bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites
Authors: Syahputra Hermawan , Wibowo Aldiva
Published: January 2025

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content.  Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.
Water Quality Monitoring with Regression Based PPM Sensor for Controlling Hydroponic Dissolved Nutrient
Authors: Adiputra Dimas , Kristanto Titus , Albana Abduh Sayid , Samuel Gilbert Wednestwo , Andriyani Syakira , Kurniawan Christian Jose Anto , Ramadaniputra Nursyahjaya , Munna Era Anzha Naelil
Published: January 2025

Hydroponic cultivation requires rigorous monitoring and control of several parameters, such as turbidity, electric conductivity, acidity (pH), dissolved oxygen and nutrient, which usually be measured once a day manually. Therefore, automation in hydroponic cultivation requires those water quality information as the controlled variable. The dissolved nutrient is especially important because it significantly affects the hydroponic plant growth. Acquiring the dissolved nutrient can be done by using a PPM (parts per million) sensor, but most of the time the sensor needs further processing to obtain the desired measurement. This study presents a reading correction of a PPM sensor based on a regression method so the desired measurement can be done. Sample water with different PPM, such 309 PPM, 290 PPM, 762 PPM, 1910 PPM and 2420 PPM are measured first using a standard PPM meter. Then, the sample PPM is measured by using the PPM sensor. The study also investigates the best regression method to map the PPM sensor measurement to the standard PPM meter measurement by comparing several line equations, such as linear, exponential, polynomial and logarithmic. The function coefficient and bias is chosen by using least square methods. After comparing, the result shows that the polynomial function provides the best reading correction with average error of 76 PPM. The error is especially few when measuring the higher PPM (more than 500 PPM), which is suitable with hydroponic cultivation. Therefore, the PPM sensor with the polynomial function shown in this study can be used to measure the dissolve nutrient accurately in the automation of hydroponic activity compare to other line equations. This study is limited to small sample sizes to prove the concept. The generalization can also be considered in the future study.
Implementation of Open Web Application Security Project for Penetration Testing on Educational Institution Websites
Authors: Sulisnawati Nani , Subektiningsih Subektiningsih
Published: January 2025

The development of information technology cannot be separated from the development of website applications, as well as the threat of security attacks that will attack website applications. Educational Institution X uses a website application as an important medium in learning activities. Therefore, penetration testing is needed to find security holes in website applications. In this study, penetration testing will be carried out with the target website for student access at Educational Institution X based on the reason that there is sensitive student data that needs to be secure. The method used in this study is an experimental method with the OWASP TOP 10 2021 standard (Open Web Application Security Project). The penetration test results obtained on the website application at Educational Institution X found 11 vulnerabilities that could be tested. Of the 11 vulnerabilities, there is one vulnerability at the medium risk level, 7 at the low risk level, and 3 at the information risk level. The vulnerabilities found relate to token authentication, policy delivery, cookie attribute, cross-site script inclusion, authorization, clickjacking, and weak transport layer security. Based on the penetration testing activities obtained, it can be concluded that the vulnerability gaps found need to be further repaired by the website application system developer, in this case, the Educational Institution X. Therefore, the final result of this study is in the form of a report document containing a list of vulnerabilities, recommendations for vulnerability repairs, and vulnerability mitigation strategies as solutions for handling security systems on website applications to make them even better.
On IPv6 Slow Adoption; Why We Might Approach it Wrongly?
Authors: Setiawan Mukhammad Andri
Published: January 2025

The slow adoption of IPv6, despite its numerous advantages over IPv4, is a pressing issue in many regions, including Indonesia. This challenge is particularly significant given the increasing demand for Internet of Things (IoT) devices and the need for a sustainable, scalable, and flexible network infrastructure. In response to this issue, our research introduces the Design Thinking-Inspired Technology Adoption (DTITA) model. This innovative approach leverages design thinking principles to facilitate the adoption of new and challenging technologies. DTITA incorporates the five stages of design thinking alongside traditional technology adoption factors, such as perceived usefulness, ease of use, and social influence. The DTITA model aims to create user-centric solutions that address new technologies' unique challenges and barriers. By placing the user at the center of the design process, we were able to develop solutions that are not only technologically advanced but also highly accessible and relevant to users. Through a survey involving individuals from the education industry, Internet Service Providers (ISPs), content providers, government institutions, and the Information and Communication Technology (ICT) industry, we identified key barriers impeding the widespread implementation of IPv6. This study provides valuable insights into the application of design thinking in the context of technology adoption, particularly in the case of IPv6. It contributes to the broader discourse on technology adoption and offers practical recommendations for stakeholders and decision-makers in Indonesia.
Descriptive Analysis and ANOVA Test with File Sending on Computer Networks Attacked with Rogue's Dynamic Host Configuration Protocol (DHCP)
Authors: Wintolo Hero , Indrianingsih Yuliani , Hamdani Wahyu , Abdie Syafrudin
Published: January 2025

