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All Journal Jurnal Buana Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Manajemen Informatika Jurnal Riset Informatika EVOLUSI : Jurnal Sains dan Manajemen Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Abdi Teknoyasa Jurnal Aruna Mengabdi (ARMI)

Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya

Universitas Bina Sarana Informatika

Author-ID : 1128107

Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Title

Support Vector Classification with Hyperparameters for Analysis of Public Sentiment on Data Security in Indonesia Siti Ernawati; Risa Wati; Nuzuliarini Nuris
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.481

The development of Information Technology makes increasing use of the internet. This raises the vulnerability of data security. Cyber attacks in Indonesia caused many tweets on social media Twitter. Some are positive, and some are negative. The problem of this study is to determine the public sentiment towards data security in Indonesia, while the purpose of this study is how the response or evaluation of the government of Indonesia to the many perceptions of people who lack confidence in data security in Indonesia. Data obtained from twitter with as much as 706 data was processed using python with a percentage of 10% test data and 90% training data. Weighting is done using TF-IDF, and then the Data is processed using the Support Vector Machine algorithm using the SVC (Support Vector Classification) library. Support Vector Classification with RBF kernel classifies Text well to obtain AUC value with good classification category. Utilizing one of the hyperparameter techniques, which is a grid search technique that can compare the accuracy of test results. The test results using SVC with RBF kernel obtained an accuracy value of 0.87, Precision of 0.82, recall of 0.94, and F1_Score of 0.87. This study is expected to be used by decision-makers related to public confidence in data security in Indonesia

ANDROID-BASED QURAN APPLICATION ON THE FLUTTER FRAMEWORK BY USING THE FOUNTAIN MODEL Siti Ernawati; Risa Wati
Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i2.64

Abstract With the development of technology, smartphones have become one of the communication tools and can be used as a tool entertainer. But smartphones have an impact on the declining interest in reading the Quran. It would be a good smartphone that can be used to remember the creator is to create a Quran application on android so that users do not need to carry the mushaf Quran while on the go. The purpose of the construction of the application is to always remember to the god that is by the way can read Quran whenever and wherever are. The Model used to build the application Model is the Fountain where at the time of building the application can be done in overlap by the needs. Quran application built using. net framework flutter with the programming language dart. To install the application at least the Android version used is version 5.0 Lollipop. Testing the application of the Quran using black-box testing. Give the questionnaire to potential users of the application to assess the feasibility of the application of the Quran. From the results of the questionnaire can be concluded that the application of the Quran is very user friendly and with the audio playing over and over can help the user to memorize the Quran.

SENTIMENT ANALYSIS OF THREE-PERIOD POLEMICS USING K-NEAREST NEIGHBOR WITH TF-IDF WEIGHTING Siti Ernawati; Risa Wati
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

The issue of changing the presidential term which was originally 2 periods of government into 3 periods raises pros and cons in the community. Many 3-period hashtags have sprung up on social media twitter. So that conducted research on sentiment analysis of presidential election polemics 3 period. The purpose of the study was to produce the value of classification on the issue of presidential election change discourse into 3 periods using the K-NN method and whether the k-NN method proved to be well used for classifying text in the review of presidential election polemics 3 periods. Dataset totaling 1152 data, data is processed using Python and Jupyter Notebook as a text editor. The data is classified into positive reviews and negative reviews, then the data is divided into training data and test data with a ratio of 90:10. Weighting words using TF-IDF and sentiment classification using K-NN method. From the results of classification using the K-NN method obtained the highest accuracy when the value of k=17 and k = 18 with an accuracy of 85.3%. The results of the analysis of public sentiment to review the issue of discourse on the change of presidential term into 3 periods tend to be negative with a percentage of 21.26% positive sentiment and 78.74% negative sentiment.

Support Vector Classification with Hyperparameters for Analysis of Public Sentiment on Data Security in Indonesia Siti Ernawati; Risa Wati; Nuzuliarini Nuris
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

The development of Information Technology makes increasing use of the internet. This raises the vulnerability of data security. Cyber attacks in Indonesia caused many tweets on social media Twitter. Some are positive, and some are negative. The problem of this study is to determine the public sentiment towards data security in Indonesia, while the purpose of this study is how the response or evaluation of the government of Indonesia to the many perceptions of people who lack confidence in data security in Indonesia. Data obtained from twitter with as much as 706 data was processed using python with a percentage of 10% test data and 90% training data. Weighting is done using TF-IDF, and then the data is processed using the Support Vector Machine algorithm using the SVC (Support Vector Classification) library. Support Vector Classification with RBF kernel classifies Text well to obtain AUC value with good classification category. Utilizing one of the hyperparameter techniques, which is a grid search technique that can compare the accuracy of test results. The test results using SVC with RBF kernel obtained an accuracy value of 0.87, Precision of 0.82, recall of 0.94, and F1_Score of 0.87. This study is expected to be used by decision-makers related to public confidence in data security in Indonesia.

Prediction Of Flight Delays Using Feature Engineering, Catboost, And Bayesian Optimization To Improve Model Performance Maulana, Ilham; Ernawati, Siti; Wati, Risa
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.346

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.

SENTIMENT ANALYSIS OF MENTAL HEALTH REVIEWS USING MACHINE LEARNING ALGORITHMS Wati, Risa; Ernawati*, Siti
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Mental health is a significant issue in the modern era due to lifestyle changes, social pressures, and technological advancements that introduce new challenges. These problems affect various aspects of life, including education, employment, social relationships, and overall quality of life. Technological development enables the use of machine learning to automatically classify large amounts of data. This study aims to analyze and compare the performance of Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) in sentiment classification on mental health issues, while simultaneously contributing to scientific development and supporting the understanding of public psychological conditions. The dataset used in this research was obtained from Kaggle and consists of 20,364 mental health–related reviews in .CSV format, processed using Google Colab with the Python programming language. The data were categorized into two groups—depression and suicidewatch—and then underwent preprocessing, data splitting into training and testing sets with an 80:20 ratio, and TF-IDF weighting. The results indicate that the SVM algorithm outperforms the other methods. Using an RBF kernel and a C parameter of 15, SVM achieved an accuracy of 72.09%, a precision of 72.11%, a recall of 72.09%, and an F1-score of 72.09%. This study not only provides scientific contributions but also supports efforts to better understand the psychological conditions experienced by society.

Co-Authors Ahmad Fauzi Akrom, Akrom Ernawati*, Siti Ernawati, Siti Fauzi Ahmad Muda Hani, Siti Hasanudin Hasanudin Hilda Rachmi Ilham Maulana Imam Nawawi Imam Nawawi, Imam Nurlaela, Siti Hani Nuzuliarini Nuris Priyanti, Widi Intan Riani, Meifana Regita Silvy Nur Azizah Siti Ernawati Siti Hani Nurlaela Widi Intan Priyanti

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