Reviewerly/exHarmony huggingface dataset:
Digital Avatar Sub-Metaverse Modeling Using Terrestrial Photogrammetry Techniques

In this study, we explore the metaverse, a virtual realm blending the digital and real worlds. The metaverse promises immersive experiences, and we focus on creating lifelike avatars and environments within a scientific hub called the Science and Technology Region (KST). We use a method called terrestrial photogrammetry to make 3D avatars, focusing on researchers at KST. The results show that Agisoft Metashape makes highly precise 3D models with symmetrical details, while the KIRI Engine is faster in processing. These findings suggest that we can make better avatars and environments in the metaverse, but the choice between precision and speed depends on the specific application.
Utilizing Knowledge Bases in Filtering IoT Data to Predict Algal Blooms

The proliferation of algal blooms in aquatic habitats has emerged as a matter of worldwide significance, owing to their consequential ecological and economic ramifications. Conventional forecast techniques frequently encounter difficulties in comprehensively capturing the intricate interaction of environmental variables that contribute to bloom occurrences. This research study presents a novel methodology incorporating knowledge bases into the data preprocessing stage to predict algal blooms. This approach aims to improve the accuracy of predictions by integrating expert knowledge, historical data, and real-time observations. The methodology encompasses obtaining and organizing domain-specific information, merging it with environmental data, and constructing prediction models. The experimental findings provide evidence of the strategy’s efficacy in enhancing predicting accuracy. This research makes a valuable contribution to the proactive algal bloom control field by integrating data-driven methodologies with ecological experience, resulting in the development of a comprehensive framework.
Study of Graph Database Implementation on Dynamic Table of BPS Website

BPS is a government agency that provides statistical data. The process of disseminating BPS statistical data to the public is carried out through the BPS website in the dynamic table feature. This feature requires high database performance to handle large data volumes. Because of this, this research implements one type of non-relational database, Graph database, then compares its execution time with the current system (relational database). The implementation of graph database using Neo4j resulted in 3,365,778 nodes and 16,810,408 relations. The performance measurement results show that Neo4j can be 1.19 to 30.26 times faster than the current system under the condition of large data and large number of users. However, PostgreSQL is also 2.74 to 7.87 times faster under the condition of small data and small number of users. This shows that Neo4j is superior to PostgreSQL on read operations when the amount of data gets larger, the number of users gets larger, and the query complexity gets higher.
The Impact of Downsampling Methods on Face Recognition in Electronic Identity Card

Electronic identity cards have limited storage capacity, necessitating the downsizing of images to be stored. Downsampling is a method used to reduce the size of images, but it can result in the loss of essential facial features, impacting face recognition performance. Therefore, the selection of an appropriate downsampling method becomes crucial. In this study, we evaluated and compared the face recognition performance of five different downsampling methods, such as Bicubic Interpolation, Bilinear Interpolation, Lanczos Interpolation, Nearest Neighbour, and Inter-Area using the Asian Face Image Database PF01. We measured the face recognition performance using False Rejection Rate (FRR) at various levels of False Acceptance Rate (FAR). Nearest Neighbour had consistently demonstrated the lowest performance across various scenarios, making it unsuitable for downsampling. In contrast, Bicubic Interpolation has consistently outperformed other methods and is favored for downsampling. In cases where downsizing to a much lower size is required, Lanczos Interpolation offers a preferable option. Our experimental results revealed that the choice of the downsampling method significantly influenced face recognition performance up to 8.41% at specific FAR values. This study highlights the critical importance of selecting the right downsampling method to preserve essential facial features, ensuring optimal face recognition performance for electronic identity cards.
Handling Data Imbalance Using Text Augmentation For Classifying Public Complaints

As a public service provider, the local government is obliged to provide services for every citizen, including complaint services as a public response to the services provided. Through the E-Lapor Jogja Istimewa service of Diskominfo DIY, the public can submit complaints or problems that occur online. The classification of complaints is an integral part of accelerating the government’s response to resolving problems. Automatic classification is expected to reduce errors in the selection of complaint categories. One of the problems in classification is data imbalance which causes low classification performance. Therefore, oversampling methods are used to balance the data and improve classification performance. This research uses text augmentation-based oversampling with the Synonym Replacement technique. With the SVM method, the results show that Synonym Replacement can improve classification performance with an accuracy value of 0.84 and an f1-score of 0. S2. This result is better than the SMOTE method which produces an accuracy value of 0.77 and an f1-score of 0.70.
The Impact of Eyebrows Region on Deep Face Recognition