The requirement for a computer that is physically connected to a computer network to be able to access existing resources on a computer network in the form of an IP address obtained statically or dynamically. On a static IP address, there are not many problems that arise because it is loaded directly into the computer, while for a dynamic IP address, security problems arise in the form of a dynamic IP address sharing server in the form of DHCP Rogue. The configuration that is added to the first router when the network is hit by a DHCP rogue attack is to configure the main router, in this case, the first router, and the switch used as a connecting device between computers. configuration on both switches is done by snooping trust which is useful for securing IP addresses to avoid IP attackers. This research was conducted to find out if a computer network with a dynamic IP address was attacked by sending files between computers. Files with the longest sending time indicate an attack on the computer network. The method used in this study is the ANOVA test with descriptive-based analysis. Based on the results of the analysis, it is known that the average file transfer time on networks affected by DHCP Rogue is higher than the average file transfer time on normal and mitigated networks, and the significant value of the ANOVA test results has a value of 0.004. In general, it can be concluded that there are differences in data transfer when the network is normal, the network is subject to DHCP Rogue, and the network has been mitigated with DHCP Rogue.
Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation
Authors: Satoto Budi Dwi , Wahyuningrum Rima Tri , Khotimah Bain Khusnul
Published: January 2025

Corn is one of the essential commodities in agriculture. All components of corn can be utilized and accommodated for the benefit of humans. One of the supporting components is the quality of corn seeds, where a specific source has the physiological qualities to survive. The problem is how to get information on the quality of corn seeds at agricultural locations and get information through the physical image alone. This research tries to find a solution to obtain high accuracy in classifying corn kernels using a convolutional neural network because there is a profound training process. The problem with convolutional neural networks is the training process takes a long time, depending on the number of layers in the architecture. This research contributes to increasing the computing time with the proposed contribution by adding Region proposals with a convex hull to use on a custom layer. The method's purpose is a region proposal area with a convex hull to increase the focus on the convolution multiplication process. It affected reducing unnecessary objects in background images. A custom layer architecture by maintaining the priority layer is an option to get a shorter computational time in constructing a model. In addition, the architecture that is made still considers the stability of the training process. The results on the classification of corn seeds are obtained by a model with an average accuracy of 99.01%—the Computational training time to get the model is 2 minutes 30 seconds. The average error value for MSE is 0.0125, RMSE is 0.118, and MAE is 0.0108. The experimental data testing process has an accuracy ranging from 77% -99%. In conclusion, using region proposals can increase accuracy by around 0.3% because focused objects assist the convolution process
Design of a Laboratory Scale Archemedes Screw Turbine Model Hydroelectric Power Station (PLTA) Simulator
Authors: Basri Muhammad Hasan , Muhtadi Ahmad , Hasan Darul
Published: January 2025

The purpose of this research is to design a new model simulator of the Archimedes Screw turbine on a laboratory scale which is simple, inexpensive, environmentally friendly and for practice at the Electrical Engineering Laboratory of Nurul Jadid University by studying the efficiency of the Archimedes turbine which utilizes kinetic energy. water flow energy from the difference in upstream-downstream water head. Methods used numerical simulations have been run to evaluate the performance coefficient of the turbine alone (without friction loss or blockage augmentation), and to extend the TSR range. Numerical simulations make it possible to generate efficiency curves of Archimedes Screw turbines in both parallel and inclined configurations. The result obtained is that the proposed geometry can be used in real-life applications, providing 0.5 kW at flow velocities between 1 and 2 m/s. Novelty of hydropower simulation studies of the Archimedes turbine screw model using numerical simulation methods.
Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models
Authors: Geni Lenggo , Yulianti Evi , Sensuse Dana Indra
Published: January 2025

