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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 489 Documents
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Noise Reduction in Brain Magnetic Resonance Imaging Using a Convolutional Autoencoder I Gede Susrama Mas Diyasa; Pangestu Sandya Etniko Siagian; Eva Yulia Puspaningrum; Wan Suryani Wan Awang; Sayyidah Humairah; Deshinta Arrova Dewi
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

In clinical practice, precise and high-quality brain Magnetic Resonance Imaging (MRI) is pivotal for diagnosing and formulating effective treatment strategies. The research objective is to assess the viability of employing a Convolutional Autoencoders (CAE) for the mitigating noise in brain MRI images. The focus is brain MRI images and the various types of noise (Salt and Pepper, Speckle, and Gaussian noise) that typically corrupt images and may lead to inaccuracies in diagnosis. The research also applies methods to artificially generate these noise types to represent real-world scenarios. Specifically, the dataset of brain MRI images is collected, pre-processed, and artificially exposed to various noise types to simulate the real-world conditions after the CAE model is used to reconstruct the corrupted images. The CAE is assessed for its high efficiency and efficacy using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The results indicate that the CAE is very effective in removing noise, particularly Salt and Pepper noise. The model achieves a PSNR of 27.0687 dB and an MSE of 0.00216246 at the lowest noise level. The model also demonstrates stability under varying levels of Speckle noise. Although performance degrades as noise increases, the model continues to demonstrate potential for further refinement. The research furthers the CAE’s analytical potential by assessing its denoising capabilities across various noise types and levels. The research adds value by outlining recommendations to the medical imaging community while identifying the need for future research on different classifications of noise and advanced regularization methods.
Enhancing Competency Level Prediction Using Machine Learning: A Data-Driven Approach Based on Psychological Assessment Data Sinung Suakanto; Joko Siswanto; Jan M. Pawlowski; Muharman Lubis; Syfa Nur Lathifah; Litasari Widyastuti Suwarsono
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

Competency level prediction plays a crucial role in competency-based human resource management such as talent management. Talent management is achieved by identifying individuals’ knowledge, skills, and attitudes through psychological assessment. Recognizing employees as a strategic asset by accurately predicting competencies supports targeted development, boosting individual and organizational performance. Current practices related to competency assessment require expert judgment from psychologists or assessors, which can be time-consuming. The research proposes a machine learning–based approach to predict competency levels using psychological assessment scores as input, designed to operate within digital, network-enabled interview platforms. Several machine learning methods, including Random Forests, k-Nearest Neighbors (KNN), and Support Vector Machines (SVMs), are applied to historical assessment datasets to identify patterns and relationships between psychological assessment scores and competency levels.The dataset comprises 1,220 records from a psychological assessment. The experimental results indicate that the Random Forest model achieves the highest accuracy of 81%, outperforming other models in competency level prediction. The key novelty lies in its data-driven methodology, which enhances the objectivity and efficiency of competency evaluation while reducing reliance on expert interpretation. By enabling automated competency prediction in network-enabled interview environments, the proposed approach supports more efficient talent decision-making, workforce development, and recruitment processes. The findings demonstrate that machine learning can accurately predict competency levels from a clean dataset of psychological assessment scores, achieving accuracy above 80%. Future research may enhance model robustness by incorporating additional assessment center criteria and real-world performance metrics.
Hybrid Stacking Model for Web Attack Classification Using LightGBM, Random Forest, and MLP Fadli Dony Pradana; Farikhin; Budi Warsito
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

The research presents a stacking-based hybrid intrusion detection framework for web application attacks, addressing the persistent limitation that minority classes, including Brute Force, Cross-Site Scripting (XSS), and Structured Query Language (SQL) Injection, are frequently underdetected in conventional Intrusion Detection Systems (IDS) due to severe class imbalance. The proposed architecture combines LightGBM and Random Forest as base learners, while a Multi-Layer Perceptron (MLP) functions as the meta-learner. The framework is supported by rigorous preprocessing, ANOVA F-testbased feature selection, and domain-informed augmentation of critical traffic features, such as Flow Inter-Arrival Time (IAT) Min, Init Win bytes forward, and Backward (Bwd) Packets/s, through optimized weighting strategies. Evaluation on the CICIDS-2017 web attack subset using 10-fold stratified cross-validation shows that the proposed model improves the macro F1-Score from 0.62 ± 0.004 to 0.76 ± 0.003 and achieves a binary accuracy of 99.67% with a macro F1 of 0.94. The observed performance gains are statistically significant (p < 0.001), confirming the robustness of the framework. These findings indicate that targeted feature engineering and heterogeneous stacking substantially improve minority-attack detection while preserving majority-class performance. In addition, the framework demonstrates sub-millisecond inference time, highlighting its practical suitability for real-time IDS deployment in resource-constrained and high-throughput operational cybersecurity environments. The proposed design also offers methodological generalizability for broader anomaly detection tasks in dynamic network environments, where reliable recognition of low-frequency but high-impact attack patterns remains increasingly critically important.
Power-Efficient Surveillance Camera Using Sleep Mode and YOLOv3 Model-Based Edge Computing Mhd. Idham Khalif; Raden Deiny Mardian; Ade Faiz Kurnia Putra; M. Dhanu Wicaksono; Tirta Akdi Toma Mesoya Hulu; Listyo Edi Prabowo
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

