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All Journal International Journal of Electrical and Computer Engineering Jurnal S2 Pendidikan Sains Kontribusia : Research Dissemination for Community Development Khaira Ummah: Jurnal Pengabdian Kepada Masyarakat
Rachmad Abdullah
Program Studi Pendidikan Sains Program Pascasarjana Universitas Sebelas Maret Surakarta, 57126, Indoensia
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : International Journal of Electrical and Computer Engineering

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Homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English machine translation Harjo, Budi; Muljono, Muljono; Abdullah, Rachmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7036-7045

Abstract

Homonym and polysemy features can influence some errors in translation from a source language to another target language, for example, from Indonesian to English. A lemma or a morphology factor can cause the configuration of Indonesian homonym features. For example, the word beruang can mean an animal beruang (bear) and can mean a verb alternation ber+uang (has/have money). The Indonesian polysemy feature can also impact an error in the translation process because it can have a literal meaning and a symbolic meaning. For example, the terms bunga melati (jasmine flower) and bunga hati (lover), where bunga does not only mean flower. Therefore, the development machine translation (MT) method needs to capture homonym and polysemy features in the form of a word or a phrase. This research proposes homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English MT. First, this research uses morphology extraction to detect sentences that contain prefixes, lemma, and suffixes. Then, the word similarity measurement functions to extract homonym and polysemy in the form of uni-gram and bi-gram using bidirectional encoder representations from transformers (BERT) embedding, named entity recognition (NER), synonym-based term expansion, and semantic similarity. This research uses neural machine translation for the translation process.
Aspect-based sentiment analysis: natural language understanding for implicit review Suhariyanto, Suhariyanto; Sarno, Riyanarto; Fatichah, Chastine; Abdullah, Rachmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6711-6722

Abstract

The different types of implicit reviews should be well understood so that the developed extraction technique can solve all problems in implicit reviews and produce precise terms of aspects and opinions. We propose an aspect-based sentiment analysis (ABSA) method with natural language understanding for implicit reviews based on sentence and word structure. We built a text extraction method using a machine learning algorithm rule with a deep understanding of different types of sentences and words. Furthermore, the aspect category of each review is determined by measuring the word similarity between the aspect terms contained in each review and aspect keywords extracted from Wikipedia. Bidirectional encoder representations from transformers (BERT) embedding and semantic similarity are used to measure the word similarity value. Moreover, the proposed ABSA method uses BERT, a hybrid lexicon, and manual weighting of opinion terms. The purpose of the hybrid lexicon and the manual weighting of opinion terms is to update the existing lexicon and solve the problem of weighting words and phrases of opinion terms. The evaluation results were very good, with average F1-scores of 93.84% for aspect categorization and 92.42% for ABSA.
Co-Authors Budi Harjo Cari, Cari Chastine Fatichah Faisal, Mohammad Juwita Sari Mildawati, Titik Muhammad Reza Pahlawan Muljono Muljono respatia, wimba Riyanarto Sarno Setyorini, Nanis Suhariyanto Suhariyanto Widha Sunarno