General election is one of the crucial moments for a democratic country, e.g., Indonesia. Good election preparation can increase people's participation in the general election. In this study, we conduct a sentiment analysis of Indonesian public opinion on the upcoming 2024 election using Twitter data and IndoBERT model. This study is aimed at helping the government and related institutions to understand public perception. Therefore, they could obtain valuable insights to better prepare for elections, including evaluating the election policies, developing campaign strategies, increasing voter engagement, addressing issues and conflicts, and increasing transparency and public trust. The main contribution of this study is threefold: (i) the application of state-of-the-art transformer-based model IndoBERT for sentiment analysis on political domain; (ii) the empirical evaluation of IndoBERT model against machine learning and lexicon-based models; and (iii) the new dataset creation for sentiment analysis in political domain. Our Twitter data shows that Indonesian public mostly reacts neutrally (83.7%) towards the upcoming 2024 election. Then, the experimental results demonstrate that IndoBERT large-p1 is the best-performing model that achieves an accuracy of 83.5%. It improves our baseline systems by 48.5% and 46.49% for TextBlob, 2.5% and 14.49% for Multinomial Naïve Bayes, and 3.5% and 13.49% for Support Vector Machine in terms of accuracy and F-1 score, respectively.
Cat Feeding Using Microcontroller Arduino Uno TCS3200 Sensor and Internet of Things
Authors: Wardana Handava , Salamah Irma , Taqwa Ahmad
Published: January 2025

Petting animals is one way for humans to reduce stress levels and entertain themselves. When coming home from work, a person needs entertainment at home, namely by keeping cute animals; one of the attractive and widely kept pets is a cat. The presence of cats in the house can help restore mood or feelings, and animals that like to be invited to play. For that, the owner must love his own pet without reducing affection for his pet; a cat's diet must be maintained even though the owner is busy working, especially outside the city, because cats need a good diet. Therefore, the purpose of doing this research is to facilitate cat owners in feeding while doing other activities outside the home. In addition, previous research still cannot feed the cat automatically but can monitor the state of cat activity. This tool uses several sensors, namely the RTCDS3231 sensor, TCS3200, Load Cell, HX711, ESP32CAM. The results obtained are Cat Feeding Using Arduino Uno Microcontroller TCS3200 Sensor, and Internet of Things is a tool system that can notify that the feed has run out, can feed the cat automatically, and can find out the activity of the cat by using the sensor. Several pet shop parties strongly agree that this tool is very helpful, namely to reduce the worry of the owner in feeding the cat.
The Implementation of H5P in Interactive Games for Cyber Security Awareness Learning Facilities for Elementary and Junior High School Students
Authors: Mat Din Mazura Binti , Mansor Shaifizat , Muhamat Dawam Siti Rafidah , Andria Andria , Laksono Ridam Dwi , Sussolaikah Kelik
Published: January 2025

The importance of digital security awareness is not only related to privacy protection but also includes responsible use, protection from cyberbullying, and understanding the consequences of online actions. In primary and secondary schools, understanding regarding cyber security awareness is still lacking; students must realize that what they do in the digital world can have long-term impacts on their real lives. An introduction and understanding of digital security awareness need to be given to elementary and junior high school students. This study intended to provide a vehicle (learning tool) and introduce cyber security awareness using interactive games. By using the Moodle platform and the H5P plugin to make games more interactive, interesting, effective, and efficient. This activity was carried out using the RnD method. With the RnD method, the development of awareness and the many ways to protect oneself on the internet can be developed significantly, according to the level of understanding of students Game platform development uses a Moodle-based learning management system. Then do the customization using H5P. Meanwhile, games are developed by taking into account cyber security awareness indicators. Game applications that are compiled can run well. The trial was conducted on 4 elementary schools and 4 junior high schools. During the trial, the platform's reliability can run smoothly when accessed by users. There were no obstacles in using games by the user during the trial. User features related to achievement badges, as an indication of the level of play each player can achieve responsively. Interactive games about cyber security awareness can be formed and run according to the design set. The contribution of this research is to increase students' understanding of internet safety awareness through interactive games and to increase students' knowledge about how to protect themselves while surfing the internet through interactive games.
The Usage of Password Generators to Enhance Data Security in Most Used Applications
Authors: Halim Erwin , Teng Angelia Hartanto , Hebrard Marylise , Sundaram David , Poba-Nzaou Placide
Published: January 2025

