Reviewerly/exHarmony huggingface dataset:
Manipulative impact of implicit communication:

This paper presents the application of a quantitative model for measuring the impact of manipulative implicit linguistic strategies on a small comparable corpus of Italian, German and French political discourses. The aim is to show the cross-linguistic applicability of the model, originally developed and put to test on Italian. Furthermore, the analysis allows a quantitative and qualitative comparison of the three comparable corpora: some statistical correlations and tendencies in the frequency and type of linguistic implicit strategies are presented and put in relation not only with the language, but also with the political orientation of the speaker and with other parameters (context, subject, rhetorical style). Results show that the model can be applied to multiple languages and that inter- and intra-linguistic tendencies in the use of manipulative implicit linguistic strategies can be appreciated.
The Virtual Reality Socialization and Training: Virtual Tour

The rapid world of virtual reality (VR) technology provides opportunities for school promotion media using VR widely, namely Virtual Tour training. FIRST This also challenges SMK MUTU to understand better the effectiveness of VR experiences in encouraging better attitudes towards promotional objectives and shaping visiting patterns. SECOND: This basic procedure is technical for taking panoramic images, identifying the resulting panoramic images owned, and observing 360-degree changes when processed in 3DVISTA software. THIRD If the panoramic image has become 360o, then make it into a series of images so that when processed, it will become a single image that is more alive in 3D like Google Street View, when in compile Virtual Tour will provide an experience where users can interact with a 3D image. FOURTH how positive are the consequences of the feeling of presence in the VR experience for the first time, the feeling of being in a virtual environment increases the peak of enjoyment of VR users both the increased touch of VR technology, such as being there results in a stronger sense of liking and preference at the destination. A positive attitude change leads to a higher level of intention to visit.
The Use of Video Blog Posted in YouTube for Teaching Spoken Recount Text

This research was conducted to investigate the effect of video blog posted in YouTube to teach spoken recount text to the secondary students. The design was the one group pretest posttest design of pre-experimental study. The sample, X MIPA 2 of SMA Negeri 2 Pontianak, was chosen from cluster random sampling technique totaling 30 students. The findings indicate that there is a significant effect of video blog posted in YouTube towards the students’ ability in spoken recount text. The t-test calculation was 25.92 which was higher than t-table 2.045 (df=29; significance α=0.05). Meanwhile, the effect size was 4.41 categorized as strong effect. From these results, it was revealed that the Ho was rejected while the Ha was accepted. Therefore, it can be concluded that video blog posted in YouTube was effective to teach spoken recount text to secondary students, especially the tenth-grade ones. Thus, the researchers recommend this technique to be used as one of alternatives to teach spoken recount text, specifically related to people’s experience.
Improved reinforcement learning path planning algorithm integrating prior knowledge

In order to realize the optimization of autonomous navigation of mobile robot under the condition of partial environmental knowledge known. An improved Q-learning reinforcement learning algorithm based on prior knowledge is proposed to solve the problem of slow convergence and low learning efficiency in mobile robot path planning. Prior knowledge is used to initialize the Q-value, so as to guide the agent to move toward the target direction with a greater probability from the early stage of the algorithm, eliminating a large number of invalid iterations. The greedy factor ε is dynamically adjusted based on the number of times the agent successfully reaches the target position, so as to better balance exploration and exploitation and accelerate convergence. Simulation results show that the improved Q-learning algorithm has a faster convergence rate and higher learning efficiency than the traditional algorithm. The improved algorithm has practical significance for improving the efficiency of autonomous navigation of mobile robots.
COMPARISON OF K-N EAREST NEIGHBOR AND NAÏVE BAYES ALGORITHMS FOR PREDICTION OF APTIKOM MEMBERSHIP ACTIVITY EXTENSION IN 2023

