Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL COOPERATIVE LEARNING TIPE SNOWBALL THROWING UNTUK MENINGKATKAN HASIL BELAJAR IPS SD

The purpose of this research was to identify, describe and analyze the results of segera studies related to dd application of the Snowball Throwing type of cooperative learning model to improve social sciences elementary school learning outcomes. The Systematic Literature Review (SLR) method was used in this study, by searching the DOAJ, Scholar and Garuda databases from 2012 to 2022. Data was collected by selecting several studies based on several inclusion criteria and a total of 20 qualified articles were obtained. This study Classifies qualified primary studies based on categories such as Educational level, research method and the effectiveness of applying the Snowball Throwing type cooperative learning model to improve social studies learning outcomes. The result is that as whole, the application of the Snowball Throwing type of cooperative learning model cantik improve social studies learning outcomes and have a positive impact on learning carried out, especially studies at the elementary level. In addition, the method often used by researchers is the quantitative method which can see how the effectiveness of learning with the use of the Snowball Throwing type cooperative learning model has on student social studies learning outcomes, with the research design that is widely used namely Classroom action research.
SCIENTIFIC ARTICLES RECOMMENDATION SYSTEM BASED ON USER’S RELATEDNESS USING ITEM-BASED COLLABORATIVE FILTERING METHOD

Scientific article recommendation still remains one of the challenging issues in education, including learning process. Difficulties in finding related articles from research history and research interest have been experienced by students in collage affecting the duration of study and research time. This paper proposed a new solution by building a search engine to collect and to recommend articles related to student research topics. The system combined the web scraping method as an article data retrieval technique on google scholar and item-based collaborative filtering to recommend the article. Parameters result produced based on items of user’s history, including item-searched, clicked, and downloaded. The system was built on a web-based scientific article recommendation system using python programming language. This system recommends articles based on the preferences of users and other users who are affiliated and who have an interest in the same item. This research showed that the validation result from the system obtained a recommendation accuracy value over 0.516801. The percentage of the RMSE error value of the recommendation system is 8.62%, or in other words that the accuracy of the recommendation system is 91.28%.
IDENTIFYING POSSIBLE RUMOR SPREADERS ON TWITTER USING THE SVM AND FEATURE LEVEL EXTRACTION

In everyday life, many events occur and give rise to various kinds of information, which are also rumors. Rumors can cause fear and influence public opinion about the event in question. Identifying possible rumor spreaders is extremely helpful in preventing the spread of rumors. Feature extraction can be done to expand the feature set, which consists of conversational features in the form of social networks formed from user replies, user features such as following, tweet count, verified, etc., and tweet features with text analysis such as punctuation and sentiment values. These features become instances used for classification. This study aims to identify possible spreaders of rumors on Twitter with the SVM classification model. This instance-based classification algorithm is good for linear and non-linear classification. In the non-linear classification, additional kernels are used, such as linear, RBF, and sigmoid. The research focuses on getting the best model with high performance values from all the models and kernel functions that have been defined. It was found that the SVM classification model with the RBF kernel has a high overall performance value for each data combination with a ratio of the amount of data is 1:1 or the difference is very large. This model gives accurate results with an average of 97.02%. With a wide distribution of data, the SVM classification model with the RBF kernel is able to map the data properly.
ANDROID MOBILE-BASED ENGLISH LEARNING GAME EDUCATION FOR CHILDREN IN INDONESIA

The increasing use of english languange and its importance as a global languange make english languange become important to learn and understand by many people. However, there are some difficulties in learning English, especially for early childhood in Indonesia, due to various factors, including: lack of vocabulary, and teaching materials and books that are less innovative. Therefore, this study aims to build an edugame that contains animal and fruit vocabulary for early childhood in Indonesia based on the CCI guidelines to make applications that are quite innovative. The ADDIE method was chosen as the research method used which consists of: pre-production, production, and post-production. The edugame application in this reseacrh contains of two main menus, are: learning menu that contain fruits and animal vocabularies alongside with sound of their spelling, and play game menu, that challenge children to guess the correct answer of every vocabulary. According to the result of pretest and posttet test that conducted on 40 children with parental assistance showed the increasing of scores in answering list of questions. Besides that, the SUS testing carried out in this research got an average point about 84.81 which means that the applicatian has a good function and can be accepted by users.
PORTRAYING ACADEMIC WRITING APPTITUDE AND THE PLAGIARISM ISSUES AMONG EFL STUDENTS

