Reviewerly/exHarmony huggingface dataset:
STORAGE SERVER DATABASE UTILIZATION FORECASTING USING HOLT-WINTERS AND ARIMA METHODS IN E-GOVERNMENT SYSTEM. STUDY AT KEMENKEU RI

Storage is a storage medium which is an important part of the system infrastructure. Storage utilization is one of the variables in determining the needs and performance of a system. If the storage is lacking or depleted it will cause the system to be hampered and the possibility of interference occurring. There were eight complaints or reports of system disturbances recorded in the Ministry of Finance's ticketing service desk application caused by anomalies in storage or disk capacity. Things like this can be anticipated if we know the storage capacity requirements for the future. In compiling ICT capacity analysis at Pusintek there has been no use of past data to plan future storage requirements. The use of data mining algorithms can be used to obtain forecast values from storage utilization. The use of the ARIMA model and Holt-Winters Exponential Smoothing as a method used to predict inventory is considered to be a fairly accurate model. Both algorithms are used to compare which accuracy is better in predicting storage requirements by measuring RMSE. Research data was obtained from monthly utilization reports from January 2021 to December 2022. From the evaluation results it can be concluded that forecasting storage server database utilization using the Holt-Winters method is better than the ARIMA method with the RMSE results for the Holt-Winters method being 14332.661717740748 and the RMSE method ARIMA is 20498.977982137938. The results of this forecasting can be utilized in planning database server storage needs.
SYSTEMATIC LITERATURE REVIEW OF THE CLASS IMBALANCE CHALLENGES IN MACHINE LEARNING

The significant growth of data poses its own challenges, both in terms of storing, managing, and analyzing the available data. Untreated and unanalyzed data can only provide limited benefits to its owner. In many cases, the data we analyze is imbalanced. An example of natural data imbalance is in detecting financial fraud, where the number of non-fraudulent transactions is usually much higher than fraudulent ones. This imbalance issue can affect the accuracy and performance of machine learning classification models. Many machine learning classification models tend to learn more general patterns in the majority class. As a result, the model may overlook patterns that exist in the minority class. Various research has been conducted to address the problem of imbalanced data. The objective of this systematic literature review is to provide the latest developments regarding the cases, methods used, and evaluation techniques in handling imbalanced data. This research successfully identifies new methods and is expected to provide more choices for researchers so that imbalanced data can be properly handled, and classification models can produce unbiased, accurate, and consistent results.
SENTIMENT ANALYSIS OF INDONESIA'S CAPITAL CITY RELOCATION USING THREE ALGORITHMS: NAÏVE BAYES, KNN, AND RANDOM FOREST

The relocation of Indonesia's capital city from Jakarta to the IKN Nusantara on the island of Borneo has become a trending topic that triggers conversations and opinions on various social media. The pros and cons of this policy are very pronounced in various media, especially on Twitter or X platform. The purpose of this research is to conduct a public sentiment analysis of public opinion related to the relocation of Indonesia's capital city. Data is taken from tweets comments collected during a certain period from June to September 2023. This research uses a Natural Language Processing approach with data pre-processing techniques to prepare the data before applying labeling and classification algorithms. This research tests the accuracy of three algorithms used in classification, namely Naïve Bayes Classifier, K-Nearest Neighbor, and Random Forest. The results of the data classification show that positive sentiment has a value of 36.8%, neutral sentiment is at 25%, and negative sentiment related to the relocation of the capital city is 38.1%. Then an accuracy test was carried out on the Naïve Bayes Classifier Algorithm method which found an accuracy value of 65.26%, the K-Nearest Neighbor Algorithm of 58.25%, and the Random Forest Algorithm of 45.05%. This shows that the Naïve Bayes Classifier Algorithm method has better accuracy than other algorithms in predicting classification in sentiment analysis. This research also identifies the frequency of key words that often appear in each sentiment which can be valuable information for monitoring public opinion on social media.
ANALYSIS OF PUBLIC SENTIMENT RELATED TO THE FAILURE OF INDONESIA TO HOST U-20 USING MULTINOMIAL NAÏVE BAYES CLASSIFIER

