Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Penjualan Produk Pertanian Pada Badan Usaha Milik Desa (BUMDES) Sinar Bulan

Technological developments that are increasingly advanced and developing can be utilized to support human work, including in the government sector. One form of good government can be seen from the services provided to the public which have been carried out effectively and efficiently. In the business world, the role of information technology and reliable application programs is no longer in doubt in supporting the ability of business units to win business competition. BUMDes Sinar Bulan Village has several business units that have been developed, one of which is the sale of agricultural products, because almost all of the people of Sinar Bulan Village are farmers, so agricultural products can be used as an opportunity by the village government to increase BUMDes income. In terms of recording buying and selling transactions, it is still done manually and does not comply with the rules in the accounting application program, resulting in an ineffective and inefficient recording system. Therefore, this research aims to create a Sinar Bulan village-owned enterprise (BUMDES) application program for WEB-based sales of agricultural products
Inorganic Waste Reduction Planning with The Implementation of Dipo Waste Bank (DWB) and Reverse Vending Machine (RVM) at Diponegoro University

Plastic waste is a global environmental problem that has existed for a long time and has not been resolved. On a worldwide scale, solid waste increased to 9.1 billion tons, of which 6.9 billion tons was plastic waste. Undip is one of the largest public universities. As one of the universities that supports the achievement of SDGs Number 12 concerning waste management, Diponegoro University has a Waste Bank, namely the Dipo Waste Bank (DWB). The method used in this study is mass balance. Based on the projected waste generation from 2021-2030, the era of inorganic waste in the composition of plastic bottles at Undip is 42,577 kg/day. DWB is expected to realize independent and sustainable waste management within Diponegoro University (UNDIP). In its course, waste management efforts are felt to be lacking due to several obstacles and participation that are not optimal. There are three scenarios of reducing inorganic waste of plastic bottles in the study, namely the baseline scenario, the Dipo Waste Bank (DWB) scenario can achieve the target of 20% of waste that can be recovered by DWB and Reverse Vending Machine (RVM).
Comprehending a Man and a Scientist (to the Anniversary of Yuri Nikolayevich Stolyarov)

The article is devoted to the 85th anniversary of Yuri Nikolayevich Stolyarov, an outstanding Russian scientist, Doctor of Pedagogical Sciences, a recognized leader in the field of library science, documentology, document science knowledge, whose contribution to the development of scientific disciplines of document-communication cycle cannot be overestimated. The purpose of the article is to consider through the prism of personal perception the enduring significance of scientific activity of Yu.N. Stolyarov, his role in training and education of scientific and professional staff. The great importance of the fundamental research of Yu.N. Stolyarov for the development of the theory and practice of library science, document science, book science, including book culture, their role in the formation of documentology as an independent scientific discipline is emphasized. His invaluable contribution to the solution of terminological problems relevant to these spheres of scientific knowledge is highlighted. The article highlights the main components of high creative achievements of Yu.N. Stolyarov. Among them are the amazing ability to always be in scientific search, to discover new knowledge, to determine the vector of its development for many years ahead, as well as the scale, systematicity, originality of thought, the highest competence in the field of philosophy and methodology of science, the widest range of scientific interests, inexhaustible energy and tireless work.
Diagnosing Poverty Eradication Through Literacy: Analysis from Indonesia National Socioeconomic Survey

Literacy can be interpreted as a tool for the development of global society. However, the low literacy rate in Indonesia obstructs the quality of human resource improvement in education. Based on data from the PISA (2019), Indonesia is ranked 62nd in the literacy level from 70 countries. Quality of education is one of the economical ways to reduce the illiteracy that is interrelated to poverty. A question arises as to whether literacy-based cognitive abilities can reduce poverty . Previous studies on education for poverty eradication were limited to the regular cognitive domain. This study attempts to fill that gap. A Probit regression model was used with the probability of becoming poor as the dependent variable. A set of literacy-based cognitive abilities, such as age, gender, education level, and geographical factor are used as explanatory variables. According to 2019 National Socioeconomic Survey data, the analysis indicated that literacy-based cognitive abilities have a significant negative impact on poverty. Literacy has often been limited to the ability to read, write, and perform basic arithmetic. In order people to function effectively in their communities, the government should put focus on education attainment that cultivates functional literacy. It refers to the practical skills needed to read, write, and perform math for real-life purposes.
Perlindungan Hukum Kepada Konsumen Terhadap Produk Skincare Tanpa Izin Edar Yang Dijual Secara Online

