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Dodi Vionanda
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Analisis Sentimen Penggunaan Aplikasi YouTube Menggunakan Metode Naïve Bayes Triana Putri; 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 Alivia Zulzila; 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 Kerin Hagia Aidillah; 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 Muhammad Luthfi Alfathan; 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.
Analisis Sentimen Pengguna Twitter Terhadap Serangan Moskow oleh ISIS dengan Algoritma Naive Bayes Cindy Pratiwi; 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.
Analisis Sentimen Program MSIB pada Aplikasi X (Twitter) Menggunakan Algoritma Naïve Bayes Nabila Husni; Dodi Vionanda; Nur Leli; Syafriandi Syafriandi
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/361

Abstract

Certified Internships and Independent Studies (MSIB) is one of the programs of the Independent Learning-Independent Campus (MBKM) curriculum as a policy of the Kemendikbudristek. A government policy, especially in terms of education, will of course give rise to stigmas or feedback from the public regarding the policy. This research aims to find out public opinion regarding the MSIB program in the X (Twitter) application by sentiment analysis using the Naive Bayes Classifier algorithm. From this analysis, it was found that 84.6% of reviews had positive sentiments, while 16.4% of reviews had negative sentiments. Evaluation using the Naïve Bayes Classifier model shows that this model succeeded in classifying 85% of all data correctly, showing quite good performance in classifying the sentiment of these reviews.
Penerapan Algoritma Extreme Gradient Boosting dengan ADASYN untuk Klasifikasi Rumah Tangga Penerima Program Keluarga Harapan di Provinsi Sumatera Barat Amelia Susrifalah; Dodi Vionanda; Yenni Kurniawati; Dwi Sulistiowati
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/369

Abstract

Program Keluarga Harapan (PKH) is a form of social protection provided by the government to overcome poverty in Indonesia. However, challenges remain in accurately predicting eligible households. Therefore, a data-based classification method is needed to identify PKH recipients based on their factors. This research was conducted in West Sumatra Province using variables from the Data Terpadu Kesejahteraan Sosial (DTKS) variable group contained in SUSENAS 2024. Based on data from Badan Pusat Statistik (BPS) of West Sumatera Province, there are 1.790 PKH recipient households and 9.810 non-recipient households, indicating a class imbalance. Considering the large amount of data and complex variables, PKH can be analyzed using the Extreme Gradient Boosting (XGBoost) algorithm because of its ability to handle large-scale data and produce high classification performance. To address data imbalance, Adaptive Synthetic (ADASYN) was applied before analysis. The application of XGBoost with the scale_pos_weight parameter shows low classification performance, with sensitivity value of 12.3% and balanced accuracy of 55.2%. To overcome this, unbalanced data was handled using the ADASYN method. The application of XGBoost after data balancing with ADASYN showed significant performance improvement, with sensitivity value 80.4% and balanced accuracy 88.1%. In classifying PKH recipient households, the variables that make an important contribution are the age of the head of household, floor area, diploma of the head of household, floor material and number of household Members. This research shows that the combination of XGBoost and ADASYN is effective in overcoming data imbalance and improving PKH recipient classification performance.
Penerapan Partial Least Squares dan Pendekatan Robust dalam Analisis Diskriminan untuk Data Berdimensi Tinggi Rahmadina Adityana; Dodi Vionanda; Dony Permana; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/396

Abstract

Classical discriminant analysis, namely linear discriminant analysis and quadratic discriminant analysis, is generally known to suffer from singularity problems when exprerienced with high-dimensional data and is not robust to outliers that make the data not multivariate normally distributed. This research focuses on investigating the classification performance of discriminant analysis on high-dimensional data by applying two approaches, namely the Partial Least Square (PLS) dimension reduction approach as a solution to high-dimensional data and a robust approach with the Minimum Covariance Determinant (MCD) estimator technique that is robust to outliers. The data used for this study is Lee Silverman Voice Treatment (LSVT) data. PLS forms five optimal latent variables that represent predictor variable information. Based on the assumption test of covariance homogeneity between groups, the test statistic value is greater than the chi-square table or the p-value is smaller than the significance level, which means that the assumption is unfulfilled, so quadratic discriminant analysis is applied. The evaluation results showed that the quadratic discriminant analysis analysis model with the MCD approach on the PLS transformed data was able to achieve 81% accuracy, 71% precision, 86% recall, and 77% F1-score. These values indicate that both approaches are able to maintain the efficiency of discriminant analysis classification performance on high-dimensional and multivariate non-normally distributed data.
PayPal Usage in Indonesia with k-Nearest Neighbor Algorithm Amannia zeze; Muhammad Ravi Azzaki; Dodi Vionanda
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/405

