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Journal : bit-Tech

Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews Eka Putri, Siti Oktavia; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2582

Abstract

The tourism industry is a key economic sector in Indonesia, with East Java ranking highest in tourist visits. This study aims to enhance tourism development by applying aspect-based sentiment analysis (ABSA) using convolutional neural networks (CNN) to analyze online reviews. CNN was selected for this study due to its proven efficiency in capturing local n-gram features and its relatively lower computational cost compared to other deep learning model. Reviews from TripAdvisor and Google Maps were collected focusing on four aspects: attraction, amenities, access, and price. Five different models were developed in this research: one multilabel aspect classifier designed to identify multiple aspects mentioned within each review, and four sentiment classifiers focused on evaluating the sentiment polarity for each specific aspect. These models were trained and evaluated using various combinations of word embeddings, including static embeddings like Word2Vec, and contextualized embeddings such as BERT and IndoBERT. Additionally, the impact of preprocessing through stemming was investigated to understand how simplifying word forms affects model performance. Results indicate that IndoBERT-CNN attains the best overall sentiment classification, reaching F1-scores up to 0.71 for attraction and 0.93 for amenities, while Word2Vec-CNN with stemming leads multilabel classification. Meanwhile stemming improves performance for static embeddings like Word2Vec by simplifying word forms, it reduces effectiveness in transformer-based models like BERT and IndoBERT that rely on natural language context. These findings highlight the benefit of choosing appropriate embeddings and preprocessing for different tasks, thus providing practical insights for improving tourism services through better tourist reviews analysis.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Dewi, Heni Lusiana; Arifiyanti, Amalia Anjani; Najaf, Abdul Rezha Efrat
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2684

Abstract

Diseases affecting tomato leaves can severely impact agricultural productivity, as they can reduce crop yields and quality significantly. A swift and dependable identification of these diseases is vital for ensuring prompt interventions and the successful implementation of disease control strategies. This study focus on evaluating and comparing the efficiency of three separate optimizers, such as Adam, RMSProp, and SGD on the pretrained Convolutional Neural Network (CNN) architecture DenseNet121. There has been no previous research that directly compares the performance of Adam, RMSProp, and SGD optimizers on the DenseNet121 model for classifying tomato leaf diseases using the Plant Village dataset. These optimizers are crucial in the training process by influencing the model’s ability to converge and generalize well on new, unseen data. Experimental procedures were performed using a labeled dataset of tomato leaf images, which included healthy leaves and various disease classes. Out of the three optimization techniques tested, the DenseNet121 model trained with the Adam optimizer consistently outperformed the others. It achieved the highest evaluation metrics, with an accuracy of 0.9800, precision of 0.9807, recall of 0.9800, and F1-score of 0.9800 on the test set. These outcomes suggest that the model has a strong and balanced classification performance, capable of correctly identifying disease conditions with minimal errors. Based on these findings, the DenseNet121 architecture combined with the Adam optimizer is considered the optimal model used to recognize various tomato leaf diseases in this study.
Co-Authors Abdul Rezha Efrat Najaf Achmad Fauzi Afandi, Mohamad Irwan Aghni Qisthina Al Rahma Agung Brastama Putra Agung Brastama Putra Akira Permata Ramadhani Al Rahma, Aghni Qisthina alathoillah, abdul hanif Ana Wati3, Seftin Fitri Ananda Lakunti A Andhyni, Cyntia Prisya Anggy Oktaviana Syafira Anita Wulansari Anita Wulansari, S.Kom., M.Kom Annisa Lusyani Zahra Anwar Sodik, Anwar Aprilia, Eka Fahira AryaRafa, Daud Audrey Septya Rosanti Bagus Utomo Basma Eno Ketherin Brahmantio Widyo Trenggono Brastama Putra, Agung Daniar, Ivan Faiz Devi, Ditha Lozera Dewi Safitri, Triyatul Dewi, Heni Lusiana Dharmawan, Ega Dhian Satria Yudha Kartika Diana Aqidatun Nisa Ditha Lozera Devi Eka Putri, Siti Oktavia Elfaretta, Syifa Saskia Fachrurrozy Nurqoulby Fandi, Rico Satria Farhan Setiyo Darusman Farhan Setiyo Darusman Fariska, Rahmah Putri Ferdiansyah, Rizky Fernaldy, Fabiyan Atha Fidyah Salsabila Putri Sillehu Firsttama, Risav Arrahman Fitri, Anindo Saka Hardiartama, Rendi I Gusti Ayu Sri Deviyanti Indira Setia Amalia Indra Fajar Novian Irwan Afandi, Mohamad Jannatuzzahra, Khoirunisa Ketherin, Basma Eno Kusumantara, Prisa Marga Kusumantara, Prisa Marga Luhur Indayanti Sugata, Tri M. Rizal Abdullah Rozi Mahanani, Anajeng Esri Edhi Marga Kusumantara, Prisa Marisca Amanda Hidayat Mashita Kustyani Maulana Arrasyid, Nizar Maulana Kharyska Abadi, Muhammad Mochamad Suhri Ainur Rifky Mochammad Fuad Pandji Mohamad Irwan Afandi Muhammad Burhanuddin F Narendra, Efriza Cahya Nilwanda, Leona Elsa Novian, Indra Fajar Nur Cahyo Wibowo Nur Rachman Nidhi Suryono, Muhammad Nurisa Rahma Shantika Nurjanti Takarini Oktania Purwaningrum Oktania Purwaningrum Oktania Purwaningrum Pandu Rizki Maulidiah Permatasari, Reisa Pradana, Rhendy May Putra, Satrio Honggonagoro Pramono Putri, Youlan Indira Putu Anggi Suryantari Rafi Purwa Syahputra Raihana Sakhi Aswanda Rendi Panca Wijanarko Rhendy May Pradana Rizka Hadiwiyanti Saka Fitri, Anindo Salma Nabila Seftin Fitri Ana Wati Sembilu, Nambi Sidhi Pamekas, Afu Solehudin Al Ayyubi Sudewantoro N M Sugata, Tri Luhur Indayanti Sulistyowati Sulistyowati Sulistyowati Sulistyowati Tri Diana Rimadhani Tri Luhur Indayanti Sugata Ubaidillah Fahmi, Rohmat Wahyuni, Eka Dyar Wati , Seftin Fitri Ana Wati, Seftin Fitri Ana Wibisono, Mahendra Priyo Wisnu Mukti Darwansah Yudha Yunanto Putra Yudha Yunanto Putra Zahra, Nabila Athifah