This research was conducted with the aim of knowing legal protection for consumers for skincare products without a marketing authorization that are sold online. This study uses normative juridical research methods, namely using data collection techniques through library research or using secondary data. Among them are official documents, books, research results, in the form of reports. In this study, it was concluded that consumers who have experienced losses as a result of skincare products without marketing authorization can take legal action through the courts or outside the courts. However, this legal protection is still underutilized by consumers in the event of a problem, so that the UUPK becomes less effective, because compensation is given within seven days after the transaction. If this provision is maintained, it will be difficult for consumers who experience losses to obtain protection. This is because the negative effects of using skincare can occur after seven days.
4 Designing Augmented Reality

Making Dinosaurs Dance: A Toolkit for Digital Design in Museums takes the reader behind the scenes to learn how the American Museum of Natural History innovates visitor digital engagement, highlighting design techniques used both there and at museums around the world. Based on the author’s six years at the landmark institution that inspired the Night at the Museum franchise, the book introduces The Six Tools of Digital Design - user research, rapid prototyping, public piloting, iterative design, youth collaboration, and teaming up – then applies them through case studies across a range of topics:
Analisis Perilaku Pelanggan menggunakan Metode Ensemble Logistic Regression

Customer behavior analysis is crucial for companies, especially Small and Medium Enterprises (SMEs), to make strategic decisions. Through customer behavior analysis, patterns of customer behavior in purchasing a product or service can be identified. This provides insights into preferences and the right strategies to maintain or increase customer loyalty. This research aims to analyze customer behavior using the ensemble logistic regression method. Data was collected from the company's customer database over the last 5 years. Customer behavior is characterized by gender, age, and the preferred vehicle transmission type. The ensemble logistic regression method was implemented to improve model accuracy. The results show that the ensemble technique can enhance the accuracy of the Logistic Regression method. Model accuracy significantly increased with boosting, achieving an accuracy of 76%, while the model with bagging achieved an accuracy of 75%.
Evaluating the Performance of Supervised Machine Learning Algorithms in Breast Cancer Datasets

Breast cancer is the leading cause of mortality globally. Several attempts have been made to use data mining methodology together with machine learning techniques to develop systems that can detect or prevent breast cancer. In line with the reviewed paper; large datasets for illness analysis have been developed. In this study, the results of selected Machine Learning algorithms are compared: Decision Table, J48, SGD, bagging, and Naïve Bayes Updateable on Wisconsin Breast Cancer Original dataset was conducted using weka tools. Exploratory data analysis, pre-processed with supervised attribute selection and class order, was used to identify potential features to aid the performance of the chosen algorithms in classification. The empirical result showed that Decision Table explores greater likelihood (74% correctly classified instances, True Positive Rate of 0.752, False Positive Rate of 0.478, Precision of 0.77, receiver operating characteristic Area of 0.682) in terms of accuracy and efficiency compared with others. This study's comparison technique is thought to aid breast cancer detection.
SegSemPuzzle: Solving Jigsaw Puzzles with Semantic Segmentation

The traditional Jigsaw Puzzle is a challenging task performed by humans, mainly due to its hardness and proven to be a NP-Complete problem. Even so, recent efforts show better performance in this task using different methods involving complex computer vision and machine learning techniques. In this sense, this paper proposes new approaches based on the semantic segmentation (SS) task to solve jigsaw puzzles (visual puzzles) in reduced training scenario. To the best of our knowledge, this is the first work in the literature that uses SS for the target application. In the performed experiments, it was possible to demonstrate that SegSemPuzzle and SegSemPuzzle-G obtained excellent results when compared with other approaches existing in literature for 3 × 3 puzzle solving tasks.
Structuring narrative information via metadata: a case of clinical narratives

The goal of the current work is to improve and assess the existing metadata for clinical narrative information. The metadata schema for clinical narrative information borrows from the domain of narrative information and clinical narration. The elements corresponding to the narrative information were from the elements of narration, narrative theories and entities in the ontology-based narrative models. The elements of clinical narration are identified from the patient stories. This study found that the narrative in general and medical narratives in specific lack a metadata schema. The work developed a metadata framework for medical narrative information. Following the development of this framework, an evaluation by experts was conducted through the Delphi method.
ANALISIS SENTIMEN TERHADAP PROGRAM MERDEKA BELAJAR-KAMPUS MERDEKA (MBKM) PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER

Merdeka Belajar-Kampus Merdeka (MBKM) is a policy of the Indonesian Ministry of Education and Culture, which plays an important role in autonomous and flexible learning in student learning activities outside of study programs. However, MBKM has pros and cons, so it is necessary to analyze and evaluate its policies to improve performance through feedback from the community. This research will conduct a sentiment analysis on the MBKM policy on Twitter user tweets from 2022 with the keywords "MBKM", "kampus merdeka" and "merdekbelajar". The Naive Bayes Classifier (NBC) is used to analyze multiclass sentiment in Indonesian tweets into 3 (Three) Sentiment classes. Dataset collection and preparation begins with feature selection, eliminating duplication and tweet selection, then pre-processing is carried out, namely case folding, tokenizing, character cleaning, normalization to stemming for use in Labeling using Textblob which is required in making the Naïve Bayes Classifier model. The results of this study resulted in the Naïve Bayes model which had been trained from training data of 300 tweets with the best accuracy value of 79.66% having a Precision value of 79%, a Recall value of 80% and an F1-Score value of 79%. from the test of 1175 data the results were dominated by positive sentiment, namely 53.44%, followed by "neutral" sentiment, namely 34.47%, and "negative" sentiment, namely 12.08%.&#x0D;
The Effect of Tax Revenue Mobilization on the Economic Growth of Ethiopia

A study on the long run and short run effects of tax revenue mobilization on the economic growth of Ethiopia was conducted using an explanatory research design and mixed research approach. Secondary data from world development indicators spanning from 1990 to 2021 were analyzed using Auto regressive distributive lag (ARDL). The study found no long run causal relationship between taxation, different tax classifications, and economic growth in Ethiopia during the study period. However, in the short run, the current period of tax revenue had a positive and significant impact on economic growth, while a one-period lag in tax revenue had a negative and significant effect. Specifically, the one-period lag in direct tax negatively affected economic growth. Indirect tax did not show a significant effect in both the short run and long run. Based on these findings, the study recommends that the Ethiopian government and the Ministry of Revenues review the tax system and implement mechanisms to shift tax revenue sources from direct tax to indirect tax. This can be achieved by introducing strong tax collection methods and emphasizing the collection of domestic indirect tax.
THE EFFECTIVENESS OF USING BINGO GAMES TO IMPROVE STUDENT’S SPELLING ABILITY AT SMP KRISTEN SA’DAN

THE EFFECTIVENESS OF USING BINGO GAMES TO IMPROVE STUDENT’S SPELLING ABILITY AT SMP KRISTEN SA’DAN
IMPROVING STUDENTS’ VOCABULARY THROUGH SPOOF TEXT AT FIRST GRADE OF SMP NEGERI 1 SUNGGUMINASA

This study was to know the ability of students of SMP Negeri 1 Sungguminasa in mastering vocabulary by using spoof text. It was conducted at VII class of SMPN 34 Makassar academic year 2021/2022 as the subject of the study. This study is categorized as a collaborative classroom action research. The writer worked collaboratively with the English teacherin the class. The CAR was done based on Kurt Lewins’ design. The writer did two cycles in which each cycle consists of planning, acting, observing, and reflecting. The data were gathered through qualitative and quantitative data. The qualitative data was gained by analyzing observation result. Then, quantitative data were obtained from the students’ writing score of pretest and posttest. The finding of this study showed that there was an improvement on the students’ ability in vocabulary. It can be seen from the mean of pretest score was 60.31. Then, the mean of posttest cycle 1 score was 66.38 and the mean of posttest cycle 2 score was 75.31. In addition, there were 4 students (9.5%) who passed Minimum Mastery Criterion – Kriteria Ketuntasan Minimal (KKM) in the pre test. Meanwhile, in the cycle 1, there were 14 students ( 33.3%) who passed Minimum Mastery Criterion – Kriteria Ketuntasan Minimal (KKM) and it gained which was in the posttest cycle 2 there were 37 students (88%) who passed Minimum Mastery Criterion – Kriteria Ketuntasan Minimal (KKM), so the criterion of success was achieved. Related to the results of the gained data, it can be concluded that spoof text can improve students’ ability in vocabulary.
The Influence of E-Word of Mouth Marketing on Students’ Selection of Management Degrees

This study aims to examine the influence of electronic word-of-mouth marketing on students’ selection of management degrees. A structured questionnaire was employed to gather data. The target population consisted of management undergraduates during the period from 2016 to 2022 in Sri Lanka, as well as potential students interested in pursuing a management degree program. A sample size of 384 respondents was initially selected, and 312 responses were collected from the management undergraduates of the Open University of Sri Lanka and five other private universities in Sri Lanka. After removing outliers, a total of 283 responses were considered for analysis. Due to the unknown size of the total population, a convenience sampling technique was utilized to select the responses from the undergraduates. The regression analysis results revealed significant and positive effects of e-word of mouth (eWOM) quality, eWOM quantity, and eWOM credibility on students' intention to select management degree programs. Notably, it was found that eWOM credibility exerted the strongest influence on students' intentions in this regard. These findings highlight the importance of considering the quality, quantity, and credibility of eWOM when assessing its impact on students' decision-making process of degree selection. The findings of this study contribute to the existing body of knowledge on e-word-of-mouth marketing in the context of higher education. The results provide insights into the extent to which eWOM marketing influences students' decision-making process and their intention to choose management degree programs. Educational institutions and marketers can leverage these insights to design effective marketing strategies and enhance their online reputation to attract and retain prospective students in Sri Lanka.
A comprehensive survey of cloud computing, encompassing architectures, data storage, services, security, and applications

