Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Informasi Kas Masuk Dan Kas Keluar Berbasis Web Pada Kantor Notaris Krisalia Wahyu Sari Kota Jambi

Krisalia Wahyu Sari Notary Office is a medium business unit located at Jln. Otto Iskandardinata No.59, which is engaged in making Authentic Deed regarding all deeds, agreements and provisions required by statutory regulations. The purpose of this study is to analyze the current system, so that it can overcome the problems faced at the Krisalia Wahyu Sari Notary Office of Jambi City, by designing Web-Based Cash In and Out Cash Information System Design at the Krisalia Wahyu Sari Notary Office in Jambi City . As for the constraints faced, company data security is not guaranteed, this is because documents are still in the Form of paper that is easily lost, damaged or lost, the information generated cannot be presented on time because it requires a long time to process data. The Research Framework that will be carried out in solving the problem discussed is identifying, searching for information based on theoretical foundations, collecting data using observation and interview methods, analyzing to find solutions to the problems faced by Krisalia Wahyu Sari's Notary Office in Jambi City. The method uses the waterfall model, the implementation of this study uses the PHP Language and MySQL DBMS. To produce data processing applications that are expected to facilitate data processing and report generation.
Analisis Kecurangan Terhadap Praktik Laporan Keuangan PT. Asuransi Jiwasraya (Persero) Di BEI Tahun 2017-2019

This research discusses fraud against the financial reporting practices of PT Asuransi Jiwasraya (Persero) 2017-2019, where the information presented in the financial statements must not have the interest of one party because it will harm the other party. This study aims to determine the occurrence of fraud in the practice of financial reporting at PT Asuransi Jiwasraya (Persero) in 2017-2019. This study uses secondary data. The population in this study is the company PT Asuransi Jiwasraya (Persero) which is listed on the Indonesia Stock Exchange in 2017-2019. This research uses descriptive quantitative method with descriptive statistical analysis tools and literature study collection methods. The results show that the income at PT Asuransi Jiwasraya (Persero) in 2017-2019 has increased quite significantly while the expenses for PT Asuransi Jiwasraya have also increased but overall it is very significant. This shows that the income at PT Asuransi Jiwasraya (Persero) has increased significantly when their expenses are high. In the case of this financial statement, the company should as much as possible reduce the expenses on the company so that they are more focused on company revenue.
Sistem Informasi Persediaan Perlengkapan Alat Alat Isi Ulang Air Minum Pada Toko Gun Cs

Gun cs shop is a company engaged in the sale of refill equipment. The process of data collection of incoming goods and goods out of stock inventory when searching for goods data is still using the manual method. With this, in order to make it easier, it is recommended to use an inventory information system. The purpose of this inventory system is to overcome the system that is currently running at the gun cs shop as well as a solution so that the store can manage inventory data more quickly. The result of this research is to produce a web-based inventory information system with the LIFO FIFO and Avarage method which provides convenience in providing information on inventory data through incoming and outgoing goods using data flow diagrams, DFD, ERD and MySQL for data processing.
Penerapan K-Medoids Clustering Dan Silhouette Method Untuk Strategi Pemasaran Program Donasi Pada Lembaga Amil Zakat

Donation data management is a complex challenge for amil zakat institutions in designing an effective marketing strategy for fundraising programs. In this study, the k-medoids algorithm was used to cluster the donation data with the aim of identifying patterns and characteristics of donors. The k-medoids algorithm was chosen because of its ability to handle unusual data and non-numeric attributes. Through clustering analysis, this study classifies donors based on attributes such as the number of donations, the frequency of donations, and the time interval for donating. And in determining the number of clusters in this study using the silhouette method to measure the quality of the resulting clustering. And getting the most optimal number of clusters is k = 3 with a silhouette score of 0.598782. The results of the study found that groups of donors had similar characteristics, such as donors who made high donations with regular frequency and donors who focused on donations for specific purposes. These findings can be used by charitable organizations in developing more effective fund management, marketing and targeting strategies
A systematic review of federated learning from clients’ perspective: challenges and solutions

