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Web-Based Incentive Calculations System at CV Karya Prima using Prototyping Method 7

Web-Based Incentive Calculations System at CV Karya Prima us...

引用

7th International Conference on New Media Studies, CONMEDIA 2023

作者： Cornelia, Monica Suryasari Universitas Multimedia Nusantara Information Systems Study Program Faculty of Engineering and Informatics Tangerang Indonesia

ISBN: (纸本)9798350307504

Consumption is an essential aspect of daily life, and fulfilling customer needs requires efficient distribution of goods. Therefore, distributors are needed and they frequently set targets for their employees to achieve, and motivation plays a significant role in employee performance. An organization that distributes goods called CV Karya Prima motivates its employees by offering incentives. However, manual incentive calculation sets out problems such as a lack of progress tracking, irregular payout dates, and probable inaccuracies. This study aims to develop a web-based incentive calculation system using the analytical hierarchy process method and a database for data retrieval. The system utilizes PHP, CodeIgniter, and MySQL and adopts the SDLC prototyping method. The developed system is focused on the sales division and variables like total sales, effective calls, calls, and outlets. The web-based system is capable of solving the issues using the features that were built based on user needs and have already been tested through user acceptance testing. © 2023 IEEE.

关键词： Websites

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The Comparison of Sentiment Analysis Algorithm for Fake Review Detection of The Leading Online Stores in Indonesia 7

The Comparison of Sentiment Analysis Algorithm for Fake Revi...

引用

7th International Conference on Informatics and Computing, ICIC 2022

作者： Christian, Pius Hans Desanti, Ririn Ikana Information Systems Study Program Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798350345711

Online store users continue to grow and develop throughout the year so that the number of reviews from customers is also increasing. Customers can write reviews about the products and services and then post them on social media, whether it's a good or bad review. The reviews written are also very diverse such as product specification review, product advantages, product disadvantages and so on. Sometimes it is difficult for readers to determine which reviews are real and correct because there are so many written reviews. Reviews really play a very important role for potential customers to decide whether to buy the product or service or not. This study used several algorithms of sentiment analysis to detect fake reviews of online stores on Twitter. The algorithms were used to calculate the accuracy value of the review. The algorithms used were support vector machine, Naive Bayes, and logistic regression. The accuracy values of the three algorithms were compared and the highest is selected. The review text that was processed to produce fake review detection comes from the 3 leading online stores in Indonesia namely Tokopedia, Shopee, and Bukalapak. The results of calculating the accuracy value for real data using support vector machine is 70.58%, using naïve bayes is 69.89%, and using logistic regression is 70.49%. Comparison of these three results determines that the most appropriate algorithm for detecting fake reviews of online stores is the support vector machine algorithm. © 2022 IEEE.

关键词： Sentiment analysis

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Patient Record System for People Living with HIV/AIDS (PLWHA) at the Srikandi Sejati Foundation 7

Patient Record System for People Living with HIV/AIDS (PLWHA...

引用

7th International Conference on information Technology systems and Innovation, ICITSI 2022

作者： Sugrinting, Angelina Sanjaya Desanti, Ririn Ikana Sulaiman, Agus Universitas Multimedia Nusantara Information Systems Study Program Tangerang Indonesia

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

Public service is held to aid in the form of services from government, organizations, non-governmental organization, and others. As a form of public service, health care service becomes a service that is needed by people as an essential service. Srikandi Sejati Foundation is as an organization that serves the public in the form of assistance services for key populations (trans women) in suppressing and controlling HIV/AIDS, especially for Indonesian trans women. With the increasing number of patients that are assisted by Srikandi Sejati Foundation, a system is needed to manage and process the patients' data so it can be used to create reports and executive supervision for People Living With HIV/AIDS (PLWHA) through the program that was held by Srikandi Sejati Foundation with EpiC Indonesia, as well as monitoring the performance and operations of the foundation. The proposed system was built using the prototyping method and the result was a website that can be accessed by staffs and executives. The website is expected to assist the management of Srikandi Sejati Foundation's in managing patient data, reporting, visualizing, and monitoring in real time. © 2022 IEEE.

关键词： Websites

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A Comparative study of Traditional and Deep Learning Models Using Emotional Customer Data in Automotive Sales Forecasting

A Comparative Study of Traditional and Deep Learning Models ...