One of the world's global issues is data breaches. These crimes can happen because most people use hackable passwords such as their birthdates and sequencing numbers or alphabets so they would not be easily forgotten. Setting weak passwords in almost or all accounts certainly raises issues and increases the possibility that classified information is hacked and leaked. When it happens, classified data can be misused and taken advantage. This research will help spread awareness to society on how important data security is and how helpful password generators can be to reduce and prevent the probability of data security crimes from happening. This quantitative research uses SMART-PLS as a statistical tool to process the data gathered and random sampling to determine its population. SMART-PLS is variance-based structural equation modeling that uses the partial squares path modeling method. Overall, researchers successfully gathered 114 datasets. Google Forms was used to gather the data. A potential limitation of the study is that all respondents are primarily based in Jakarta. Expanding the geographic focus for further study to gain more insights is highly recommended. 48% of the respondents came from the age group of under 20, occupation as students. Factors significantly affecting people's intention to comply with password generators are perceived password effectiveness, perceived ease of use, subjective norms, and attitude. Eventually, the intention to comply may arouse actual compliance. The result of the study can be used to raise educational campaigns on the usefulness of password generators to promote data security. Based on the result, 78.9% of the respondents are willing to increase their data security. This research contribution is to see how aware people are of data security, how well they know password generators as a technology to generate strong passwords, and how welcome they are with the idea of using password generators.
Detection of COVID-19 Based on Synthetic Chest X-Ray (CXR) Images Using Deep Convolutional Generative Adversarial Networks (DCGAN) and Transfer Learning
Authors: Anhar Anhar , Septiandi Dandi
Published: January 2025

The global COVID-19 pandemic has significantly impacted the health and lives of people worldwide, with high numbers of cases and fatalities. Rapid and accurate diagnosis is crucially important. Radiographic imaging, particularly chest radiography (CXR), has been considered for diagnosing suspected COVID-19 patients. CXR images offers quick imaging, affordability, and wide accessibility, making it pivotal for screening. However, the scarcity of CXR images remains due to the pandemic's recent emergence. To address this scarcity, this study harnesses the capabilities of Deep Convolutional Generative Adversarial Networks (DCGAN). DCGAN is a convolution-based GAN approach, has the potential to alleviate the scarcity of CXR data by generating authentic-looking synthetic images. This study combines synthetic CXR images with real CXR images to bolster model performance, resulting in an Extended Dataset. Extended Dataset comprises 7,345 images, with 34.63% being original CXR images and 65.37% being synthetic images produced by DCGAN. Expanded Dataset then utilized to train three pre-trained models: ResNet50, EfficientNetV1, and EfficientNetV2. The outcomes are remarkable, showcasing considerable enhancement in detection accuracy. Especially for the EfficientNetV1 model, it takes the lead with an impressive accuracy of 99.21% after merely ten epochs, achieved within a brief training period of 6.18 minutes. This surpasses the prior accuracy of 98.43% observed when used the Original Dataset (without synthetic CXR images). Overall, this research offers a solution to mitigate the scarcity of synthetic CXR images for COVID-19 detection. For future endeavors, refining the quality of synthetic images stands as an area for exploration, enhancing the overall efficacy of this approach.
Design and Implementation of IoT-Based Monitoring Battery and Solar Panel Temperature in Hydroponic System
Authors: Rahmatullah Rizky , Kadarina Trie Maya , Irawan Bagus Bhakti , Septiawan Reza , Rufiyanto Arief , Sulistya Budi , Santiko Arief Budi , Adi Puput Dani Prasetyo , Plamonia Nicco , Shabajee Ravindra Kumar , Atmoko Suhardi , Mahabror Dendy , Prastiyono Yudi
Published: January 2025

