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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Articles 40 Documents
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Search results for , issue "Vol 9, No 3 (2025)" : 40 Documents clear
Entity Extraction in Indonesian Online News Using Named Entity Recognition (NER) with Hybrid Method Transformer, Word2Vec, Attention and Bi-LSTM Zainuddin, Zahir; Mudassir, -; Tahir, Zulkifli
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2902

Abstract

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that identifies entities such as person names, locations, and organizations within the text. While many NER studies have concentrated on the English language, there is a significant need for further research on Indonesian NER. Indonesia presents unique challenges due to its structural complexities, polysemy, and ambiguities. Conventional machine learning and deep learning techniques have been widely applied in NER; however, more detailed exploration into integrating these methods for performance improvement is needed. This study introduces a novel hybrid model, TWBiL, which combines Transformer mechanisms, Word2Vec embeddings, Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention mechanisms to enhance NER performance on Indonesian text. TWBiL harnesses the strengths of each component to generate superior word vector representations, extract intricate sentence features, and disambiguate entities contextually. Our experimental results demonstrate the effectiveness of the proposed hybrid model, revealing a significant improvement in NER performance. Specifically, TWBiL achieves an F1-Score of 85.11 on an Indonesian online news dataset, outperforming the traditional Bi-LSTM model, which achieved a score of 75.18. The results indicate that TWBiL effectively reduces ambiguity and captures context more accurately, enhancing entity recognition. Future research should priorities reducing computational time when handling larger datasets without compromising overall NER performance. This study underscores the potential of integrating advanced deep learning techniques to tackle the unique challenges of Indonesian NER, thus providing a solid foundation for further advancements in the field.
Applying Data Mining on Personal Computer for Document Classification Chai, Ian; Salleh, Ahmad Zarif
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3473

Abstract

The typical user creates documents over many years of computer usage. As people move from computer to computer, they tend to copy the files to the new computer, because "you never know when we might need to refer to something from the past." Hence, the collection grows larger and larger, expanding to hundreds and thousands. This collection soon exceeds the ability of most people to remember what each document was, even if they have been keeping them in some order in folders – and many people fail to anticipate how the folders and subfolders should be arranged as time passes – and by the time they realize it, most find it too daunting a task to reclassify them all manually. Therefore, we sought to solve this problem using a data mining-based solution, specifically multinomial naive Bayes. We developed a document classification program to automatically categorize all documents stored on a person's personal computer hard drive, eliminating the need for manual classification. The proposed algorithm achieved a score of 0.853 for accuracy, 9,833 for precision, 0.661 for recall, and 0.767 for the F1 metric. It should be possible, with further refinement and improvement, for example by balancing the dataset and increasing its size, for this technique to be applied in practical applications that enable automatic document classifications on the computers of most computer users.
Development of Conventional Lathe Machine Manual User by Using Augmented Reality Frameworks Hamid, Abdul; Puan, Loretta Anak; Tamin, Norfauzi; Maslan, Andi; A.S, Darmawan
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2712

Abstract

Machining is one of the familiar subjects in the field of Technical and Vocational Education and Training (TVET) and has been offered at several Vocational Colleges and Institutes of Higher Education (IPT) throughout Malaysia. However, the level of dominance is limited to a handful of students in understanding the learning content and achieving learning outcomes at the end of the course's teaching and learning process. Therefore, this research intends to design and develop a machine manual using an interactive multimedia concept characterized by Augmented Reality (AR). The method of creating forms and developing interactive multimedia routinely uses the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model as a reference model and guideline for implementing learning. The research instruments used were product development expert review forms and student investigation questionnaires. The research respondents consisted of 80 TVET students from Universiti Tun Hussein Onn Malaysia (UTHM) and Tanjung Piai Vocational School. The data obtained is collected and analyzed periodically using statistical-based software. An evaluation is conducted on the product's design, form, content, and functionality. The results of the analysis on the use of interactive multimedia concepts indicate that the average minimum standard for all variables exceeds 3.25, which is interpreted as Highly Acceptable for the Use of Multimedia-Based Learning. Three experts in the field of multimedia and engineering agree that the product developed has a shape that matches the design and can function effectively. Overall, the research found that the design form, content, and functionality of conventional interactive machines can enhance students' visualization abilities in the teaching and learning process, as well as improve their skills when practicing with the devices.
Comparative Study of CNN Architectures for Real-Time Audio-Based Car Accident Detection on Edge Devices Ilahi, Ahmada Haiz Zakiyil; Irwansyah, Arif; Oktavianto, Hary
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2985

