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CommIT (Communication & Information Technology)
Published by Universitas Bina Nusantara
ISSN : 19792484     EISSN : 24607010     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Communication and Information Technology (CommIT) focuses on various issues spanning: software engineering, mobile technology and applications, robotics, database system, information engineering, artificial intelligent, interactive multimedia, computer networking, information system audit, accounting information system, information technology investment, information system development methodology, strategic information system (business intelligence, decision support system, executive information system, enterprise system, knowledge management), e-learning, and e-business (e-health, e-commerce, e-supply chain management, e-customer relationship management, e-marketing, and e-government). The journal is published in affiliation with Research Directorate, Bina Nusantara University in online and free access mode.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol. 19 No. 1 (2025): CommIT Journal (in press)" : 2 Documents clear
Impact of Statistical and Semantic Features Extraction for Emotion Detection on Indonesian Short Text Sentences Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul; Setyawan, Arif Fitra
CommIT (Communication and Information Technology) Journal Vol. 19 No. 1 (2025): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v19i1.11680

Abstract

The ability to detect emotions in short texts is crucial because interpreting emotions on platforms like Twitter can offer insight into social trends and responses to specific events. Additionally, examining emotions in product reviews assists companies in comprehending customer sentiment, allowing them to improve the quality of their products and services. Most research on Indonesian language emotion detection utilizes statistical feature extraction, with limited discussion on the impact of both statistical and semantic feature extraction. Thus, the research aims to detect emotions in short texts equipped with an analysis of the impact of statistical and semantic features. Analysis of the impact of statistical and semantic features on short texts is necessary to identify the most effective approaches, improve detection accuracy, and ensure that the developed systems can better handle the variety and complexity of informal language. The data used are a public dataset originating from Twitter texts and product review texts in e-commerce. The research utilizes statistical features such as Term Frequency Inverse Document Frequency (TF-IDF) and semantic features such as Bidirectional Encoder Representations from Transformers (BERT). The evaluation results show that using semantic features significantly improves the performance of emotion detection in short texts by 13–24%. It is higher than using statistical features. Deep Learning (DL) algorithms based on neural networks have also been proven to outperform Machine Learning (ML) algorithms in detecting emotions in short text. The experimental results and outlines show the potential directions for future development.
Hybrid Stacked Ensemble Regression Model for Predicting Parkinson’s Progression on Protein Data Aditya, K. Shastry; Mohan, M.; Deepthi, K.
CommIT (Communication and Information Technology) Journal Vol. 19 No. 1 (2025): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v19i1.12079

Abstract

Parkinson’s Disease (PD) is a progressive neurological disorder marked by both motor and nonmotor symptoms. Accurate prediction of disease progression is critical for effective patient management. The research presents a Hybrid Stacked Ensemble Regression (HSER) model for predicting PD progression using protein and peptide data measurements, leveraging the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDSUPDRS) scores. The researchers integrate three datasets: clinical data, protein data, and peptide data into a comprehensive feature-engineered dataset. The dataset is split into training and testing sets in four configurations for predicting the four UPDRS scores, namely updrs 1, updrs 2, updrs 3, updrs 4. The hybrid approach combines stacking and blending techniques. The researchers select ridge regression, gradient boosting, and extra trees as base models. A meta-model is trained using the algorithms’ out-of-fold estimates (ridge regression). The final predictions are obtained by averaging the predictions of the base models on the test data. The proposed HSER model exhibits enhanced performance compared to baseline models. These results underscore the promise of the hybrid model to enhance the prediction of PD progression, providing valuable insights for personalized treatment strategies. Future research can focus on refining model weights and exploring additional biomarkers to improve predictive accuracy.

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