Reviewerly/exHarmony huggingface dataset:
Analisis Perspektif Auditor Kementerian Keuangan atas Pengawasan Intern Jarak Jauh

The study aims to analyze the perspective of government auditors on the use of remote internal supervision. A qualitative method with a case study approach is used in this study to get an overview of the use of remote internal control in organizations based on the UTAUT2 theory of technology use. Interviews were conducted with several auditors from various levels of Itjen Kemenkeu. The study finds that that the PIJJ method will be used by Itjen Kemenkeu auditors in the future. To maintain and increase the use of PIJJ in the future, the Itjen Kemenkeu needs to consider several factors that support the use of PIJJ, namely expectations of effort, expectations of performance, facilitative conditions, social influence, hedonic motivation, and habits. Organizations can provide training according to competency needs in PIJJ; use technology that is easy to use and user-friendly; promote the advantages and benefits of PIJJ for auditor performance; establishing a reward system for PIJJ's performance; managing organizational policies and culture; and arrangement of assignment teams. The results of this study have implications for the Inspector General of the Ministry of Finance and other government internal auditor organizations. Organizations can anticipate actions that need to be taken to increase the use of PIJJ.
Search Optimization of PIP Scholarship Recipients In Web-Based Student Data Application Using The Levenshtein Distance Algorithm

Realizing that education is very important, the government supports every citizen to get education. One of the government programs is the Smart Indonesia Program. PlP is a scholarship designed to help school-age children from poor/vulnerable families to continue to receive education services, both through formal elementary to high school/vocational schools and non-formal pathways from package a to package c and special education. SDN II Babakanloa has not been touched by technology for processing student data. So that the student section has difficulties in recording and updating student data. Student names have unique identities and errors often occur in typing the keywords to be searched. This results in an information that is desired or sought can not be found. Therefore we need a web-based data application that can provide keyword corrections in searching for student names. This study aims to create a web-based student data application by optimizing corrections to typing keywords searched by implementing the Levenshtein Distance Algorithm and also making it easier to process and search student data. The development method used is the Rational Unified Process (RUP) with the stages of Inception, Elaboration, Construction, and Transition. Designed using the CodeIgniter Framework with the PHP and JavaScript programming languages. The application of the Levenshtein Distance Algorithm can optimize the search for student data and reduce the occurrence of search errors by School Operators. The application of the Levenshtein Distance Algorithm produces a very good accuracy rate of 94% of the results of student data correction. accordance with the expectations of the School Operator. So it shows that the application of the Levenshtein Distance Algorithm is appropriate to use in optimizing the search.
Paradigmatic relations in Construction Morphology: the case of Dutch Noun+Verb compounds

The formal and semantic properties of Noun+Verb compounds in Dutch can be accounted for by means of paradigmatically related word-constructional schemas. We can form verbal compounds with an infinitival head from nominal compounds with a deverbal head noun, for instance boekbespreken ‘to book-review’, derived from boekbespreking ’book review’ through substitution of ‑ing by ‑en. The paradigmatically related constructions of verbal compounds with an infinitival head and nominal compounds with a deverbal head thus constitute a paradigm for word formation. The paradigmatic origin of verbal compounds makes the rare use of finite forms of these compounds understandable. The form of nominalizations for Noun+Verb compounds provides additional evidence for the paradigmatic origin of these verbal compounds. The analysis proposed lends support to morphological frameworks in which paradigmatic relations between words play a crucial role, such as Construction Morphology.
The Performance of the Equal-Width and Equal-Frequency Discretization Methods on Data Features in Classification Process

The classification process often needs help with suboptimal accuracy values, which can be attributed to various factors, including the dataset's wide range of attribute values. Discretization methods offer a solution to address these issues. This study aims to compare the effectiveness of Equal-Width and Equal-Frequency discretization methods in enhancing accuracy during the classification process using datasets with varying sizes. The research employs Naïve Bayes, Decision Tree, and Support Vector Machine as classification models, with three datasets utilized: Bandung City Traffic data (3804 records), Bandung City COVID-19 cases data (2718 records), and Bandung City Dengue Fever Disease Index data (150 records). Three experimental scenarios are executed to assess the impact of the two discretization methods on accuracy. The first scenario involves no discretization, the second employs Equal-Width, and the third applies Equal-Frequency discretization. Experimental results indicate significant accuracy improvements post-discretization. The Naïve Bayes model achieved 94% accuracy for the Traffic dataset, while the Decision Tree achieved 71% accuracy for the COVID-19 dataset and an impressive 98% for the Dengue Fever Disease dataset. These outcomes demonstrate that applying Equal-Width and Equal-Frequency discretization methods addresses the challenge of wide attribute value ranges in the classification process.
Sentiment Classification of Fuel Price Rise in Economic Aspects Using Lexicon and SVM Method