We investigate the importance of the information contained in the area of the eyebrows for deep face recognition. An isotropic 2D Gaussian low-pass filter of varying bandwidth is used to remove discriminative information in the probe and reference images gradually. We measure the recognition performance of two deep learning-based face recognition systems as a function of the bandwidth of the low-pass filter applied to the eyebrows. Methods are tested on the frontal face and high-resolution images from the PUT database. The results showed that even though the eyebrows are important for recognition, deep learning still works well on facial images with removed eyebrows. Furthermore, we found that the discriminative information provided by eyebrows comes from their shapes, not their textures.
Procedural 2D Dungeon Generation Using Binary Space Partition Algorithm And L-Systems

In game development, procedural method is a common method to reduce the high cost of content generation by artist. The idea of procedural content generation is to automate the generation content by developing a set of algoritmhs and rules based on the property of the content. Dungeon map generation plays a crucial role in creating immersive and challenging experiences in top-down roleplaying games (RPGs). This paper presents an approach to enhance dungeon map generation by combining the Binary Space Partition (BSP) algorithm and L-System that able to generate game-ready dungeon map. The proposed method uses BSP algorithm to perform an efficient space partition an L-System to generate diverse and visually appealing corridor between rooms. To validate the proposed system, we build a simple RPG game that employs our generator to generate a random dungeon network on each game session. The results shows that our method able to generate playable dungeon with a good level of playability.
The Effectiveness of Integrated Dynamic Archival Information System (SRIKANDI) Using DeLone and McLean Model on Local Government Organisation of Surakarta City

The Local Government Organisation (LGO) of Surakarta City has employed SRIKANDI application to maximize archive management. To enhance public services and streamline administration processes, system evaluation is necessary. According to Peraturan Arsip Nasional Republik Indonesia Nomor 4 Tahun 2021 about the Implementation Guideline of SRIKANDI, technology dimension as one of indicators in SRIKANDI implementation is assessed from the aspect of information and system qualities. This is supported by DeLone and McLean who measure information system success through system quality, information quality, service quality, use, intention, user satisfaction, and net benefits. Based on DeLone and McLean, the study proposes to discover the effectiveness level of SRIKANDI on the LGO of Surakarta City using data collected from 134 SRIKANDI users who are archival managers on the LGO. The results of the study indicated that SRIKANDI was considered to be Sufficiently Effective based on the System Quality and Information Quality dimensions, and Very Effective according to the Service Quality, Use, User Satisfaction, and Net Benefits dimensions. The results align with the previous study, indicating that a good system is judged not only by its complexity but also by user acceptance and satisfaction with the provided information system
Design of Question Generator System (QPGS) Using Fisher-Yates Shufling Algorithm

The process of generating question papers for educational assessments is a crucial task in the field of academia. The traditional manual approach to question paper creation is time-consuming and prone to human error. In response to this challenge, the Question Paper Generation System (QPGS) has emerged as a powerful solution that leverages technology to automate and streamline the question paper generation process. The Question Paper Generation System has a profound impact on the education sector by enhancing the quality and efficiency of assessments. It empowers educators to create assessments that align with educational goals and standards, ultimately benefiting both teachers and students. Moreover, it contributes to the overall modernization of educational practices by integrating technology seamlessly into the assessment process. This paper provides an overview of the Question Paper Generation System, highlighting its key features, benefits, and impact on educational institutions. QPGS combines advanced algorithms, database management, and user-friendly interfaces to efficiently produce high-quality question papers tailored to specific subjects, courses, and assessment levels. For efficient randomization and shuffling of the questions in the question bank, the Fisher-Yates shuffle algorithm, also known as the Knuth shuffle or the Durstenfeld shuffle, was used. A hybrid of the Object-Oriented Analysis and Design (OOAD) together with the Structured System Analysis Methodology (SSADM) with Unified Modeling Language (UML) was adopted for the design of the system. The system was implemented using HTML, CSS and JavaScript as the front end, while the back end which deals with the system’s logic was implemented using PHP and MySQL. The results obtained were tested using several test strategies.
CFPChain: An Optimization of Research Funding Selection System Employing A Consortium Blockchain Based Approach

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies.
Comparison of the Convolutional Neural Network Architectures for Traffic Object Classification