Abstract

The development of information and digital technology has had a significant impact on the financial sector. In Indonesia, digital payment technologies such as PayPal, Gopay, Shopeepay, OVO, and DANA have become an integral part of the modern payment system. Since the implementation of the national electronic clearing system, RTGS, and ATMs in 2005, transactions have become increasinglyconvenient. This study analyzes user sentiment toward PayPal in Indonesia to understand user experience and provide insights for service development, marketing strategies, and brand reputation management. Review data from the PayPal app was collected from Google Plat via web scrapping and processed to yield 597 clean data points. Initial sentiment was categorized into positive, neutral, and negative, wordcloud visualization displayed positive and negative sentiment, while neutral sentiment was analyzed numerically. Automatic labeling was performed using the NLTK library based on rating values, above 3 positive, below 3 negative, and exactly 3 neutral. The results showed 146 positive reviews, 451 negative reviews, and a few neutral reviews. Sentiment classification using the K-Nearest Neighbor (K-NN) method yielded adequate accuracy, indicating that PayPal's acceptance in Indonesia is largely influenced by users' negative experiences. These findings provide a foundation for developing strategies to improve service quality and update PayPal's operational policies in the Indonesian market.
Factors Affecting Households Program Keluarga Harapan Recipients in West Sumatra: Binary Logistic Regression Analysis Sonia Ardhi; Dodi Vionanda; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/406

Abstract

Poverty is still a complex issues in Indonesia. Poverty rate in West Sumatra province has increased over the past 3 years. One of the government's initiatives to address poverty is the Program Keluarga Harapan (PKH), which is a social protection program that provides conditional cash transfers to poor and vulnerable Keluarga Penerima Manfaat (KPM) on condition that they are registered in the Data Terpadu Kesejahteraan Sosial (DTKS). Although PKH has a positive impact on poverty alleviation and enhanced access to health, education, and social welfare, the implementation still faces major challenges such as data inaccuracies, particularly in targeting accuracy. Therefore, an analysis is needed to determine the factors that significantly affects PKH recipient households in West Sumatra Province. This research used variables from the DTKS variable group contained in SUSENAS 2024 using two stages one phase stratified sampling method with 11,600 observations consisting of 1,790 receiving PKH and 9,810 not receiving PKH. The dependent variable is PKH recipient status (Yes = 1, no = 0). Data were analyzed using binary logistic regression with a significance level of 5%. Based on the results of the analysis, it can be concluded that floor area of ​​the house, age of the household head, household size, education level of the household head, and floor material of the house have a significantly effect on PKH recipient households. Household size has the most influence on PKH receipt with a 40,3% probability of receiving PKH.
Co-Authors Abilya Amanda Alivia Zulzila Amannia zeze Amelia Susrifalah Cindy Pratiwi Dina Fitria Dio Afdal Putra Dony Permana Dwi Sulistiowati Fadhilah Fitri Fakhri Kamil Fayyadh Ghaly Fitri Hayati Fitri Kerin Hagia Aidillah Latifah Jayatri Febiola M. Farel Rusde Putra Maulidya Hernanda Muhamad Rayhan Nanda P Muhammad Luthfi Alfathan Muhammad Ravi Azzaki Nabila Husni Nikma Hasanah Nonong Amalita Nufhika Fishuri Nur Leli Rahmadina Adityana Salma, Admi Siti Nurhaliza Sonia Ardhi Susi Jumiati Syafriandi Syafriandi Tessy Octavia Mukhti Triana Putri Ully Martha martha Zamahsary Martha Zilrahmi, Zilrahmi