Abstract

Traffic accidents often result in fatalities for both drivers and bystanders. Traditionally, accident information relies heavily on community reports, which can delay the provision of victim assistance. To address this issue, a system capable of detecting accidents responsively in various weather conditions and traffic densities is necessary. One approach involved using audio analysis techniques to evaluate collision sounds. Thus, this study proposed an audio classification system for detecting car accidents using Convolutional Neural Networks (CNNs). The system’s performance was evaluated on personal computers and edge devices, such as the Raspberry Pi 4 and NVIDIA Jetson Nano, to compare inference times and power consumption. To enhance the dataset, segmentation and augmentation techniques were applied before converting the audio data into a 2D Mel-spectrogram. The dataset was then trained and assessed with four CNN architectures: custom sequential, custom with shared input layer, transfer learning EfficientNetB0, and transfer learning MobileNetV2. Both original and Lite models were deployed on experimental devices. Results showed that the custom CNN model had faster inference times across devices in both original and lite forms, though it had a 4% increase in the false positive rate. The Lite MobileNetV2 model recorded the fastest inference time on edge devices at 86 ms. Jetson Nano exhibited faster inference times compared to Raspberry Pi 4. However, Raspberry Pi 4 showed a minor increase in power consumption of 0.6 watts during inference. In future work, this system can be tested in real-time environments using embedded systems to evaluate its robustness against noise and varying environmental conditions.
Personalized Tourism in Surabaya: A Bayesian Network Approach Faradisa, Rosiyah; Badriyah, Tessy; Maulana, Hanan Ammar; Assidiqi, Moh Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3376

Abstract

This study investigates the application of Bayesian Networks in developing a personalized tourist destination recommendation system focused on Surabaya, Indonesia. The research incorporates push and pulls factors alongside tourist activities as key input variables to model decision-making processes. Two distinct Directed Acyclic Graph (DAG) structures are evaluated: one proposed based on existing theoretical frameworks and another generated from empirical respondent data. The dataset comprises responses from 1,350 tourists visiting twenty-five popular attractions in Surabaya. The analysis reveals that Bayesian Networks effectively identify correlations between various influencing factors. From the tests carried out, the accuracy obtained from the two DAG structures did not significantly differ. The proposed DAG achieved 35% accuracy for the top-ranked destination recommendations, while the data-driven DAG was 25%. Both achieved 75% accuracy in the top five recommendations. The accuracy increased as the number of output states was reduced. Meanwhile, in the test with binary output, BN was able to accurately classify tourist destinations with an average accuracy of 95% for both DAGs. These findings highlight the potential of Bayesian Networks to enhance tourism decision support systems by providing nuanced insights into tourists' preferences and motivations. For further research, hybridization or feature engineering can be employed to improve model accuracy. In addition, determining more appropriate push factors and tourist activities based on the tourism case studies also needs to be done to obtain better tourist preferences. This research highlights the promising role of Bayesian Networks in improving the personalization and effectiveness of tourist recommendations.
Prediction Analysis of Greeting Gestures Based on Recurrent Neural Networks Wibowo, Angga; Kurnianingsih, -; Sato-Shimokawara, Eri
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2917