The number of vehicles on the road increases from year to year. This causes road infrastructure to be frequently upgraded to meet the needs of road users. To find out the number of a kind of vehicle, we have to classify moving objects on the road. Convolutional Neural Network is one of the deep learning techniques for image processing and pattern recognition. It can be used to classify moving objects on the roads. In this paper, we implement and compare three Convolutional Neural Network architectures to classify moving objects from traffic CCTV videos. The three architectures are SSD MobileNet, RetinaNet, and EfficientNet. The objects that we try to classify are cars, motorcycles, trucks, and people. Our simulation results show that these three architectures can perform good detection and classification with an average accuracy above 83.4%. However, there is still room for improvement in small-area detection in the videos.
Top Layer Selection in Pretrained Models for Sentiment Analysis on Biodiversity Tweets in Bahasa Indonesia

This study investigates sentiment analysis on Bahasa Indonesia biodiversity-related tweets using the pre-trained language model IndoBERTweet. The research explores the influence of including different numbers of top layers from the IndoBERTweet model during fine-tuning. To ensure robustness, we conducted 50 experiments for each configuration, ranging from excluding all layers to including five top layers, utilizing various random seeds. The F1 score is employed as the performance metric. The results reveal that incorporating 3 top layers of the pre-trained IndoBERTweet model produces the most optimal performance for sentiment analysis with an average F1 score of 0.7782 from 50 experiments. Furthermore, a violin plot visual inspection and a Kolmogorov-Smirnov test confirm statistically significant differences among the various layer configurations, supporting the observed performance disparities. This work offers valuable insights into leveraging pre-trained models for sentiment analysis in Bahasa Indonesia, particularly for biodiversity tweets. Additionally, it underscores the importance of top-layer selection during fine-tuning when using a pre-trained model.
Improvement of DART Algorithm Teletsunami Detection Response through Lengthening the Prediction Time

The DART (Deep-ocean Assessment and Reporting of Tsunamis) tsunami detection algorithm, developed by the National Oceanic and Atmospheric Administration (NOAA) in 1997, is an automatic and real-time tsunami detection method used in various Tsunami Early Warning Systems, including the Indonesia Tsunami Early Warning System (InaTEWS). The DART algorithm’s performance was evaluated using data from several tsunami cases in the Pacific Ocean from 2006 to 2022, and it was discovered that teletsunamis were not always correctly detected by the DART algorithm. There have been situations where the DART algorithm successfully detected tsunamis but required an extended period (more than 2 hours from the arrival of the tsunami), and cases where the DART algorithm was unable to detect the arrival of a tsunami signal properly. The proposed solution in this paper is to increase the prediction time of the DART algorithm up to 7.75 minutes, which has been empirically proven to reduce the detection time of teletsunamis by at least 10 minutes and up to 2.64 hours and can identify previously undetected teletsunamis.
Goal-Oriented Functional Suitability Measurement on Loan Origination System Based on ISO/IEC 25010

Nowadays, all industries need to be fast-paced to provide customer needs, including the banking industry. To attract customers, banks must be able to meet all customer needs easily and quickly. The Loan origination System is a software system used by financial institutions to facilitate the process of loan application, loan approval and settlement. The biggest advantages using this application is it allows financing applications to be made within one working day called “One Day Service” as long as all financing requirements can be completed by the prospective customer. Because of its high usage, it’s important to keep the application meets the requirement and functionality. To achieve that, this research will measure the functional suitability aspects based on ISO/IEC 25010 combining with Goal Question Metric Approach. The measurement using Goal Question Metric shows lower result but get more objective because it depends on the institution goals and done by the users. And from this measurement, it can be used as a future evaluation and improvement for Loan origination System development.
Web-based Software Development of Movie Comparison using Laravel Framework

Indonesia has provided substantial support for the growth of its film industry through the enactment of the 2009 Film Law. Our proposed application is a valuable tool for movie enthusiasts, particularly cinephiles, as it offers various functionalities that enable users to compare and assess available movies, as well as add movies they are interested in to their watchlist. The application serves as a platform for users to access information about movies, including synopses, actors, and ratings from various sources. The compare movies feature provides users with the ability to determine which movies align with their tastes and generates movie recommendations that correspond to their preferences. Additionally, the watchlist feature enables users to save movies they intend to watch and monitor their progress. Through this application, users can effortlessly find, compare, and save movies that suit their interests and preferences, thereby making informed decisions about which movies to watch.
DESPP-DETR: A Dense Connection Efficient Spatial Pooling DEtection TRansformer for Vehicle Detection

