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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 1,172 Documents
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Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms Kusnawi, Kusnawi; Ipmawati, Joang; Asadulloh, Bima Pramudya; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Rahardi, Majid
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2453

Abstract

This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.
IoT and Deep Learning Enabled Smart Solutions for Assisting Menstrual Health Management for Rural Women in India: A Review Kesavan, Revathi; Palanichamy, Naveen; Thirumurugan, Tamilselvi
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2399

Abstract

A global medical issue, primarily raised in underdeveloped nations, is inappropriate Menstrual Hygiene Management (MHM) among teenage girls. Menstrual hygiene is a global concern because there are over 0.6 billion teenage females (about 8% of the population). The Asian and African continents are home to over 80% of these teenagers. Throughout, 355 million girls and women in India have periods. However, MHM causes discomfort and a lack of respect for millions of women all over the country. In alignment with today's technologies like cloud computing, artificial intelligence (AI), and Internet of Things (IoT), the MHM can be handled effectively. A quantitative survey was carried out among 184 random volunteers aged 18-22 to reveal the current status of MHM in India. The result of the survey confirmed that 72.8% of girls encountered stress during their period, 45% of them were unaware of hygiene products to be used while in the menstruation cycle, 65.2% of them used sanitary pads, and 57.6% of them received disrespectful treatments. This work aims to empower women with the MHM by facilitating knowledge on the menstrual cycle and guiding them about safe-to-use products and disposal strategies in home, work, or community places with the help of technological advancements. Further, introduce a simple friendhood discussion forum through an intelligent chatbot like "Sirona, " a chatbot built over Whatsapp that facilitates a complete ecosystem for MHM.
Students' Behavior in the Learning Process Using Zoom Meeting Media: Problems and Solutions Zaim, Muhammad; Zakiyah, Muflihatuz; Zaim, Rifqi Aulia
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2226

Abstract

Recent digital technologies have increased the flexibility of learning. Online learning is now seen as a good alternative, not a force anymore. Among all online learning media that can aid, Zoom is an interactive online learning media mainly used by lecturers to carry out synchronous learning processes when offline lectures cannot be carried out. Using Zoom, lecturers and students can interact synchronously, like face-to-face learning in class. Many studies have been conducted to see the impacts of online learning on students’ learning behaviors using various media, yet studies on how students behave while learning using Zoom have not yet been explored in more detail. This research aims to reveal how students behave in the learning process by using Zoom in English classes through a survey study. Data were collected through a questionnaire delivered online to some 142 English Department students of Universitas Negeri Padang who experienced online learning. They voluntarily took part in this survey. Carrying quantitative analysis, the research showed that most students did not follow the Zoom-mediated learning process as well as they did face-to-face learning, which was carried out offline, for various reasons. Several positive and negative behaviors were found when implementing the learning process using Zoom. Therefore, for the learning process to run well, it is necessary to agree on the ethics of the learning process by using Zoom. The findings of this research can provide a reference for making conventional ethics of online learning using Zoom or other media.
Multi-Objective k-Nearest Neighbor for Breast Cancer Detection Nataliani, Yessica; Arthur, Christian; Wellem, Theophilus; Hartomo, Kristoko Dwi; Wahab, Nur Haliza Abdul
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2669

Abstract

Early detection of cancer is crucial. This study aims to increase the efficiency of breast cancer detection using the modified k-nearest neighbor (k-NN) algorithm. Since k-NN faces challenges with sensitivity to k values and computational complexity, a modification of k-NN was proposed, namely a multi-objective k-NN model. It was developed to incorporate multi-objective optimization and local density to create a more robust and efficient classification algorithm. The model dynamically determines the k value based on the sample density, optimizing accuracy and efficiency. Breast cancer data were collected from the University of Wisconsin Hospitals, Madison. The experimental results showed that the multi-objective k-NN model outperformed traditional k-NN and k-NN with feedback support. The proposed model achieved an accuracy of 93.7%, with precision values of 93% for the negative cancer class and 94% for the positive cancer class. These results indicate that the multi-objective k-NN model provides superior accuracy and precision in breast cancer detection, demonstrating its potential for clinical applications.
Enhancing Vision-Based Vehicle Detection and Counting Systems with the Darknet Algorithm and CNN Model Rangkuti, Abdul Haris; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2550