The case of Indonesia's failure to host the U-20 World Cup in 2023 has become a hot topic of discussion in Indonesia. The rejection of the Israel U-20 national team and security factors by FIFA are considered the main reasons for the cancellation. This raises many issues and controversies from various parties. In this study, sentiment analysis using the Naive Bayes algorithm was conducted. Researchers use the naive bayes algorithm because this algorithm has high accuracy with simple calculations. The data obtained in this study came from 250 tweets of Twitter data with a ratio of training and test data of 7:3. The results showed good data classification with 97.26% accuracy, 93.33% precision, and 100% recall. In conclusion, the classification model developed can describe public sentiment related to Indonesia's failure in the U-20 World Cup well.
THE EFFECT OF UNIGRAM AND BIGRAM IN THE NAÏVE BAYES MULTINOMIAL FOR ANALYZING OF COMMENT SENTIMENT OF GOJEK APPLICATION IN GOOGLE PLAY STORE

In sentiment classification systems that use Naïve Bayes Classifier, a commonly used feature extraction method is TF-IDF with unigram and bigram, where the two is used separately. In the reality, most of texts contain single or composed word,so it is needed to use the combination of unigram and bigram to maximize the accuracy of the classification results. In this research, the impact and performance improvement between classification systems using unigram or bigram solely and those using a combination of both are studied. Using 1000 data of reviews with ratings 1 (negative) and 5 (positive) from Gojek users on the Google Play Store, and performing performance validation with K-Fold at K=10, the system that uses the combined TF-IDF feature extraction of unigrams and bigrams achieves the best performance among the three systems with an accuracy of 0.84, however the accuracy of the system that uses unigrams solely has accuracy of 0.83, and 0.7 for the system that uses bigram. From the results of the research, it can be concluded that the use of the combination of unigram and bigram can increase the accuracy of the classification result.
Comparison of Decision Tree and K-Nearest Neighbor Performance for Determining Mustahik

The problem of poverty is one of the fundamental issues of concern to the Indonesian government. One of the methods used by Islam to alleviate poverty is through zakat from Badan Amil Zakat Nasional (BAZNAS). Currently, the distribution of zakat is divided into two, namely in the form of consumptive zakat and productive zakat. Productive zakat is aimed at people who need business capital. To assist zakat managers in managing their funds, a mechanism is needed that can process mustahik data so that it can be selected more quickly and precisely using data mining. In this research, the data mining methods that will be used are K-nearest neighbor (KNN) and Decision Tree. The dataset used in this research is data obtained from BAZNAS and has been preprocessed to obtain a dataset with 7 attributes and 144 records. Decision trees, KNN Manhattan, and KNN Euclidean are used to predict mustahik candidates who are worthy of receiving zakat. The performance of the third method was tested using AUC and confusion matrix namely Accuracy, Precision, Recall, and F1 in each dataset split scenario of 70%:30%, 75%:25%, and 80%:20%. Based on the number of false positive and false negative results, the best performance obtained is KNN Euclidean with a dataset division scenario of 80%:20%.
IMPLEMENTATION OF TEXT PROCESSING FOR SENTIMENT ANALYSIS OF TAX PAYMENT INTEREST AFTER THE "RUBICON" PHENOMENON

In February 2023, an incident occurred involving the child of an official from the Indonesian Directorate General of Taxes who committed violence against a member of the GP Ansor organization. The news spread widely and brought a new issue, namely suspicious reporting of the official's wealth with an amount of up to 56 billion Indonesian Rupiahs. In order to determine public sentiment towards the "RUBICON" case, which was receiving attention, sentiment analysis of tax payment interest was conducted using text mining techniques. Data processing was done using the R language and RStudio application, taking a dataset of 23,785 tweets from the public about paying taxes on Twitter. Next, text cleaning was done to remove numbers, symbols, and URLs, as well as text processing using stemming, tokenizing, stopword removal, and TF-IDF methods. The TF-IDF method shows that the words "rafael" and "case" are the top keywords. This study used a supervised model by comparing SVM, KNN, and Naive Bayes algorithms, and evaluation was done using a confusion matrix with accuracy results in descending order of 0.8922, 0.8049, and 0.7369. The conclusion of this study is that the SVM algorithm successfully classified sentiment with the highest level of accuracy and obtained the highest negative sentiment of 5,616 sentences.
AUTOMATIC GIT REPOSITORY DEPLOYER IN UBUNTU USING PYTHON, JENKINS AND CLOUD FIRESTORE AT PT XYZ