Real-time vehicle detection is a challenging and vital task in intelligent transportation systems. The key requirements for a vehicle detection model are speed and accuracy. However, existing real-time vehicle detection models often sacrifice one of these qualities in favor of the other. This trade-off makes them unfit for real-time deployment, where both speed and accuracy are equally important. Additionally, occlusion, which refers to the obstruction or partial covering of vehicles, further complicates detection and affects the system’s accuracy. In this study, we propose DESPP-DETR, a one-stage detection network for real-time vehicle detection. It is based on bipartite matching and a transformer encoder-decoder architecture, with the addition of a dense connection block and enhanced spatial pyramid pooling. The presence of dense connection block strengthens feature extraction. The enhanced spatial pyramid pooling eliminates the fixed-size constraint and increases the network’s learning capacity. When compared to existing models, DESPP-DETR achieves greater accuracy in real-time vehicle detection. On the MS COCO 2017 dataset, the proposed model achieves an improved mean average precision (mAP) of 75.53%, making it a promising solution for intelligent transportation systems.
Enhancing Sentiment Analysis Models through Multi-Technique Data Augmentation: A Study with IndoBERT

In deep learning-based models, such as those used for sentiment analysis, one critical challenge is ensuring robust and generalized performance. While data augmentation has been identified as a key strategy for enriching datasets, its effectiveness in terms of improving model generalization, reducing overfitting, handling data imbalances, capturing contextual variations, and decreasing sensitivity to label noise has yet to be comprehensively studied. This study conducted several experiments involving three distinct data augmentation techniques. The primary goal is to prepare training data for sentiment analysis models using the fine-tuning method with the IndoBERT model. The techniques include Sequence Generative Adversarial Network (SeqGAN), Easy Data Augmentation (EDA), and An Easier Data Augmentation (AEDA). We have conducted various experiments by combining augmentation techniques. The highest macro-average F1-score was obtained from the combination of augmentation experiments using the EDA, AEDA, and SeqGAN methods, reaching 93.61%. This result represented an improvement compared to the baseline model without data augmentation, which achieved an F1-score of 92.45%. These figures show that our approach gives a promising result and can be implemented using another dataset.
Enhancing Machine Learning Models through Hyperparameter Optimization with Particle Swarm Optimization

Hyperparameter tuning plays a crucial role in optimizing the performance of machine learning algorithms. This study explores the effectiveness of Particle Swarm optimization (PSO) in fine-tuning the hyperparameters of three popular machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Support Vector Machine. The studies use two publicly available datasets: the Heart Failure Clinical Records Dataset and the Iris Dataset. For additional comparative analysis, we also compare with the common optimization technique using Random Search. The result reveals that PSO and Random Search have varying impacts on the performance of different algorithms and datasets. The findings provide valuable insights into the behavior of different optimization techniques and their effect on model performance, paving the way for better and more reliable machine learning models in various domains. Through effective hyperparameter optimization, machine learning models can achieve enhanced accuracy.
Traffic Incident Detection in Jakarta on Twitter Texts Using a Multi-Label Classification Approach

Congestion is a phenomenon or traffic incident that often occurs in big cities. These incidents certainly can be detrimental to existing road users. Therefore, it is necessary to detect traffic incidents. Twitter is one of the media that can be used to find information about an incident in a real-time. In this study, a multi-label classification was carried out using data sourced from the Polda Metro Jaya TMC Twitter account to identify various traffic situations, such as smooth traffic, heavy/jammed traffic, weather conditions, and traffic accidents. This study uses machine learning with Support Vector Machine, Complement Naive Bayes and Logistic Regression, deep learning with Long-short Term Memory, and transfer learning from a pre-trained model IndoBERT. Based on the evaluation results of classification models, IndoBERT has the highest evaluation score with 99.10% f1-score and 99.26% accuracy.
Unlocking the Power of Linked Data for Metadata Enrichment

Effective metadata management is crucial in the era of growing digital repositories and abundant information. Traditional metadata may not provide sufficient depth and richness for comprehensive insights in many repositories. Metadata enrichment techniques are increasingly used to address this issue by adding valuable information from other resources. Two metadata enrichment methods are examined: using Wikidata as a central hub and directly querying relevant repositories. We present a case study on the Indonesia Nucleotide Archive (InNA), a DNA/RNA data repository, to evaluate the effectiveness of these methods. Our findings showed that both enrichment methods have advantages and considerations. We discovered that 18 out of the total 27 species exhibited potential for enrichment, although with varying levels of comprehensiveness, by utilizing Wikidata as a central resource. On the other hand, directly querying pertinent repositories resulted in a significantly greater quantity of valuable information for each of the 27 species. The enrichment result suggests that the process of enhancing metadata has a substantial impact on enhancing the contextualization of resources within the InNA. This includes various attributes such as taxonomy, morphology, culture, ecological data, molecular biology, relevant links, etc.