Abstract

This study focuses on developing an algorithm that accurately calculates the volume of vehicles passing through a busy crossroads in Indonesia using object recognition. The high density of vehicles and their proximity often pose a challenge when distinguishing between vehicle types using a camera. Therefore, the proposed algorithm is designed to assign a unique identity (ID) to each vehicle and other objects, such as pedestrians, ensuring that volume calculations are not repeated. The objective is to provide an equitable comparison of road density and the total number of detected vehicles, enabling the determination of whether the road is crowded. To accomplish this, the algorithm incorporates the Non-Max Suppression function, which displays bounding boxes around objects with confidence values and counts the objects within each box. Even when objects are nearby, the algorithm tracks them effectively, thanks to the support of the Darknet Algorithm. The main capabilities of this algorithm for improving vehicle detection include enhanced accuracy, speed, and generalization ability. Typically, it is used in conjunction with the You Only Look Once (YOLO) object detection framework. Five convolutional neural network models are tested to assess the algorithm's accuracy: YOLOv3, YOLOv4, CrResNext50, DenseNet201-YOLOv4, and YOLOv7-tiny. The training process utilizes the Darknet Algorithm. The best-performing models, YOLOv3 and YOLOv4, achieve exceptional accuracy and F1 scores of up to 99%. They are followed by CrResNext50 and DenseNet201-YOLOv4, which achieve accuracy rates of 92% and 98% and F1 scores of 94% and 98%, respectively. The YOLOv7-tiny model achieves an accuracy rate and F1 score of 86% and 88%, respectively. Overall, the results demonstrate the algorithm's success in accurately detecting and calculating the volume of vehicles and other objects in a busy intersection. This makes it a valuable tool for regional government decision-making.
Knowledge-Based Intelligent System for Diagnosing Three-Wheeled Motorcycle Engine Faults Ary Setyadi, Heribertus; Supriyanta, Supriyanta; Nurohim, Galih Setiawan; Widodo, Pudji; Sutanto, Yusuf
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2487

Abstract

Three-wheeled motor engine damage is one of the most serious problems with all motorcycles. When problems appear, it becomes difficult for users to repair and diagnose faults because knowledge about machine breakdown symptoms is minimal. Most motorcycle repair shops don’t have mechanics who understand tricycle motorbike engines, so they are less accurate in diagnosing damage symptoms, only based on estimates. Three-wheeled motorbikes have several differences in structure and spare parts compared to motorcycles because tricycle motorbikes have an axle like a car. For this problem, an information system is needed with a method that combines an expert's experience, expertise, and knowledge to develop expert system applications based on several cases that have been experienced and are known as case-based reasoning. This research aims to produce a web-based expert system to diagnose and solve tricycle motorbike engine damage problems. The case-based reasoning method with the K-Nearest Neighbor algorithm is used to assist in analyzing engine damage and give solutions to the issues in three-wheeled motorbike engines. Using two methods is appropriate because of the answers found and the similarities calculated by the cosine similarity method, which experts then review to get the proper solution. From testing using 20 samples of diagnostic data, an accuracy percentage of 85% was obtained. The calculation result for precision is 85%, and recall is 85%.
Automatic Feature Extraction of Marble Fleck in Digital Beef Images to Support Decision Preferences Pranata, Feriantano Sundang; Adif, Anjjani Mardhika; Na'am, Jufriadif
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2813

Abstract

Beef is one of the essential food ingredients to meet human nutritional needs. These nutrients are fundamental to the growth and development of the human body. The primary nutrient found in beef is protein. The nutritional value of protein in beef can be observed by the quality of the beef itself. An indicator of the protein level is the amount of marbling or white streaks in the meat. Marbling is characterized by a marble-like pattern in the meat layers. This study aims to process beef images to automatically identify marbling. The data processed is secondary data obtained from Kaggle.com, consisting of 60 images with a resolution of 800 by 800 pixels. This study develops a highly subjective method to produce fast and accurate classification. The processing stages used are pre-processing, segmentation, and extraction. The automatic stage is in the extraction, by developing a filtering algorithm. The results of this study can identify the marbling fleck ratio of each beef image very well, where each beef image has marbling flecks. The area of marbling flecks varies greatly depending on the quality of the meat, with the lowest quality having a ratio of 1.0% and the highest being 71.39%. This ratio level becomes an indicator in determining the quality of the meat, which is the primary preference in making accurate decisions in selecting meat quality. Thus, this study can serve as an indicator in determining the appropriate meat preference choice.
Harmonizing Emotion and Sound: A Novel Framework for Procedural Sound Generation Based on Emotional Dynamics Hariyady, Hariyady; Ag Ibrahim, Ag Asri; Teo, Jason; Md Ajis, Ahmad Fuzi; Ahmad, Azhana; Md Yassin, Fouziah; Salimun, Carolyn; Weng, Ng Giap
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3101