So far APTIKOM as the Informatics and Computer Higher Education Association has provided many opportunities for registered members to participate in discussions on the development of science among fellow association members, access to various professional experts, as well as technical and non-technical guidelines in the field of education. With the various opportunities above, it is hoped that all members will support the activities of each member who has joined or has just joined so that a good association can be created. This study aims to find out about the problems that occur in APTIKOM, namely members who have registered as members but rarely renew their membership which results in data accumulation in APTIKOM. This research method uses the k-nn and naïve Bayes algorithms by using data sets from 2012 to 2022. The dataset used is APTIKOM member data and has 5 attributes namely name, gender, last education, institution and validation secret. To calculate the research test using a rapid miner. The purpose of this study is to predict whether in the following year there will be a membership renewal process for all APTIKOM members who have been recorded from 2012 to 2022. Furthermore, the results of this study have a different level of accuracy. Where for k-nn the resulting accuracy is 94.00% and for the result of naïve Bayes is 91.35%.
Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches

Stroke is one of the deadly diseases. This is illustrated in stroke deaths in Indonesia which reached a death rate of 131.8 cases. Some of the things that cause a stroke to become a disease with the highest mortality rate are related to transitions in human life in 4 aspects, namely epidemiology, demography, technology, and economics, socio-culture. Of the many influencing aspects, one of the transition points of human life in the technological aspect can be an alternative solution and prevention. Aspects of technology with the utilization of data can be used as a preventive measure for stroke. One approach is to use data mining techniques, which can provide an initial picture regarding the chances of getting a stroke so that it can be used as an early warning for patients. With so many techniques in data mining, this study used a classification or grouping approach using 2 algorithms, namely K-Nearest Neighbor and one of the Neural Network groups, namely Multi-Layer Perceptron. This research will focus on finding the accuracy and best results of the two algorithms in classifying. The final result of this study is that the K-Nearest Neighbor algorithm has a better accuracy of 95% compared to the Multi-Layer Perceptron which produces an accuracy of 88%
Design of IoT-Based Tomato Plant Growth Monitoring System in The Yard

The development of tomato plants to produce good fruit cannot be separated from environmental factors that affect their growth and development of tomato plants. These factors include soil moisture, soil pH, temperature, or the amount of light received by tomato plants. The need for water in tomato plants is also very important for their continued growth. Monitoring the development of tomato plants in home gardens based on IoT (Internet of Things) is a monitoring system that utilizes IoT technology to collect, transmit, and analyze data about tomato plants in real-time. In this system, sensors connected to the internet network will be installed on tomato plants to measure several parameters such as soil moisture, air temperature, light intensity, and soil nutrient / pH levels in plants. The collected data will be sent to an IoT platform that will be able to analyze the data. The results of the analysis will be used to make decisions regarding plant care, such as providing water or nutrients that the plants need to grow properly. With cameras to monitor the physical development of the plants, plant height, and fruit development. With this system, communities and farmers can grow tomato plants and can monitor and control plant conditions in real-time through smartphone applications. By utilizing IoT technology, monitoring the development of tomato plants becomes more efficient and accurate. Communities and farmers can take preventive measures to avoid plant disorders and diseases before it's too late, to increase the production and quality of crops.
Implementation Opinion Mining For Extraction Of Opinion Learning In University

Opinion mining is a field of Natural Language Processing (NLP) that is used to carry out the process of extracting and processing textual data which functions to obtain information through sentiment analysis from a document in the form of text, among others, to detect attitudes towards objects or people. Sub-processes in opinion mining can use documents of subjectivity, opinion orientation, and detection targets to find out the data used as sentiment analysis, sentiment analysis aims to assess emotions, attitudes, opinions, and evaluations conveyed by a speaker or writer towards a product or towards a public figure. In this study, an opinion mining system was developed to analyze learning in college. The methodology used is quantitative descriptive, while the processing of sentiment analysis data uses Azure machine learning. Sentiment analysis results are very good at assessing opinions or opinions and emotions, and attitudes conveyed by someone. The learning process is the main element that must run well so that competency and achievement in learning can be maximally conveyed to students. Documents that identified opinions were then classified into negative, neutral, and positive opinions based on the results. In general, it can be concluded that the value obtained by sentiment analysis using Azure Machine Learning tools is quite good, judging from the results of a positive class of 0.79 and a neutral class of 0.53. The use of cleaning and labeling techniques and other classifications is still very possible to use. To get a better accuracy value.
Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3