Surveillance cameras play a vital role in a wide range of monitoring applications, particularly in ensuring real-time security and observation. However, conventional surveillance systems often face limitations in energy efficiency, especially when deployed in remote locations or powered by battery sources. Although many surveillance cameras offer high-resolution capabilities, only a few incorporate power management strategies to optimize energy usage. The research presents the design and implementation of a low-power surveillance camera system based on the ESP32-CAM platform, incorporating a sleep mode to enhance power efficiency. Two operational scenarios are tested: one with enabled sleep mode and one without. Experimental results show that the camera without sleep mode achieves a higher frame rate of up to 17.01 FPS than the sleep-enabled camera with a maximum of 3.53 FPS. Despite the reduced frame rate, the system successfully performs object detection using the YOLOv3 model processed via edge computing. Furthermore, the average wake-up time from sleep mode is 1.414 seconds, indicating a fast, responsive system suitable for low-power embedded applications. In terms of energy consumption, the sleep-enabled device consumes only 3475.543 mW over 2 hours of operation, compared to 5561.639 mW for the device without sleep mode, resulting in an energy saving of approximately 37.5%. These findings confirm that implementing sleep mode is effective in managing power consumption without compromising core surveillance functionality. The research contributes to the development of sustainable and energy-efficient monitoring solutions and highlights the potential for further enhancement through advanced edge computing platforms in future work.
Magnetic Resonance Imaging (MRI)-Based Breast Cancer Detection Using Graph Convolutional Network (GCN) with Advanced Texture Feature Extraction Ferdaus Anam Jibon; Sujan Chandra Roy; Hadia Razin Mou; Md. Ashraful Islam; Utpal Kanti Das; Ripa Sarkar; Ratna Rani Sarkar
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

Breast cancer is the leading cause of death for women worldwide, and it is predicted to be an important factor in public health. Therefore, early and accurate detection is crucial to enhancing survival rates. Recently, Magnetic Resonance Imaging (MRI) has become a superior option to biopsies due to its exceptional soft tissue imaging capabilities, making it highly effective for detecting and monitoring breast cancer. However, it requires a competent radiologist to perform the procedure. The researchers introduce an approach for breast cancer detection and classification that employs Graph Convolutional Networks (GCNs) to distinguish breast MRI images. The combination Dual-Tree Discrete Wavelet Transform (DTDWT) with GCNs enhances feature extraction, while the Gray-Level Co-Occurrence Matrix (GLCM) identifies texture patterns distinguishing normal, benign, and malignant tissues. The research also employs t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, improving pattern interpretation. This approach classifies the four breast cancer types using a dataset comprising 200 Dynamic Contrast-Enhanced (DCE)-MRI images from Radiopaedia, allocated as 160 training and 40 validation instances in categories including ductal carcinoma, lipoma, triplenegative breast cancer, and inflammatory breast cancer. A comparative analysis confirms the validity of the approach, which is the first to address these four categories in MRI. The experimental results indicate significant improvements, achieving an accuracy of 0.9821 in classifying breast tumors as benign or malignant, thereby establishing a new diagnostic standard.
Fine-Tuning Hybrid Deep Learning for Sentiment Analysis of Indonesian Product Reviews Arwin Halim; Roni Yunis; Erlina Halim
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

The research aims to build a hybrid deep learning model for sentiment analysis of Indonesian ecommerce product reviews, which represent the expressed opinions of customers. A major challenge in the domain is the presence of non-standard language and highly imbalanced sentiment classes, which hinder accurate classification. Most existing Indonesian sentiment analysis studies rely on relatively small and balanced datasets and primarily use attention mechanisms, an ensemble model, as well as a sequential fusion method. In the research, a large-scale dataset of Indonesian product reviews is collected from the largest e-commerce site in the country. The dataset consists of review text and corresponding product ratings. After preprocessing, semantic features are extracted using a pre-trained Indonesia Bidirectional Encoder Representations from Transformers (IndoBERT) model. The features are then fed into a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers through parallel feature-level fusion. Model hyperparameters are optimized using the Tree-Structured Parzen Estimator (TPE), while data imbalance is addressed through resampling methods. Regularization strategies are also applied to mitigate overfitting, and the model is evaluated using stratified k-fold cross-validation. The model hyperparameters are validated using a learning curve, showing a stable and consistent curve following the trend. The results show that the hybrid CNN-LSTM model, combined with Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE), achieves superior performance in distinguishing positive and negative reviews. This outcome reaches Receiver Operating Characteristic - Area Under the Curve (ROC AUC) score of 92.48%, outperforming baseline and conventional machine learning models. These results also show good generalization ability, characterized by consistent values with a very low standard deviation of 0.0009 for each fold.
CNN-LSTM Architecture for Multi-Task Sentiment and Emotion Classification on Large-Scale Indonesian TikTok Application Reviews Wahyu Fajar Setiawan; Afif Amirullah; Ilham Putra Ariatama; Ratih Nur Esti Anggraini
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

