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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 478 Documents
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Leaf Temperature Measurement Using Low-Resolution Thermal Camera Based on Thresholding and Clustering Techniques Soetedjo, Aryuanto; Hendriarianti, Evy
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.10706

Abstract

Leaf temperature can indicate photosynthetic rates, leaf water status, and stomata conductance. Leaf temperature can be measured using thermal resistance sensors, thermocouple devices, infrared thermometers, or infrared thermal imaging devices. Additionally, measuring leaf temperature using a thermal camera is simple and efficient. Therefore, the research proposes a leaf temperature measurement method using AMG8833, a low-resolution (64 pixels) thermal camera. The proposed system adopts an image segmentation technique to extract the leaf area from a thermal image. The leaf temperature is then calculated by averaging the temperature values on the leaf area. The proposed system aims to utilize a low-cost and low-resolution thermal camera for measuring the leaf temperature. The proposed approach is evaluated using real images of the Dieffenbachia plant, a popular ornamental plant that can be easily planted. In the experiments, fourteen segmentation methods consisting of eight thresholding techniques and six clustering techniques are evaluated. The experimental findings on the Dieffenbachia plant indicate that the most accurate leaf temperature measurements are obtained using local thresholding with an absolute error of 0.0109 and k-means clustering with an absolute error of 0.0134. The proposed method provides a simple, effective, and low-cost leaf temperature measurement system compared to the existing systems which employ high-cost commercial thermal cameras and complex measurement methods.
The Comparison of Deep Learning Models for Indonesian Political Hoax News Detection Rachmawati, Oktavia Citra Resmi; Darmawan, Zakha Maisat Eka
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.10929

Abstract

Indonesia is the world’s fourth most populous country and has a diverse sociopolitical landscape. Political fake news exacerbates existing social divisions and causes political polarization in Indonesian society. Hence, studying it as a specific challenge can contribute to broader discussions on the impact of fake news in different contexts. The researchers propose a hoax news detection system by developing a deep learning model with various lapses against a data set preprocessed using term-frequency and token filtering to represent the most prominent words in each class. The researchers compare the layers with the potential to have high performance in predicting the falsity of Indonesian political news data by observing the models based on training history plots, model specification, and performance metrics in the classification report module. The deep learning models include One-Dimensional Convolution Neural Networks (1D CNN), Long-Term Short Memory (LSTM), and Gated Recurrent Unit (GRU). The news data are obtained from the Kaggle site, containing 41.726 rows of data. Based on the experiments with the text data that has been preprocessed in the form of vectors and the specific parameters before starting, the results show that GRU achieves the highest performance value in accuracy, recall, precision, and F1 score. Although GRU becomes the model with the smallest file size, it is the slowest model to generate predictions from text news data. It also has a higher potential to be an overfitted model due to parameters than a simple RNN.
Determinant Factors of Logistics Firm Performance Mediated by Optilog Adoption Using a Mixed Method with an Explanatory Sequential Analysis Melianie, Melianie; Widuri, Rindang; Gamayanto, Indra; Sundjaja, Arta Moro
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.11090

Abstract

Recently, trucking logistics service providers are focusing on adopting technology and Information Technology (IT) capability to improve performance. Hence, it is essential to investigate how the IT capability of the firm can enhance logistics performance and impact technology adoption on performance. Therefore, the research examines the determinant factors of logistics firm performance mediated by Optilog adoption. The research design is a mixed method with an explanatory sequential design using quantitative and qualitative data analysis. Quantitative data analysis uses Structural Equation Modeling (SEM), while qualitative analysis adopts in-depth interviews. The analysis population includes 41 Optilog users in the logistics firm in East Jakarta, with the experts adopting a census sampling method. For qualitative phase, the researchers also recruit four respondents holding supervisory positions or higher using Optilog for daily operations. The results show that logistics firm performance is influenced by service excellence and Optilog adoption. Additionally, perceived risk, traceability, and IT capability positively affect Optilog adoption, which mediates the effects on logistics firm performance. The results confirm the rejection of the hypothesis that Optilog adoption mediates IT capability on logistics firm performance. The thematic analysis further concludes that the system, implemented in March 2022, does not impact logistics firm performance significantly, according to the respondents. Consequently, IT departments depend on responsiveness to address users’ needs and issues since when the vendor develops the system, it faces significant constraints due to the development and maintenance costs.
Deciphering Digital Discourse: Detecting Cyberbullying Patterns in Filipino Tweets Using Machine Learning Naga, January F.; Lavilles, Rabby Q.
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.11094