Abstract

Human activity recognition, such as rehabilitation, sports, human behavior, etc., is developing rapidly. A Recurrent Neural Network (RNN) is a practical approach to human activity recognition research and sequential data. However, studies on recognizing human activities rarely study culture, including greeting gestures. And studies seldom use small datasets when employing the RNN approach, as they typically utilize large amounts of data to conduct such studies. This study aims to predict greeting gestures from Japan and Indonesia with limited data. This study proposes and compares six RNN architecture methods, including Long Short-Term Memory (LSTM), Bidirectional RNN (BRNN), Gated Recurrent Unit (GRU), Vanilla RNN (VRNN), Deep RNN (DRNN), and Hierarchical RNN (HRNN), which have been modified with regularization to handle overfitting. We evaluate using Mean Squared Error (MSE), Root Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). The experimental results show that LSTM has the best MSE, RMSE, and MAE values, with MSE of 0.0773479, RMSE of 0.2781149, and MAE of 0.2402451, while GRU has the best R² value of 0.0267571. The conclusion of this study indicates that LSTM and GRU are more suitable than other models for solving this problem. Therefore, it can be beneficial for future research to address the challenges of small data and overfitting in sequential data and human activity recognition, particularly in the context of greeting gestures. Future work can utilize data augmentation, proper parameter selection, and incorporate data from multiple individuals to enhance the accuracy of the model.
Low-Resolution Face Image Reconstruction Using Multi-Stage FSRCNN to Improve Face Detection and Tracking Accuracy in CCTV Surveillance Tommy, -; Siregar, Rosyidah; Rahman Syahputra, Edy
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3160

Abstract

Face detection and tracking under real-world condition remain challenging under different illumination, crowded scenes, partial occlusions and small or low-resolution face images. In traditional face tracking schemes, these factors often cause the false positive rate to be high and the accuracy to be low. Specifically, little or no detailed information is presented for small or distant faces, here the reliability of detection is diminished and non-face-object can provoke false alarms thus degrading the performance of a system in general. Such problems are not unclear and need a sophisticated solution to improve the resolution and detection performance in various scenarios. In this paper, a new face tracking system based on a cascade classifier, a two-step model of Fast Super-Resolution Convolutional Neural Network (FSRCNN) and DLib face validator is presented. The low-resolution facial parts are first enhanced by the FSRCNN to optimize the detection by the cascade classifier. The DLib face validator improves the approach by validating the discovered faces, and reducing false positives. The system was tested over a CCTV scenario video corpus of several challenging conditions represented by crowded environments, dynamic object and human faces of different sizes and locations. The performance analysis focused on performance metrics such as precision, recall, and false positive rate, which provided a comprehensive overview of the system's robustness. The results demonstrate a significant improvement in face detection accuracy, as high as 98% precision and very few false positive detections. The synergy between the FSRCNN method and the DLib validation was especially effective on small and far-away faces, which are normally difficult to perceive. Whilst their improvements on memory consumption were small, they proved effective for face detection in challenging conditions. The ability of the system to maintain high measurement accuracy while avoiding errors makes it well suited for use in surveillance, security and monitoring systems. In conclusion, this research highlights the effectiveness of combining super-resolution techniques with traditional face detection methods to address the limitations of existing systems. The future work will focus on increasing recall rate and constantly maturing the extraction system to work well in various realistic conditions, thus making it effective and general for different applications.
Deep Learning-Based Early Dropout Prediction in University Online Learing Park, Hee-Sun; Yoo, Seong-Joon; Gu, Yeong-Hyeon
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.4258

Abstract

With the global transition of universities to online education due to COVID-19, the high dropout rate in online learning has become a critical challenge for higher education institutions. To address this issue, this study aims to develop a deep learning model for early dropout prediction in university online education. The proposed model was built by collecting and analyzing daily learning history data stored in the Learning Management System (LMS). Unlike previous studies that primarily relied on data collected at the end of the online learning period, this study analyzes students' behavioral data over time to more accurately identify students at risk. The research utilized data from a cyber university located in Seoul, South Korea, including approximately 30,574 student records and 12,014,610 learning history entries from the academic management system. To validate the model’s performance, data from the following academic year, which was not used for model training, was employed. The study compared the effectiveness of traditional machine learning methods with deep learning techniques (DNN and LSTM). Specifically, it proposed the LSTM-DNN model, which effectively learns both static learner information data and sequential learning history data. The results demonstrated that the LSTM-DNN model achieved a prediction accuracy of over 92%, confirming its effectiveness in providing real-time dropout risk assessments and predictive insights. Ultimately, this study proposes a novel approach to integrating real-time dropout prediction services into university Learning Management Systems (LMS), thereby contributing to student retention and academic success in online learning environments.
An Enhanced Routing Protocol For Vehicular Ad Hoc Networks With Swarm Intelligent Tareq, Mustafa; Farhan, Yasir Hadi; Nafea, Mohammed Mansoor
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3298

