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All Journal Dinamisia: Jurnal Pengabdian Kepada Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer
Faldy Irwiensyah
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Computer Science & IT Social Sciences Other

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Analisis Sentimen Ulasan Pengguna Aplikasi Netflix Pada Google Play Menggunakan Algoritma Naïve Bayes Ananda Bagas Pranata; Allif Rizki Abdillah; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1964

Abstract

The rapid development of information technology has advanced rapidly, including advancements in film technology. In this modern era, watching movies no longer requires going to the cinema, as there are applications available to watch movies anytime and anywhere. One popular application for watching movies is Netflix, a widely used streaming platform for films and series. Netflix also ranks 10th in terms of access in Indonesia. This study focuses on identifying user satisfaction levels with the Netflix application based on reviews on the Google Play Store. The research aims to analyze user review sentiment of an application available on Google Play, namely Netflix. These reviews will be used to gauge user satisfaction with the Netflix application. Researchers obtained these reviews using a Python web scraper with a total of 1000 unprocessed data points. After processing these 1000 data points by removing duplicates and symbols, researchers obtained 893 data points ready for sentiment analysis using RapidMiner. Out of the 893 data points, researchers manually labeled 635 data points, while 258 data points were labeled automatically using machine learning, namely Naive Bayes. Researchers also created a confusion matrix to determine the accuracy level of the algorithm used in this study. The accuracy result of the confusion matrix obtained by researchers in this study is 93.39%. The positive class precision value of 85.52% indicates that most positive reviews were identified accurately, while the negative class precision value of 100% demonstrates excellent capability in identifying negative reviews. In conclusion, the Netflix application receives diverse responses from users, and the algorithm used effectively identifies reviews accurately
Analisis Sentimen Aplikasi Tokocrypto Berdasarkan Ulasan Pada Google Play Store Menggunakan Metode Naïve Bayes Rizki Adi Saputra; Dion Parisda Ray; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1707

Abstract

The advancement of increasingly sophisticated technology has brought numerous changes and conveniences for humans in all aspects, including the financial sector. Cryptocurrency has emerged as an innovation in the financial world. A cryptocurrency exchange is an electronic platform that enables sellers and buyers to conduct cryptocurrency trading transactions through a website or mobile application. Currently, many cryptocurrency exchange applications suffer from poor service, unreliable security, lengthy withdrawal processes, high administrative fees, and other issues. As a result, many people in Indonesia rely on reviews on the Google Play Store to check user feedback before deciding to use these cryptocurrency exchange applications. Many Indonesians seek information on cryptocurrency exchange applications that provide the best services for buying and selling cryptocurrency. One such application, according to reviews on the Google Play Store, is Tokocrypto. This study aims to understand the sentiment towards user reviews of the Tokocrypto application using the Naïve Bayes algorithm for data classification. The data obtained consists of 2,000 reviews from the Google Play Store in February 2024, collected using Google Colaboratory. The research stages include data scraping using web scraping techniques, data labeling, preprocessing, TF-IDF weighting, implementing the Naïve Bayes algorithm, and evaluation. The cleaned data resulted in 1,000 reviews, with 396 positive sentiments and 604 negative sentiments. The results of sentiment analysis research using the Naïve Bayes algorithm method show 74.22% for accuracy, 63.25% for precision, and 81.40% for recall.
Analisis Sentimen Ulasan Aplikasi Samsat Digital Nasional Pada Google Playstore Menggunakan Algoritma Naïve Bayes Deni Wijaya; Rizki Adi Saputra; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1738

Abstract

Digital transformation has become a major factor of change in various aspects of modern life, including business, education, and government. In the current era of digital transformation, the government is trying to improve efficiency and services to the community through the implementation of various technological innovations. The application of digital technology in public services is increasingly widespread, including in the administrative service sector such as the National Digital Samsat (SIGNAL) which allows people to make online vehicle tax payments through the SIGNAL application. User evaluations of this application can provide important insights for service providers. This research aims to analyze the sentiment of user reviews of the National Digital Samsat application on the Google Playstore platform using the Naïve Bayes algorithm. This method is used to classify user reviews into positive and negative sentiment categories. From 2000 reviews taken, 1,665 reviews were categorized as positive and 335 reviews as negative after manual labeling. Data preprocessing using RapidMiner includes cleaning, transform cases, tokenizing, stopword filter, token by length filter, and stemming. TF-IDF weighting is used to give weight to each word in the document. Evaluation of the Naïve Bayes model resulted in an accuracy of 63.61%, with 307 True Positives, 74 True Negatives, 26 False Positives, and 192 False Negatives. Precision was 92.19% and recall was 61.52%. The overall analysis shows that user reviews tend to be more positive towards the SIGNAL app, although there are some negative reviews. This conclusion gives an idea of users' positive perception of the app
Analisis Sentimen Pengguna Terhadap Aplikasi Bing Chat di Google Play Store dengan Metode Naïve Bayes Dimas Cahyo Ramadhan; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1769

Abstract

The development of technology that occurs at this time is increasingly rapid, so it can be said to be an era of technological revolution where at this time almost all activities in society have used technology. One of the technologies that emerged in the current era of technological development is artificial intelligence(AI) technology. Artificial intelligence refers to the ability of computers to learn, adapt, and make decisions based on data. Currently, there are many artificial intelligence technologies in the form of applications that can be easily downloaded for free on the Google Play Store, one of which is the application resulting from the partnership between Microsoft and OpenAI, namely Bing Chat. The presence of Bing Chat as one of the artificial intelligence applications on the Google Play Store raises various user reviews while using the artificial intelligence technology. Based on this, a method is needed to analyze the various reviews on the Bing Chat application. This research aims to analyze user sentiment reviews of the Bing Chat application on the Google Play Store with the Naïve Bayes method. A total of 2000 user sentiment review data for the Bing Chat application on the Google Play Store in the January to February 2024 timeframe were collected using the web scrapping method. After going through the analysis process, 1877 sentiment data were obtained with 1653 positive sentiment data and 224 negative sentiment data. The evaluation results of this research on the sentiment of the Bing Chat application on the Google Play Store with the Naïve Bayes algorithm method get the results of the accuracy value of 67.16%, precision 93.53%, and recall 67.39%.
Video Editing Training Utilizing Capcut Application at SMKM 7 East Tebet: Pelatihan Editing Video Menggunakan Aplikasi Capcut di SMKM 7 Tebet Timur Oktarina Heriyani; Faldy Irwiensyah; Rifky; Dan Mugisidi; M. Mujirudin
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 6 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i6.19300

Abstract

The significance of video editing skills in conveying messages in the digital era has prompted the implementation of a training program at SMKM 7 Tebet Timur, focusing on the utilization of the CapCut application. This topic was selected to address the challenge of insufficient understanding and skills among students in applying video editing techniques, aligning with contemporary demands in visual communication. The training method encompassed material preparation, theoretical presentation, demonstration, hands-on practice, evaluation, and discussion. The evaluation revealed a notable improvement in the students' understanding and skills post-training, with average scores experiencing significant increments. The conclusion underscores the importance of a holistic approach in multimedia training and recommends further collaboration with higher education institutions. The training outcomes also fostered positive relations between SMKM 7 Tebet Timur and FTII UHAMKA. This training makes a tangible contribution to preparing students to meet the demands of an increasingly visually connected professional world.
Co-Authors Allif Rizki Abdillah Ananda Bagas Pranata Dan Mugisidi Deni Wijaya Dimas Cahyo Ramadhan Dion Parisda Ray M. Mujirudin Oktarina Heriyani Rifky, Rifky Rizki Adi Saputra