Abstract Federated learning (FL) is a machine learning approach that decentralizes data and its processing by allowing clients to train intermediate models on their devices with locally stored data. It aims to preserve privacy as only model updates are shared with a central server rather than raw data. In recent years, many reviews have evaluated FL from the system (general challenges) or server’s perspectives, ignoring the importance of clients’ perspectives. Although FL helps users have control over their data, there are many challenges arising from decentralization, specifically from the perspectives of clients who are the main contributors to FL. Therefore, in response to the gap in the literature, this study intends to explore client-side challenges and available solutions by conducting a systematic literature review on 238 primary studies. Further, we analyze if a solution identified for one type of challenge is also applicable to other challenges and if there are impacts to consider. The conclusion of this survey reveals that servers and platforms have to work with clients to address client-side challenges.
SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO

Abstract— ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages ​​without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword "CahtGPT OpenAI". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..
KLASIFIKASI KONDISI BAN KENDARAAN MENGGUNAKAN ARSITEKTUR VGG16

Tyres are the main component that a vehicle needs to work with reducing vibration due to uneven road surfaces, protecting the wheels from wear to provide stability between the vehicle and the ground helping to improve acceleration to facilitate travel while driving. Wear ensures stability between the vehicle and the ground helps improve acceleration for easy movement and driving. Caused including components that are often used, tires can experience damage such as the appearance of cracks in the tires. Cracks in tires can be triggered by factors such as age or the cause of the road that has been exceeded. Detection of tire cracks at this time is still carried out conventionally, where users see directly the state of the tire whether the tire is in good condition or cracked. Conventional methods are important because they maintain tire quality and rider safety. The Conventional Method certainly has weaknesses because vehicle users must have good vision and the ability to distinguish normal tires or cracked tires, but this method is considered less effective because it still uses human labor, causing the risk of human error (human negligence) which can hinder the process of identifying tire cracks. Based on this problem, this study will develop a deep learning model that can classify cracked tires using the VGG16 architecture. In this study, the model was created using 8 scenarios by changing the value of epochs, to get the best parameters in making the model. The results of the 8 scenarios carried out in this study are the best scenario obtained in scenarios 1,3,4 which get 98% accuracy in model testing
Sistem Informasi Pelayanan Administrasi Kependudukan Kelurahan Kenteng Berbasis Website dengan Uji Kualitas Sistem Menggunakan Metode Mccall Software Quality

Kenteng Village provides population administration services to the community by means of residents coming to the village and village officials processing administrative applications needed by residents and residents taking printed letters. This is not considered effective and efficient because in making the letter, residents still have to wait to take the letter. Therefore, it is necessary to build a website-based population administration service information system for the Kenteng sub-district that can assist the service process effectively and efficiently. This system is built using the codeignter framework and the system development method using Prototype. The websitebased population administration service information system sub-district was tested for quality using the McCall Software quality method and overall quality was produced in a good category with an overall percentage of 62.18% which included the Correctness factor with a percentage of 69%, Reliability with a percentage of 76.75%, Efficiency with a percentage of 89.25%, Integrity with a percentage of 71%, Usability with a percentage of 70%, Maintainability with a percentage of 52.35%, Flexibility with a percentage of 50.25%, Testability with a percentage of 69%, Portability with a percentage of 66%, Reusability with a percentage percentage of 51% and Interoperability with a percentage of 67%.
Enhanced Recommendation Systems: A Survey on the Impact of Auxiliary Information

In the age of big data, recommendation systems have become a critical tool for helping users navigate the overwhelming amount of online information. Enhanced recommendation systems take this one step further, leveraging the latest algorithms and data-driven insights to deliver highly personalized and relevant recommendations. This research paper provides a comprehensive overview of the recent progress in enhanced recommendation systems, cov- ering the current state-of-the-art techniques and discussing the opportunities and challenges practitioners face. The article explores a range of approaches, including deep learning techniques and hybrid models that integrate both user and item data, and presents the essential concepts, methods, and applications driving the advancement of recommendation systems. We recognize the press- ing hurdles in the field as sparsity and diversity, thereby focusing on intent- based models that exploit the additional/auxiliary information by aggregating implicit feedback from user-item interactions. We have gone one step further by compiling the benchmarks in the field, enabling new researchers to explore and innovate at a much more thoughtful and faster pace.
PENGENDALIAN INTERNAL SISTEM AKUNTANSI PENGGAJIAN MI NURUL BAHRI ALMASHOOLIH

