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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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Search results for , issue "Vol 9, No 5 (2025)" : 50 Documents clear
Comparative Analysis of Robust Imputation Techniques for Enhancing Cervical Cancer Prediction with Missing Data Mizan, Muhammad Thaqiyuddin; Ernawan, Ferda; Kasim, Shahreen; Erianda, Aldo; Mohd Fauzi, Abdullah Munzir
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.9.5.4501

Abstract

Handling missing data is a critical challenge in machine learning applications, as it can significantly affect the accuracy and reliability of predictive models. Addressing this issue is crucial for developing robust systems that can deliver high-performance results. This study provides a comparative analysis of the robust imputation technique for cervical cancer prediction with incomplete information. This study has investigated the importance of robust imputation techniques, particularly Soft Imputer, in addressing missing data challenges and enhancing model performance. This study investigates the impact of various imputations across five distinct approaches: KNN imputer, PCA imputer, MICE imputer, XGBoost imputer, LightGBM imputer, and feature selection methods. These imputation data are tested on several machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Decision Tree (DT), Support Vector Classifier (SVC), Logistic Regression (LR), Extra Trees Classifier (ETC), CatBoost Classifier, Stochastic Gradient Descent (SGD), and Gradient Boosting (GB) for improving classification accuracy of cervical cancer prediction. The evaluation reveals that the soft imputer method achieves a balanced and effective handling of missing data, significantly improving the reliability of the models. Among the tested methods, LightGBM and XGBoost deliver strong results, each achieving an average accuracy of 96.91%. MICE demonstrated the lowest average accuracy at 95.94%, although it still performs reliably in managing missing data. The findings provide valuable insights for enhancing predictive accuracy in future work by integrating advanced imputation strategies for high-dimensional and complex datasets.
Optimizing Online Retail: Uncovering Opportunities with Unsupervised Learning Techniques Yung Jun, Yu; Ru Poh, Tan; Wong, Kenneth; Siew Mooi, Lim; Kar Eun, Hew
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.9.5.3082

Abstract

This project explores the application of unsupervised learning techniques to analyze an online retail transaction dataset. The primary objectives are to gain insights into customer behavior, product relationships, and opportunities for improving marketing strategies, product recommendations, and inventory management in the online retail business. This dataset comprises comprehensive information, including transactions, items, and consumers, across 24 countries, with the majority of sales occurring in the United Kingdom. Interesting trends in client geography, sales trends over time, and the presence of outliers in quantity and unit pricing are identified through exploratory data analysis. Examples of unsupervised learning techniques are K-means clustering, DBSCAN, fuzzy C-means, and Spectral Clustering. Researchers addressed the problem statements using these techniques. Different client groups, depending on purchase habits and top-selling products, are identified within every segment of product analysis and customer segmentation using K-Means. Information on product linkages and unusual buying patterns was offered when products were clustered using DBSCAN, and outliers were identified. Product association analysis and customer segmentation, using fuzzy C-Means, visualized the found clusters and assessed the ideal segment count. Depending on the country of origin and total sales of consumers, Spectral Clustering was used for geographic-based customer segmentation. The performance of the clustering models was evaluated using Silhouette Scores and Davies-Bouldin Indices, with Fuzzy C-Means demonstrating the highest clustering quality. The insights gained from this analysis can be leveraged by online retail businesses to enhance marketing strategies, product recommendations, cross-selling, and inventory management, ultimately improving the overall customer experience.
Developing Augmented Reality as a Teaching Material to Enhance Cultural Awareness in Secondary Schools Pattaufi, -; Makawi, Faizal Erlangga; Aswan, Dedy; Cahyadi, Dian
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.9.5.4540

