomes of the SVM classification. In addition, kernel types, k-fold values, and sampling techniques also influenced classification accuracy. LibSVM with a kernel type of RBF, a k-fold of 8, and shuffled sampling classified 200 data with an accuracy of 68.40%.
Importance of digital technology application in the development of professional competence of future chemistry teachers

This article describes the preparation of a virtual laboratory in the subject and its application during the lesson. The aim of this study is to improve the professional competence of future chemistry teachers using digital technologies. Accordingly, the subject of the study is to determine the impact of digital technologies on improving the quality of education. This study is carried out by using experimental method which is a kind of quantitative research method. In the course of the work, a study of the influence of a virtual laboratory on the quality and increase in the level of students’ knowledge in a pedagogical experiment was made. The level of students’ knowledge was determined by the formula for calculating progress. A group of students who used the virtual laboratory showed a high level of knowledge. These data prove the effectiveness of conducting laboratory classes in chemistry using a virtual laboratory, which is one of the digital technologies. The effectiveness of digital technology application in the development of professional competence of future chemistry teachers is shown: -chemical experiments were carried out using a virtual laboratory;-students had the opportunity to self-correct and understand laboratory work at re-watching;-prepared with the help of animation and color images, a virtual laboratory has increased the interest of students;-at discussion the results of the lesson, a test was conducted on the “quizizz” program;-to determine the significance of the virtual laboratory, the lesson was conducted in two groups, at analysis of the lesson results in the group where the virtual laboratory was used, the level of knowledge was high;-it can be observed that the professional competence of students (motivation, skills, knowledge) has increased.
PENERAPAN MODEL PEMBELAJARAN BERBASIS MASALAH PADA MATERI ZAT ADITIF MAKANAN BERKAITAN DENGAN KESEHATAN TERHADAP HASIL BELAJAR KOGNITIF, SIKAP DAN AKTIVITAS SISWA SMP INSHAFUDDIN KOTA BANDA ACEH

This study aims to determine the improvement of students' attitudes towards snacks containing harmful additives for health and student cognitive learning outcomes. In this study, the method of classroom action research (classroom action research) consists of several stages, namely planning, action, observation and reflection. The population in this study were all students of class VIII SMP Inshafuddin Banda Aceh City. The research sample was one class which was randomized by random sampling. Instruments in the form of cognitive ability tests, student attitude scale questionnaires, interview guidelines, observation sheets. The data obtained is processed using the activity criteria that have been determined. The results of this study indicate that, 1) There was an increase in students' attitudes towards snacks containing additives that are harmful to health after the implementation of a problem-based learning process on food additives related to health with an average attitude value of 61% in cycle I and an average of 61%. the average value of the attitude of the second cycle is 83% and the gain value is 56%. 2) There is an increase in learning outcomes in each cycle where in the first cycle 10% of students complete with a classical score of 36.81, in the second cycle 65% of students complete with a classical score of 65.8 and in the third cycle 90% of students complete with a classical score of 80.8 after the application of the process problem-based learning on food additives related to health.
Automatic Measurement Application of Heart Area from Chest X-Ray Images Using the U-Net Deep Learning Method

Heart health is a basic human right and a crucial component of global health justice. In an ever-more-advanced age, every task becomes simple due to science, technology, and information development. However, certain tasks are still performed manually. Therefore, innovation in computerized system design is required. The human heart area calculation was performed by combining image processing and deep learning techniques. Deep learning is a scientific subfield of machine learning, while image segmentation is a step in image processing. This study employed the U-Net segmentation method to identify different stages of heart area calculation. U-Net could conduct image segmentation with the small training dataset accurately. This study’s population consisted of 800 chest X-ray images obtained from the Kaggle website, with human hearts as the sample. The findings revealed that the training data with the U-Net architecture model acquired an accuracy of 09.98. However, the testing data accuracy was still determined manually. In this work, the U-Net model employed an input shape measuring 256x256, a kernel size of 3x3, and 50 epochs.
Features of the implementation of interaction in the conditions of the professional and pedagogical training of the teacher-tutor