After being hit by COVID-19 for a long time around the world which resulted in the paralysis of all countries, especially the economic aspects of all countries that dropped dramatically, the world was again shocked by the conflict between Russia and Ukraine which resulted in an increase in world oil prices including in Indonesia, many people complained and opposed the government's policy of increasing fuel prices because fuel affects various aspects, including economic aspects. Based on these problems, researchers use sentiment analysis methods that aim to find out people's opinions on issues that are being discussed throughout Indonesia and this research focuses on comparing the SVM algorithm with TF-IDF feature extraction then using K-Fold Cross Validation after that it is compared with the Lexicon Inset dictionary, in this case the model with Lexicon Inset which contains weighting on each word. In this study, it was found that the dataset model using the SVM algorithm with TF-IDF feature extraction and then using K-Fold Cross Validation obtained an average accuracy of 0.85 using the SVM algorithm. While the model using the automatic labeling dataset using the Indonesian sentiment Lexicon (Lexicon Inset) obtained an average accuracy of 0.68. Classification using SVM with TF-IDF feature extraction is superior to using Lexicon Inset.
The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification

Marine fauna are all types of organisms that live in the marine environment. Marine fauna is also an important part of the marine ecosystem that has an important role in maintaining environmental balance. However, the survival of marine fauna is threatened due to activities carried out by humans, such as pollution, overfishing, industrial waste disposal into marine waters, plastic pollution and so on. Therefore, efforts are needed to monitor and protect marine fauna so that marine ecosystems can remain stable. One way to monitor marine fauna is by using classification technology. One of the technologies that can be used in marine fauna classification technology is Convolutional Neural Network (CNN). CNN is one of the classification methods that can be used to classify objects in images with a high level of accuracy. The CNN architecture models used are MobileNet, Xception, and VGG19. Furthermore, the method used to improve the performance of the CNN algorithm is the Transfer Learning method. The test results show that the MobileNet architecture model produces the highest accuracy value of 91.94% compared to Xception and VGG19 which only get an accuracy value of 87.64% and 88.42%. This shows that the MobileNet model has a more optimal performance in classifying marine fauna.
Comparison of Sentiment Analysis Methods on Topic Haram of Music In Youtube

Sentiment analysis on video lectures on YouTube that discuss the haram of music is an exciting topic to find out public opinion. This study aims to find what factors affect the model's accuracy in sentiment analysis, especially on video lecture content on YouTube. The data used is comment data on three video lectures that discuss the haram of music, which has been labelled into two categories: positive and negative. The data is divided into two categories, namely primary data, as many as 2099 data that have not been normalized, while secondary data has 1001 data that have been normalized. The experiment shows that the validity of the data, labelling the data, the amount of data, and preprocessing are essential points in forming a good sentiment analysis classification model because, from the test results, it was found that imbalance techniques such as SMOTE, word embedding word2Vec and FastText, and SVM and KNN classification algorithms do not provide maximum accuracy if the data used primary data. However, the data imbalance process, such as oversampling and SVM and KNN classification algorithms, will provide better model accuracy if used with secondary data. Based on the trial results, it is found that when using the SVM algorithm, primary data produces the highest accuracy at 58.35%, while secondary data is 72.23%. If using KNN, the primary data provides the highest model accuracy at 53.54%, while the secondary data has the highest accuracy at 72.81%. Based on these results, it was found that the validity of the data or data must be appropriate and related to the case raised and labelling the data must be done carefully because the most crucial is the inappropriate data in preprocessing the data must be done correctly, if data preprocessing is done in an inappropriate way then data imbalance techniques such as oversampling do not have enough influence on increasing accuracy, but if on the contrary then accuracy will increase. The selection of the right word embedding also affects accuracy. It is necessary to do many experiments to select the correct algorithm and follow the data owned because selecting the correct algorithm will provide maximum accuracy model results
Comparison of NB and SVM in Sentiment Analysis of Cyberbullying using Feature Selection

