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All Journal JURNAL PENDIDIKAN TAMBUSAI Pelita Eksakta Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Dodi Vionanda
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences Other

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Perbandingan Analisis Diskriminan Kuadratik dengan Analisis Diskriminan Kuadratik Robust martha, Ully Martha; Dodi Vionanda; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/315

Abstract

This study compared the performance of quadratic discrimination analysis and robust quadratic discrimination analysis using the Iris dataset from Kaggle. The robust quadratic discriminant analysis, designed to handle outliers and non-normal distributions, shows better performance with an Apparent Error Rate (APER) of 2.5%. In contrast, the quadratic discriminant analysis, used for data with multivariate normal distribution and different variance-covariance matrices among groups, yields an APER of 3.03%. These results indicate that robust quadratic discriminant analysis is more accurate in classification on this dataset compared to quadratic discriminant analysis. Keywords: Apparent Error Rate, Quadratic Discrimination Analysis, Robust Quadratic Discrimination Analysis
Sentiment Analysis of The Constitutional Court Decision Regarding Changes to The Age Limit for Presidentian and Vice Presidential Candidates Using Support Vector Machine Amanda, Abilya; Nonong Amalita; Dodi Vionanda; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/321

Abstract

The Constitutional Court (MK) as a judicial institution granted a judicial review on October 16, 2023 related to the Election Law Article 169 (q) Law No.7 of 2017 number 90/PUU-XXI/2023. The Constitutional Court approved the material test, leading to changes in the age limit for presidential and vice presidential candidates. This change caused controversy because it was considered to benefit one of the candidate pairs. This research aims to see the trend of public opinion towards policy changes by the government. This research uses the Support Vector Machine (SVM) method which divides the data into two classification classes. The application of linear, Radial Bias Function (RBF), and polynomial kernels resulted in the highest accuracy of 84%. The calculation of accuracy, precision, and recall is 84%, 22%, and 90%, respectively. Based on the resulting wordcloud, Positive words indicate backing for presidential and vice presidential candidates. Meanwhile, negative sentiments express disapproval of the Constitutional Court's decision concerning the changes to the age limit requirements for presidential and vice presidential candidates.
How MUI Fatwa Changes Indonesia Mindset towards Pro-Israel Boycott Products using the Naïve Bayes Classification Method Jumiati, Susi; Dodi Vionanda; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/326

Abstract

Boycotting pro-Israel products has become a popular topic on social media, both in Indonesia and globally. This research aims to analyze the sentiments of Indonesian using the Naive Bayes classification method regarding the boycott before and after the issuance of MUI Fatwa No.83/2023. Through sentiment and word cloud analysis of 3327 tweets, it was found that discussions remained consistent and were not influenced by MUI Fatwa. The sentiment of the majority of Indonesian regarding the boycott of pro-Israel products is positive, with full support for this action. MUI Fatwa has had an impact on the sentiment of Indonesian, as can be seen from the increase in positive sentiment after the fatwa was released. Word cloud analysis shows that both before and after November 8, 2023, the top one word that appears in the word distribution is exactly the same, namely 'boycott'. This similarity shows that the discussion topics that developed on the Twitter platform remained consistent, both before and after the release of MUI Fatwa Indonesian netizens have uniformly discussed boycotting products that support Israel as a form of rejection of the genocide carried out by that country in Gaza, Palestine.
Sentiment Analysis of Twitter User Government Official of Indonesia Vacancy in 2024 Using Naive Bayes Classification Larissa, Dwika; Vionanda, Dodi
Jurnal Pendidikan Tambusai Vol. 9 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i1.25497

Abstract

Pengumuman seleksi CPNS merupakan momen penting yang selalu ditunggu-tunggu oleh masyarakat Indonesia setiap tahunnya. Hal ini tidak terlepas dari tingginya animo masyarakat untuk menjadi bagian dari Aparatur Sipil Negara. Penelitian ini menganalisis sentimen masyarakat terhadap pengumuman seleksi CPNS tahun 2024 dengan menggunakan metode klasifikasi Naive Bayes. Data dikumpulkan dari 2001 tweet di Twitter yang berkaitan dengan Lowongan CPNS 2024, dan dilakukan preprocessing sebelum dilakukan analisis sentimen. Hasil penelitian menunjukkan bahwa mayoritas respon masyarakat adalah netral dengan 1788 tweet, sedangkan 94 tweet positif, dan 10 tweet negatif. Ketidakpastian mengenai jumlah formasi, proses seleksi, persyaratan, dan kebijakan lainnya menjadi faktor utama yang membuat sebagian besar masyarakat cenderung netral. Hasil analisis juga menunjukkan bahwa model klasifikasi Naive Bayes memiliki akurasi sebesar 92%, menunjukkan kemampuan yang baik dalam mengkategorikan data sentimen. Penelitian ini memberikan masukan yang berharga bagi pemerintah dan lembaga terkait dalam merancang kebijakan yang lebih transparan dan jelas untuk meningkatkan dukungan masyarakat terhadap pembukaan lowongan CPNS di masa mendatang.
Analisis Sentimen Penggunaan Aplikasi YouTube Menggunakan Metode Naïve Bayes Putri, Triana; Siti Nurhaliza; Dodi Vionanda
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/343