Hydroponics is currently widely used for the effectiveness of farming in narrow areas and increasing the supply of food, especially vegetables. This hydroponic technology grew until it collaborated with the internet of things technology, allowing users to monitor hydroponic conditions such as temperature and humidity in the surrounding environment. This technology requires electronic systems to obtain cost-effective power coverage and have independent charging systems, such as power systems using solar panels, where the power received by solar panels from the sun is stored in batteries. It must ensure that the condition of the battery and solar panels are in good condition. The research contribution is to create a solar panel temperature monitoring system and battery power using Grafana and Android Application. Apart from several studies, solar panels are greatly affected by temperature, which can cause damage to the panels. If the temperature is too high, the battery and panel temperature monitoring system can help monitor the condition of the device at Grafana and Android application with sensor data such as voltage, current, temperature and humidity that have been tested for accuracy. Accuracy test by comparing AM2302 sensor with Thermohygrometer and INA219 sensor with multimeter and clampmeter, both of which have been calibrated. The sensor data gets good accuracy results up to 98% and the Quality-of-Service value on the internet of things network is categorized as both conform to ITU G.1010 QOS data based on network readings on the wireshark application. QOS results are 0% Packet loss with very good category, 14ms delay with very good category and Throughput 71.85 bytes/s.  With the results of sensor accuracy and QOS, the system can be relied upon with a high level of sensor accuracy so that environmental conditions are monitored accurately and good QOS values so data transmission to the server runs smoothly.
Validation of Varian Clinac iX Model on 6 MV Photon Beam Using Fast Monte Carlo Simulation
Authors: Manik Josua Timotius , Okselia Anisza , Gaspersz Daniel Gibbor , Haryanto Freddy
Published: January 2025

Monte Carlo (MC) is widely recognized as the most accurate method for dosimetry analysis in radiotherapy due to its precision. However, successful MC dose calculation hinges upon the validation of the linac model employed in simulations. This study aims to verify the PRIMO model of the Varian Clinac iX and to determine the optimal initial electron energy. The comparison of one-dimensional dose distribution between simulations and measurements serves as the foundation for assessment. The Varian Clinac iX on 6 MV photon beam was meticulously modeled with the initial electron energies spanned from 5.2 to 5.8 MeV in increments of 0.2 MeV. The dose calculation were performed for a field size of 10 cm × 10 cm and a source-to-surface distance (SSD) of 100 cm. The Dose Planning Method (DPM) was adopted as the simulation engine for expedited MC simulation. A number of particle histories–approximately 4.0 × 108–were simulated, resulting in the generation of around 109 particles from the linac head. The investigation revealed that an initial electron energy of 5.8 MeV achieves good agreement with measurement by attaining the smallest difference in percentage depth dose (PDD) of about 0.98%. The lateral dose deviation of approximately 4.63% serves to validate the precision of the secondary collimator design. Additionally, a comparative analysis of DPM and PENELOPE for dose calculation was conducted. In contrast to the PENELOPE, the DPM speeds up simulation time by approximately 3.5 times, reduced statistical uncertainties to 0.59% and afford better accuracy in dose calculation. The result underscore the suitability of the PRIMO model for MC simulation for dose calculation, given its robust agreement with the measurements.
Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review
Authors: Hidayat Rahmad , Lestari Ninik Sri , Sujana Ahmad , Mahardika Andrew Ghea , YS Herawati , Ramady Givy Devira
Published: January 2025

Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference.
Performance and Emissions of Nanoadditives in Diesel Engine: A review
Authors: Ghazaly Nouby M. , Abdulhameed Ahmed N.
Published: January 2025

Nowadays, the demand for energy and fossil fuels has widely increased as a result of the continuous growth of the population. However, the continued use of traditional fuels as the primary source of energy has resulted in various environmental challenges related to climate change and global warming. This has prompted researchers to look for more eco-friendly and sustainable fuel alternatives with a minimal amount of engine modification and emission treatment techniques. Amongst the suggested alternative fuels, biofuels, biofuel/diesel blends, and the incorporation of nanoparticles into fuels. The nanoparticle diesel additives played a vital role in increasing engine performance as well as retarding harmful emissions such as nitrogen oxides (NOx), carbon monoxide (CO), unburned hydrocarbon (UHC), and particulate matter (PM). Metal-oxides nanoadditive such as aluminum oxide (Al2O3), ceric oxide (CeO2), and titanium dioxide (TiO2) act as oxygen catalysts and promote proper mixing of fuel and air, resulting in more efficient combustion and decreased emissions. The incorporation of nanometal-based additives, including iron (Fe), copper (Cu), and aluminum (Al) accelerated the fuel evaporation rate and increased the probability of fuel ignition. Carbon-based nanoparticles such as carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and graphene oxide (GO) are promising fuel nanoadditives owing to their metal-free composition. In addition, carbon-based additives enhanced the thermal conductivity of fuel and increased active sites available for chemical reactions, which led to improved engine performance.
Optimized Machine Learning Performance with Feature Selection for Breast Cancer Disease Classification
Authors: Koirunnisa Koirunnisa , Siregar Amril Mutoi , Faisal Sutan
Published: January 2025