The purpose of this research is to find out whether the implementation of the internal control of the payroll accounting system at MI Nurul Bahri Almashoolih is in accordance with the theory. This research is a qualitative research using descriptive techniques and case studies at MI Nurul Bahri Almashoolih. Data collection is done through observation, interviews, and documentation. The first step is to describe the payroll system that exists at MI Nurul Bahri Almashoolih. The second stage is to compare theory with practice in the payroll accounting system of MI Nurul Bahri Almashoolih. Based on the findings of the data analysis, it is clear that the internal control of the MI Nurul Bahri Almashoolih payroll system is not in accordance with the theory. Because one of them is a dual function task.
Denoising Production Volumetric Rendering

Denoising is an integral part of production rendering pipelines that use Monte-Carlo (MC) path tracing. Machine learning based denoisers have been proven to effectively remove the residual noise and produce a clean image. However, denoising volumetric rendering remains a problem due to the lack of useful features and large-scale volume datasets. We have seen issues such as over-blurring and temporal flickering in the denoised sequence. In this work, we modify the production renderer to generate potential features that might improve the denoising quality, and then run a state-of-the-art feature selection algorithm to detect the best combination. We collect thousands of unique volumetric scenes from our recent films to create a large dataset for training. Our evaluation shows a good amount of quality gain compared to the version currently in use.
Web-based Consulting Company Employee Timesheet

Work timesheet is a form of work activity reporting that is usually implemented by a company to monitor the work of their employees. In this study, the researcher wanted to develop a Web-based Consulting Company Employee Timesheet which aims to facilitate the process of reporting and checking data in terms of work timesheets or reporting of work activities in a consulting company. The features developed are made in such a way that it is expected that the company's needs can be met according to the previous system of the consulting company. The method used for website application development is the waterfall method where the development stages occur sequentially. The method applied is through ERD ( Entity Relationship Diagram), Flowcharts, Use Case Diagrams, Activity Diagrams. After this web-based was created, the researchers conducted tests and interviews with admins, managers and employees of the consulting company, totaling 5 people. From the results of interviews with 5 respondents obtained a positive response to the development of a Web -based Timesheet for Consulting Company Employees.
Using Fun Learning Method with Iqro' Block Media to Improve the Reading Ability of Paud Hijaiyah Students

Religious education should be introduced to children from an early age. At PAUD KB Ceria Serang, Islamic religious education was introduced to students, one of which was by teaching the hijaiyah letters to students. To improve students' ability to read hijaiyah letters, one of the methods used is to use the iqra' block media. This research is a quantitative research with the type of experimental research with a quasi-experimental design. The population in this study were all students of PAUD KB Pelangi Ceria Serang with a sample of 24 students in class B. Based on the results of data analysis, the average pretest score was 53.21 and the posttest average was 74.46. And based on the independent sample t-test output table in the equal variances assumed section, it is known that the sig2teiled value is 0.000 &lt;0.05, so it can be concluded that there is an increase in students' reading skills ability after using the iqra' beam media.
SELEKSI ATRIBUT PADA DATA TIDAK SEIMBANG NASABAH KOPERASI DENGAN OPTIMASI SMOTE DAN ADABOOST

The credit procedure is the provision of credit on the basis of the bank's belief in the ability and ability of the customer to repay. In this study, the customer data tested was divided into 3 classes, namely 39 LOW customers, 84 MEDIUM customers, and 11 HIGH customers. Unbalanced datasets are pre-processed using the Synthetic Minority Over-sampling (SMOTE) technique. Classification methods such as Random Forest and Support Vector Machine will test cooperative customer data. The data is tested based on the attribute that has the highest matching value using the Particle Swarm Optimization method, this test is also optimized with the Adaboost method to increase its accuracy. The results of tests carried out using the Random Forest method obtained an accuracy of 89.05% and the Support Vector Machine algorithm obtained an accuracy of 81.75%. Meanwhile, testing with two methods optimized with Adaboost showed an increase in accuracy with Random Forest getting 91.24% accuracy and Support Vector Machine getting 82.48% accuracy. The highest accuracy in the cooperative customer data classification test was obtained from the Random Forest algorithm which was optimized with Adaboost at 91.24%
Simplifying Facial Animation using Deep Learning based Phoneme Recognition

We present a machine learning framework for facial animation that is simple, easy to implement and integrates well into an artist friendly workflow. The framework employs a pre-trained deep learning model used for phoneme extraction. Each phoneme is mapped to artist defined face shapes resulting in sparse key frames generating quick and synchronized looking dialogue animations, which can optionally be enhanced using familiar artist friendly workflows.
Automatic Modulation Recognition Based on a New Deep K-SVD Denoising Algorithm

