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Analisis Sentimen Terkait Konflik Palestina-Israel Pada Media Sosial X Menggunakan Algoritma Naïve Bayes Classifier Simamora, Silvia Damayanti; Irwiensyah, Faldy; Hasan, Firman Noor
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5344

The conflict between Palestine and Israel has been ongoing for approximately 76 years, during which the Zionist movement has attempted to establish a Jewish homeland in Palestinian territory. In October 2023, news about this conflict resurfaced and has continued up until June 2024. This issue has drawn global attention, including from the Indonesian public. On the social media platform X, numerous comments and posts both negative and positive regarding the Palestine-Israel conflict have appeared as a result of the ongoing challenges faced by Palestine. This study aims to analyze the sentiment expressed on the social media platform X regarding the Palestine-Israel conflict. The data collected focuses solely on comments and posts from Indonesia, totaling 1,715 entries. The study employs the Naïve Bayes Classifier algorithm, with an 80% to 20% ratio of training data to test data, following a pre-processing phase. The results of this study indicate an accuracy of 94%, precision of 91%, recall of 100%, and an F1-Score of 95%. The analysis reveals a positive sentiment, suggesting that the Indonesian public's response on the social media platform X predominantly shows positive support towards Palestine

Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE Sunata, Muhamad Hafidz Ardian; Irwiensyah, Faldy; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7708

In the era of digital advancement, the utilization of social media has surged, enabling individuals to express their viewpoints openly. This research underscores the utilization of social media platform X as the primary avenue for users to express their opinions, particularly on political matters, notably within the framework of the presidential election. Sentiment analysis techniques, specifically employing the Naïve Bayes algorithm and the Synthetic Minority Oversampling (SMOTE) method, have been the central focus of inquiry to infer people's inclinations toward presidential candidates. Despite numerous antecedent studies, deficiencies persist in terms of precision and data imbalance. This study endeavors to enhance the efficacy of sentiment analysis by integrating the Naïve Bayes approach with SMOTE. By scrutinizing tweets on social media X spanning from December 12, 2023, to March 31, 2024, the data is categorized into positive and negative sentiments. The findings reveal that employing SMOTE bolstered accuracy to 88% for the Ganjar-Mahfud dataset, whereas accuracy without SMOTE languished at approximately 69% for the Anies-Imin dataset. Out of 1589 tweets conveying positive sentiments, approximately 27.7% were directed towards Anies-Imin, 28.7% towards Prabowo-Gibran, and 43.5% towards Ganjar-Mahfud. The preponderance of negative sentiments was aimed at Anies-Imin (41.5%) and Prabowo-Gibran (40.8%).

Analisis Sentimen Pendapat Netizen Indonesia Terhadap Pengungsi Rohingya Pada Aplikasi X Menggunakan Algoritma Naive Bayes Lestari, Putri; Irwiensyah, Faldy
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.440

Rohingya ethnic group is one of the victims of genocide atrocities committed by Myanmar as a result of past grudges. Because of this, several ethnic groups chose to leave Myanmar and decided to flee to other countries, including Indonesia, as a result, this issue continued to rise on social media. This research was conducted with the aim of analyzing public opinion sentiment regarding the inclusion of the Rohingya ethnic group on social media X using the Naive Bayes method. The data carried out using the Web Scrapping method was 1124 with a time span of May 2023 to January 2024 and the data was continued with the preprocessing stage and removing duplications in the data which made the data change to 701. After going through these stages it can be implemented into Naive Bayes. The results of implementing Naive Bayes in this research about the opinions of Indonesian netizens regarding Rohingya refugees in the X application obtained an accuracy value of 98.57%, precision 100%, and recall 97.35%.