This article is the research result of teaching-learning Academic Writing to English Students Department, UNG in the fourth semester of 2020. This research objective is to enrich applying theories accessed from online books and international Journals by avoiding plagiarism in students’ writing essay as theoretical bases of their research. Besides, they would be able to communicate in the target language of writing skill as a maximum standard. There are three categories of the rubric to analyze essay in this qualitative method; a) choosing ideas in introductory sentences before taking quotations, b) accuracy diction selection, and c) linkage ideas in well sentences formulation after choosing the quotation/paraphrase. The result, from 25 students’ writing, there are 15 students who got value of A category (60%), 5 students with A- category (20%), 3 students got B+ category (12%), and 2 students got C (8%).
EFFECT OF AUDIT STANDARD COMPLIANCE, DIGITAL AUDIT, TIME BUDGET PRESSURE ON RED FLAG AND AUDIT DELAY

The purpose of this study was to test the ability of red flags as mediation of audit delay. This research was conducted because it is to test that red flags are signs of fraud or indications of fraud that auditors must pay attention to avoid audit delay. A total of 40 questionnaires were distributed to auditors from junior to partner level, but only 37 questionnaires were returned and processed. Hypothesis testing in this study uses analysis with the SmartPLS application with the Outer Model and Inner Model Test. The results showed that red flag is a mediation showing that red flag has a significant effect on audit delay. This shows that in the presence of red flags, auditors must be more careful and take immediate action to prevent audit delay through the variables tested, namely, Audit Standards Compliance, Digital Audit, Time Budget Pressure. Audit delay can occur due to red flags so that the auditor's decision to make a decision to provide an opinion will take a longer time.
Investigating EFL Students Reading Preferences (Digital or Printed)

Reading is crucial to the process of learning a language. Students can use digital technology to support reading activities in today's modern culture. As a result of students' need for information in the digital age, digital text is now virtually everywhere. This study investigated EFL students reading preferences (digital or printed). The participants of this study were 102 EFL students from private universities in South Sumatera. The data was collected through 20 items of questionnaire distributed through google form. Based on the result of the study, it could be concluded that first, most participants enjoy reading. They read for a variety of purposes, including to get the knowledge, to get information, to get the entertainment, and for other purposes. The second, the participants prefer reading digital texts. The practicality of the digital texts is the primary motivating factor why they chose it. They acknowledged that they do not need to hold thick texts or books while reading, they only need to bring their devices. Furthermore, they said one device can store and read multiple texts. They added that reading materials on digital devices are considerably more intriguing and enjoyable than those on printed. Third, the researchers noted that all readers—whether they preferred reading printed or digital texts—felt at comfortable with the reading equipment they had selected. . Finally, it is revealed from the data that most participants are more inspired to read when they use digital reading texts.
Peningkatan Kualitas Pembelajaran Keterampilan Berbicara Bahasa Indonesia melalui Teknik Bercerita (Penelitian Tindakan Kelas Pada Siswa Kelas VII-3 SMPN 13 Bandar Lampung Tahun Pelajaran 2022/2023)

The ability to speak is one of the language skills that a person needs to have. That is someone who lives in the community and school environment and anywhere. Speaking is common to us, but the failure of learning speaking skills is still widely heard among schools. This study used a classroom action research method that lasted for three months, carrying out various activities. The results possessed in learning Indonesian speaking skills through storytelling techniques can increase, this can be seen from the results of the pre-test nlai average children 40.5 while in the results of the children's test 77.15, and cycle 1 children get an average score of 63.3 and cycle II an average of 73.58. The above improvement can be seen that learning Indonesian language skills through storytelling techniques at SMPN 13 Bandar Lampung can increase, this shows that storytelling techniques are feasible and can be used to be applied in schools because they provide good results, especially in speaking skills.
Melejitkan Hasil Belajar Peserta Didik pada Mata Pelajaran Ilmu Pengetahuan Sosial melalui Metode Jigsaw

The research aimed to improve the students’ outcome in Social Science subject through Jigsaw. The subjects of this research were the grade IX students of class IX B at SMP Negeri 30 Bandarlampung year 2018/2019. Therefore, the classroom action research by using the Jigsaw model was conducted. This classroom action research consisted of 3 cycles. The subject of the research was 27 students from class IX B. The data of students’ activity level was obtained from the observation sheet and the result of students group work assignment. The data of student’s learning outcome was obtained from individual written evaluation. The research showed that there was an increase in students’ activity level and learning outcome from cycle I through cycle III. Those result proved that through Jigsaw model, the learning outcomes in Social Science subject of students in class IX B at SMP Negeri 30 Bandarlampung year 2018/2019 significantly increased.
DECISION SUPPORT SYSTEM SELECTING CRYPTOCURRENCY EXCHANGE USING AHP METHOD

