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Mirfan
STMIK Handayani
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Analisis Sentimen Ulasan Produk di E-Commerce Bukalapak Menggunakan Natural Language Processing Elsa Sera; Hazriani Hazriani; Mirfan Mirfan; Yuyun Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research discusses the analysis of sentiment in product reviews on E-Commerce Bukalapak using Natural Language Processing (NLP). The study aims to fill the knowledge gap regarding the analysis of product reviews in online stores in Indonesia, specifically Bukalapak. The data used in this research were collected from various product categories, such as clothing, electronics, cosmetics, and others. The method employed in this study was the TF-IDF method to train the Naive Bayes model. The results of the research show that the Naive Bayes model trained using the TF-IDF method achieved an accuracy of 88%. This indicates that the model has good capability in predicting the sentiment of product reviews. The analysis of positive reviews reveals customer satisfaction with product quality, fast delivery, reasonable pricing, and receiving items as expected. On the other hand, the analysis of negative reviews uncovers the mismatch between customer expectations and the actual conditions regarding color, delivery, and product orders. This study contributes to a deeper understanding of sentiment analysis in product reviews on E-Commerce Bukalapak. The insights from this analysis can be utilized by Bukalapak to enhance the quality of their products and services, providing a more satisfying experience for customers.
Klasifikasi Kesegaran Buah Apel Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android Respaty Namruddin; Mirfan Mirfan; Irfandi Irfandi
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Apple fruit is one of the important agricultural commodities in the farming industry. The maturity level of apple fruit is a key factor that affects its quality and shelf life. Manual determination of apple fruit maturity levels is often time-consuming and subjective. Therefore, this research aims to develop an automation system that can classify the maturity levels of apple fruit using the Convolutional Neural Network (CNN) method on the Android platform.Image data of apple fruit at various maturity levels were collected and processed in this study. A CNN model was designed, trained, and optimized using this data to identify the maturity levels of apple fruit from images. The final outcome of this research is a user-friendly Android application that can assist apple farmers in quickly and accurately classifying the maturity levels of apple fruit.The research results indicate that the CNN model can recognize the maturity levels of apple fruit with high accuracy, and the generated Android application provides easy access for users to support apple farming by enhancing efficiency in harvest management and apple processing
Co-Authors Asriyani Ismail Diky Wahyudi B Elsa Sera Guntur Guntur Hazriani, Hazriani Herman Irfandi Irfandi JUMARLIS, MILA Karman Jaya Mahka, Muh Fachrur Razy Manga, Abdul Rachman Muh Fachrur Razy Mahka Respaty Namruddin Ronal Taruk Padang sari, Sri Kurniyan Suardi M. SYAMSU ALAM Yuyun Yuyun