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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining Digital Zone: Jurnal Teknologi Informasi dan Komunikasi DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi The Indonesian Journal of Computer Science
Lidya Maukar, Anastasia
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Sentiment Analysis of Public Responses on Social Media to Satire Joke Using Naive Bayes and KNN Putra Selian, Rasyid Ihsan; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Febrian Rusdi, Jack
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13721

Abstract

This study examines the use of Satire Joke as a humorous communication style in conveying criticism of the government through social media. Satire Joke is often used to depict the government's inability to address important social issues, such as slow bureaucratic processes and unfulfilled political promises. The aim of this research is to analyze public sentiment towards Satire Joke expressed on the YouTube social media platform. The methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN) due to their effectiveness in data classification. The results of this study are expected to help gain an understanding of social issues for the community and public knowledge. This research is also expected to contribute to the development of sentiment analysis methods in the future. The analysis results show that 400 data have neutral sentiment, 850 data have negative sentiment, and 947 data have positive sentiment. Based on testing, both Naive Bayes and KNN methods show good performance. The Naive Bayes method achieved the best accuracy of 90.29%, while the KNN method achieved an accuracy of 60.75%.
Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods Damayanti, Erika; Vitianingsih, Anik Vega; Kacung, Slamet; Suhartoyo, Hengki; Lidya Maukar, Anastasia
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 2: JULI 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i2.478

Abstract

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.
Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection Pujiono, Halim; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1165

Abstract

Plant diseases, particularly in staple crops like rice, significantly affect the stability of rice production in Indonesia. Crop failure caused by rice plant diseases present a critical challenge for farmers.  Early diagnosis is crucial for preventing and managing rice diseases, as it facilitates more effective preventive measures, reduces yield losses, and boosts overall agricultural production. This study aims to apply the Faster Region Convolutional Neural Network (Faster R-CNN), a deep learning approach, to detect rice plant diseases. The Grid Search method was employed as a hyperparameter tuning technique to identify the optimal parameter combination for enhancing algorithm performance. Experimental results demonstrate the model's performance, achieving an accuracy rate of 88%, recall and precision of 100%, and an F1 Score of 93%. These findings indicate that the Faster R-CNN method effectively recognizes and classifies rice plant diseases with a high degree of accuracy.
Analisis dan Perancangan Sistem Informasi Manajemen Pegawai Menggunakan Metode Waterfall Berbasis Web Vitianingsih, Anik Vega; Fardhan Maulana, Abelardi; Kacung, Slamet; Lidya Maukar, Anastasia; Wati, Seftin Fitri Ana
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 2 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.2.237

Abstract

In today's digital era, the need for an employee management information system is increasingly urgent. Organizations must be able to overcome the complex challenges of managing their human resources to remain competitive in the ever-changing market. With the right Personnel Information System, organizations can optimize the management of personal, performance, and administrative information of their employees efficiently. One important aspect of a Personnel Information System is the mapping of workforce needs, which enables organizations to plan appropriate employee recruitment and development strategies. In addition, efficient scheduling is also a key focus, as proper placement and wise resource allocation can improve overall productivity. However, manually managing employee data is no longer sufficient in this digital age. Errors, delays, and loss of information often occur in manual processes, causing losses in terms of both time and finances. Therefore, the implementation of a robust and efficient Personnel Information System is a must. The Waterfall method, with its structured step-by-step approach, was able to provide clear guidance in the development of this system. A comprehensive analysis stage ensures that the needs of the organization are well understood, while the design stage guarantees that the system design meets the right specifications. With the results of this study, it is expected that organizations will be able to develop a Personnel Information System that suits their needs, improve the efficiency of human resource management, and optimize overall performance. Thus, the Personnel Information System is not only an administrative tool, but also one of the key factors in organizational success in this digital era.
Recommendation System to Determine Achievement Students Using Naïve Bayes and Simple Additive Weighting (SAW) Methods Jazaudhi’fi, Ahmad; Vitianingsih, Anik Vega; Kristyawan, Yudi; Lidya Maukar, Anastasia; Yasin, Verdi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.19746