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
The peculiarities of teaching the discipline “Basics of Academic Writing” for the students of the specialty “Software Engineering”

International scientific conference «Innovative aspects of the development of philological sciences» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 216 pages.
GOVERNMENT AND COMMUNITY EFFORTS IN FLOOD DISASTER RISK MANAGEMENT CASE STUDY: TOAHA VILLAGE - PAKUE DISTRICT - KOLAKA UTARA - SOUTHEAST SULAWESI - INDONESIA

This reseach is motivated because the flood disaster that occurred in Toaha village was caused by having a lowland area, overflowing river water exceeding the capacity of the river, low topography, the effect of the collapse of the embankment and the amount of sediment in the sewer. This study discusses the efforts of the government and the community in managing flood risk in Toaha Village, Pakue District, North kolaka Regency seen from 1) Efforts, 2) Impact, 3) Cooperation Relationships, 4) Government Policies, the purpose of this research in general is to get an overview of the efforts of the government and the community of Toaha Village, Pakue District, Nort Kolaka Regency. This type of research is qualitative. The type of sampling used is purposive, assign informants based on research criteria. Collecting data carried out interview techniques. Based on data analysis using analysis that makes an explanation that can later provide a concrete explanation. From the result of this study indicate that the efforts made by the Toaha village community before the flood occurred were to be alert and careful when the rainy season arrived, when the flood came, people ran to a high and safe place, after the flood the people returned home, the second effort made by the government before the flood occurred was to urge the public to be careful during the rainy season, when the flood came, the local government contacted the sub-district head and the central government to the community, when the plood recedes, the government directs its people to return home and clean up the garbage that enters the house, the three impeacth that often occur after a flood are that people cannot carry out their usual activities because people are busy cleaning their homes, children cannot go to school. Fourth, the relationship between the community and the government is good, the government has facilitated in terms of flood disaster management. Government policy.
Literatur Review: Peran Aplikasi SAAS Dalam Kegiatan Bisnis E-Commerce

The existence of cloud computing will clearly cause changes in the way information technology systems work in a business. This is due to cloud computing through the concept of virtualization, standardization and other fundamental features that can reduce costs, simplify the management of information technology services and speed up service delivery. One of the cloud computing infrastructures, namely Software as a Service (SaaS), is currently the choice for many companies that are developing their business, especially in the E-Commerce business. As a typical form of cloud services, SaaS has been leading the adoption of cloud to enable E-Commerce by the industry. The development of a framework for modeling complex business systems as a SaaS model in the Cloud has proven to be very effective. Literature review is carried out based on issues, methodologies, and similarities. Of the 12 studies conducted, several methods are used with algorithms that are appropriate to the existing problems. Based on 12 studies, it was found that there are several factors that encourage the use of SaaS and cloud in general in business processes spread across several countries. In one of the studies, there is a method used to support SaaS-oriented business processes. Discussion: The application of the SaaS model to E-Commerce business processes provides an important and very profitable role in providing services to customers.
Advanced Development of a Prayer Schedule Bot Application on Telegram Using PHP

A bot is a web robot or an assistant with computer intelligence. The usefulness of a bot is very much starting from the web, messenger, and others. One application possessing a provider to make a bot is a Telegram. The development of technology, such as social media applications, has made them prevalent for the public, including most Muslims. Sometimes, people will be negligent in worshiping when using the application. Therefore, creating a prayer schedule bot is necessary to prevent people from doing so. A framework creation aimed to facilitate the stages of making a bot using a PHP programming language. The bot-making results could be utilized in various countries with the calculation method found in a bot, providing a preference for users. Telegram bot is preferred for smartphone users wanting storage efficiency and having an internet connection. In contrast, the conventional prayer scheduler application does not care much about the storage taken and does not have an internet connection.