Sentiment and emotion analysis of mobile application reviews has attracted significant attention as a means to understand users’ perceptions and experiences. The research proposes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model for multi-task sentiment and emotion classification on Indonesian TikTok application reviews. A large-scale corpus consisting of 500,000 reviews is collected from the Google Play Store and preprocessed through cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labels (positive, negative, and neutral) are assigned using a lexicon-based approach, while emotion labels are annotated through emoji analysis and word matching based on five basic emotions: anger, fear, happiness, love, and sadness. The proposed CNN-LSTM model is evaluated against a hybrid Bidirectional Encoder Representations from Transformers – Convolutional Neural Network (BERT-CNN) architecture. Experimental results show that the CNN-LSTM model outperforms the BERT-CNN model, achieving an accuracy of 91.30% for sentiment classification and 99.15% for emotion classification, compared to 42.43% and 72.85%, respectively, obtained by the BERT-CNN model. These findings indicate that the CNN-LSTM architecture is more effective in capturing sequential patterns and contextual features in Indonesian review texts, particularly in a multi-task learning setting. Despite its strong performance, the research is limited by its focus on a single platform and the use of lexicon-based automatic labeling, suggesting future work on cross-domain evaluation and manual annotation refinement.
An Adaptive DTN Routing Protocol Using a Q-Learning Framework for Archipelagic Emergency Networks Agussalim Agussalim; Henni Endah Wahanani; Andreas Nugroho Sihananto
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

Natural disasters in archipelagic regions often disrupt communication networks, particularly in geographically isolated islands where terrestrial infrastructure is limited and highly vulnerable. Hence, adaptive, infrastructure-independent solutions are required to maintain connectivity during emergencies. The research proposes an adaptive routing protocol for Delay Tolerant Network (DTN), named Q-learning-based Forwarding Routing (QFR), designed to enhance data delivery performance in disaster scenarios characterized by intermittent connectivity and constrained resources. QFR employs a lightweight, tabular Q-learning framework to make intelligent forwarding decisions based on real-time state information, including buffer occupancy, encounter history, and local node density. The protocol further integrates adaptive replica control and prioritybased scheduling mechanisms to regulate congestion and optimize bandwidth and buffer utilization. Performance evaluation is conducted using the ONE Simulator with realistic maritime mobility traces derived from vessel movement patterns around Madura Island, Indonesia, representing inter-island emergency communication conditions. The results indicate that QFR consistently outperforms benchmark protocols such as Epidemic and PRoPHETv2, particularly in maintaining a high delivery ratio under heavy traffic loads while keeping routing overhead moderate and latency stable. Time-series analysis further demonstrates QFR’s ability to improve its performance over time as the agent learns. The key finding is that a lightweight, adaptive algorithm based on a tabular Q-learning framework provides a practical and effective solution for reliable communication in resource-constrained emergency networks, avoiding the computational complexity of deep reinforcement learning approaches.
Advancing Cross-Cultural Natural Language Processing with a Focus on Sundanese Language and Contextual Nuances Anggi Muhammad Rifai; Ema Utami; Amali Amali; Muhamad Fatchan; Muhamad Ekhsan
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

The Sundanese language, as one of Indonesia’s regional tongues, holds deep cultural value but is still underrepresented in computational linguistics. The research addresses this gap by developing a translation model between Sundanese and Indonesian using a transformer-based sequence-to-sequence (Seq2Seq) architecture. With a parallel dataset of 3,616 sentence pairs, the model is fine-tuned to capture linguistic and contextual subtleties. The evaluation yields strong results: Bilingual Evaluation Understudy (BLEU) score of 44.12, Recall - Oriented Understudy for Gisting Evaluation (ROUGE)-1 F1-Score of 0.72, and ROUGE-L F1-Score of 0.71. Those demonstrate high translation quality despite limited data. Unlike earlier Sundanese translation studies that rely on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), or standard transformer models, this research uniquely leverages the multilingual pretrained M2M100 Transformer, enabling transfer learning from high-resource languages to improve low-resource performance. These outcomes highlight the model’s potential for real-world applications, such as translation tools for education and cultural exchange. The research emphasizes the importance of improving access to Sundanese texts and promoting its digital presence to aid in language preservation. Overall, the research not only advances Natural Language Processing (NLP) research for low-resource languages but also reinforces the importance of integrating regional languages like Sundanese into modern technology. Building upon prior studies on Indonesian–Sundanese translation, the research novelty lies in fine-tuning a multilingual Seq2Seq Transformer that captures both linguistic and contextual nuances, thereby setting a new benchmark for lowresource language processing.

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