PT XYZ is a retail company using Cloud Computing to support its business operations. The company requires the back-end application deployment process to be done automatically on each server computer. The problem occurs insecure in the process of the back-end application deployed manually causing human error vulnerabilities and inefficient when the version of application change occurs to operate on each server computer takes time as long as the number of server computers that need to be configured. The research aims to design a deployer system that help company to deploy back-end applications on each Ubuntu server computer automatically efficiently and securely. The research method uses Research and Development to create system. The research produces a design and build of an automated deployer system using Jenkins to create data configuration that aim the version of back-end application as target deploy and stored in the Cloud Firestore database. The stored data causing deployer system change version back-end application as stored data using GIT command and provide application to operate as service on each server computer. Operation result of application recorded as data deployment that stored in the Cloud Firestore then Jenkins detect it to store data as deployment log. Based on the results, the deployer system considered is able to operate back-end application automatically according the target version on each server computer as eficient and secure deployment process. This conclusion is supported by the results of the questionnaire, which obtained a 85% percentage and was classified as "Strongly Agree" with the created system.
ENHANCING SENTIMENT ANALYSIS WITH CHATBOTS: A COMPARATIVE STUDY OF TEXT PRE-PROCESSING

Text pre-processing plays a crucial role in the Sentiment Analysis process. Machine Learning models like Chat GPT-3.5 by OpenAI and Google Bard serve as alternative methods for text pre-processing. This study aims to evaluate the capabilities of both Chatbots in the text pre-processing stage while assessing their performance using a dataset obtained by crawling from source X. The study involves a comparison of Chat GPT-3.5 and Google Bard using Decision Tree and Naïve Bayes algorithms. The validation process employs K-Fold Cross Validation with a K value of 10. Additionally, three sampling methods, namely Linear, Shuffled, and Stratified Sampling, are utilized. The findings reveal that Chat GPT-3.5 performs best when using the Decision Tree algorithm with a K-Fold Cross value of 10, and employing Stratified Sampling, achieving an Accuracy of 90.68%, Precision of 90.63%, and Recall of 100%. On the other hand, Google Bard's optimal performance is achieved with the Decision Tree algorithm, a K-Fold Cross value of 10, and Shuffled Sampling, resulting in an Accuracy of 74.00%, Precision of 72.73%, and Recall of 98.77%. The study concludes that Chat GPT-3.5 and Google Bard are viable alternatives for text pre-processing in Sentiment Analysis. Performance measurements indicate that Chat GPT-3.5 outperforms Google Bard, achieving an Accuracy of 90.68%, Precision of 90.63%, and Recall of 100%. These results were validated by comparing them to human annotations, which achieved an accuracy score of 85.20%, Precision of 85.71%, and Recall of 99.03% when using the Decision Tree algorithm with a K-Fold Cross value of 10 and employing Stratified Sampling. This suggests that Chat GPT-3.5's text pre-processing performance is on par with human annotations.
FACEMASK DETECTION USING YOLO V5

The use of facemasks is one of the obligations when carrying out activities outside the home during the COVID-19 pandemic, but despite the COVID-19 pandemic, the use facemasks is still needed. One of the supporting factors driving this is air pollution. The use of facemasks can reduce the risk of respiratory diseases, because it is important to use facemasks when carrying out activities in place with a high risk of air pollution such as industrial areas, this is done to maintain the safety of its users both in term of healh and comfort. So consistency is needed for users to use masks, through current technological developments detecting the use of masks is one of the right solutions to this problem. One of the mask detection methods used in this study is YOLO (You Only Look Once). YOLO is a method that detects objects using a single neural network consisting of several layers of convolution networks for image feature extraction, then prediction of bounding box coordinates is performed simultaneously. The YOLO v5 training model in this study was carried out with a combination of minimum values ​​on img, batch, and epoch resulting in a maximum F1 value and mAP@50 of 86%.
RECOGNITION OF REAL-TIME HANDWRITTEN CHARACTERS USING CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE

Pattern recognition, including handwriting recognition, has become increasingly common in everyday life, as is recognizing important files, agreements or contracts that use handwriting. In handwriting recognition, there are two types of methods commonly used, namely online and offline recognition. In online recognition, handwriting patterns are associated with pattern recognition to generate and select distinctive patterns. In handwritten letter patterns, machine learning (deep learning) is used to classify patterns in a data set. One of the popular and accurate deep learning models in image classification is the convolutional neural network (CNN). In this study, CNN will be implemented together with the OpenCV library to detect and recognize handwritten letters in real-time. Data on handwritten alphabet letters were obtained from the handwriting of 20 students with a total of 1,040 images, consisting of 520 uppercase (A-Z) images and 520 lowercase (a-z) images. The data is divided into 90% for training and 10% for testing. Through experimentation, it was found that the best CNN architecture has 5 layers with features (32, 32, 64, 64, 128), uses the Adam optimizer, and conducts training with a batch size of 20 and 100 epochs. The evaluation results show that the training accuracy is between 85, 90% to 89.83% and testing accuracy between 84.00% to 87.00%, with training and testing losses ranging from 0.322 to 0.499. This research produces the best CNN architecture with training and testing accuracy obtained from testing. The developed CNN model can be used as a reference or basis for the development of more complex handwriting pattern recognition models or for pattern recognition in other domains, such as object recognition in computer vision, facial recognition, and other object detection.
ANALYSIS OF IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION (PSO) METHOD ON LECTURERS ASSIGNMENTS TO STUDENTS

When preparing a program for a conference, it is very important to divide teaching and learning tasks according to the areas in which you are involved for teaching and learning to be effective. At the University, the assignment process is still done manually which is very time consuming. Therefore, an appropriate optimization method is needed to handle this. This problem can be solved using a population-based heuristic approach, Particle Swarm Optimization (PSO) has been applied to various fields such as scheduling and assignment. The data used in this research is lecturer assignment data in the form of prioritizing lecturer interest in teaching certain subjects. Based on the calculation results, a test was carried out to determine the effect of the test parameters on the fitness value obtained. From the results of the PSO parameter test, the best number of particles is 100, the best number of repetitions is 100, and the speed combination parameters c1 and c2 are 1.5 and 1.5 with the appropriate value of 94878. The system results, the solution obtained gives good results, i.e. always within tolerance limits, the error scores obtained by placing teachers on subjects that suit their preferences are lower
Implementation of Random Shuffle Algorithm to Randomize Questions in Anti-Corruption Prevention Game

Socializing anti-corruption education in Indonesia is crucial to fostering public aversion towards corruption. Games are a highly effective medium for disseminating anti-corruption prevention during school years. This research aims to implement anti-corruption education within a game using the shuffle random algorithm to ensure the successful randomization of anti-corruption questions. This data was obtained through a literature review. The method used in this study is the Multimedia Development Life Cycle (MDLC). The results of this research indicate that the testing yielded a 100% accuracy rate for the shuffle random algorithm in randomizing the questions. The accuracy rate demonstrates that the shuffle random algorithm can effectively randomize the questions and is easy to implement.
Uncovering Hidden Realities: A Phenomenological Approach to Financial Crime in Public Procurement

This research aims to uncover the hidden reality of financial crime in the context of public procurement using a phenomenological approach. Through in-depth analyses of the experiences of financial criminals, procurement officials, and the communities involved, the research identifies the factors that drive fraud in the procurement process. The results show that the complexity of the procurement system, local social, economic and cultural pressures play a key role in the dynamics of financial crime. Dissatisfaction with complex procurement procedures and pressure to achieve a certain status are the main triggers for fraud. The implications of this research underscore the need for reforms in the procurement system, increased transparency and accountability. Practical suggestions involve additional education and training for procurement officials, addressing social pressure through social programs, and strengthening internal and external oversight. By highlighting the long-term impact of fraud in public procurement, this research provides a basis for policy and practice changes to create an environment of greater integrity and efficiency in the use of public funds. Collaboration with stakeholders, including researchers and academics, was identified as a strategic step towards achieving this goal.
Analysis of Web-Based E-Learning Management System Business Proccess to Increase Learning Effectiveness at SMAABC Bandung

The results of the review of the implementation of differentiated learning activities at SMA ABC Bandung which are in accordance with the vision of an independent curriculum based on a digital school as well as the diverse characteristics of students at SMA ABC Bandung support the development of a digital technology-based learning management system with the E-Learning Management System of SMA ABC Bandung which is built in 2020 in line with the LFH (Learning From Home) learning scheme. This supports innovation and creation in the development of digitalization-based learning in this school. This research aims to evaluate the design of E-Learning products that use Moodle as a platform, with a focus on the product's ability to increase the effectiveness of differentiated learning at SMA ABC Bandung. This research uses a Research and Development approach with a one group pre-test post-test design. The samples for this research were students in classes X, XI, XII and several E­Learning Management System operators at SMA ABC Bandung. Based on the results of the existing design analysis, it can be seen and concluded that the E­Learning Management System application for SMA ABC Bandung has good effectiveness. In this way, it can be utilized effectively by all school components that use it.