Abstract

Giving appreciation to outstanding students can motivate students to compete with each other in learning. MA Tanwirul Qulub Tanggungan often experiences difficulties in determining outstanding students due to There is no application that can assist school management in identifying outstanding students, the implementation is considered less than optimal. besides that, the determination of outstanding students is still based on report cards that are only ranked, and there are no criteria that refer to the K-13 curriculum. The purpose of this research is to offer a solution to create a recommendation system for selecting outstanding students using the parameters of midterm exams, final exams, assignments, attendance, attitude, extracurricular activities, organizations, and award certificates using decision support system techniques. Extracurricular grades are taken from Scouting activities only because students are generally required to participate in them. Naïve Bayes and Simple Additive Weighting methods are used in this research, where the Naïve Bayes method classifies the categories of outstanding students and not, while the SAW method is used for ranking. The contribution of this research has the potential to increase school efficiency in student assessment and support efforts to improve the quality of education by rewarding students appropriately. The validation test results of Naïve Bayes and SAW techniques get an accuracy value of 100%, which shows that the SAW method can produce the best alternative recommendations
Comparative Analysis of Deep Learning Methods for Predicting the Value of the Standard & Poor's Global Supply Chain Intelligence (S&P GSCI) Nickel Stock Index Rahmansyah, Ragada; Vitianingsih, Anik Vega; Hamidan, Rusdi; Lidya Maukar, Anastasia; Budi Suprio, Yoyon Arie
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36129

Abstract

The development of information technology has opened up new opportunities in stock market forecasting, especially in nickel commodities, which are increasingly strategic in the global energy transition. This study uses a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a Gated Recurrent Unit (GRU) to forecast the movement of the S&P GSCI Nickel stock index value. Yahoo Finance time series data for the years 2018–2024 are used in the dataset. The study's findings are used to evaluate each model's capacity to forecast changes in nickel stock prices. The RNN model is used in this study because it can work with sequential information, while LSTM works with three memory gates (input, forget, output), and GRU works with 2 gates, namely update and reset. Mean Absolute Percentage Error (MAPE) presents the results of open and closed variable forecasting errors with the lowest average for the RNN model of 2.08%, the LSTM model of 2.505%, and the GRU model of 1.505%. This study is expected to contribute to investor decision-making and the identification of the most accurate forecasting model for the nickel stock index
Sentiment Analysis on the FIFA U-20 World Cup in Argentina Using Support Vector Machine Warsito Sujatmiko, Achmad; Vitianingsih, Anik Vega; Kacung, Slamet; Cahyono, Dwi; Lidya Maukar, Anastasia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3973

Abstract

The decision made by FIFA regarding the selection of the soundtrack and the host country for the FIFA U-20 World Cup has sparked emotional reactions among the public and raised concerns about the event, especially on social media platform X. This is due to FIFA’s decision to choose a soundtrack not from the host country, Argentina, but from the previous host, Indonesia. FIFA should advocate for the creation of a soundtrack by the host country to reflect its distinctive characteristics or atmosphere. Concerns about the U-20 World Cup in Argentina have also been fueled by the country’s economic crisis, which is feared to affect the facilities and infrastructure for the young players representing their nations. This research focuses on filtering public responses to FIFA’s decisions regarding the soundtrack selection and the host country for the U-20 World Cup into positive, neutral, and negative categories using the Support Vector Machine (SVM) method. The research aims to provide policy recommendations regarding the host selection process and cultural representation in international sports events. Additionally, this study is expected to provide a deeper understanding of the preferences and values held by the public regarding international sports. The research steps include data collection, pre-processing, labeling, weighting, and classification using a Support Vector Machine. The data for this research were obtained through crawling on social media platform X, totaling 2400 data points. The performance evaluation of the SVM algorithm using a 50:50 ratio of training and testing data yielded an average accuracy of 85.71%, Precision of 85.98%, Recall of 85.71%, and F1-score of 85.58%.
Co-Authors Anik Vega Vitianingsih Budi Suprio, Yoyon Arie Damayanti, Erika DWI CAHYONO Fardhan Maulana, Abelardi Febrian Rusdi, Jack Fitri Ana Wati, Seftin Hamidan, Rusdi Hengki Suhartoyo, Hengki Jazaudhi’fi, Ahmad Pujiono, Halim Putra Selian, Rasyid Ihsan Rahmansyah, Ragada Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Warsito Sujatmiko, Achmad Wati, Seftin Fitri Ana Yasin, Verdi Yudi Kristyawan, Yudi