Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Nuansa Informatika
Nisa Hanum
Universitas Logistik Bisnis Internasional
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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
Co-Authors Ruth Diana Purnamasari Ruth Diana Purnamasari Woro Isti Rahayu