Tomato diseases vary greatly, one of which is tomato leaf disease. Some variants of leaf diseases include late blight, septoria leaf, yellow leaf curl virus, bacteria, mosaic virus, leaf fungus, two-spotted spider mite, and powdery mildew. By knowing the disease on tomato leaves, you can find medicine for the disease. So that it can increase the production of tomatoes with good quality and a lot of quantity. The problem that often occurs is that farmers cannot determine the disease in plants, they try to find suitable herbal medicines for their plants. After being given the drug, many plants actually died due to the pesticides given to the tomato plants. This is detrimental to tomato farmers. This problem is caused by incorrect disease detection. Therefore, this study aims to solve the problem of disease detection in tomato plants, in a more specific case, namely tomato leaves. Detection in this study uses a deep learning algorithm that uses a Convolutional Neural Network, specifically GoogleNet and EfficientNetB3. The dataset used comes from kaggle and google image. Both data sets have been pre-processed to match the data set class. Image preprocessing is performed to produce appropriate image datasets and improve performance accuracy. The dataset is trained to get the model. The training using GoogleNet resulted in an accuracy of 98.10%, loss of 0.0602 and using EfficientNetB3 resulted in an accuracy of 99.94%, loss: 0.1966.
Analysis of the SVM Method to Determine the Level of Online Shopping Satisfaction in the Community

Online shopping is an activity of buying goods done online (virtual). This online shopping process is done because it doesn't waste a lot of time. With online shopping, it is very easy for people. Just need open mobile phone view and select the desired item and then order goods and goods will be delivered to the house. But online shopping sometimes also has drawbacks which are one of the reasons people don't want to shop online, such as long shipping times, expensive shipping costs. Therefore a study was made about the level of public satisfaction in online shopping. Researchers will make a data classification about the level of public satisfaction in online shopping using the SVM method. This study aims to see the level of public satisfaction with online shopping, many or nope satisfied people when shopping online. The first step is to collect data that will be used in the data mining process. After that, data preprocessing will be carried out planning the design of the SVM method and finally the prediction process to get Classification results. Then the classification results obtained using the SVM method in data mining show that 34 people are satisfied with online shopping (for a representation result of 59.65%), 23 people are dissatisfied with online shopping (for a representation result of 40.35%). These results state that there are still many people who are satisfied with shopping online and there are some people who are dissatisfied with online shopping
Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation.
Application of the Naïve Bayes Algorithm in Determining Sales Of The Month

One important factor for creating a healthy and growing company is the existence of sales rewards for employees to achieve sales targets every month. Assessing employees is not an easy thing when there are so many employees. This will make the assessment team have to look at the criteria carefully and carefully. Data manipulation can occur because it is difficult to make decisions with such large criteria and data without automated data mining. As a result, the company will not get competitive human resources. Sales targets are one of the keys to sales success because with sales targets, the sales prediction value can be used as a guide as a reference in determining product sales. One way to make better sales predictions is by utilizing data mining processing using the Naive Bayes algorithm. The Naive Bayes algorithm calculates the probability value of each of the attributes examined including attendance, sales targets and sales returns. Research with employee absence criteria, monthly sales and monthly sales invoice returns. From the results of the research that has been done, it can be concluded that the application of the Naive Bayes classifier method to the target data set for sales of goods achieves an optimization level of 95.78%, with attendance criteria greatly affecting employee performance so that product sales targets each month can be achieved
Sentiment Analysis of Beauty Product Applications using the Naïve Bayes Method

The number of beauty products that appear on the market makes every producer compete in attracting consumers. One of the facilities provided by manufacturers to make it easier for consumers to shop is an online shopping application that can be accessed via gadgets. Where the feature of the application is the availability of user review services User reviews are often used as a recommendation for the product to be purchased. The more positive the reviews that appear, the greater the consumer's confidence to buy the product; conversely, the more negative the reviews that appear, the more reluctant consumers are to buy. This study aims to find out how much accuracy the Naïve Bayes algorithm has in conducting sentiment analysis on user reviews of beauty product applications with different combinations of training and test data. Furthermore, it is also important to know the frequency of words that often appear in the review. The sentiment class used is divided into three, namely, positive, negative, and neutral. This research method includes a number of stages, namely: data collection, data labeling, text pre-processing, data visualization, TF-IDF, sentiment analysis, etc., until the results are obtained. This research has produced the highest accuracy rate of 90.08% in the Naïve Bayes algorithm, with a composition of 90% training data and 10% test data. While the word that often appears in user reviews is "application," with a frequency of 446 occurrences, it is followed by the word "product," 444 times, and the word "price," 312 times. The greater the amount of training data used, the higher the level of accuracy resulting from the Naïve Bayes algorithm. Meanwhile, the greater the amount of test data used, the lower the resulting accuracy value.
Black Box Testing of the "Hybrid Engine" Application Using Boundary Value Analysis Technique

