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All Journal Bulletin of Electrical Engineering and Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama Akademika
Irawan Irawan
Universitas Dinamika Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering

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HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS Jasmir; Maria Rosario; Irawan Irawan; Agus Siswanto; Tiko Nur Annisa
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7488

Abstract

The development of internet technology and social media has driven the increasing use of sentiment analysis to understand public opinion. This study aims to improve the classification performance of sentiment analysis by proposing a hybrid model that combines FastText-BLSTM and BERT. The dataset used consists of 900 Indonesian-language Netflix app user reviews obtained through crawling using Google Play Scraper. The research stages include text preprocessing, feature extraction using FastText and BERT, and classification using BLSTM, which are then combined in a concatenation layer to produce a richer feature representation. Experimental results show that the FastText-BLSTM-BERT hybrid model provides the best performance with an accuracy of 94.22%, a precision of 95.98%, a recall of 95.68%, and an F1-score of 95.83%. This achievement is superior to the single models of FastText-BLSTM and BERT. The main novelty of this research lies in the integration of contextual embeddings from BERT with subword-level semantic and sequential representations from FastText-BLSTM, which has not been extensively explored in prior studies on Indonesian sentiment analysis. This hybridization demonstrates significant improvement in model generalization and robustness for low-resource language texts
Public complaint tweet data feature analysis for sentiment classification Errissya Rasywir; Yovi Pratama; Irawan Irawan; Marrylinteri Istoningtyas
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7172

Abstract

The perception of the public regarding a government's performance significantly impacts a city's advancement. This research involved analyzing complaint tweets from Jambi City residents directed at the government to gauge sentiment. In the testing phase, 500 Twitter accounts were examined to categorize sentiment as positive, negative, or neutral. Training data was prepared by extracting tokens through feature selection techniques such as information gain (IG) and mutual information (MI). For testing, all tokens are entered as data in the input layer in the recurrent neural network (RNN). From the tests carried out, the average use of feature selection can achieve a good value compared to no feature selection. But more specifically the use of IG produces better accuracy compared to the use of MI. From the research conducted, Twitter data is classified using a RNN and several tests by adding feature selection to produce differences. The results are proven to improve classification performance. With a recall value of 92.243%, it shows the system's success rate in sentiment classification and a precision of 92% indicates a level of accuracy that is sufficient to support the government's sentiment assessment.
Evaluasi SMOTE dan SMOTE+Tomek untuk Mengatasi Ketidakseimbangan Kelas pada Prediksi Stunting Balita Berbasis Pembelajaran Mesin Marrylinteri; M. Irwan Bustami; Irawan Irawan; Maria Rosario B; Sansan Rosita
JURNAL AKADEMIKA Vol 18 No 2 (2026): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/sajy3k06

Abstract

Class imbalance is a recurring obstacle in machine learning based screening of child nutritional status. This study evaluates and compares the effect of SMOTE and SMOTE+Tomek Links on the classification of toddler nutritional status using K-Nearest Neighbours (KNN) and Random Forest (RF). The data consist of 9,426 anthropometric records with four predictors, labelled into three classes: normal (7,212; 76.51%), moderate stunting (1,612; 17.10%) and severe stunting (602; 6.39%), a majority to minority ratio of about 12:1. Min-Max scaling and resampling were fitted on training data only, inside a pipeline, and the models were assessed on an independent 20% test set (n = 1,886) with stratified 5-fold cross validation. At baseline, RF reached 0.975 accuracy and 0.932 macro-F1, while KNN displayed an accuracy paradox: 0.913 accuracy but only 0.392 recall on severe stunting (47 of 120 cases). SMOTE raised KNN recall to 0.733 and macro-F1 from 0.753 to 0.816. For RF, SMOTE improved both criteria at once: recall rose from 0.825 to 0.908 (99 to 109 cases) and macro-F1 from 0.932 to 0.949. SMOTE+Tomek performed almost identically, removing only 30 of 17,307 training samples. RF with SMOTE is therefore recommended
Co-Authors Abdul Rahim Afrizal Nehemia Toscany AGUS AGUS SETIAWAN Agus Siswanto Akwan Sunoto Ali Sadikin Chindra Saputra Desi Kisbianty Desi Kisbianty, Desi Errissya Rasywir FA Bambang Sukoco Gilang Prakoso, Amandha IBNU SANI Istoningtyas , Marrylinteri Jasmir KARMAN, ZULFI Kisbianty , Desi Lola Yorita Astri, Lola Yorita M Irwan Bustami Maria Rosario Maria Rosario B Marrylinteri Marrylinteri Marrylinteri Istoningtyas Nugroho, M. Restu ROBY SETIAWAN Sansan Rosita SIKA, XAVERIUS Tiko Nur Annisa Wardani, Muhammad Willy Yovi Pratama yulia