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All Journal Bulletin of Computer Science Research Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Budiati, Heani
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Computer Science & IT Control & Systems Engineering

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Deteksi Lintasan Gerak Tangan Berbasis Pengolahan Citra Menggunakan Metode K-Means Isnawati, Nurvia; Himamunanto, AR.; Budiati, Heani
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.476

Abstract

Technological developments have demanded continuous research so that it becomes more advanced in bringing benefits to computer science in particular. As is known, hand gestures are often used to convey meaning and can be used to interact with computers. Research by building software using Matlab R2020a which processes video input using a method approach based on image processing. The aim of the method approach in this research is to compute the coordinates of hand gesture objects to obtain hand movement trajectories. The research results of the K-Means method which was implemented to describe trajectory patterns can process the quality of segmented objects with exposure to dim light. The quality of segmentation that obtains the correct area of the hand gesture object influences the K-Means method in presenting trajectories in describing hand movement action patterns. The K-Means method can describe the trajectory of hand movements on the quality of segmented objects with dim light exposure. The longer the duration, the longer the processing will take, with the reality found that object detection processing based on image processing still takes longer than video viewing. With the hope that precise trajectories with hand movements can be understood as patterns in conveying meaning.
Identifikasi Tingkat Intensitas Opini dalam Analisis Sentimen Berbasis Aspek Menggunakan Enhanced Triplet Extraction Jimmy Richardo Chastelo B, Gabriel; Berutu, Sunneng Sandino; Budiati, Heani
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1074

Abstract

Conventional sentiment analysis often overlooks variations in the intensity of opinions within text reviews. This is due to the limitations of the Aspect-Based Sentiment Analysis (ABSA) approach, which is restricted to three main triplet components. This study aims to develop and expand the Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) framework to extract entity relationships and sentiment polarity by integrating opinion intensity detection. This study implements the ABSA approach by expanding the triplet structure into four components: aspect, opinion, intensifier, and sentiment (Enhanced Triplet). Data was collected via web scraping of Twitter (X) comments related to the Free Nutritious Meals program, which served as a case study to test the model’s ability to analyze public sentiment. The data then undergoes pre-processing and BIO Tagging, and is classified using a fine-grained sentiment approach to capture the nuances of emotional intensity in greater detail. A Transformer-based model, namely IndoBERT, was used to understand the context and intensity of meaning in the Indonesian language. Evaluation results on the test data show that the model achieved an accuracy of 88% and an average F1-score of 0.88 in sentiment polarity classification between entities, indicating strong model performance. These results demonstrate that providing a framework that is more sensitive to the intensity of opinions when classifying the nuances of public sentiment is a highly effective solution. 
Co-Authors Himamunanto, AR. Isnawati, Nurvia Jimmy Richardo Chastelo B, Gabriel Sunneng Sandino Berutu