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.
Classification of E-Commerce Product Descriptions with The Tf-Idf and Svm Methods

The rapidly growing e-commerce sector presents a significant challenge in navigating an abundance of products. Understanding and classifying product descriptions efficiently and accurately is crucial to improving user experience and business operations. This research employed the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Support Vector Machine (SVM) for the classification of e-commerce product descriptions into four categories: Electronics, Household Items, Books, and Clothing. The initial phase involved pre-processing of text data which incorporated text cleaning, tokenization, part-of-speech tagging, entity recognition, and conversion into a vector representation. The resulting model was trained and tested using the SVM algorithm. Our model demonstrated a high degree of accuracy, achieving 99.2% during the training phase and 95.7% in the testing phase. This model provides a valuable tool for e-commerce businesses, as it allows for accurate classification of products based on their descriptions. This could lead to improved user navigation and overall user experience on e-commerce platforms.
User Interface Design for Baduy Ecotourism Website Using User Centered Design Method

Baduy is one of the ecotourism destinations which offers captivating natural and cultural attractions, making it a worthy place to visit. The lack of available information in the media has piqued the author's interest in designing a visually appealing website to attract tourists. To address this issue, a user interface design will be created for the Baduy Tribe Ecotourism website using the User Centered Design (UCD) method. This method focuses on users and involves four stages, specifying the context of use, specifying requirements, designing a solution, and evaluating the design. Furthermore, to assess how well users interact with a product, usability testing will be conducted using the System Usability Scale (SUS) and Single Ease Question (SEQ). The usability testing results on the created website interface obtained a SUS score of 81 and generated 3 rating components in the SEQ method, fairly easy, easy, and very easy. Therefore, it can be concluded that the usability score falls within the category of good and is acceptable to users. Through this research, the author hopes that the user interface design for the Baduy Tribe Ecotourism website will meet users' needs, providing them with easy access to valid information and a seamless experience in discovering the natural wonders of the region.
Identification of 10 Regional Indonesian Languages Using Machine Learning

Language Identification plays a pivotal role in deciphering the rich tapestry of Indonesia's diverse regional languages, encompassing a wide spectrum of scripts, and spoken forms. Language Identification, an integral component of Natural Language Processing, is frequently addressed through Text Classification. In this study, we embark on the task of identifying 10 Indonesian languages, leveraging the NusaX dataset, with the overarching objective of contextual language determination. To achieve this, we harness a diverse array of machine learning techniques, including Support Vector Machine, Naïve Bayes Classifier, Decision Tree, Rocchio Classification, Logistic Regression, and Random Forest. We complement these methods with two distinct feature extraction approaches: N-gram and TF-IDF. This comprehensive approach enables us to construct robust models for language identification. Our findings unveil the strong efficacy of these models in discerning Indonesian languages, with the Naïve Bayes Classifier emerging as the frontrunner, achieving an impressive accuracy rate of 99.2% with TF-IDF and an even more remarkable 99.4% with N-Gram. To gain deeper insights, we delve into error analysis, revealing that misclassifications often stem from shared words across different languages. This research is underpinned by the necessity for a robust language identification model, underscoring its critical role within the complex linguistic landscape of Indonesian regional languages. These results hold great promise for applications in automated language processing and understanding within this diverse and multifaceted linguistic context.
Multidimensional Knapsack 0-1 Solution With Algorithm Evolution Pso-Ga

This paper develops the particle swarm optimization (PSO) method and uses a genetic algorithm (GA) by changing the distribution of articles in the initialization of the initial position. PSO at this time the search and speed of particles will always go to the best solution so that by narrowing the search area will be faster by updating the best position of PSO. While the Genetic algorithm plays a role to get an expanded search area for PSO solutions by utilizing crossover and mutation in GA. So that GA will expand the range of candidates for the best solution in PSO. From each of the advantages of PSO Update and GA will be combined to get Evolutionary PSO-GA (EVPGA) that can minimize error and speed up computation (itation) in finding the best solution. By using the Multidimensional Knapsack data set, the results of EVPGA get an average speed of 24.9s with an error of 1.49%.
Comparison of Algorithms for Sentiment Analysis of Operator Satisfaction Level for Increasing Neo Feeder Applications in PDDikti Higher Education LLDIKTI Region VI Semarang Central Java

