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All Journal Nuansa Informatika
Nisa Hanum
Universitas Logistik Bisnis Internasional
Computer Science & IT

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Sentiment Analysis for Delivery Pricing Optimization : Naïve Bayes Evaluation on Gojek Reviews Nisa Hanum; Woro Isti Rahayu; Ruth Diana Purnamasari
NUANSA INFORMATIKA Vol. 20 No. 1 (2026): Nuansa Informatika 20.1 Januari 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i1.520

Abstract

The development of digital technology in Indonesia has driven the growth of application-based transportation services such as Gojek. However, amid this growth, user dissatisfaction with pricing strategies often appears in social media reviews, but has not been systematically explored as a basis for optimizing delivery pricing using an automated text mining approach. Nevertheless, empirical studies that explicitly utilize sentiment analysis as a foundation for delivery pricing optimization remain limited. Existing sentiment analysis studies on online transportation services have mostly focused on overall service satisfaction, with limited attention to how sentiment patterns reflect customer price sensitivity. This study aims to analyze user sentiment as a diagnostic basis for evaluating and supporting delivery pricing optimization strategies. A comparative evaluation of four text representation methods: TF-IDF, Bag-of-Words (BoW), Word2Vec, and TF-IDF with Chi-Squared feature selection was conducted using 282 Gojek-related reviews collated from X (Twitter). The results of the experiment show that the Naïve Bayes model with BoW representation achieved the best performance, with 70% accuracy and an F1-score of 0.68, outperforming more complex approaches. Further analysis identifies that negative sentiment correlates with the keywords “expensive” and “promo,” providing a strong indication that pricing issues are a major point of concern for customers. These findings serve as a basis for management to optimize dynamic pricing strategies based on real-time user feedback.
Deep Learning Evaluation for Interactive Dashboard-Base Mail Classification: Evaluasi Pembelajaran Mendalam untuk Klasifikasi Email Berbasis Dasbor Interaktif Ruth Diana Purnamasari; Nisa Hanum
NUANSA INFORMATIKA Vol. 20 No. 1 (2026): Nuansa Informatika 20.1 Januari 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i1.546

Abstract

The management of incoming mail archives at a large national logistics company in Indonesia generates a large volume of unstructured textual data, making manual classification inefficient and error-prone. This study evaluates the performance of deep learning models for administrative mail archives classification using data collected between 2023 and 2025. Three models are examined, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN). Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. Experimental results indicate that CNN achieves the highest accuracy of 85.82%, outperforming LSTM and Bi-LSTM models. This superior performance is attributed to CNN’s ability to capture local textual patterns through convolution operations, which are well-suited to the structured and repetitive language characteristics of official correspondence. To support practical interpretation, an interactive dashboard is implemented as a visualization tool for model evaluation results, classification outcomes, and clustering analysis. These findings demonstrate that deep learning-based approaches integrated with visual analytics can significantly improve the efficiency and accuracy of unstructured mail archive management
Development of a Mobile Application for Booking Meeting Rooms at PLN UP2D West Java: Pengembangan Aplikasi Mobile untuk Pemesanan Ruang Rapat di PLN UP2D Jawa Barat Wafid Adzka Haifan Mukhayyar; Nisa Hanum
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.556

Abstract

At PLN UP2D West Java, the meeting room booking process still often faces several problems. Employees frequently experience schedule conflicts, difficulty finding clear information about room availability, and delays caused by manual confirmation. These issues can make employees confused and waste time, especially when meetings need to be arranged quickly. As a result, work activities do not always run smoothly and meeting schedules are sometimes disrupted. To solve these problems, a mobile meeting room booking application was developed. This application allows employees to check room availability in real time and make reservations easily without going through a long manual process. The system also helps prevent double bookings, so each meeting room can be used more effectively. The application is designed with a simple and user-friendly interface, making it easy for all employees to use anytime and anywhere. With this application, the meeting room booking process becomes more organized, efficient, and supports smoother daily work activities at PLN UP2D West Java
Co-Authors Ruth Diana Purnamasari Ruth Diana Purnamasari Wafid Adzka Haifan Mukhayyar Woro Isti Rahayu