Abstract

The research addresses the escalating challenge of cyberbullying in the Philippines, a concern magnified by widespread social media use. A dataset of 146,661 tweets is analyzed using a pre-trained natural language processing model tailored to detect derogatory Filipino terms. The methodology is designed to preprocess data for clarity and analyze derogatory phrases, using the 23 key terms to indicate cyberbullying. Through quantitative analysis, specific patterns of derogatory term co-occurrence are uncovered. The research specifically focuses on Filipino digital discourse, uncovering patterns of derogatory language usage, which is unique to this context. Combining data mining and machine learning techniques, including Frequent Pattern (FP)-growth for pattern identification, cosine similarity for phrase correlation, and classification technique, the research achieves an accuracy rate of 97.91%. To assess the model’s reliability and precision, a 10-fold cross-validation is utilized. Moreover, by examining specific tweets, the analysis highlights the alignment between automated classifications and human judgment. The co-occurrence of derogatory terms, identified through methods like FP-growth and cosine similarity, reveals underlying cyberbullying narratives that are not immediately obvious. This approach validates the high accuracy of the models and emphasizes the importance of a comprehensive framework for detecting cyberbullying in a linguistically and culturally specific context. The findings substantiate the effectiveness of the targeted approach, providing essential insights for developing cyberbullying prevention strategies. Furthermore, the research enriches the literature on digital discourse analysis and online harassment prevention by addressing cyberbullying patterns and behaviors. Importantly, the research offers valuable guidance for policymakers in crafting more effective online safety measures in the Philippines.
Indonesian-English Textual Similarity Detection Using Universal Sentence Encoder (USE) and Facebook AI Similarity Search (FAISS) Krisnawati, Lucia D.; Mahastama, Aditya W.; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.11274

Abstract

The tremendous development in Natural Language Processing (NLP) has enabled the detection of bilingual and multilingual textual similarity. One of the main challenges of the Textual Similarity Detection (TSD) system lies in learning effective text representation. The research focuses on identifying similar texts between Indonesian and English across a broad range of semantic similarity spectrums. The primary challenge is generating English and Indonesian dense vector representation, a.k.a. embeddings that share a single vector space. Through trial and error, the research proposes using the Universal Sentence Encoder (USE) model to construct bilingual embeddings and FAISS to index the bilingual dataset. The comparison between query vectors and index vectors is done using two approaches: the heuristic comparison with Euclidian distance and a clustering algorithm, Approximate Nearest Neighbors (ANN). The system is tested with four different semantic granularities, two text granularities, and evaluation metrics with a cutoff value of k={2,10}. Four semantic granularities used are highly similar or near duplicate, Semantic Entailment (SE), Topically Related (TR), and Out of Topic (OOT), while the text granularities take on the sentence and paragraph levels. The experimental results demonstrate that the proposed system successfully ranks similar texts in different languages within the top ten. It has been proven by the highest F1@2 score of 0.96 for the near duplicate category on the sentence level. Unlike the near-duplicate category, the highest F1 scores of 0.77 and 0.89 are shown by the SE and TR categories, respectively. The experiment results also show a high correlation between text and semantic granularity.
Evaluating Airline Passengers’ Satisfaction during the COVID-19 Pandemic: A Case Study of AirAsia Services through Sentiment Analysis and Topic Modelling Yu, Lee Jie; Md Saad, Nor Hasliza; Kun, Zhu; ElSayad, Ghada
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.11364

Abstract

AirAsia has emerged as a dominant force among prominent low-cost airlines in recent years. However, the COVID-19 pandemic outbreak has severely impacted airline services, including AirAsia. There is a strong need for airline services to monitor customer experience and satisfaction from online customer reviews on the website to keep pace with changing customer perceptions toward their service quality. A growing number of travelers choose to express their experiences and emotions on online customer review platforms, resulting in substantial online airline service evaluations. The research analyzes 796 online customer reviews from Skytrax, a well-known online airline review website. The information hidden in customer-generated reviews is analyzed with the text mining technique, including topic modeling and sentiment analysis. The research uses the Latent Dirichlet Allocation (LDA) model for topic analysis and the Valence Aware Dictionary for Sentiment Reasoning (VADER) model for sentiment analysis. The sentiment ratio for AirAsia’s online reviews is approximately 59% positive and 41% negative. Only four reviews are neutral. The findings indicate that the online review of AirAsia has a greater proportion of positive sentiments than negative sentiments. In addition, the topic modeling shows hidden topics with the top high-probability keywords concerned with interior and seat, baggage, online service, staff service, flight schedule, and refund. The research demonstrates using sentiment analysis and topic modeling on customer review data as a more thorough alternative to survey-based models for researching airline service. The research contributes to the methodological advancements in text mining analysis and expands the current knowledge of customer review data.
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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