The prevalence of breast cancer is relatively high among adults worldwide. Particularly in Indonesia, according to the latest data from the World Health Organization (WHO), breast cancer accounts for 1.41% of all deaths and continues to increase. In order to address this growing issue, a proactive approach becomes essential. Therefore, the objective of this study is to classify the diagnosis of breast cancer into two categories: Benign and Malignant. Moreover, this classification pattern can serve as a benchmark for early detection and is expected to reduce mortality and cancer rates in breast cancer cases. The dataset used in this study is obtained from Kaggle and consists of 569 rows with 32 attributes. Various machine learning algorithms, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), are employed for the classification analysis in this disease. . This study uses Principal Component Analysis (PCA) for optimized feature selection techniques with dimension reduction are employed on the dataset prior to modeling the data. Our highest accuracy model is the Support Vector Machine (SVM) with an RBF kernel, utilizing c-value selection. Additionally, the Logistic Regression (LR) model achieves an accuracy of 97.3%. However, it is worth noting that the precision and recall of the SVM model are both 100%. Moreover, the Receiver Operating Characteristic (ROC) curve indicates that the SVM graph surpasses the LR graph, which can be attributed to the results obtained from the confusion matrix calculation, where the False Positive Rate is found to be 0. Consequently, the overall performance evaluation of the SVM model with an RBF kernel, along with the utilization of the c-value selection approach, is significantly superior. This is primarily due to the fact that the SVM model does not make any incorrect predictions by classifying something as positive when it is actually negative.
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting
Authors: Pebrianti Dwi , Kurniawan Haris , Bayuaji Luhur , Rusdah Rusdah
Published: January 2025

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method.
Trans Arterial Embolization of Spontaneous Abdominal Muscle Bleeding
Authors: Biondetti Pierpaolo , Amato Gaetano Valerio Davide , Lanza Carolina , Carriero Serena , Ascenti Velio , Grilli Chiara , Xhepa Edon , Angileri Salvatore Alessio , Ierardi Anna Maria , Carrafiello Gianpaolo
Published: January 2025

Spontaneous abdominal muscle bleeding is an uncommon condition that usually arises in elderly and fragile patients. Commonly affected areas include body wall muscles, particularly the rectus sheath and the iliopsoas, with clinical manifestations such as abdominal pain, groin pain, anemia, and in severe cases, hemorrhagic shock. A computed tomography multiphase scan is the preferred modality of examination, as it allows the characterization of hematomas and the assessment of active bleeding. Conservative management of coagulopathy is standard care for stable patients while embolization and surgical options are reserved for those with hemodynamic instability or significant bleeding. Current literature provides incongruous results on outcomes, while some authors consider embolization only after conservative treatment failure, other authors advocate for a more aggressive approach in patients who are at high risk of developing a poor outcome. This discrepancy underlines the need for further standardized research to optimize management approaches in this fragile patient population.
Substantiation of the temperature regime of the differential pump of electromagnetic action
Authors: Korobko Bogdan , Pavlikov Andriy , Kivshyk Anton
Published: January 2025

The article discusses the temperature of the working body caused by Foucault currents circulating inside of the working body of a differential pump of electromagnetic action. The article begins with a theory describing the nature of the Foucault current and the causes of the plunger heating. Then there is a characteristic of the differential pump of electromagnetic action. The following is a description of the structures of the working bodies of the differential pump of electromagnetic action. The estimated data of the working body are entered in the table. These tables are taken into account when plotting. The theory substantiates the conditions of heating of the working body, the existence of eddy currents, and also the influence of the temperature of the actuator on the operation of the differential pump. The description shows what role the plunger plays in the operation of the differential pump. The article described in detail the interaction of the working body and the solenoid of the differential pump.
Determination of spatial interaction of the individual road train links
Authors: Orisenko O , Skoryk M. , Kryvorot A , Rassoha I.
Published: January 2025