Abstract

A Vehicular Ad Hoc Network (VANET) is a transient network of wireless mobile nodes operating without centralized administration or pre-existing infrastructure. VANETs are a subset of Mobile Ad Hoc Networks (MANETs) designed to facilitate vehicular communication. This allows vehicles to communicate directly with roadside devices or with each other. These networks are appropriate for applications like infotainment services, traffic control, and accident avoidance since they are dynamic, decentralized, and highly flexible. However, their lack of established infrastructure presents serious difficulties, especially when preserving dependable routing and energy efficiency. Path selection in VANETs usually attempts to limit the number of intermediary nodes required to reach a destination to reduce latency and possible points of failure. However, as the distance between nodes increases, so does the required transmission power, directly impacting the network's energy consumption. As a result, energy-efficient routing is crucial to maintain network longevity and performance. This paper introduces the Bee Destination Sequenced Distance Vector Routing (B-DSDV) protocol, utilizing swarm intelligence principles via the Artificial Bee Colony (ABC) algorithm to enhance energy efficiency within a DSDV framework. This integration incorporates the Bee Algorithm into the discovery mechanism of DSDV to identify the most accessible node and the shortest route based on node distances. The algorithm assesses both the power levels of nodes and their distances to others. Route selection is optimized by considering the power consumption of intermediate nodes between the source and destination. Performance evaluation of the B-DSDV protocol is compared with established protocols, demonstrating its effectiveness in selecting high-power optimal paths and improving overall performance. The simulation results were conducted based on average throughput, average energy consumption, average end-to-end delay, and packet delivery ratio performance metrics. We conducted a simulation study using Network Simulator (NS) version 2.35 to evaluate the performance metrics of the routing protocols. Regarding energy consumption, the B-DSDV protocol achieved superior results, approximately 0.10% concerning packet size, compared to other protocols.
Analysis of Emission Reduction in Indonesia's Power Generation Sector for the Centennial Milestone using Grammatical Evolution and ARIMA Raharjo, Jangkung; Wijayanto, Inung; Nur Ikhsan, Rifki Rahman; Indra Wijaya, Igpo; Nugroho, Bambang Setia; Rokhmat, Mamat
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3067

Abstract

This study examines the Indonesian government's commitment to reducing electricity production, a crucial element in achieving sustainable energy. Historically, Indonesia depends on non-renewable energy sources, including coal and oil. Indonesia is presently transitioning to cleaner energy alternatives. This policy is done to align with the objective of global sustainability. This pivotal action by the Indonesian government aims to accelerate the adoption of low-carbon technology by society. Through careful planning, Indonesia aims to establish a sustainable and resilient energy framework that addresses both current and future environmental challenges. The active participation of both the state and private sectors is crucial to support this transition. For instance, investment in research and development of sustainable technology by the private sector can accelerate the improvement or creation of a more sustainable energy framework. Innovative technologies, such as solar, hydropower, and wind, can significantly contribute to reducing carbon footprints. This study conducted an extensive observation and evaluation of the contribution of Indonesia's power generation sector to achieving net-zero emissions. This study utilizes the Autoregressive Integrated Moving Average (ARIMA) and Grammatical Evolution (GE) to predict the overall electrical capacity trajectory leading up to Indonesia's Centennial in 2045. By utilizing the exponential grammar, GE outperforms ARIMA in predicting energy forecasts. This research sheds light on Indonesia's transformative efforts, contributing to a broader understanding of how to cultivate a sustainable and environmentally responsible energy future.

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