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NUR HAMID
Jurusan Fisika, Fakultas Sains dan Matematika, Universitas Diponegoro, Semarang
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Journal : Jurnal Teknik Informatika (JUTIF)

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Evaluating Lexicon Weighting and Machine Learning Models for Sentiment Classification of Indonesian Mangrove Ecotourism Reviews Chahyadi, Ferdi; Uperiati, Alena; Pratiwi , Risdy Absari Indah; Hamid, Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5563

Abstract

Sentiment analysis on ecotourism reviews presents specific challenges due to descriptive writing styles, the use of ambiguous words, and contextual meaning shifts (contextual polarity shift). These characteristics often cause lexicon-based approaches to produce unstable polarity labels. This study aims to evaluate the influence of two lexicon weighting methods, namely Mean Weighting and Summation Weighting, on the initial sentiment labeling of mangrove ecotourism reviews and to assess the performance of machine learning models trained using these labels. The research method includes text preprocessing, lexicon-based scoring using the InSet lexicon, feature extraction with Term Frequency–Inverse Document Frequency (TF–IDF), and the training of two classification algorithms, Support Vector Machine (SVM) and Logistic Regression (LR). The results show that the Mean Weighting method produces more stable polarity scores and higher model performance. The combination of SVM with Mean Weighting achieves the best results with an accuracy of 0.902, macro precision of 0.876, macro recall of 0.819, a macro F1-score of 0.841, and a weighted F1-score of 0.899. Meanwhile, LR with Mean Weighting reaches an accuracy of 0.891 with a similar performance pattern. In contrast, the Summation Weighting method results in lower performance for both algorithms. Error analysis indicates that neutral sentences and ambiguous words such as “bagus” and “ramai” frequently lead to misclassification. These findings highlight that the choice of lexicon weighting method plays a crucial role in improving sentiment classification accuracy and contributes to the development of hybrid approaches in text mining and sentiment analysis for the Indonesian language.
Co-Authors Abdul Rouf Alghofari Adeni, Adeni Adissa, Kurnia Nur Agus Riyadi Agus Riyadi Agus, Abu Hasan Ali, Mukti Alwi HS, Muhammad Aly, Muhammad Nilzam Ancho, Inero Valbuena Annisa Nur Falah, Annisa Nur Aroyandini, Elvara Norma Budi Astuti Budi Nurani Ruchjana Chahyadi, Ferdi Dharma Putra, Ngurah Made Dicky Muslim Didik Aryanto Dina Ruslanjari Döngül, Esra Sipahi Efrita Norman Endang Rusyaman Farah, Nada Alfa Fettahlıoğlu, Ömer Okan Fianti Fianti, Fianti Firanti, Annisa HAFIIZH, MOCHAMMAD Hashim, Mohmadisa Hendarmawan Hendarmawan, Hendarmawan J Juhadi, J Jauza', Nur Faridatul Jawad, Muhammad Juhadi Juhadi Kasmuri Kasmuri Kasmuri Kasmuri, Kasmuri Lukmanul Hakim Maghfirah, Hidayatul Mahat, Hanifah Marpaung, Leonard Misbah, Muhammad Khoirul Mochammad Najmul Afad Mudhofi, M. Mudhofi, Mudhofi muflihah muflihah muhammad khoiruman, muhammad Mukti, Beta Pujangga Muttaqin, Rodhotul Nur Fajariyah, Nur Nur Kholis Nurul Jannah Pahlefi, Dwi Muhidin Pintaka Kusumaningtyas Pratiwi , Risdy Absari Indah Putra, Ngurah Made Dharma Putut Marwoto Qadriah, Lailatul Rabiatul Adawiyah Restasari, Afni Rohmah, Ucik Saidatur Rohmah, Umi Amanatur Rusnaenah, Andi Saadah, Nuris Sa’diyah, Sa’diyah Sadiyah, Sadiyah Saerozi Saerozi, Saerozi Saiful Anwar Sa’diyah, Sa’diyah Setiyanti, Anis Setyaningsih, Natalia Erna Sismawarni, Wuri Utami Dea Sofiana, Naela Su'aedi, Ifran Fuad Su'aibah, Siti Sugianto Sugianto Sukma, Adi Sukma, Rahmawati Sulhadi - Suratman Suratman Syaiful Anam Syarifatul Azaliyah, Syarifatul Syauqi, Wildan Uperiati, Alena Usman Usman Uswatun Hasanah Widyowati, Iis Intan Zhafirah, Azizah Zurqoni