When designing traction-coupling devices and studying the operational properties of road trains, it becomes necessary to determine the size and direction of applying loads that are transmitted from one link of the road train to another. This is convenient to carry out, first by designing a spatial system of forces acting on a separate link, on the axis of the spatial coordinate system, which is fixedly connected with this link, and then bringing these projections to the coordinate system, which is fixedly connected with another link. So, the problem is to develop a mathematical apparatus that allows you to make the transition between coordinate systems that are rigidly connected with individual links of the road train. For a category M1 road train, it is proposed to make a transition between coordinate systems using a table, which is a transformed product of rotation matrices around the coordinate system axes, which is accepted as stationary. At the same time, stationary, depending on the task, can be considered a coordinate system associated with any link of the road train. Since the product of matrices is not commutative, and the positions of individual links during movement change all the time according to an arbitrary sequence of turns, the resulting matrix will depend on the accepted sequence of turns, which will further affect the projections magnitude of the force one link influences on another part.
The effect of gas hydrates self-preservation in the process of their industrial development
Authors: Zezekalo Ivan , Kobolev Volodymyr , Lukin Oleksandr , Vynnykov Yuriy , Safronov Anatoly
Published: January 2025

The negative influence of the gas hydrates (GH) self-preservation effect  on the course of the technological process of their industrial development by the depressurization method is considered. The main parameters of the self-preservation  manifestation: the porosity of the hydrate and its morphological characteristics are justified. An important practical conclusion was made that solving the problems of gas hydrates self-preservation  should be facilitated by the selection of the optimal operation mode for the production well, which would harmonize the hydrate porosity  and its morphological characteristics, and at the same time ensure the maximum possible production and control over the processes of gas hydrates dissociation.
Dynamical Analysis of a Three Species Food-WebModel With Extended Holling Type II Functional Response
Authors: Kamal Bakhan Bahman , Mustafa Arkan
Published: January 2025

This paper deals with dynamical analysis of a food-web including three logistically growing interaction species, prey, intermediate predators and apex predators. The intermediate predator species predate the prey species according to the Holling type-II functional response, while the apex predator species predate both prey species and intermediate predator species according to extended Holling type II functional response for two prey species. Firstly, the criteria that make the model permanent are determined and then all biologically possible steady states are explored, and their local stability as well as global stability is analyzed based on the sample parameters. The occurrence of Hopf-bifurcation of thee model near all steady states, are discussed. Finally, with the help of MATLAB program, it is performed numerical simulations to support the evidence of our analytical results.
Statistical Insights into Stochastic Glucose-InsulinDynamics: Modeling Insulin Degradation with the Michaelis-Menten Function
Authors: Alzahrani Salem Mubarak
Published: January 2025

In this study, a glucose-insulin model with the Michaelis-Menten function as the rate of insulin degradation is analyzed using stochastic differential equations. Further, we solve the stochastic glucose-insulin model using the Milstein method, which is based on truncated Ito-Taylor expansions. Comparison of the approximation solution of a stochastic and deterministic model is illustrated by comparing the approximation solution with the deterministic model. A stochastic model allows random fluctuations in glucose-insulin diseases. Furthermore, the stochastic glucose-insulin model's numerical solution provides insight into its variability. The model predicts glucose-insulin dynamics accurately, which is a powerful tool for managing diabetes. Analytical and simulation results are consistent. Improved treatment strategies and personalized medical interventions could result. Treatments and insulin injections are sensitive to these parameters. Numerical simulation corroborates theoretical results.
Fuzzy \((m, n)\)-Ideals and \(n\)-Interior Ideals in Ordered Semigroups
Authors: Khramrot Pannawit , Iampan Aiyared , Gaketem Thiti
Published: January 2025

