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All Journal Jurnal Simetris JOIV : International Journal on Informatics Visualization
Rifqi Mulyawan
Universitas Amikom Yogyakarta
Computer Science & IT

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PENINGKATAN AKURASI TOPIC MODELING PADA KINERJA KEPOLISIAN REPUBLIK INDONESIA DI TWITTER MENGGUNAKAN ALGORITMA SPELL CHECKER Isra Andika Bakhri; Rifqi Mulyawan; Nurfajri Asfa; Ema Utami; Agus Fatkhurohman
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 13, No 1 (2022): JURNAL SIMETRIS VOLUME 13 NO 1 TAHUN 2022
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v13i1.7186

Abstract

Kinerja polisi republik Indonesia saat ini menjadi soratan terutama munculnya banyak komentar dari netizen terutama di Twitter dengan diramaikannya hashtag #percumalaporpolisi dan #1hari1oknum. Hal ini mesti direspon cepat oleh kepolisian republik indonesia untuk melakukan counter issue dengan pertama-tama menggali topik tersembunyi dari sekian banyak tweet yang beredar salah satu caranya yaitu menerapkan topic modeling pada Twitter. Dalam rangka memetakan issue atau topik yang akan dibenahi menurut padangan publik terlebih dahulu. Salah satu kelemahan penerapan topic modeling ialah adanya typo maupun singkatan disengaja yang dapat menggangu keakuratan model yang akan dibangun. Maka penting diterapkannya peningkatan perbaikan teks tweet yang diakuisisi sebelum dimasukkan ke dalam model. Normalisasi teks dapat dikaloborasikan dengan algoritma spell checker yang dapat membantu menormalkan typo dan singkatan yang disengaja untuk membantu meningkatkan akurasi model. Penerapan algoritma spell cheker terbukti mampu meningkatkan akurasi model yang mengakibatkan topik yang dihasilkan sebelum dan sesudah diterapkan spell checker berbeda, ditandai dengan tidak ditemukannya lagi singkatan atau typo pada term yang diolah oleh LDA.
Pre-Trained CNN Architecture Analysis for Transformer-Based Indonesian Image Caption Generation Model Rifqi Mulyawan; Andi Sunyoto; Alva Hendi Muhammad Muhammad
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1387

Abstract

Classification and object recognition in image processing has significantly improved computer vision tasks. The method is often used for visual problems, especially in picture classification utilizing the Convolutional Neural Network (CNN). In the popular state-of-the-art (SOTA) task of generating a caption on an image, the implementation is often used for feature extraction of an image as an encoder. Instead of performing direct classification, these extracted features are sent from the encoder to the decoder section to generate the sequence. So, some CNN layers related to the classification task are not required. This study aims to determine which CNN pre-trained architecture or model performs best in extracting image features using a state-of-the-art Transformer model as its decoder. Unlike the original Transformer’s architecture, we implemented a vector-to-sequence way instead of sequence-to-sequence for the model. Indonesian Flickr8k and Flick30k datasets were used in this research. Evaluations were carried out using several pre-trained architectures, including ResNet18, ResNet34, ResNet50, ResNet101, VGG16, Efficientnet_b0, Efficientnet_b1, and Googlenet. The qualitative model inference results and quantitative evaluation scores were analyzed in this study. The test results show that the ResNet50 architecture can produce stable sequence generation with the highest accuracy value. With some experimentation, finetuning the encoder can significantly increase the model evaluation score. As for future work, further exploration with larger datasets like Flickr30k, MS COCO 14, MS COCO 17, and other image captioning datasets in Indonesian also implementing a new Transformers-based method can be used to get a better Indonesian automatic image captioning model. 
Co-Authors Agus Fatkhurohman Alva Hendi Muhammad Muhammad Andi Sunyoto Ema Utami Isra Andika Bakhri Nurfajri Asfa