Abstract

This study aims to analyze user sentiment towards the YouTube application using the Naive Bayes method. With the rapid growth of YouTube users worldwide, understanding user preferences and experiences is crucial. Sentiment analysis, a process of processing or extracting textual data to obtain information by categorizing positive or negative sentiment The Naive Bayes algorithm, a statistical approach commonly used in natural language processing and sentiment analysis, was applied due to its simplicity and efficiency. The research involved data collection through web scraping, followed by preprocessing steps such as cleaning, case folding, tokenization, stopword removal, and stemming. Feature selection was performed using TF-IDF (Term Frequency-Inverse Document Frequency) to assign weights to words based on their importance. The Naive Bayes classifier was then trained on the preprocessed data, and its performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed an accuracy of 82%, precision of 83%, recall of 98%, and an F1-score of 89%, indicating the effectiveness of the Naive Bayes method in sentiment analysis for the YouTube application. This study provides valuable insights into user sentiment towards YouTube, enabling developers and content creators to enhance user experiences and marketing strategies.
Analisis Sentimen Masyarakat Terhadap Korupsi Berdasarkan Tweet Menggunakan Klasifikasi Naive Bayes Zulzila, Alivia; Latifah Jayatri Febiola; Dodi Vionanda
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/345

Abstract

Corruption is one of the big problems faced in Indonesia. The still high rate of corruption can damage the integrity of government, hamper economic growth, and reduce public trust in public institutions. Even though the government has made efforts to eradicate corruption, such as the formation of the Corruption Eradication Commission (KPK), these big challenges remain. Social media, especially Twitter, has become an important platform for people to voice opinions and criticize corruption issues. Sentiment analysis is used to detect opinions in the form of judgments, evaluations, attitudes and emotions of a person. The textual classification algorithm used in this research is Naive Bayes. This research aims to determine public sentiment towards corruption in Indonesia in positive, negative and neutral categories. This is done by data preprocessing, data labeling, and classification. The results of sentiment classification using the Naïve Bayes method obtained positive sentiment of 11, negative sentiment of 14, and neutral sentiment of 1485. So it can be concluded that Indonesian society tends to have neutral sentiments towards corruption that occurs in Indonesia
Analysis on Scopus Articles Padang State University Based on SINTA Website Aidillah, Kerin Hagia; Dodi Vionanda; Dony Permana
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/346

Abstract

Universities have the responsibility to carry out education, research, and community service as mandated by Law Number 20 of 2003 on the National Education System in Article 20. The flagship research theme set by Universitas Negeri Padang (UNP) for the period 2020-2024 is "Development of Digital Learning Services and Development of Minangkabau Cuisine based on Local Potential." The focus of the flagship research activities at Padang State University encompasses two main research areas: 1) Digital Learning Services; and 2) Minangkabau Cuisine. The objective of this research is to compare the flagship research theme with the Scopus articles from Universitas Negeri Padang on the SINTA website. By analyzing the trends of Scopus article topics on the SINTA website using web scraping techniques and wordcloud visualization, it is concluded that there is a match between the trending topics of UNP's Scopus articles and UNP's flagship research theme, particularly in the field of Digital Learning Services. From the wordcloud results, which show keywords such as Learning, Development, Student, and Model. This research allows us to easily observe from the wordcloud visualization the trend of research topics in Scopus articles on SINTA at Universitas Negeri Padang, reflecting the realization of Universitas Negeri Padang flagship research theme for the period 2020-2024
Analisis Sentimen Review Aplikasi Chatting di Google Play Store Menggunakan Alghoritma Naïve Bayes Classifer Alfathan, Muhammad Luthfi; Dodi Vionanda; Nufhika Fishuri
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/347

