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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Sistem Informasi dan Aplikasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Lidya Maukar, Anastasia
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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%.
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
Analisis dan Perancangan Sistem Informasi Berbasis Website di Bebras Biro Universitas Dr. Soetomo Mustafa, Zulfikar Amirul; Vitianingsih, Anik Vega; Kristyawan, Yudi; Lidya Maukar, Anastasia; Yasin, Verdi
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 2 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i2.38767

Abstract

Universitas Dr. Soetomo holds a free challenge event every year. However, there is no application that helps with student data collection, activities and scoring assessment results because it still uses Microsoft Office Excel. In addition, there is no data processing such as graphs that report which schools each year participate in the competition and which students receive the highest scores in the free challenge. The Waterfall method is used to create a more organized web-based information system to overcome this problem. A clear framework is provided by this method from needs analysis to implementation. In addition to simplifying the registration and competition assessment process, this system provides educators, students and organizers with direct access to information. The results show increased efficiency, accessibility and transparency, and that this method helps improve quality and engagement in Bebras competitions. This shows that the appropriate use of information technology can significantly improve the quality of education.
Sentiment Analysis of Cyberbullying Detection on Social Networks using the Sentistrenght Method Yunior, Kevin Heryadi; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Dwi Arumsari, Andini
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4226

Abstract

In today's swiftly changing digital realm, social media has emerged as a pervasive means of communication, yet it has also fostered the rise of cyberbullying, especially among young demographics. This research strives to develop an application that assesses public sentiment on Instagram regarding cyberbullying instances, categorizing sentiments as positive, negative, or neutral. Drawing data from Instagram accounts such as kumparandotcom, merdekadotcom, and okezonedotcom, the approach combines lexicon-based text labeling and sentiment analysis employing Sentistrength. Findings demonstrate the method's effectiveness, achieving accuracy, precision, and recall rates exceeding 85% while offering precise visualization of predictions. This study contributes to combatting cyberbullying, aiming to improve victims' mental well-being by providing clearer insights into social sentiment. The dataset comprises 4500 comments collected through web crawling, categorized into positive (735 entries), negative (2478 entries), and neutral (1288 entries) sentiments. The evaluation highlights the commendable performance of Sentistrength, achieving the highest accuracy at 93.85%.
Emotion-Based Multi-Class Sentiment Analysis Of FirstMedia Customers Reviews Using SVM With Kernel Comparison Ongko, Bagus Kustiono; Vitianingsih, Anik Vega; Cahyono, Dwi; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15644

Abstract

The advancement of digital technology has made users increasingly reliant on online services, with user reviews serving as an essential resource for evaluating the quality of service provided by companies such as FirstMedia. However, these valuable data have not undergone comprehensive analysis to assess users’ emotional responses. This study aims to classify FirstMedia customers’ emotions into four categories (joy, sadness, anger, and neutral) and to evaluate the Support Vector Machine (SVM) method using four different kernel functions. Most existing studies primarily focus on polarity-based sentiment analysis and do not explicitly examine multi-emotion classification or kernel comparison in machine learning models. A total of 4,001 reviews were collected through web scraping from the Google Play Store and the X app and processed through several preprocessing steps. Emotion classification was conducted using the NRC Indonesian Emotion Lexicon, while word significance was determined using TF-IDF weighting. After preprocessing, 3,069 labeled reviews were retained and distributed as 1,065 neutral, 748 anger, 692 joy, and 564 sadness reviews, which were used for emotion classification. Model performance was evaluated using a hold-out validation scheme with an 80:20 train-test split and assessed through a confusion matrix. To address class imbalance, undersampling was applied, resulting in a balanced dataset for model training. The evaluation results show that the Linear kernel achieved the highest performance, with an accuracy of 82.63%, precision of 82.86%, recall of 82.63%, and an F1-score of 82.60%, outperforming the Gaussian, Polynomial, and Sigmoid kernels. This study demonstrates that multi-emotion sentiment analysis provides a more comprehensive understanding of user perceptions beyond conventional sentiment polarity, thereby supporting more informed evaluations of digital service quality.  
Sentiment Analysis E-Wallet Application Services Using the Support Vector Machine and Long Short-Term Memory Methods Arya Darmansyah, Mochammad Dzikri; Vitianingsih, Anik Vega; Lidya Maukar, Anastasia; Yuliani, SY.; Fitri Ana Wati, Seftin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apedaz75

Abstract

The rapid growth of financial technology services in Indonesia has increased the volume of user reviews, yet their utilization for sentiment-based insights remains limited in the e-wallet sector. This study compares the effectiveness of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) in classifying the sentiment of 3,185 DANA e-wallet reviews collected from the Google Play Store and Instagram. The research process includes text preprocessing, lexicon-based labeling, and feature extraction using TF-IDF for SVM and word embeddings for LSTM. Model evaluation is conducted using a confusion matrix based on accuracy, precision, and recall, without inferential statistical testing. The results show that LSTM outperforms SVM, achieving an accuracy of 86.66%, a recall of 81.86%, and a precision of 82.09%, while the best SVM variant with an RBF kernel attains an accuracy of 84.93%. This study contributes by identifying key service-related factors influencing user satisfaction and dissatisfaction and by providing practical, sentiment-based insights to support service quality improvement. The novelty lies in the multi-platform analysis of Indonesian e-wallet reviews and the direct comparison of classical machine learning and deep learning approaches without statistical hypothesis testing. These findings confirm the effectiveness of deep learning for sentiment analysis of unstructured Indonesian text.
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
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 Arumsari, Andini Dwi Arya Darmansyah, Mochammad Dzikri Budi Suprio, Yoyon Arie DWI CAHYONO Efendi, Kacung Fardhan Maulana, Abelardi Febrian Rusdi, Jack Fitri Ana Wati, Seftin Hamidan, Rusdi Jazaudhi’fi, Ahmad Mustafa, Zulfikar Amirul Ongko, Bagus Kustiono Pujiono, Halim Putra Selian, Rasyid Ihsan Rahmansyah, Ragada Slamet Kacung, Slamet Slamet Winardi Verdi Yasin Warsito Sujatmiko, Achmad Wati, Seftin Fitri Ana Yudi Kristyawan, Yudi Yuliani, SY. Yunior, Kevin Heryadi