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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.
Arjuna Subject : -
Articles 1,172 Documents
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Recommender System Based on Social Network Analysis of Student Workshop and Event Activities Compared to GPA and Department Setiawan, Esther; Santoso, Joan; Cahyadi, Billy Kelvianto; Afandi, Acxel Derian; Saputra, Daniel Gamaliel; Ferdinandus, FX; Fujisawa, Kimiya; Purnomo, Mauridhi Hery
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.9.3.2943

Abstract

This research uses social network connections and academic data to create a recommender system that helps students choose seminars and events that suit their interests. The aim is to address the issue of students' hesitation in selecting activities. This project investigates the use of social network analysis (SNA) to provide individualized suggestions by analyzing student involvement in workshops and events, as well as their grade point average (GPA). The materials contain student data gathered between 2018 and 2023 from Institut Sains dan Teknologi Terpadu Surabaya (ISTTS), emphasizing the student's social media interactions and event participation. Metrics like centrality are employed to identify prominent nodes inside the network, and the approach combines graph-based SNA and cosine similarity for event recommendation. The network of student involvement in events was represented by a dataset comprising 2,293 edges and 602 nodes. The results show that the relevance of recommendations is improved when social network data is integrated with GPA, rather than GPA-based systems alone. The system identified key nodes, such as specific lectures, that significantly impacted student involvement and were rated highly in terms of centrality. Future research implications recommend expanding the dataset to encompass a broader range of events and refining the algorithm by including content-based filtering. The system's application is not limited to educational environments; it may also be tailored for career counselling or professional development.
Performance Improvement for Hotspot Prediction Model Using SBi-LSTM-XGBoost and SBi-GRU-XGBoost Sukmana, Husni Teja; Aripiyanto, Saepul; Alamsyah, Aryajaya; Henry, Amir Acalapati; Nandaputra, Riandi
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3047

Abstract

Forest fires damage ecosystems and harm all living beings, often triggered by low rainfall that worsens fire spread. Climatic factors such as the El Nino–Southern Oscillation (ENSO) also contribute to reduced rainfall and prolonged dry seasons. This study aims to enhance the performance of fire prediction models to support forest fire mitigation. Modified artificial neural network algorithms—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with bidirectional stacked layers—are employed as baseline models. An experimental approach was used to compare the performance of LSTM and GRU models with their ensemble versions, where XGBoost was added to improve prediction accuracy. The results show that the proposed ensemble algorithms significantly outperform the baseline models in multivariate fire prediction. The SBi-LSTM-XGBoost and SBi-GRU-XGBoost models demonstrated more than a 40% performance improvement compared to the original SBi-LSTM and SBi-GRU models. In multivariate modelling, the ensemble models achieved an R-value of 1.0000, with an average MAE of 0.0007, RMSE of 0.0009, and MAPE of 0.0008. This study also identified limitations of the LSTM and GRU models in processing ENSO data due to their non-linearity and weak correlation with hotspot data. As a contribution, our experiments show that integrating XGBoost into LSTM and GRU models effectively overcomes these limitations, significantly improving hotspot prediction accuracy and supporting better forest fire mitigation strategies.
Crypto Forecast: Integrating Web Scraping and Data Analysis for Cryptocurrency Price Prediction Gadge, Krutika; Daduria, Shreyash; Sarodaya, Abhishek; Borkar, Pradnya Sulas; Badhiye, Sagarkumar Shridhar; Agrawal, Pratik K
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2767

Abstract

Accurately predicting cryptocurrency prices is still a difficult task because of the extremely volatile nature of the market. This study introduces a new methodology combining web scraping, data analysis, and machine learning to further improve prediction accuracy. A live cryptocurrency monitors gathers data from various sources such as trading volumes, price volatility, and sentiment in market to create a rich data set. Feature engineering is used to convert raw data into useful inputs for machine learning algorithms to further enhance prediction functions. Utilizing Python libraries including Beautiful Soup, Pandas, Scikit-learn, and deep learning libraries, the correct predictive model is designed and strictly tested for precision, performance, data quality, usability, scalability, and cost. The proposed hybrid model is a combination of traditional statistical methods with deep learning models to overcome the constraints of conventional forecasting methodologies. The output reflects the performance of the model in identifying the trends in the market and rendering data-driven insights to traders and investors. Future studies can employ different data sources, including social media sentiment analysis, financial news articles, and web-based cryptocurrency forums, to enhance predictability. Further advancement in time series forecasting through deep learning models, including transformer models, may also enhance the precision of long-term forecasting. A deeper insight into how external forces, including government intervention, macroeconomic trends, and emerging blockchain technologies, would complement our understanding of cryptocurrency market dynamics. This study contributes to complementing predictive analytics in financial markets by providing useful insights to investors, researchers, and policymakers. 
Automated UML Class Diagram Generation from Textual Requirements Using NLP Techniques Meng, Yang; Ban, Ainita
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3482