Cryptocurrency is a digital currency that functions as a standard currency and can be traded virtually without service fee and have a reliable institution. Investors and traders are interested in buying or acquiring cryptocurrencies and expect to make a profit when selling them. Apart from mining, cryptocurrencies can obtain by buying through cryptocurrency exchanges. The cryptocurrency exchange is helpful as an intermediary for buying and selling crypto assets. There are many types of cryptocurrency exchanges circulating, and it makes users have to select carefully the exchanger they will use. Based on this problem, a decision support system is needed to facilitate the decision-making. The method used in this research is the Analytical Hierarchy Process (AHP). This method is selected because it helps multi-criteria decision-making, solves complex problems into small forms, and receives input from human understanding by defining problems, making hierarchies, conducting comparative assessments, determining priorities, and calculating consistency values. The final result of this research using the AHP method can be seen that Binance is the most recommended cryptocurrency exchange to be used for investing or trading because it earns a percentage of 35.34%, followed by FTX at 28.35%, Huobi Global at 18.05%, Gate.io at 6.96%, KuCoin at 5.76%, and Indodax 5.55%. The results of this calculation are obtained by calculating the priority criteria and alternatives.
IMPLEMENTATION OF DATA MINING WITH CLASSIFICATION AND FORECASTING METHOD USE MODEL GAUSSIAN NAÏVE BAYES FOR BUILDING STORE (STUDI CASE: TB SINAR JAYA)

In the construction industry, the building materials business is a necessity, one of the businesses that is currently growing rapidly is the property business. The public’s need for a place to live becomes a business opportunity sought by the public. The Industry 4.0 transformation brought building materials stores to the online market, one of which is Tokopedia. TB. Sinar Jaya has a dataset of inventory and sales with a total of approximately 15,000 data rows which are updated each month. With large amounts of data, data mining and machine learning methods are needed in data management. 5 years rapid development of TB. Sinar Jaya has not been without problems, such as competition with online stores that offer lower prices than offline stores and a lack of strong marketing strategy. In this case, TB. Sinar Jaya wants help in making marketing strategy decisions by utilizing information system technology and minimizing existing problems. Based on these problems, it is necessary to implement data mining and machine learning gaussian algorithms naïve bayes to find out the average prices available on Tokopedia to increase sales and carry out classification, forecasting and TSA (Time Series Analysis) at TB Sinar Jaya. Based on the results of testing/research, the gaussian naive Bayes algorithm has good accuracy results with an accuracy level of 0.71 and gains insight, that is, for potential buyers they can do Wishlist efficiency, and for segments that generate profits below 50% of the total profit the researcher recommends carrying out a campaign program according to customer profile in order to improve the resulting profitability.
IMPLEMENTATION OF STEGANOGRAPHY ON DIGITAL IMAGE WITH MODIFIED VIGENERE CIPHER ALGORITHM AND LEAST SIGNIFICANT BIT (LSB) METHOD

Digital image can be utilized as a media to hide messages. There should be a special treatment in securing the messages so that the information accessibility can be controlled. Hiding messages to an image can be conducted using steganography. It can be combined with cryptography algorithms in order to make it harder to be accessed by unauthorized parties. The modification of cryptography algorithms can improve the confidentiality of the message content. This research is aimed to know the implementation of steganography in desktop applications using Least Significant Bit (LSB) that is combined with the modification of Vigenere Cipher algorithms. The developed software namely waterfall supports the creation of applications. The result of the research shows that the encrypted picture will change into gray or display in grayscale so that the hidden message will be more difficult to be accessed by the attacker. The encrypted picture will also have a bigger size file than the original version and the extended file. The LSB method will work precisely according to the formula of the modified cryptography algorithm. The further research is suggested to focus on the variation of media for content message and the picture format in their encryption.
TONE DETECTION ON TERANIKA MUSICAL INSTRUMENT USING DISCRETE WAVELET TRANSFORM AND DECISION TREE CLASSIFICATION

Musical instruments are one of the cultures that must be preserved. Teranika is one of the traditional musical instruments from the Majalengka area, which is made of clay. Currently, the manufacture of conventional musical instruments is still done manually, so there are still differences in the tone produced. Meanwhile, the quality of a musical instrument is determined by the accuracy of the technique. Therefore, we need a system that can accurately detect the method's accuracy. The author designed a tone detection system for Teranika musical instruments to help artisans carry out quality control. This system will detect whether or not the musical instrument is successfully matched with the right tone and agent. The technique contained in this musical instrument is Do Re Mi Fa So La Si Do high. To overcome these problems, the author makes this tone detection system using the Discrete Wavelet Transform method and the Decision Tree classification. The working principle of this system is that the recorded sound of musical instruments will be transmitted to this system. Then the sound will be processed as input and matched with the essential voice in the database. The output of this system produces samples according to the sampling frequency used. The test results show the best results at decomposition level 6, a thresholding value of 0.05, and a Fine Tree classification type with an accuracy of 87.5%
DATA WAREHOUSE MODEL BASED ON KIMBALL METHODOLOGY TO SUPPORT DECISION MAKING IN ASSET MAINTENANCE

