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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Al Ghazali, Nabiel Muhammad
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Computer Science & IT Education

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Sentiment Classification in E-Commerce Using Naïve Bayes and Combined Lexicon - N-Gram Features Al Ghazali, Nabiel Muhammad; Sibaroni, Yuliant
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6157

Abstract

This study investigates sentiment classification in e-commerce using Naïve Bayes with lexicon-based, N-gram, and combined lexicon-N-gram features. While previous research has employed various e-commerce platforms and achieved varying degrees of accuracy using Naïve Bayes for sentiment analysis, the combination of lexicon and N-gram features with Naïve Bayes has not been extensively explored in e-commerce contexts. This study proposes to evaluate three models: Naïve Bayes with Lexicon Features, Naïve Bayes with N-Gram Features, and Naïve Bayes with Combined Lexicon-N-Gram Features. The research analyzes 10,000 customer reviews of the Shopee application from the Google Play Store. Results show that the Naïve Bayes model using combined lexicon-N-gram features achieved the highest performance among the three approaches. Using 10-fold cross-validation, the combined model achieved an average accuracy of 83.4%. The N-gram model showed strong performance with an average accuracy of 82.8%, while the lexicon-based model demonstrated lower performance with an average accuracy of 77%. These findings contribute to the field of sentiment analysis in e-commerce, highlighting the effectiveness of combining lexicon and N-gram features when used with Naïve Bayes classifiers. The study provides insights into optimizing sentiment classification techniques for e-commerce platforms, emphasizing the importance of leveraging both semantic and contextual information in sentiment analysis tasks.
Strategi Sourcing Berkelanjutan MBG Berbasis Advanced Analytics: Analisis PESTLE dan Rantai Pasok Gifari, Briyan; Al Ghazali, Nabiel Muhammad; Fajar, Abdullah
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 1 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.1.530

Abstract

The Free Nutritious Meal Program (MBG) is a national strategic initiative facing complex challenges in balancing supply security and environmental sustainability. This study aims to implement an Advanced Analytics framework to evaluate supplier performance based on the Triple Bottom Line principle. Using real logistics datasets for strategic commodities (Rice and Soybeans), this study applies the Weighted Scoring Model algorithm with parameters of logistics efficiency (40%), environmental impact (30%), and volume security (30%). The analysis of the supply chain shows a total carbon footprint (Scope 3) of 112.11 tons of CO2 with an average distribution distance of 297 KM. The model successfully identified the best "Green Suppliers," with the Pelalawan–Siak route recording the highest sustainability score (0.89) due to the balance of volume and distance. This study recommends the adoption of a data-driven scoring system to mitigate carbon emissions in the MBG supply chain.
Co-Authors Fajar, Abdullah Gifari, Briyan Yuliant Sibaroni