Sentiment analysis on the satisfaction level of PDDikti operators is very important to find out how PDDikti operators feel after the version of the academic reporting application for higher education was upgraded, namely Neo Feeeder. The increase in the version of this application causes some of the features in it to not function properly. So some academic reporting activities from tertiary institutions experience problems. As a result of this condition, the most felt impact is students, where students experience delays in graduation. Then it is necessary to evaluate through sentiment analysis from PDDikti operators to find out the response from operators and be able to provide positive suggestions to developers from the PDDikti reporting application. This study applies several classification methods for sentiment analysis at once, including the Random Forest algorithm, the Support Vector Machine algorithm, the Multinomial Naïve Bayes algorithm, the Decision Tree algorithm, and the K-Nearest Neighbor algorithm. Of the 5 methods applied, the results of their performance accuracy will be compared. The performance of the highest classification algorithm is the K-Nearest Neighbor (K-NN) algorithm which produces an accuracy value when testing data, which is up to 90% using the oversampling technique in unbalanced classes. While the lowest classification accuracy performance value is in the Multinomial Naïve Bayes (MNB) algorithm with a value of 76%. It is proven that oversampling can help the performance of the classification algorithm to be more optimal. Thus, it should be noted that the balance of data classes is an important factor when applying the classification method.
UPAYA GURU DALAM MENINGKATKAN HASIL BELAJAR SISWA DENGAN MENGGUNAKAN METODE MINDMAPPING PADA MATA PELAJARAN IPA DI SDN DURI KEPA 03

The research was conducted in class III B at SDN Duri Kepa 03 in the odd semester of the 2021/2022 academic year with 32 students. The stages of this research were carried out in two cycles: (1) the planning stage, (2) the action implementation stage, (3) the observation stage, and (4) the reflection stage. Data collection in the study included observation sheets of student learning activities, teacher observation sheets of activities when the mind mapping learning method was used, and test questions that measure student learning outcomes in terms of material changes in the shape of objects used. Data analysis techniques in this study used qualitative and quantitative analysis. The results of this study indicate that the use of the mind mapping learning method on changing the shape of objects can improve student learning outcomes at SDN Duri Kepa 03. These results can be seen from Cycle I, where the average class achievement was 72.81 at the completeness level of 20 students (62, 5%), while in Cycle II the class average was 72.81 at the completeness level of 28 students (87.5%). score increases. By using mind mapping techniques in this learning activity, you will find that learning outcomes and students' responsiveness to learning materials increase. Classroom learning encourages students to be more active and curious about what is being taught.
KNPC Blockchain Platform &amp; the Digitization of the Shipment Nomination Process

Abstract This study examines the potential of blockchain technology to streamline the Shipment Nomination and Preparation Process in the oil and gas sector. With the industry grappling with challenges related to inefficiencies, errors, and delays in traditional systems, there is a pressing need for digital transformation. The primary objective of this research is to explore how blockchain, with its inherent attributes of decentralization, immutability, and transparency, can enhance the efficiency, accuracy, and cost-effectiveness of this process. A custom-built blockchain application was designed and implemented, focusing on digitizing the entire process, including real-time data sharing, digitization of physical documentation, and enhancing the coordination and communication among multiple stakeholders. The transition to the blockchain-based digital process required meticulous planning, effective stakeholder cooperation, and a comprehensive understanding of the traditional system to successfully integrate the existing procedures into the new digital platform. The results revealed significant improvements in efficiency, accuracy, and cost savings, with reduced manual procedures, enhanced data accuracy, and considerable time savings. The blockchain application demonstrated a marked improvement by introducing digital capabilities into a traditionally manual system, mitigating many longstanding issues. Moreover, the study highlighted the potential challenges in such a transition, including the integration of existing procedures into the digital platform, ensuring seamless stakeholder communication and cooperation, and implementing robust security measures and protocols. The research findings provide valuable insights into the applicability of blockchain technology beyond its current applications, suggesting its potential to drive digital transformation across various industries and processes. The study also proposes avenues for future research, including an investigation into the integration of blockchain with other emerging technologies, expanding its transformative potential. It sets the stage for broader application and exploration of blockchain technology across various industries, emphasizing the transformative potential of digital technologies in streamlining operations and enhancing process transparency.