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  • Enhancing The Attractiveness of Coloring Books Using Live Texturing Augmented Reality

    Authors: Hanhan Maulana; Jajang Saeful Anshor; Hideaki Kanai

    DOI: 10.1109/INCITEST59455.2023.10396956

    Abstract

    This study aims to build live texturing augmented reality to enhance the attractiveness of coloring books. This research has four main stages, namely data gathering, object preparations, software development and evaluations. This study uses UNITY 3D in building a Live Texturing Augmented Reality System. The AR method used is the Marker method. Users can color the marker. The 3D object will follow the color given to the marker in real time. This system is expected to increase children’s attraction to coloring. It is hoped that children’s understanding of color can also be improved. Furthermore, this system is expected to help children in deciding on color selection so that children’s creativity is getting better. © 2023 IEEE.

    Author keywords

    Augmented Reality; Coloring books; interactive system; Live Texturing; Multimedia

    This article can be accessed at: https://www.scopus.com/pages/publications/85185195328

  • Support Vector Machine for Satellite Images Classification 

    Using Radial Basis Function Kernel Method

    Authors: Nur Suhaili Mansor ; Hapini Awang ; Sarkin Tudu Shehu Malami ; Amirulikhsan Zolkafli ; Mohammed Ahmed Taiye ; Hanhan Maulana 

    DOI: 10.1007/978-981-99-9589-9_23

    Abstract

    Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

    Author keywords

    Geospatial; Image Classification; Linear Kernel; Radial basis function (RBF) Kernel; Support Vector Machine

    Indexed keywords

    Engineering controlled terms

    Classification (of information); Complex networks; Data handling; Forestry; Large datasets; Learning algorithms; Nearest neighbor search; Radial basis function networks; Remote sensing; Satellite imagery; Statistical tests; Support vector machines

    Engineering uncontrolled terms

    Geo-spatial; Geospatial data processing; Images classification; Kernel-methods; Linear kernel; Performance; Polynomial kernels; Radial basis function kernels; Satellite image classification; Support vectors machine

    Engineering main heading

    Image classification

    This article can be accessed at https://www.scopus.com/pages/publications/85185723250

  • IoT (Internet of Things) as a Tool to Help Regulate Water Altitude on Dams

    Authors: Sri Supatmi, Dita Ardisura Pamungkas, Desta Rifaldi Nugraha, A Febrian, Audi Citra Fadilah

    DOI: 10.1109/INCITEST64888.2024.11121455

    Abstract

    In each dam in the Citarum River, workers from the management officers must go to each dam to arrange the doors of irrigation and record for the report if the officers who visit each dam and make irrigation reports can be minimized to be more efficient. Therefore, this research aims to help regulate water altitude on dams through a dam monitoring system employing the Internet of Things (IoT). IoT is a structure where objects, people who are provided with an exclusive identity, and the ability to move data over a network without requiring two-way between humans to humans is the source of human purpose or interaction to a computer. The way it works from IoT is that objects must have an Internet Protocol (IP) address. Furthermore, Internet Protocol (IP) addresses in these objects are connected to the Internet network. The idea is expected to be an alternative solution for dam officers in managing irrigation in the dam to raise the officers’ performance in working because of the many times created from this monitoring system. Data collection was conducted through interviews in the hall of Management of River Basin Water Resources (PSDA WS) in Citarum, West Java Province. This research result is the prototype of irrigation monitoring tools to facilitate the modeling of the devices. © 2024 IEEE.

    Author keywords

    dam monitoring system; IoT; regulate water altitude

    This article can be accessed at https://www.scopus.com/pages/publications/105015867292

  • Comparison of SAW and MABAC Methods in Determining Strategic Tourism Destinations with Entropy Weighting Integration

    Authors: Muhlas Adiputra, Yeffry Handoko Putra

    DOI: 10.1109/INCITEST64888.2024.11121470

    Abstract

    Bima Regency, with its natural and cultural diversity, has great potential as a strategic tourism destination that requires proper management. However, the main challenge faced is determining the most potential destinations appropriately and supported by accurate data. In an effort to address the problem, this research utilizes the Entropy method for criteria weighting and applies two multi-criteria decisionmaking methods, Simple Additive Weighting (SAW) and MultiAttributive Border Approximation area Comparison (MABAC), to evaluate their effectiveness in selecting tourist destinations in Bima Regency. The results show that both methods consistently identify Lariti Beach as a superior destination. From the comparison of the two methods, the SAW method provides more stable and consistent results, is suitable for less complex conditions, and requires long-term reliability. Meanwhile, the MABAC method shows higher sensitivity to data changes, with more significant fluctuations in results. Thus, the SAW method is considered more appropriate for use in the selection of strategic tourist destinations in Bima Regency, considering that this method provides consistent results as well as efficiency and ease of application. © 2024 IEEE.