Abstract

This study investigates the development of Augmented Reality (AR)-based teaching materials to enhance students’ understanding of local wisdom and cultural diversity in secondary schools in South Sulawesi, Indonesia. Employing a quasi-experimental design, the research involved 304 students across four major ethnic regions, namely Makassar, Bone, Toraja, and Mandar, which were divided equally into experimental and control groups. The experimental group utilized AR-integrated materials, whereas the control group employed traditional methods. Data were collected through pre-tests, post-tests, and student perception surveys. Results revealed a significant increase in students' cultural awareness and understanding after the intervention, particularly among Bugis Makassar and Bugis Bone groups. The mean improvement in the experimental group was statistically significant (p < 0.001), confirming the pedagogical benefits of AR in promoting cultural literacy. Additionally, students expressed strong appreciation for AR’s interactive features, which enhanced their engagement and motivation. This study underscores the importance of integrating AR and local wisdom in educational content to foster inclusive, culturally responsive learning environments. The findings emphasize that augmented reality (AR)-based instructional resources serve as a powerful tool in enhancing students' understanding of indigenous knowledge and cultural diversity within secondary education. The incorporation of local wisdom into educational content not only enhances students’ learning experiences but also cultivates a profound appreciation of their cultural heritage. The results highlight the potential of AR as a transformative technology that can bridge the deficiencies inherent in conventional pedagogical approaches, particularly in fostering cultural awareness among learners.
Smart Control Upgrade: IoT-Enhanced Remote Management of Straightening Machine with NodeMCU ESP8266 Irawan, Bambang; Muhlasin, Muhlasin; Erzed, Nixon; Herwanto, Agus; Rahaman, Mosiur; Mooduto, Hanriyawan Adnan
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.9.5.2975

Abstract

Straightening machines are essential in the oil and gas industry. These machines are intended to straighten bent drill pipes after the drilling process. The operator must be near the straightening machine for manual operation and control. This limitation prevents the operator from monitoring the machine's performance directly and efficiently. In response to this challenge, this study proposes to use the Internet of Things to enable remote control and monitoring of the straightening machine using the NodeMCU ESP8266 microcontroller. The proposed system integrates sensors and electronic components to collect and process critical data from the straightening machine. The INTER-METH method develops an IoT system to control the straightening machine remotely. The RemoteXY cloud server receives the collected data via a Wi-Fi network using the Message Queuing Telemetry Transport protocol. This setup enables real-time data retrieval by an Android-based application. The proposed system allows the user to monitor the machine's operational status and control it remotely, thereby increasing efficiency by up to 45% and reducing the total cost of operation by about 60%. With the implementation of IoT, the operator could remotely monitor the performance of the straightening machine, thereby increasing operator safety by more than 50%. These advances improve operators' safety conditions and simplify maintenance and operational processes. In addition, future research could focus on robust data security measures. These developing systems could be operated with other industrial IoT platforms and utilize data analytics for predictive maintenance, extending the machine's life.
Multilingual Parallel Corpus for Indonesian Low-Resource Languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almu’iini; Arya Astawa, I Nyoman Gede; Andika Dwiyanto, Felix
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.9.5.3412

Abstract

Indonesia has an extraordinary number of languages, with more than 700 regional languages such as Javanese, Madurese, Balinese, Sundanese, and Bugis. Despite the wealth of languages, digital resources for these languages remain scarce, making the preservation and accessibility of digital languages a significant challenge. Research was conducted to address this gap by building a multilingual parallel corpus consisting of more than 150,000 phrase pairs extracted from Bible translations in five regional languages in Indonesia. Rigorous preprocessing, normalization, and Unicode tokenization were performed to improve data quality and consistency. The encoder-decoder architecture was a key focus in the development of the NMT model. Evaluation focused on forward and backward translation directions, which were measured using BLEU scores. The results show that forward translation consistently outperforms backward translation. The Indonesian Javanese model produced a score of 0.9939 for BLEU-1 and 0.9844 for BLEU-4, indicating a high level of translation quality. In contrast, reverse translation tasks, such as translating from Sundanese to Indonesian, presented significant challenges, with BLEU-4 scores as low as 0.3173. This illustrates the complexity of the translation system from Indonesian to local languages. If future research focuses on transformer-based models and incorporates additional linguistic parameters to enhance the accuracy of natural language processing (NLP) models for Indonesia's underrepresented regional languages, this work provides a dataset that can be utilized for that purpose.
Course Timetabling using Genetic Algorithm and Fuzzy Cross-Over Maspiyanti, Febri; Gatc, Jullend; Nursari, Sri Rezeki Candra; Murtako, Amir
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.9.5.3196