The "Hybrid Engine" application is an introduction to a hybrid engine that is packaged attractively and can be accessed online, this application is very important for conveying information about hybrid engines, if an error occurs in the functional application there can be misunderstandings about the information conveyed. Therefore it is necessary to test to ensure the quality of the application that has been produced. Testing is an evaluation process of assessing the functional quality of software to check whether the software meets the expected process or not. Functional processes that have not been maximized can cause inequalities in the data information to be displayed. Applications that have been designed must go through the testing stages to ensure the level of functional quality. Of the several types of black box testing methods, one of them is Boundary Value Analysis. The method tests the maximum and minimum number of digits to produce a valid value and is easy enough to test "hybrid engine" applications. The first stage carried out in this research is to identify the functionality to be processed and ensure that the maximum and minimum number of digits matches the predetermined system arrangement. The result of applying the method used is that the quality of the application is under its function, and can be utilized properly by the user. The results of the Boundary Value Analysis test show that the application is following the expected system and instructions with a success percentage of 78.245615%.
Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects

Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that is well-known for its effectiveness in image recognition tasks, while DETR utilizes Transformers to study object representations directly from images. We tested our system performance on the COCO dataset and demonstrated that ResNet-50 + DETR achieves a better level of accuracy than DETR models that do not use ResNet-50. In addition, we also show that ResNet-50 + DETR can detect objects more quickly than similar traditional CNN models. The results of our research show that the use of ResNet-50 in the DETR system can improve object detection performance in images by about 90%. We also show that using ResNet-50 in DETR systems can improve object detection speed, which is a huge advantage in real-time applications. We hope that the results of this research can contribute to the development of object detection technology in images in the world of computer vision.
Analysis of the Neural Network Method to Determine Interest in Buying Pertamax Fuel

Fuel is one of the needs that is used by the community as a material to be used on motorcycles or cars. Fuel has become an important need for society, because when there is no fuel, a motorbike or car that is owned by someone cannot be used. Each vehicle has its own fuel, for motorbikes the fuel is pertalite, Pertamax, Pertamax Turbo and for cars the fuel is diesel and dexlite. For the fuel used in motorbikes, there are some people who are interested in Pertalite fuel and there are not many people who are interested in Pertamax fuel. So researchers will make a study of public interest in Pertamax fuel. This research will be made using the neural network method by classifying community data in data mining. This study aims to see the public's interest in purchasing Pertamax fuel. The research process was carried out with the initial stages of collecting and selecting data to be used, then preprocessing, then designing the neural network method and finally the testing process to obtain classification results using the neural network method. The results obtained from data classification using the neural network method state that there are 23 people who are interested in Pertamax fuel and 18 people who are not interested in Pertamax fuel. It turns out that many people are interested in Pertamax fuel.
Aiding and abetting: How police media information units shape local news coverage

&lt;p&gt;[Para. 1]: "The Toronto Police Service is a $930-million-a-year operation that in-cludes a sophisticated public-information unit offering reporters all the news the police deem fit to print. In 2010 the unit issued 2,138 press releases, published 246 Internet stories, and maintained a website that made everything from homicide maps to then-Police Chief Bill Blair’s videotaped remarks available to 1.5 million visitors. In addition to the three uniformed officers who worked full time in the unit answering questions from journalists, three civilians with other responsibilities also responded to media inquiries."&lt;/p&gt; &lt;p&gt;Chapter in Chris Richardson and Romayne Smith Fullerton (Eds.), &lt;em&gt;Covering Canadian Crimes: What Journalists Should Know and the Public Should Question &lt;/em&gt;(pp. 193-216)&lt;em&gt;.&lt;/em&gt; Toronto, ON: University of Toronto Press. &lt;/p&gt;