Abstract

Translating textual requirements into precise Unified Modeling Language (UML) class diagrams poses challenges due to the unstructured and often ambiguous nature of text, which can lead to inconsistencies and misunderstandings during the initial stages of software development. Current methods often struggle with effectively addressing these challenges due to limitations in handling diverse and complex textual requirements, which may result in incomplete or inaccurate UML diagrams. This study aims to propose a Natural Language Processing (NLP) model that analyzes and comprehends textual requirements to extract relevant information for generating UML class diagrams, ensuring accuracy and consistency between the diagrams and requirement descriptions. The research employs a four-step approach: preprocessing to handle text noise and redundancy, sentence classification to distinguish between "class" and "relationship" sentences, syntactic analysis to examine grammatical structures, and UML class diagram generation based on predefined rules. The results show that the model achieved a classification accuracy of 88.46% with a high Area Under the Curve (AUC) value of 0.9287, indicating robust performance in distinguishing between class definitions and relationships. This study highlights that existing methods may not fully address the nuances of translating complex textual requirements into accurate UML diagrams. This study successfully demonstrates an automated method for generating UML class diagrams from textual requirements and suggests that future research could expand datasets, optimize feature extraction, explore advanced models, and develop automated rule generation methods for further improvements.
An Innovative Approach for Improving Navigation Performance of Robust Land-Based Vehicles Ghouneem, Mostafa Mahmoud; Wassal, Amr
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2647

Abstract

The Extended Kalman Filter (EKF) stands as a prominent choice within navigation systems, particularly in scenarios involving the integration of a Reduced Inertial Sensor System (RISS) with the Global Positioning System (GPS). However, despite its widespread adoption, the EKF grapples with many challenges, including the propensity to underestimate filter uncertainties, contend with unreliable GPS signals, and confront errors stemming from linearization processes. These issues invariably contribute to a decline in overall system performance. Considering these challenges, this paper endeavors to introduce a groundbreaking integration algorithm to mitigate the inherent shortcomings of the EKF. The proposed algorithm employs innovative strategies to address these challenges comprehensively. Firstly, it incorporates a dynamic self-tuning mechanism meticulously designed to improve filter configuration in real-time, ensuring adaptability to varying operating conditions. The algorithm also integrates a meticulously engineered GPS Integrity algorithm to filter out mistaken readings and bolster the reliability of the navigation solution. Furthermore, the algorithm adopts the Unscented Kalman Filter (UKF), renowned for handling non-linearities directly, thereby cutting the need for the cumbersome linearization procedures inherent in the EKF. Comparative evaluations against the traditional EKF method prove the effectiveness of the proposed approach. Significant performance enhancements are evident using two datasets from a VTI SCC1300-D04 IMU unit compared to high-precision Novatel SPAN ground truth data. These improvements are quantified through RMSE analysis, showing substantial strides in navigation accuracy. Overall, the results underscore the transformative potential of the proposed integration algorithm in advancing navigation system capabilities.
Evaluation of Extreme Rainfall Occurrences Using Short-term and Long-term Standard Precipitation Index (SPI) Razuki, Nurul Dayana; Abdul Rauf, Ummul Fahri; Zainol, Zuraini; Mohd Isa, Mohd Rizal; binti Jamaludin, Nor Azliana Akmal
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.9.3.2213