引用

Advancement in Data Science, E-learning and information systems (ICADEIS), International Conference on

作者： Gempar Bambang Godata Irmawati Information Systems Study Program Universitas Multimedia Nusantara Banten Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This study investigates the effectiveness of traditional and deep learning models in predicting automotive sales outcomes using customer acoustic and emotional parameters extracted from sales call recordings. A primary dataset of sales conversations was processed using an internal tool, which converts audio into numerical data based on 11 acoustic features such as pitch, volume, and speech rate. Traditional machine learning models, including AdaBoost, Support Vector Machine (SVM), and Random Forest, were compared with deep learning models like Dense Neural Networks (DNN) and TabNet. The optimized DNN model achieved the highest performance, with an F1-Score of 0.7298, balancing precision and recall effectively. TabNet provided competitive results with an F1-Score of 0.7237, offering additional benefits of feature interpretability through its attention mechanism. These findings suggest that while traditional models, particularly AdaBoost, perform adequately, deep learning models, especially DNN and TabNet, are better equipped to capture the complex, non-linear relationships in acoustic and emotional data. The results provide actionable insights for optimizing sales strategies through data-driven decision-making. It offers a foundation for future advancements in predictive analytics within automotive industries using voicebased datasets.

关键词： Deep learning Support vector machines Neural networks Predictive models Feature extraction Data models Acoustics Numerical models Recording Automotive engineering

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Efficiency Comparison of Employee Management Automation Using Selenium and Playwright: A Case study of Technology Consulting Company

Efficiency Comparison of Employee Management Automation Usin...

引用

International Conference on information Technology systems and Innovation (ICITSI)

作者： Muhammad Rio Apriansah Ririn Ikana Desanti Information Systems Study Program Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

The research employed a case study methodology to investigate a technology consulting company. The company implements an employee management application to facilitate the approval procedure for granting employee benefits. Before approval, the procedure of gathering and analyzing allowance data is conducted. The current procedure requires manual processing of each task individually, with each permission list taking approximately 3 to 5 minutes. This approach is not only time-consuming but also highly prone to errors, leading to inefficiencies and inconsistent outputs that fail to meet the company’s standards. An effective solution to this problem is to develop an automated program using Selenium and Playwright, alongside the Python programming language. The program will be created following the agile software development methodology, and its testing will involve both black box testing and User Acceptance Testing (UAT). After successfully developing both automation programs, a detailed comparison was conducted using data from 10 allowance lists. Testing showed that the Selenium automation program completed all operations in 55.03 and 49.25 seconds, while the Playwright program required only 47.14 and 47.71 seconds. These results indicate that the Playwright automation program demonstrates a higher level of efficiency compared to Selenium.

关键词： Technological innovation Automation Scalability Agile software development Companies Selenium Service-oriented architecture Standards Testing Python

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A Comparative Analysis of Transformer Models for Bird Song Recognition Using Mel-Spectrograms

A Comparative Analysis of Transformer Models for Bird Song R...

引用

International Conference on information and Communications Technology (ICOIACT)

作者： Deandra Yasin Winarno Irmawati Information Systems Study Program Universitas Multimedia Nusantara Banten Indonesia

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

This study explores the application of Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), and Swin Transformer models for bird species classification using mel-spectrogram representations of bird songs. The research utilized the BirdClef 2024 dataset, consisting of 24,641 audio recordings across 182 bird species. The dataset was processed into mel-spectrograms and split into training, validation, and test sets to evaluate model performance. Each model was trained over 75 epochs with early stopping mechanisms in place. The results indicated strong training accuracy for all three models; however, validation accuracy plateaued between 78% and 84%, revealing overfitting issues. To address these challenges, various optimizations were implemented, including advanced learning rate scheduling, targeted dropout rates, weight decay adjustments, and data augmentation techniques. Despite these modifications, some overfitting persisted, particularly in the DeiT model, which showed decreased test accuracy after optimization. These findings highlight the effectiveness of transformer-based models for audio-based bird species classification, while also underscoring the need for further regularization techniques and fine-tuning. Future work should focus on exploring additional transformer architectures, refining regularization methods, and utilizing larger datasets to enhance model generalization and accuracy in complex classification tasks.

关键词： Training Deep learning Accuracy Biological system modeling Refining Transformers Birds Data augmentation Optimization Overfitting

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Detection and Enhancement of Bahasa Standardized Word in Online News Draft using Levenshtein Algorithm :U-Tapis

Detection and Enhancement of Bahasa Standardized Word in Onl...

引用

International Seminar on Research of information Technology and Intelligent systems (ISRITI)

作者： Jezreel Kosasih Marlinda Vasty Overbeek Niknik Mediyawati Rudi Sutomo Samiaji Bintang nusantara Informatics Study Program Universitas Multimedia Indonesia Tangerang Indonesia Digital Journalism Study Program Universitas Multimedia Nusantara Tangerang Information System Study Program Universitas Multimedia Nusantara Tangerang

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The research on standardized word error detection and correction is based on technological advances and artificial intelligence as well as the problem of errors that are common in writing journalistic articles due to time constraints and work demands. The research on standardized word error detection and correction is also based on previous research, namely U-Tapis research. The research is a development of U-Tapis research to create a text-based application machine learning to detect and correct word or sentence errors in Indonesian. The research uses the Levenshtein Distance algorithm as the main algorithm for processing input articles. This algorithm will match each character in a word and produce a ratio or similarity value. The research shows success with the accuracy of nonstandard word detection up to 99% and F1 score reaching 92.5%. The research proved to be effective in detecting nonstandard words in Bahasa news articles and was able to replace the word with a standard word.