    Author keywords

    determination of strategic tourism destinations; DSS; entropy; MABAC; MCDM; SAW

    This article can be accessed at https://www.scopus.com/pages/publications/105015860453

  • Technology Acceptance Model Analysis on The Implementation 

    of Accounting Information Systems

    Authors: Harpa Sugiharti; Lulu Sri Hamdiati; Ajang Mulyadi; Apriani Puti Purfini; Nurul Amalia Ramdan

    DOI: 10.1109/INCITEST64888.2024.11121488

    Abstract

    This research aims to find out the level of reception users through the Technology Acceptance Model (TAM) analysis of the Accounting Information System implementation for MSMEs using the Paper.id application. This research is quantitative research with methods of descriptive verification. The research sample consisted of 155 MSME users of the Paper.id application that implements the Accounting Information System. Calculation method samples using the Inverse Square Root technique. Validity test instrument using convergent validity and reliability tests using composite reliability. The primary data in this research is the respondents’ answers collected by distributing questionnaires. The data analysis technique used is Structural Equation Modeling Analysis Based on Partial Least Square (PLS-SEM) with the help of SmartPLS 4.1 software. They are testing the hypothesis using the T-Test with the help of Bootstrapping. Test results show that perception usability and perception convenience influence attitude to usage positively, and perception usability and perception convenience influence positive use in an actual way. Furthermore, the attitude to use influential positives should be used in an actual way. © 2024 IEEE.

    Author keywords

    attitude to usage; paper.id application; perceived uses; perception ease; use by actual

    Indexed keywords

    Engineering controlled terms

    Inverse problems

    Engineering uncontrolled terms

    Accounting Information Systems; Attitude to usage; Id application; Modeling analyzes; Paper.; Perceived use; Perception ease; Technology acceptance model; Use by actual; Validity tests

    Engineering main heading

    Information systems; Information use

    This article can be accessed at https://www.scopus.com/pages/publications/105015850795

  • Comparison of Bidirectional-LSTM and GRU Models for Sentiment Analysis in Bahasa Indonesia

    Authors: Muhammad Rajab Fachrizal, Annisa Paramitha Fadillah, Lusi Melian

    DOI: 10.1109/INCITEST64888.2024.11121460

    Abstract

    Bidirectional-LSTM and GRU are models that can be used to process sequential data, including text data. This study, compares the two deep learning models to classify text for sentiment analysis using the Bahasa. The dataset used is the JKN BPJS Kesehatan mobile application user review data obtained from the Google Play Store site. After text preprocessing, the amount of data to be processed is 93517 with three target labels, positive, negative, and neutral. By using several model parameters such as Number of Units, Activation, Batch Size, Dropout, and other parameters, the test results obtained are that the Bidirectional-LSTM model has a slight accuracy value of 96.70% and higher precision, recall, and F1Score values compared to the GRU model. © 2024 IEEE.

    Author keywords

    Bahasa; Bi-LSTM; deep learning; GRU; sentiment analysis; text classification

    This article can be accessed at https://www.scopus.com/pages/publications/105015853213

  • Communication-Based Train Control Simulator to Optimize Train Headway

    Authors: Ferry Stephanus Suwita; Rin Rin Nurmalasari; Rangga Julfian Hakim

    DOI: 10.1109/INCITEST59455.2023.10395923

    Abstract

    The development of public transportation, especially for large urban areas, is mandatory, where the need for time efficiency and integrated mass transportation to provide optimal services to customers. Trains in Indonesia generally use traditional signaling systems while currently, some countries have developed the use of modern communication-based train control (CBTC) Internet of Things (IoT) technology. The use of CBTC can be a solution to optimize train headway in Indonesia with the concept of communication carried out between trains and control stations to determine the position of the train so that the train can determine the optimal speed to reach the destination and create comfort as well as accidental safety. This study analyzes the use of the concept of communication-based train control to optimize the railroad track. The research was conducted by simulating using a miniature model train scale HO or 1:87. This research will be elaborated on the literature review, design and implementation, testing and analysis, conclusions from the train system simulator specifically the results of the use of MQTT in the railroad scheduling subsystem. The results of the study indicate that in the simulation the train headway had an average distance of 12.83cm. © 2023 IEEE.