Automatic modulation recognition has a wide range of applications in wireless communication. To solve the problem that the previous methods convert signal modulation recognition into image recognition, leading to increased time costs and information loss, an automatic modulation recognition approach consisting of the improved deep K-SVD denoising algorithm is suggested. First, the effectiveness of the model for random sine wave denoising is demonstrated by simulation. Second, the original I/Q signals are fed into the modified deep K-SVD model for denoising, skipping the complicated image processing steps. Finally, the noise-reduced signals are input into a multi-channel convolutional long short-term neural network to complete the modulation recognition. To solve the slow convergence problem of Iterative Shrinkage Thresholding Algorithms in sparse coding, the Fast Iterative Shrinkage Thresholding Algorithm is adopted to improve the computational efficiency and obtain a better denoising effect. The experiments show that the improved model has an average recognition accuracy of 91.26% at different SNRs from -2 dB to 18 dB, which is better than the state-of-the-art modulation recognition models.
Smart tourism chatbot system using Multi-domain Tourism Information DST

The smart tourism service provides tourists with travel planner services and tour guide services for easy and convenient travel throughout the entire travel process. In this paper, we develop the AI-based chatbot service using a pretrained language model (PLM) and provide tourism information so that tourists can make their travel plans. The proposed chatbot system consists of the DST server, the Neo4J graph DB and MySQL DB servers, and the natural language generation (NLG) server. The dialogue state tracking (DST) server understands the intention of tourists’ questions to overcome the shortcomings of the previous rule-based chatbot system [7]. We define the domains and slots of the tourism information DST model with the 4W1H method and develop the dataset [12] for transfer learning of the SOM DST model [14]. The Neo4J and MySQL web servers search tourism information from the tourism information knowledgebase and the smart tourism information system, respectively. The NLG server provides the searched tourism information to the smart tourism app.
A novel Sentimental Analysis framework using Gated Recurrent Units for Text Transliteration

Transliteration is the process of converting the characters of one writing system into the corresponding characters of another writing system. It is often used to represent the sounds of words from one language using the characters of another language. There were multiple Artificial Intelligent techniques which were extensively used for sentimental analysis. Natural Language Processing (NLP) is one of the effective ways for sentimental analysis that generates the language interpretation. This enables the way for enabling transliteration from one language to another or a combination. This article proposes a way of deploying a Gated Recurrent Unit (GRU) which is a type of Recurrent Neural Network (RNN) architecture that is commonly used in NLP and sequence-to-sequence learning tasks.
File System for Aircraft Maintenance Records Based on Blockchain and IPFS

This study aims to enhance the traceability, data integrity, and transparency of an aircraft's maintenance records while ensuring the records' privacy using blockchain technologies. In this study, a blockchain-based and decentralized model has been developed that enables the digital distribution of maintenance record reports for air platforms, which can be shared peer-to-peer while ensuring report integrity and content confidentiality upon request. An Ethereum blockchain network with a PoS consensus algorithm is preferred for the application of this study. The proposed secure maintenance record storage system for creating a blockchain network is implemented using Ganache and Truffle. This decentralized model provides a reliable file-sharing process that enhances information visibility and reliability among maintenance, repair, and overhaul (MRO) service providers and other stakeholders.
Peningkatan Keterampilan Injeksi Pasien Diabetes dengan Menggunakan Self Insulin Injection Simulation Tool (SIIST)

This study aims to identify the effectiveness of using the Self Insulin Injection Simulation Tool (SIIST) to improve insulin injection skills in Diabetes Mellitus patients. The method used is a pretest-posttest control group design. The results showed that the increase in the average skill score in the SIIST intervention group was higher than in the control group, and there was a significant difference (p-value 0.001). In conclusion, education using SIIST is effective in improving the injection skills of Diabetes Mellitus patients. Instruction using the media simulation tool makes it easier for Diabetes patients to practice self-injection correctly and correctly.&#x0D; &#x0D; Keywords: Diabetes, Injection, Insulin, Skills, Simulation Tool
The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems.In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals.However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied.Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance.First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one vs.other and marginal restrictions.Secondly, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability.In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world.Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.