Abstract

The present work proposes a novel framework for emotion-driven procedural sound generation, termed SONEEG. The framework merges emotional recognition with dynamic sound synthesis to enhance user schooling in interactive digital environments. The framework uses physiological and emotional data to generate emotion-adaptive sound, leveraging datasets like DREAMER and EMOPIA. The primary innovation of this framework is the ability to capture emotions dynamically since we can map them onto a circumplex model of valence and arousal for precise classification. The framework adopts a Transformer-based architecture to synthesize associated sound sequences conditioned on the emotional information. In addition, the framework incorporates a procedural audio generation module employing machine learning approaches: granular and wavetable synthesis and physical modeling to generate adaptive and personalized soundscapes. A user study with 64 subjects evaluated the framework through subjective ratings of sound quality and emotional fidelity. Analysis revealed differences among samples in sound quality, with some samples getting consistently high scores and some getting mixed reviews. While the emotion recognition model reached 70.3% overall accuracy, it performed better at distinguishing between high-arousal emotions but struggled to distinguish between emotions of similar arousal. This framework can be utilized in different fields such as healthcare, education, entertainment, and marketing; real-time emotion recognition can be applied to deliver personalized adaptive experiences. This step includes acquiring multimodal emotion recognition in the future and utilizing physiological data to understand people's emotions better.
Grade Classification of Agarwood Sapwood Using Deep Learning Hatta, Heliza Rahmania; Nurdiati, Sri; Hermadi, Irman; Turjaman, Maman
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2257

Abstract

The agarwood tree (Aquilaria sp.) is a tree that produces agarwood, which is a black resin that has a distinctive fragrant smell. In Indonesia, one that is commonly traded is sapwood agarwood. Agarwood sapwood is black or brownish-black wood obtained from the parts of the agarwood-producing tree containing a strong aromatic mastic. Based on the Indonesian National Standard (SNI) 7631:2018, agarwood sapwood has three classes: Super Double, Super A, and Super B. However, many agarwood farmers need to learn to differentiate and classify the agarwood sapwood classes, and traders exploit this to buy cheap. So, deep learning can be used to classify the agarwood sapwood class. One of the uses of deep learning is in image processing. Image processing is used to help humans recognize or classify objects quickly and precisely and can process many data simultaneously. One of the deep learning algorithms used in image processing is the Convolutional Neural Network (CNN). In this study, it is proposed that the deep learning model used is CNN with batch normalization. The dataset used is 72 agarwood sapwood images with a white background, each consisting of 24 Super A, 24 Super B data, and 24 Super Double data. The dataset is divided into 80% training and 20% testing data. The evaluation results of the proposed method at 100 epochs show an accuracy of 87.5%. The research implications will help agarwood tree farmers differentiate and classify agarwood sapwood so that farmers get the right price from buyers.
Validating a Quality Model through Expert Review for Green Information Systems Muhammad, Shireen; Jusoh, Yusmadi Yah; Haizan Nor, Rozi Nor; Jussupbekova, Gulzat Tyrysbekovna; Baidibekova, Aidin Orisbayevna
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3648

Abstract

Software developers use a quality model as a guide to help them determine the quality factors of a software product they are designing. This study aims to validate a quality model for green information systems, or green IS, towards achieving eco-sustainability. Thus, this study aims to identify quality factors for green IS that will contribute to eco-sustainability. This study's methodology included two rounds of expert evaluation with four experts. Two strategies were deployed in each round to discover the quality factors. In the first round, the strategy was to take existing software quality factors and interpret them in the context of eco-sustainability. In the second round, existing eco-sustainability goals were adopted and clustered into categories; in this study’s context, quality factors were aligned with the eco-sustainability goals. An initial model consisting of 35 quality factors was synthesized from a green IS design framework, sustainable software, and social media literature. The experts presented and assessed the model in the first round. Thus, 18 quality factors have been selected for the next second-round review. Five quality factors— accuracy, completeness, accessibility, customization, and collaboration emerged from the second-round review. Each quality factor was aligned to the eco-sustainability sub-goals of eco-efficiency, eco-equity, and eco-effectiveness, which resulted in the development of a proposed model. The experts concluded that the revised model could be employed in a data collection survey since it closely resembles the green IS quality model for eco-sustainability.

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