Abstract

Course timetabling at universities presents a complex problem due to the limited timeframe to create schedules that avoid conflicts between activities. This issue becomes more challenging as the number of activities increases while the available rooms remain constant. Numerous studies have attempted to automate the scheduling process, but their success is often limited to specific cases, meaning their effectiveness may not be applicable in different institutions. One method that has shown potential in solving timetable problems is the genetic algorithm, either as a standalone approach or combined with other techniques. Despite criticisms regarding computational time complexity, genetic algorithms serve as practical global optimization tools, making them suitable for timetabling when computational time constraints are manageable. A hybrid Genetic Algorithm combined with Fuzzy Partitioning is essential for determining the crossover point, one of the key operators in genetic algorithms. In this study, we use a hybrid genetic algorithm with fuzzy crossover to address the course timetabling problem at Pancasila University, focusing on two departments, Informatics and Electro, which share classrooms on the same floor. In this study, we use data from 31 courses; our experiment achieved convergence at generation 78, with a fitness function score of zero, indicating the complete elimination of scheduling conflicts. For further improvement, adjustments could be made to the fitness function to penalize inefficient room usage, reducing the total number of generations to decrease execution time without compromising solution quality, and reducing the mutation rate to enhance solution stability.
Predictive Analytics for Employability in Malaysian TVET with a Hybrid of Regression and Clustering Methods Mahdin, Hairulnizam; Nurwarsito, Heru; Baharum, Zirawani; Kamri, Khairol Anuar; Hassan, Azman; Haw, Su-Cheng; Arshad, Mohammad Syafwan
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.9.5.4516

Abstract

Graduate employability remains a high concern for Technical and Vocational Education and Training (TVET) institutions, particularly within Malaysia’s Technical University Network (MTUN), where producing industry-ready graduates is a central goal. While machine learning has transformed fields like healthcare and finance, its application in vocational education remains underexplored—particularly for employability prediction. This study addresses this gap by hybridizing decision trees and clustering to uncover non-linear patterns in student survey data. Guided by Human Capital Theory and SERVQUAL, which inform variable selection (e.g., technical skills as productivity investments), this study integrates multiple linear regression, decision tree regression, and K-Means clustering to identify significant predictors and uncover latent student groupings. Using a publicly available dataset of Likert-scale responses from MTUN students, technical skills and supervisory support consistently emerged as the most impactful employability predictors. Communication showed moderate influence, while training delivery and problem-solving exhibited variable effects depending on the modelling approach. Unlike regression, decision trees revealed non-linear interaction thresholds. For example, students with SVR < 3.5 and TS < 4.0 had 40% lower employability scores, suggesting targeted mentoring could yield disproportionate improvements. Clustering revealed three distinct student profiles, which could support data-driven interventions. This hybrid framework demonstrates the potential for integrating machine learning into institutional analytics for proactive support of employability.
Application of IoT-based Intelligent Control Devices Empowered with Fuzzy Inference System in the Garment Industry Rizki, Agung Mustika; Ashari, Faisal; Yuliastuti, Gusti Eka; Haromainy, Muhammad Muharrom Al; Aditiawan, Firza Prima; Amnur, Hidra
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.9.5.3344