Analisis Sentimen Terhadap Ulasan Pengguna Pada Aplikasi Traveloka Menggunakan Metode Naïve Al Hakim, Muchammad Gamma; Irwiensyah, Faldy
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6119

The proliferation of user-generated reviews on digital platforms provides in-depth information to improve services. The purpose of this study is to apply the Naïve Bayes approach to analyze the sentiment of user evaluations of the Traveloka application sourced from the Google Play Store. Through online search, 10,000 evaluations were collected. Case folding, stopword elimination, tokenizing, and stemming are some of the pre-processing techniques used. Based on the review scores, the sentiment data was classified into two groups: positive and negative. Furthermore, the Naïve Bayes model was used for classification, and a confusion matrix was used to assess the results. The results showed an accuracy of 89.35%, precision of 88.44%, recall of 95.05%, and F1-Score of 91.62%. These results demonstrate the effectiveness of the Naïve Bayes approach in categorizing user reviews, providing Traveloka with important information about customer perceptions and how to improve their service quality. The findings from this study are expected to be the basis for future advancements in sentiment analysis on travel and accommodation-related applications.

Analisis Sentimen Ulasan Pengguna Aplikasi Alibaba.Com pada Google Playstore Menggunakan Naïve Bayes Rahman, Rafi Fadhlur; Irwiensyah, Faldy
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6132

Alibaba.com, as one of the leading platforms, continues to strive to improve its services based on user feedback. One approach used is the collection of user reviews on the Google Play Store. To enhance service quality and user experience, sentiment analysis of these reviews becomes crucial. In this study, the Naive Bayes algorithm is applied to analyze the sentiment of the reviews with the aim of determining whether the sentiment is positive or negative. The data, consisting of reviews, was obtained through web scraping, resulting in 998 reviews that were processed through preprocessing stages. The dataset was then divided into training and testing data with a 60:40 ratio, where 599 reviews were manually labeled for training, and 399 reviews were used as test data. The Naive Bayes algorithm subsequently categorized the reviews as either positive or negative sentiment. An evaluation with a confusion matrix was then used to assess performance, this model showed an accuracy of 77.44%, precision of 83.39%, and recall of 85.16%. A total of 721 reviews were categorized as positive sentiment, while 277 reviews were categorized as negative sentiment. The main issues identified in the negative reviews included challenges related to language and payment. Additionally, there were complaints regarding online buying and selling fraud, which is a significant issue on this platform. Many users reported negative experiences related to transactions that did not match expectations, items that were not received, or products that did not match their descriptions. This highlights the importance of better verification and security systems to protect users from fraud. This study demonstrates that the Naive Bayes algorithm is quite efficient in analyzing user review sentiments on the Alibaba.com application.

Pelatihan Pembuatan Konten Promosi Bagi Pelaku UMKM Dengan Menggunakan Aplikasi CANVA Irwiensyah, Faldy; Febriandirza, Arafat
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 12 : Januari (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Micro, Small, and Medium Enterprises (MSMEs) are currently facing increasingly intense competition, particularly in the fields of digital marketing and branding. These two aspects have become essential skills that MSMEs must possess to compete in the digital space. The rapid development of digital competencies requires MSMEs to be ready to adapt by optimizing their use of digital media. Effective and targeted use of digital media is one of the key factors for MSMEs to achieve success. Therefore, MSME entrepreneurs need to enhance their ability to create engaging and interactive marketing content for promotion on digital platforms. As part of a community service initiative, training and mentoring activities were conducted to support MSME entrepreneurs, specifically DINARNIA Muslim Clothing Store. This training aimed to teach strategies for creating promotional content on digital media using the Canva application. Through this program, MSME entrepreneurs learned to utilize Canva, starting from creating an account, logging into the application, selecting templates, and adding images and text to the chosen templates. It is hoped that this training will assist MSMEs in promoting their products or services digitally, increasing sales, and ensuring business sustainability. The participants gained valuable insights into effectively using Canva, enabling them to advance their MSMEs to a better level.

Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE Sunata, Muhamad Hafidz Ardian; Irwiensyah, Faldy; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7708

In the era of digital advancement, the utilization of social media has surged, enabling individuals to express their viewpoints openly. This research underscores the utilization of social media platform X as the primary avenue for users to express their opinions, particularly on political matters, notably within the framework of the presidential election. Sentiment analysis techniques, specifically employing the NaÃ¯ve Bayes algorithm and the Synthetic Minority Oversampling (SMOTE) method, have been the central focus of inquiry to infer people's inclinations toward presidential candidates. Despite numerous antecedent studies, deficiencies persist in terms of precision and data imbalance. This study endeavors to enhance the efficacy of sentiment analysis by integrating the NaÃ¯ve Bayes approach with SMOTE. By scrutinizing tweets on social media X spanning from December 12, 2023, to March 31, 2024, the data is categorized into positive and negative sentiments. The findings reveal that employing SMOTE bolstered accuracy to 88% for the Ganjar-Mahfud dataset, whereas accuracy without SMOTE languished at approximately 69% for the Anies-Imin dataset. Out of 1589 tweets conveying positive sentiments, approximately 27.7% were directed towards Anies-Imin, 28.7% towards Prabowo-Gibran, and 43.5% towards Ganjar-Mahfud. The preponderance of negative sentiments was aimed at Anies-Imin (41.5%) and Prabowo-Gibran (40.8%).

Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time Pratama, Ridho Danang Budi; Irwiensyah, Faldy
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7819

This study implements a direct facial expression detection system via the web using teachable machine and tensorflow.js. This system utilizes machine learning technology that operates directly in the browser without the need for a special server. With the transfer learning method, the model is trained to recognize various facial expressions such as happy, sad, angry, and neutral. This implementation uses a convolutional neural network (cnn) architecture that has been optimized for web activities. The results of the test show a detection accuracy level of 85-90% with a response time of under 200ms. This solution provides a lightweight option for emotion recognition applications that can be easily accessed via a web browser. The main advantages of this system are ease of implementation, cross-platform support, and maintaining data privacy because the process is carried out locally.

Analisis Sentimen Terhadap Ulasan Pengguna Pada Aplikasi BCA Mobile Menggunakan Metode Naïve Bayes Al Hakim, Muchammad Gamma; Irwiensyah, Faldy
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5343

Technological developments have made the payment process easier, which has resulted in a plethora of smartphone applications. As mobile phones become more prevalent, commercial and public organizations are looking to improve the services they provide by implementing mobile-based solutions. The banking industry has seen tremendous expansion, as evidenced by the use of mobile banking solutions by companies such as BCA Bank. Especially in the midst of the pandemic, the BCA Mobile app is an important advancement in online banking that provides benefits and convenience to individuals who frequently transact online. Bank BCA can continue to offer the most useful features to customers while proactively improving services that are currently lacking. This study emphasizes the importance of improving sentiment analysis techniques to understand customer feedback more fully and provide better mobile banking services. This study uses the Naïve Bayes approach to analyze user sentiment towards the BCA Mobile application on the Google Play Store by finding and categorizing user reviews based on the sentiment they exhibit i.e. positive, negative, or neutral is the objective of this study. Through online data mining, 2000 user review data were collected on January 11, 2024, resulting in 1173 sentiments, 163 positive reviews and 1010 negative reviews in total. The Naïve Bayes algorithm produced an accuracy of 86.83%, precision of 52.78%, and recall of 46.91%.

ANALISIS KEPUASAN MASYARAKAT TERHADAP PERILAKU KORUPSI PEMERINTAH BERDASARKAN KOMENTAR PADA SOSIAL MEDIA MENGGUNAKAN NAIVE BAYES CLASSIFIER Irwiensyah, Faldy; Hasan, Firman Noor
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.576

Corrupt behaviour by government officials often occurs and becomes a problem that can disturb the public and threaten the integrity of the government system. Social media has become an important means for the public to voice their opinions and sentiments on social issues, including corrupt behaviour by government officials. This study aims to analyze the corrupt behaviour of government officials based on public sentiment on social media using the Naïve Bayes Classifier method. Data was obtained from Twitter with keywords closely related to corruption cases involving government officials, data obtained in a certain period. The Naïve Bayes Classifier method was applied to classify tweets related to corrupt behaviour by government officials to later be categorized into positive sentiment, and negative sentiment. The results of this study conclude that the TF-IDF Weighting Process, 3 words are very dominant and often appear in public sentiments, namely the words "Corruption", "Official" and the word "Tax". This shows that the public is very angry and disappointed, and this results in a very low level of trust in corrupt behaviour carried out by government officials. Especially those carried out by tax officials

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