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All Journal Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah JURTEKSI
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Sistemasi: Jurnal Sistem Informasi

 1 
ALGORITMA NAÏVE BAYES UNTUK ANALISIS SENTIMENT REVIEW BLIBLI.COM DI GOOGLE PLAY STORE Siti Nur Fadhilah; Fandy Setyo Utomo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3887

Abstract

Currently, there are approximately 354 million active mobile phones in Indonesia, placing the country fourth globally in terms of the highest number of mobile phone users. E-Commerce, as a form of online transaction, enables the digital exchange of goods and services to meet daily needs. This research aims to implement sentiment analysis using the Naive Bayes classification algorithm as a method to gather user opinions. Thus, the study not only provides insights into customer satisfaction with Blibli.com but also serves as a basis for potential improvements in services or feature development to enhance the online shopping experience. Overall, the Naive Bayes algorithm successfully achieved an accuracy of around 84%, demonstrating its proficiency in categorizing sentiment in reviews. When focusing on negative data, the Naive Bayes algorithm exhibited a precision of approximately 79%, recall of around 95%, and an f1-score of about 86%, indicating its success in identifying and classifying negative reviews with high precision and sensitivity. On the positive side, the Naive Bayes algorithm achieved a precision of about 91%, recall of around 83%, and an f1-score of about 87%.
Sentiment Analysis of pegipegi.com Review on Google Play Store with Naïve Bayes Muhamad Naufal Burhanuddin Balit; Fandy Setyo Utomo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3913

Abstract

In the current era, a shift in consumer behavior is evident in the use of online platforms for booking tickets, involving various services such as flights, hotels, trains, buses, and entertainment. PegiPegi.com, as a rapidly growing online travel agent in Indonesia, demonstrates success by understanding the value of technology and maintaining strong partnerships. This phenomenon also impacts sentiment analysis, where users of this platform often provide reviews. This research aims to apply the Naïve Bayes classification method in sentiment analysis of PegiPegi.com reviews, focusing on understanding customer satisfaction and service improvement. By combining these approaches, the study contributes to a deeper understanding of user responses to OTA services and presents the evaluation results of the Multinomial Naive Bayes classification model with an accuracy rate of 89.5%. The high precision in the Negative class indicates the model's ability to identify negative reviews. However, there are challenges in classifying the Neutral class, suggesting potential for further improvement. Nevertheless, the F1-score of 0.522 reflects a good balance between overall precision and recall.
Sales Data Visualization to Determine Business Insight Using Metabase in a Global Retail Company Fandy Setyo Utomo; Zuhriyatul Lubna
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.3870

Abstract

In the era of dynamic business globalization, sales data analysis and visualization are key to strategic decision making. The application of Metabase as the main tool for visualization and analysis of sales data in the context of global retail companies, especially in the online sales sector. XYZ Company became the subject of research with complex challenges in managing extensive and diverse sales data. Metabase was adopted as a solution to deal with this complexity, enabling the company to gain deep insights into sales trends, consumer preferences, and hidden growth opportunities. Data visualization, through Metabase, plays a key role in transforming complex information into easy-to-understand visual representations, helping analysts and business stakeholders spot important patterns and trends. Research results reveal patterns of concurrent product purchases, providing opportunities to increase sales through promotions or product bundling. The identification of product categories that customers are interested in within a single transaction provides important insights for stock management and marketing strategies. Analysis of customer gender preferences opens up opportunities to direct more specific marketing strategies, focusing on the majority of a particular gender. The resulting recommendations include increased promotion or bundling of frequently purchased products together, as well as implementation of more focused marketing strategies based on product category preferences and customer gender. This article aims to contribute to the scientific literature on the practical application of data visualization in the context of sales analysis, with a focus on developing effective business decisions and marketing strategies.
Co-Authors Afit Ajis Solihin Aji Saeful Bagus Adhi Kusuma Berlilana Berlilana Hanif Hidayatulloh Jamie Mayliana Alyza Mohd Sanusi Azmi Muhamad Naufal Burhanuddin Balit Pungkas Subarkah Ria Indriyani Rifqi Arifin Ilham Rio Fadly Ramadhan Rujianto Eko Saputro Sarmini - Sarmini Sekhudin Sekhudin Siti Nur Fadhilah Wiga Maulana Baihaqi Yuli Purwati Zuhriyatul Lubna