Generally, enterprises store data in internal storage and establish firewalls to protect against intruders attempting to access the data. In cloud computing, data is stored in storage provided by service providers. Information privacy protection involves storing data in encrypted form. The cloud system is responsible for both storage and the encryption or decryption of data, allowing system administrators to access encrypted data and decryption keys. This capability poses a risk to information privacy as it enables unauthorized access to information. This survey discusses Data Storage in Cloud Computing, focusing on the concept of storing encryption and decryption services alongside the storage service. Additionally, the survey examines storage system architecture, security challenges, services, and user application systems to provide a comparison of various cloud models. In addition to the fundamental aspects of data storage and encryption, this survey delves into the evolving landscape of cloud computing, addressing the dynamic nature of storage system architectures. Enterprises increasingly rely on cloud service providers for scalable and flexible storage solutions, prompting a critical examination of the security challenges associated with this paradigm shift. The survey examines how storage services in different cloud models affect user applications and privacy. It stresses the importance of understanding cloud providers' services amidst evolving technology and growing threats. The study delves into trends like edge computing and distributed storage, evaluating their impact on cloud storage security. Overall, it aims to provide a comprehensive overview for enterprises and researchers to make informed decisions on data security in Cloud Computing.
Deep Learning Application – Identifying PII (Personally Identifiable Information) to Protect

ABSTRACT This paper presents application of deep learning and machine learning models in detecting personally identifiable information (PII) in unstructured text (emails). The proposed models use support vector machine (trained using sequential minimal optimization) and long short term memory (LSTM) artificial neural network. Synthetic email dataset has been used to train and validate the proposed models and the outcomes are measured by standard measures of accuracy, precision, recall and F1-score of each of the proposed model. The experimental results on the model that uses support vector machine (trained using sequential minimal optimization) showed most promising results on detecting the personally identifiable information in the email dataset. The LSTM model also showed equally promising results. Keywords: Personally Identifiable Information, Deep Learning in detecting PII, Machine Learning in detecting PII, Artificial Intelligence in protecting privacy, Protecting Personally Identifiable Information.
Alat Pencegah Dini Kecelakaan Pada Kendaraan Bermotor Berbasis Internet of Things (Studi Kasus Jalur Sitinjau Lauik)

The creation of an Internet of Things-based motor vehicle early prevetion tool (Sitinjau Lauik Case Study) has been completed.. The aim of this research is to develop a tool to minimize accidents caused by human physiological factors such as fatigue and drowsiness. This device utilizes the MPU 6050 sensor, pulse heart sensor, buzzer, vibration motor, GPS, Arduino Uno and Wemos D1 Mini. The research method began with the construction of the device and the permance testing of the system. The results indicate that the system of this device can detect drowsiness or fatigue by measuring the heart rate at less than 70 bpm using the pulse heart sensor and detecting head movements using the MPU 6050 sensor, where the X- axis angle is &gt; 29,23 ° &lt; - 30,63 ° and the Y-axis angle is &gt; 29,33° &lt; -20,12°. When drowsiness is detected, the device sends notifications via the Telegram application containing the driver’s condition, heart rate, andlocation through a Google Maps link. Subsequently, when the driver is drowsy, the buzzer sounds to provide a warning and the vibration motor provides vibration to give a sensory wake-up signal to the driver, helping to awaken them from a drowsy state. Overall, the device functions effectively.
Prediksi Daya Listrik Pada Pembangkit Listrik Siklus Gabungan Berdasarkan Kondisi Lingkungan Menggunakan Metode Machine Learning

The utilization of machine learning methods in energy simulation enables the optimization of energy use and improves energy efficiency. In this research, the modeling of predicting power output was conducted under full load conditions in a Combined Cycle Power Plant (CCPP) based on the surrounding environmental conditions. Historical data of CCPP operation were used to model and predict power output under various environmental conditions. In this study, four machine learning algorithms, namely Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), were compared and evaluated for their performance. The evaluation metrics used to measure the model performance were Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Squared. The research results indicate that the Random Forest (RF) model achieved the best performance compared to other models with MAE of 2.314, RMSE of 3.372, and R-squared of 0.961. Additionally, the RF model also performed the best compared to other models in external testing with new data, where RF obtained values of MAE 2.579, RMSE 3.315, and R-squared 0.957. These results are consistent with the previous testing, indicating that RF has stable and reliable performance in predicting larger and more diverse datasets. This research contributes to understanding the potential application of machine learning in the power generation industry, especially in CCPP.