Abstract

The main objective of this study is to investigate the Standard Precipitation Index (SPI), a method commonly used to determine extreme rainfall occurrences. It is also used to gauge the severity and duration of drought in meteorological studies. To highlight exceptional extreme rainfall events in selected areas, a methodology for calculating the SPI was provided in this paper using a range period and thresholds. The Standard Precipitation Index (SPI) is used to analyze monthly precipitation data from several selected rain gauge stations between 1970 and 2014. The goal of this study is to monitor the extremely moist conditions that may eventually lead to flooding. Precipitation index data from several rain gauge sites in the selected region are used to calculate the SPI time series. Additionally, SPI readings for 3 months or less may usually be used for basic drought monitoring, values for 6 months or less may be useful for monitoring agricultural impacts, and values for 12 months or more may be useful for monitoring hydrological impacts. In this study, two states affected by the monsoon season were selected: Johor and Kelantan. Two rain gauge stations were selected from these two states to calculate the SPI results. From this study, statistics on the occurrence of dry and wet events in specified areas were determined based on the SPI readings for 3-month, 9-month, 12-month, and 24-month periods. To summarize, this research demonstrates the potential of SPI to enhance our understanding of extreme rainfall events in Peninsular Malaysia.
Semantic Multi-Query Model for Cultural Computing of Image Search System Barakbah, Ali Ridho; Suryani, Indah Yudi; Kusumaningtyas, Entin Martiana
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4294

Abstract

The proliferation of digital images on the internet has increased the need for image search systems, especially for culturally significant images that contain a collection of impressions. However, traditional image search systems typically rely on a single query, making it difficult to discern user intent accurately. This paper introduces a novel model for describing user impressions using a semantic multi-query function for cultural computing in image search systems.  This model provides a culture-centric semantic multi-image query system to generate representative query impressions.  The proposed multi-query model provides an analytical tool to semantically construct representative query color attributes, involving four stages: (1) Local normalization of 3D-Color Vector Quantization, (2) Color distribution measurement, (3) Adaptive representative color adjustment, and (4) Representative color identification. For the experimental study, we evaluate our system with two types of experiments: (1) Multi-query image for image search to ensure that our multi-query model enhances the accuracy of the retrieval outcomes, and (2) Multi-query image for semantic image search of cultural paintings. In the first experiment using the SIMLIcity dataset, our proposed multi-query model achieved better retrieval performance across most categories, reducing the single-query error from 26.67% to 20%. In the second experiment using the Indonesian cultural painting dataset, our proposed multi-query model achieved better retrieval performance across most categories, improving the single-query average similarity from 46.6% to 72%.
Developing and Comparing Machine Learning Algorithms for Music Recommendation Bau, Yoon-Teck; Mohd Reza, Puteri Ainna Ezzurin; Lee, Kian-Chin
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2947

Abstract

The increasing prevalence of song skipping in music streaming applications negatively impacts user satisfaction and subscription retention. Dissatisfaction often arises when users encounter songs they actively dislike, highlighting a gap between user expectations and the value offered by these services. To address this, music recommendation algorithms were researched and developed. Initially, data collection is started. Data collection is through the Spotify application programming interface. This initiation step sets the stage for subsequent exploratory data analysis. Exploratory data analysis examined the collected data to plot a bar chart for total songs released over the years, plot a bar chart for the popularity of songs based on the year it is released, visualize word cloud on frequently mentioned music genres, chart count plot for explicit songs, and chart count plot for song modalities. Data preprocessing involved cleaning the data, handling missing values, and checking for null values to prepare the application of machine learning algorithms. Four machine learning algorithms were applied, k-means, mini-batch k-means, Gaussian mixture, and density-based spatial clustering of applications with noise (DBSCAN), to analyze musical features like rhythm, tempo, and other relevant music attributes. The results showed that the k-means outperforms all other algorithms evaluated regarding recommendation quality, as measured by the Calinski-Harabasz score. Based on the evaluation, the best machine learning will then be applied to identify similarities between songs and be used to generate music recommendation results.
TPPSO: A Novel Two-Phase Particle Swarm Optimization Shami, Tareq M.; Summakieh, Mhd Amen; Alswaitti, Mohammed; Jahdhami, Majan Abdullah Al; Sheikh, Abdul Manan; El-Saleh, Ayman A.
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2331

Abstract

Particle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems.
Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms Wisky, Irzal Arief; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3091

Abstract

This study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education.

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