关键词： Seminars Machine learning algorithms Accuracy Machine learning Writing Natural language processing Time factors Intelligent systems Standards Testing

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U-Tapis Melting Words : an Artificial Intelligence Application for Detecting Melt Word Errors in Indonesia Online News

U-Tapis Melting Words : an Artificial Intelligence Applicati...

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International Seminar on Research of information Technology and Intelligent systems (ISRITI)

作者： Kevin Gleneagles Marlinda Vasty Overbeek Niknik Mediyawati Rudi Sutomo Samiaji Bintang nusantara Informatics Study Program Universitas Multimedia Indonesia Tangerang Indonesia Digital Journalism Study Program Universitas Multimedia Nusantara Tangerang Information System Study Program Universitas Multimedia Nusantara Tangerang

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Rapidly advancing NLP technology can assist journalists in correcting linguistic errors, including melt words, which frequently occurs in news writing. A software application called U-Tapis is being developed to assist in correcting spelling mistakes. However, U-Tapis currently lacks the feature to fully correct misspelled words. Therefore, one of the developments being pursued is a Machine Learning model utilizing the Damerau-Levenshtein Distance algorithm and BERT to detect and rectify these errors with high accuracy. Testing of this model yielded F1 Scores of 96.79%, 96.01%, and 96.01% for evaluations on 100, 150, and 200 Tribunnews articles, respectively. The percentages of correct recommendations provided by the model were 88.89%, 85.23%, and 84.68% for the same sets of articles. Therefore, this research is expected to detect errors in word softening and provide appropriate word recommendations, thereby maintaining the quality of language used by journalists and the credibility of a media outlet, which can serve as a model for society.

关键词： Seminars Accuracy Machine learning algorithms Software algorithms Writing Media Software Natural language processing Softening Testing

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Forecasting the NBA's Most Valuable Player: A Regression Analysis Approach

Forecasting the NBA's Most Valuable Player: A Regression Ana...

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Big Data Analytics and Practices (IBDAP), International Conference on

作者： Arnando Harlianto Johan Setiawan Information Systems Study Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

Basketball is immensely popular in the United States, with the National Basketball Association (NBA) recognized as the leading professional league worldwide. Each season, the NBA awards the Most Valuable Player (MVP) title to the player considered the best based on performance statistics and a fair voting process. Predicting the MVP often sparks debates among NBA fans, with various media outlets offering their forecasts. This study aims to accurately predict the MVP by analyzing statistical data from NBA players over a specific period. We employ three different regression algorithms—Decision Tree, Linear Regression, and Support Vector Regression—to compare their effectiveness in MVP prediction. The research findings will highlight the potential MVP winner based on their performance statistics, presented in a simplified dashboard format for easy comprehension.

关键词： Support vector machines Fans Linear regression Media Prediction algorithms Vectors Sparks

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Customer Loyalty Prediction and Customer Segmentation: A Random Forest Prediction and PCA-Integrated K-Means++ Approach

Customer Loyalty Prediction and Customer Segmentation: A Ran...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Geovanny Cellyn Natalia Ririn Ikana Desanti Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This research discusses the prediction of company loyalty. Every company typically has a loyalty program in place. Customer loyalty is a key driver of business success, especially in competitive retail environment. Understanding the condition of customer loyalty presents a unique challenge for companies. It is also crucial for companies to understand the characteristics of their loyal customers. This research proposes a predictive model to assess the loyalty of a company's customers and builds a clustering model from the predicted loyal customers. Using the company's transaction data from January 1, 2023, to September 31, 2024, a predictive Random Forest model is developed. The Random Forest model is evaluated using ROC-AUC, accuracy, F1 Score, and Recall, showing ROC - AUC 0.93, accuracy of 87%, a F1 Score of 91 %, and a recall of 92 %. performance. The Random Forest model will generate customers predicted to be loyal and those predicted to churn. K-Means++ clustering will be built using the data of customers predicted to be loyal by the Random Forest model. Clustering is implemented by PCA (Principal Component Analysis) to reduce data dimensionality. Loyal customers are divided into 3 clusters, each displaying its distinct characteristics. This research identifies customers who have the potential to become loyal, and the clustering results reveal the characteristics of each cluster. Companies can leverage the findings of this research to develop better strategies to enhance customer loyalty.

关键词： Measurement Accuracy Consumer behavior Refining Companies Predictive models Data models Reliability Churn Principal component analysis

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