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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Teknik Elektro dan Komputer TRIAC Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) JHCLS SmartComp INJECT Interdisciplinary Journal of Communication
Christian Sri kusuma Aditya, Christian Sri kusuma
Teknik Informatika, Institut Teknologi Sepuluh Nopember
Religion Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Sentimen Ulasan Produk pada Platform E-Commerce di Indonesia dengan Menggunakan Model Pre-Trained IndoBERT Aji, Bayu Puspito; Aditya, Christian Sri Kusuma
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6968

Abstract

In today's digital era, sentiment analysis of product reviews on e-commerce platforms is becoming increasingly important, especially on Tokopedia, one of the largest marketplaces in Indonesia. Tokopedia provides facilities for users to leave reviews after making transactions, which play an important role in helping businesses understand customer perceptions of products. This research aims to classify the sentiment of product reviews on Tokopedia using the IndoBERT model and evaluate its performance compared to LSTM-based methods combined with FastText, Glove, and Word2Vec embedding. The LSTM-FastText model in previous research achieved the highest accuracy of 85.08%. In this study, the sentiment classification of product reviews on Tokopedia was carried out with a total of 5400 data and the sentiment classification process was divided into two categories, namely positive and negative, with the division of the dataset into three groups: training, validation, and testing. The contribution in this research is to explore the effectiveness of the IndoBERT model performance compared to previous methods that implement the LSTM model with FastText, Glove, and Word2Vec embedding. Based on the research results, the IndoBERT model achieved an accuracy of 97%, with the same F1-score value for both sentiment categories of 97%. Specifically designed with pre-training on a large Indonesian corpus, IndoBERT is able to understand the context of the text better than the LSTM model used in previous studies. This allows IndoBERT to produce higher accuracy, as it can understand product reviews in Indonesian more effectively.
Optimalisasi Model CNN dengan Teknik Kontras Lokal CLAHE untuk Klasifikasi Pneumonia pada Citra X-Ray Salma, Rania Alfita; Aditya, Christian Sri Kusuma
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8271

Abstract

Pneumonia is a lung infection that has a widespread impact on public health, particularly in areas with limited access to healthcare services. Chest X-ray imaging plays an important role in diagnosing this disease; however, low contrast quality often becomes an obstacle to automated classification using deep learning methods. This study aims to evaluate the effectiveness of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method in enhancing the visual quality of chest X-ray images and to analyze its impact on the performance of a Convolutional Neural Network (CNN) model in detecting pneumonia. CLAHE enhances the visibility of radiographic details through local contrast redistribution with a clip limit, allowing previously indistinct pathological structures to be more clearly recognized by the CNN. The dataset used consists of 2,623 X-ray images that are divided into two classes, namely Normal and Pneumonia. The training process was conducted under two scenarios, without and with the application of CLAHE. The evaluation results show that the CNN model without CLAHE achieved an accuracy of 96.18%, while the model with CLAHE improved to 99.69%. This improvement is significant as it reduced the classification error rate from approximately 3.8% in the model without CLAHE to only 0.3% in the model with CLAHE, while also increasing precision, recall, and f1-score across all classes. Therefore, combination of CLAHE and CNN can be applied as an effective approach for pneumonia detection that is accurate, consistent, and efficient, especially in environments with limited computational resources.
Co-Authors Agus Zainal Arifin AINA, LISTYA NUR Aji, Bayu Puspito Alif Akbar Fitrawan, Alif Akbar ASHURI, PUTRI INTAN CAHYANI, INDAH ARDHIA Chandranegara, Didih Rizki Chastine Fatichah Diana Purwitasari Evi Dwi Wahyuni Ferdiansyah, Danny Firdausita, Nuris Sabila Fuad Nasvian, Moch Gayatri, Agnes Ayu Maharani Dyah Hamdan Nafiatur Rosyida Handoko, Chanavaro Bayu Hani’ah, Mamluatul K., Halif Fachrizal Erliawan Kusumo, Wisnu Prayogo Lailiyah, Asmaul Nur Putri Hidayah Purwandhani, Septiannisa Alya Shinta S, Vinna Rahmayanti Sajiatmoko, Aletta Agigia Novta Salma, Rania Alfita Setyarini, Dela Ananda Sunan, Rohid Aji Ujilast, Novia Adelia Wicaksono, Galih Wasis Yuda Munarko Yufis Azhar