ITSM e-Prime is an ICT service management application based on ITSM framework owned by Pusintek that includes service desk, incident management, problem management, change management, release management, and configuration management processes. Currently there is a problem in determining the number of devices that will be included in the device maintenance contract or determining the number of devices that need to be replaced in a given year. The objective of this research is to build an asset management data warehouse so that it can be utilized by the Data Analysis and Presentation Team to produce a dashboard that presents data on network infrastructure assets that need to be maintained or replaced for budget planning needs. This descriptive verification analysis research used nine out of ninety tables from the ITSM e-Prime application and applied dimensional modeling Kimball to build a data warehouse because this methodology offers high query performance and understandable by end-user. The resulting data warehouse were tables in the form of star-schema. The tests were carried out by qualitative methods, namely quality testing by users (user acceptance test and blackbox testing) and quantitative method, namely comparing the number of infrastructure devices included in the maintenance contract in 2022. The final result of this research is a data warehouse consisting of fact table F_infrastructure and dimension table D_Merk, D_Area, D_Kategori, D_EoS, D_Garansi, and D_StatusPemeliharaan with acceptance percentage of 95% based on the test results.
IMAGE DETECTION IN THE AIMBOT PROGRAM USING YOLOV4-TINY

Cheats are a way for players to gain an unfair advantage. The rise of cheats in online games encourages game producers to increase the security of their games by implementing an anti-cheat system. However, the currently widely circulated anti-cheat system only monitors incoming and outgoing raw data. With the widespread use of image detection systems, we can fool most of today's anti-cheat systems. This can be done by capturing the image that appears on the screen and then processing it through the image detection system. From the process, it can be seen whether there are opponents that appear on the screen. If there is, the program will move the mouse to the place where the enemy is and shoot it. This program is built on the core of the YOLOv4-tiny image detection system.
OPTIMIZING SENTIMENT ANALYSIS OF PRODUCT REVIEWS ON MARKETPLACE USING A COMBINATION OF PREPROCESSING TECHNIQUES, WORD2VEC, AND CONVOLUTIONAL NEURAL NETWORK

This research attempts to identify the most accurate and effective model in performing sentiment analysis on product reviews in marketplaces using preprocessing techniques, word2vec, and CNN. We collected 20,986 reviews from 720 products in a marketplace using scrap method, then cleaned and labeled the data to include 515 positive reviews, 490 negative reviews. We then performed preprocessing on the data using four different scenarios and identified word vector representation using word2vec. Subsequently, we applied the results of word2vec to the CNN architecture to classify sentiment in product reviews. After trying various variations of each technique, we found that a combination of the third preprocessing technique (case folding, punctuation removal, word normalization, and stemming), the second word2vec parameter combination (size 50, window 2, hs 0, and negative 10), and the fourth CNN parameter combination (kernel size 2, dropout 0.2, and learning rate 0.01) had the best accuracy of 99.00%, precision of 98.96%, and recall of 98.96%. We also found that the word normalization technique greatly helped to increase model accuracy by correcting improperly written or incorrect words in the reviews. Based on the evaluation of word2vec, the hs 0 method produced a higher average accuracy compared to the hs 1 method because the hs 0 method used negative sampling which helped the model understand the context of the trained words. In the CNN parameter, higher learning rates can cause the model to learn faster, but can also cause the model to be unstable, while lower learning rates can make the model more stable but can also cause the model's learning process to be slower.
IMPLEMENTATION OF CERTAINTY FACTOR METHOD IN PEST AND DISEASE DIAGNOSIS IN HYDROPONIC PLANTS

Pest and disease attacks often occur in hydroponic plants so that many plants are damaged and result in losses for farmers or crop failure. The problem of pests and plant diseases is the main obstacle in increasing agricultural productivity, these conditions affect farmers' income and the provision of hydroponic plant food. The farmers do not know what types of pests and symptoms of diseases attack plants, making it difficult to handle diseases on hydroponic plants. Likewise for hydroponic plants located at the Agricultural Service of Asahan Regency which are attacked by pests, hydroponic experts say that the appearance of small leaf spots appears on the upper side of the leaves that damage hydroponic plants and because the cause of pests that cause disease in hydroponic plants is unknown, so farmers are hesitant to take countermeasures. An expert system is a computer program designed to make decisions such as decisions taken by an expert or expert. Therefore, an expert system with the Certainty Factor method is made in the diagnosis of pests and diseases in hydroponic plants. The purpose of this study was to apply the certanity factor method to diagnose pests and diseases in hydroponic plants. The results of the pest data test found spider mites with a confidence level of 79% and wet rot disease with a confidence level of 62%. The conclusion obtained is that the certainty factor method can detect early types of pests and diseases on hydroponic plants quickly and accurately.