Abstract

The garment industry in Indonesia has experienced significant development in recent years. A critical aspect of this development is the increasing role of Micro, Small, and Medium Enterprises (MSMEs). Swari Garment Industries (SGI) is an example of an MSME that focuses on the garment sector. In practice, various problems and negligence can affect the course of the production process. One potential issue is using the machine inappropriately or excessively, which can lead to a short electrical circuit. Short electrical circuits are one of the problems that must be faced because they can cause various severe impacts, including equipment damage and even fire. Based on this risk analysis, a possible solution to be applied to SGI, one of the MSMEs in the garment sector, is the implementation of an intelligent control device. The implementation of intelligent control tools based on the Internet of Things (IoT) can enhance the efficiency of the production process and mitigate significant risks to workers and the environment. The Fuzzy Inference System, in which the equity, temperature, and humidity are the input values of the Intelligent Control Device. A hardware device for temperature and humidity control, accessible through an Android phone application, was implemented in SGI. Experiments have verified that we can achieve excellent results. The average percentage of temperature measurement error was 0.2% and for humidity, 0.26%. The average percentage of measurement error from the comparison between the system and MATLAB is 0.49%.
Push-Pull-Mooring Theory and The Moderating Effect of Inertia on Switching Intention to Mobile Learning Seta, Henki Bayu; Theresiawati, Theresiawati; Niqotaini, Zatin; Trahira, Juwita Istiqomah; Assegaf, Najwaa Nahda
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.9.5.2867

Abstract

Inertia is a hindering factor in transition, which is essential to investigate as a push-pull-mooring factor influencing the switching intention to use mobile learning. Mobile learning research in Indonesia is still new, and only a few studies analyze the moderating effect of inertia on switching intention to mobile learning.  The research aims to examine students' intentions to adjust to mobile learning at universities in Indonesia and analyze the moderating effect of inertia in weakening the correlation between pull and push factors and shifting intention. This study employed a quantitative method involving a sample of 163 students. To explain inertia, this study adopted habits, switching costs, student innovation, network externalities, and technological self-efficacy as independent variables leading to inertia. This research reveals that inertia moderates learning convenience, learning autonomy, and task technology fit. Meanwhile, inertia is influenced by habit, switching costs, student innovativeness, and technological self-efficacy. This research also confirms that service quality, perceived enjoyment, and task technology fit significantly impact switching intention to employ the use of mobile learning. This research reveals that inertia moderates learning convenience, learning autonomy, and task technology fit. Meanwhile, inertia is influenced by habit, switching costs, student innovativeness, and technological self-efficacy. This research also confirms that service quality, perceived enjoyment, and task technology fit have a significant effect on switching intention to use mobile learning. University management and practitioners must increase students’ awareness of the benefits of mobile learning in higher education institutions.  Further research should test additional variables such as gender and student satisfaction with mobile learning.
Machine Learning Model to Predict Manganese Micronutrient Content in Oil Palm Plantation Soil Using Sentinel 1A and Sentinel 2A Image Integration Suhendi, -; Boro Seminar, Kudang; Sudradjat, -; Liyantono, -; Munir, Sirojul; Az Zahra, Fatimah
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.9.5.3306

Abstract

This study aims to predict manganese micronutrients in oil palm plantation soil using machine learning. Materials and technological tools use remote sensing with the integration of Sentinel 1A and Sentinel 2A satellites for monitoring micronutrients in peat soil in oil palm plantations. Integrating Sentinel 1A with Sentinel 2A will complement the shortcomings of Sentinel 2A, which is not free from cloud cover. Sentinel 1A has the advantage of being free from cloud cover. Meanwhile, Sentinel 2A has a high spectral resolution with 12 to 13 bands, which Sentinel 1A does not have, and only has dual polarization (VV-VH) and local incident angle (LIA). This study uses a machine learning method to obtain a model with a random forest regression algorithm and 103 soil samples in Central Kalimantan and Riau locations. The results of the model performance evaluation using integration showed MAPE and correctness of 25% and 75%, respectively. Suppose using Sentinel 1A, MAPE, and accuracy are 59.63% and 40.23%. Using Sentinel 2A, the MAPE and accuracy obtained are 48.40% and 51.59%. These results suggest that the integration of Sentinel 1A and Sentinel 2A plays a significant role, given their good predictive power. The implications of this study are the status of nutrient distribution maps, which can help determine the status of manganese micronutrients in soil in oil palm plantations for fertilizer application plans according to the needs of each oil palm plant.

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