    Author keywords

    CBTC; HEADWAY; MQTT; TRAIN

    This article can be accessed at: https://www.scopus.com/pages/publications/85185194586

  • Digital Encyclopedia: The Product of the Integration between Islam and Science

    Authors: Buchori Muslim; Nahadi; Sjaeful Anwar; Heli Siti Halimatul Munawaroh; Neng Sri Nuraeni; Tatan Tawami

    DOI: 10.1109/INCITEST64888.2024.11121463

    Abstract

    Digital encyclopedia is produced from the integration of Islam with science. Integration between religious knowledge and general knowledge or vice versa seeks to provide information that all knowledge comes from God, while the content and development of both are a form of worship to the creator. The presence of a dichotomy between faith education (religious sciences) and general education (science) makes it interesting to study in more depth. This causes the integration between scientific disciplines to be lost. In fact, today’s educational products tend to be oriented towards intellectual development, but are not balanced with spiritual, emotional, social and moral aspects. The aim of this research is to produce a digital encyclopedia based on the integration of Islam and science. The research method used in this research is descriptive with the Design and Development model popularized by Allesi and Trollip. The instruments used in this research are the alpha test instrument and the beta test instrument. Data analysis is carried out by simplifying the data into a form that is easier to read and interpret. The research results show that religion and science need each other and do not conflict. Because, any knowledge does not stand alone (self-sufficient). The presence of the digital encyclopedia brings the message that knowledge can be enjoyed by anyone, is no longer limited by time and space, and is more interesting. © 2024 IEEE.

    Author keywords

    dichotomy; digital encyclopedia; islam and science; islamic integration; learning media

    Indexed keywords

    Engineering uncontrolled terms

    Dichotomy; Digital Encyclopedias; General education; General knowledge; Islam and (religious science) and general education; Islamic integration; Learning media; Science education; Scientific discipline; Test instruments

    Engineering main heading

    Integration

    This article can be accessed at https://www.scopus.com/pages/publications/105015889788

  • Sigma Level Quality Analysis with Simulation Approach at PT. Mitra Rajawali Banjaran

    Authors: Diana Andriani, Iyan Andriana, I Made Aryantha Anthara, Muhammad Rifky Naufal, Ira Rustiendah

    DOI: 10.1109/INCITEST64888.2024.11121464

    Abstract

    PT. Rajawali Mitra Banjaran is a company engaged in the field of medical devices. One of the products of this company is the ADS (Auto Disable Syringe) syringe, but production in 2022 has a fairly high defect rate of 13% of total production and shows a sigma level of 3.56. It is known that the factors causing defects are man, machine, and method. To increase the company’s sigma level, quality control and the creation of a conceptual model from a simulation using ProModel are needed, then a comparison is made between actual production conditions and simulation conditions, it is hoped that this can reduce the number of defects and increase the company’s sigma level. Based on the results of the study using the ProModel application, the output of the proposed product has a difference of 18%, defective products decreased to 6.5% from the initial model, and the sigma value increased to 4.02. © 2024 IEEE.

    Author keywords

    model simulation; Production process; quality control; sigma level

    This article can be accessed at https://www.scopus.com/pages/publications/105015892369

  • Optimizing Forest Fire Detection Using PSO, Neural Networks, 

    and k-Fold Cross-Validation

    Authors: Nina Kurnia Hikmawati; Yudi Ramdhani; Doni Purnama Alamsyah

    DOI: 10.1109/ICIC64337.2024.10956756

    Abstract

    Preventing Forest fires by identifying events based on specific criteria using the Neural Network (NN) algorithm. To ensure result stability, test and training data are divided using k-fold cross-validation with variations k-5, k-10, and k-20. The NN algorithm performed significantly better after being optimized with Particle Swarm Optimization (PSO), with the average accuracy value going from 97% to 98.78% and the AUC value increasing from 0.997 to 0.999. A weight study of PSO reveals variability in values for each attribute, with Day and Month having no significant effect on classification. The results of the Paired Two-sample t-test for Means show that using PSO improves accuracy significantly. The merging of PSO with Neural Networks has a considerable positive influence on forest fire prediction and is suggested for applications requiring high accuracy. © 2024 IEEE.

    Author keywords

    Forest Fires; Machine Learning; Neural Networks; Particle Swarm Optimization

    Indexed keywords

    Engineering controlled terms

    Stability criteria

    Engineering uncontrolled terms

    Forest fire detection; Forest fires; K fold cross validations; Machine-learning; Neural networks algorithms; Neural-networks; Particle swarm; Particle swarm optimization; Particle swarm optimization-neural networks; Swarm optimization

    Engineering main heading

    Particle swarm optimization (PSO)

    This article can be accessed at https://www.scopus.com/pages/publications/105004579202