Abstract

Chatting application is a medium used to connect two or more people through social media platforms. Based on the results of the survey report, there are 5 chat applications that are often used as a medium of communication, including WhatsApp, Facebook, Telegram, Instagram and Line applications. This research aims to see the sentiment of chat application users, and see how naive bayes performs in analyzing the sentiment of chat application users. The purpose of sentiment analysis in this research is to assess whether a comment related to an issue is negative or positive, as well as a guide in improving the quality or service of a product. From the analysis results obtained, the Naïve Bayes model showed mixed performance depending on the type of application and sentiment. The model generally showed better performance in identifying positive reviews, especially on Facebook, Telegram, and Instagram apps, where recall reached 100%. However, the model performed very poorly in identifying neutral reviews across all apps. To increase accuracy and more balanced sentiment detection capabilities, improvements in data preprocessing, handling data imbalance, or the use of more complex classification methods are needed.
Implementation of Text Mining for Emotion Detection Using The Lexicon Method (Case Study: Tweets About Pemilu 2024) Afifah Salsabilah Putri; Eujeniatul Jannah; Dodi Vionanda; Syafriandi
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/348

Abstract

The presidential election is a five-year event that is an important and crucial moment in the realisation of democracy in the Unitary State of the Republic of Indonesia (NKRI). In the modern political era, the development of information technology has had a significant impact in changing the way people interact and express their views on political issues, including in the Presidential election.  One of the social media platforms that is often used to debate political and social issues is Twitter. The analysis method used in this research is sentiment and emotion analysis with a lexicon-based approach. The research stages consist of twitter data collection, data preprocessing, and emotion feature extraction. The first word to be highlighted in the 2024 election series on twitter social media is Anies. Trust is the most dominant emotion towards the three candidate pairs, namely Anies Muhaimin, Prabowo Gibran, and Ganjar Mahfud, showing high public trust.
Analisis Sentimen Pengguna Twitter Terhadap Serangan Moskow oleh ISIS dengan Algoritma Naive Bayes Pratiwi, Cindy; Dodi Vionanda; Fayyadh Ghaly
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/349

Abstract

This study aims to analyze public sentiment towards the ISIS attack in Moscow, Russia on March 22, 2024 through twitter data using the Naive Bayes classification method. The attack had a significant impact on people's perceptions and reactions as reflected in the tweets of twitter social media users. To analyze this, 3005 English tweets from 22 March 2024 to 30 April 2024 relating to the event were collected using the crawling method with the phyton programming language. Preprocessing was done on the data to clean the data, then data labeling was done using phyton TextBlob. Naive Bayes algorithm is used to classify the sentiment of tweets into positive, and negative classes. The results of the research using Naive Bayes show that public sentiment tends to be negative towards the attacks that occurred. Naive Bayes classification results are quite good with an accuracy value of 70%, but there is an imbalance of data that tends to be biased towards negative sentiment. This research provides insight into how public opinion responds to events that occur and the performance of the Naive Bayes model in classification.
Co-Authors Admi Salma Admi Salma Afdhal, Afdhal Rezeki Afifah Salsabilah Putri Aidillah, Kerin Hagia Aldwi Riandhoko Alfathan, Muhammad Luthfi Amanda, Abilya Amannia zeze Andini Yulianti Aprilla Suhada Ardhi, Sonia Atus Amadi Putra Bahri Annur Sinaga Cindy Pratiwi, Cindy defal aditya defran Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Dony Permana Dwi Sulistiowati, Dwi Eujeniatul Jannah Fadhilah Fitri Fadhillah Fitri Fashihullisan Fayyadh Ghaly Fayza Annisa Febrianti FAZHIRA ANISHA Febri Ramayanti Findri Wara Putri Fitri, Fadhilah Fitri, Fitri Hayati Fitria Panca Ramadhani Hariati Ainun Nisa Husni, Nabila Ichlas Djuazva Ihsanul Fikri Isra Miraltamirus Jimmi Darma putra Jumiati, Susi Kamil, Fakhri Larissa, Dwika Latifah Jayatri Febiola Lifia Zullani Mardhiatul Azmi martha, Ully Martha Muhammad Ravi Azzaki Muhammad Tibri Syofyan Mukhti, Tessy Octavia nabillah putri Nanda P, Muhamad Rayhan Nazifatul Azizah Nikma Hasanah Nonong Amalita Nufhika Fishuri Nur Leli Nurul Afifah Permana, Dony Putra, Dio Afdal Putra, M. Farel Rusde Putri, Triana Rahmadina Adityana Rahmanesta, Frandito rama novialdi Rivani, Putri Rizki Akbar Robiati, Silfi Salma, Admi Seif Adil El-Muslih Shavira Asysyifa S Sherly Amora Jofipasi Silvia Agustina Siti Nurhaliza Susrifalah, Amelia Syafriandi Syafriandi Syafriandi Syahfitrri, Nindi Syifa Azahra Tessy Octavia Mukhti Wood, Raihan Attaya Yarman Yarman, Yarman Yenni Kurniawati Yunistika Ilanda Zamahsary Martha Zilrahmi, Zilrahmi Zulzila, Alivia