In 2019, Ahsan et al. developed the concept of fuzzy \((m, n)\)-ideals in semigroups. Later, in 2022 Tiprachot studied \(n\)-interior ideals in ordered semigroups, which is a generalization of fuzzy ideals and interior ideals in semigroups. The aim of this paper is to study the concept of fuzzy \((m, n)\)-ideals and \(n\)-interior ideals in ordered semigroups and investigate the properties of fuzzy \((m, n)\)-ideals and \(n\)-interior ideals.
On Length and Mean Fuzzy Ideals of Sheffer Stroke Hilbert Algebras
Authors: Rajesh Neelamegarajan , Oner Tahsin , Iampan Aiyared , Senturk Ibrahim
Published: January 2025

This paper presents a detailed exploration of Sheffer stroke Hilbert algebras, introducing the innovative concepts of length fuzzy ideals and mean fuzzy ideals within an interval-valued fuzzy framework. These new constructs extend classical ideal theory by incorporating fuzzy logic, providing precise mathematical tools to analyze and measure membership gradations. Specifically, the study establishes critical relationships between length fuzzy ideals and mean fuzzy ideals, their hierarchical subsets, and their implications for algebraic consistency and computational logic. Key findings demonstrate that length fuzzy ideals align closely with interval-valued fuzzy subsets, while mean fuzzy ideals offer a unique averaging perspective for understanding ideal structures. These contributions significantly advance the field of fuzzy algebra, offering theoretical insights and potential applications in computational logic, uncertainty modeling, and algorithmic design.
Efectos del ejercicio aeróbico sobre la capacidad de resolución de problemas en estudiantes universitarios
Authors: Pauvif Carcamo Francisca , Segueida-Lorca Álvaro , Beltrán-Espinosa Dominique , Jeria-Romero Valentina , Bahamondes-Jordan Daian , Jiménez-Guzmán Esteban
Published: January 2025

El objetivo del presente estudio fue analizar el efecto del ejercicio físico aeróbico del 65-80% de intensidad en la capacidad de resolución de problemas (visoespacial) en estudiantes de pedagogía en educación física de una universidad de Santiago de Chile. Metodología: cuantitativa con un diseño cuasiexperimental. La muestra estuvo constituida por 21 estudiantes (Edad: 21.2 ± 1.7 años) de Pedagogía en Educación Física. El instrumento utilizado fue el Test de Cubos de WISC. El test fue aplicado y una semana posterior a esto, se realizó una sesión de ejercicio aeróbico al 65 - 80% de la frecuencia cardíaca máxima, con una duración de 30 minutos. Posterior a este proceso se aplicó el mismo test. Resultados: los principales hallazgos fueron que el grupo experimental mejoró significativamente en la capacidad de resolución de problemas visoespaciales, en tiempo total de ejecución (p=.003) y en los intentos no logrados (p=.045). En los resultados de la investigación se puede observar que la capacidad de resolución de problemas (visoespaciales) se puede mejorar mediante el ejercicio físico aeróbico. Conclusión: existen mejoras en la resolución de problemas (visoespaciales) luego de la realización del ejercicio físico de 30 minutos entre 65 y 80% de la FC máx.
Efecto de la inclusión del bagazo cervecero sobre el rendimiento productivo y parámetros económicos de terneras en la Patagonia Chilena
Authors: Daza Jose I. , Beltrán Ignacio E. , González Verónica , Estroz Daniel , Reyes Camila , Urrejola José L. , Moldenhauer Rodrigo
Published: January 2025

Los sistemas ganaderos, en la Patagonia chilena, basan su producción en sistemas pastoriles en donde las praderas no presentan crecimiento durante el invierno. Una forma de suplir las deficiencias es el uso de forrajes conservados, aunque generalmente no cumplen con los requerimientos nutricionales de los animales. Entonces los ganaderos utilizan suplementos que mejoran la calidad nutricional de la dieta y esto eleva el costo de producción. El objetivo fue evaluar el efecto de la inclusión de bagazo cervecero húmedo (BCH) sobre el rendimiento productivo y los parámetros económicos de terneras durante el invierno. Se emplearon dos tipos de alimentación (HB: Heno de alfalfa + BCH; H: solo Heno de alfalfa) durante 78 días con 27 terneras Angus x Hereford. La inclusión de BCH disminuyó el consumo de EM, pero se mantuvieron los valores de PC y FDN, que se tradujo en ganancias diarias de peso superiores (0.2 kg/d) con respecto a H. Además, el costo diario de la dieta disminuyó en un 16%. La inclusión de BCH en un 20% de la dieta se convierte en una alternativa para la alimentación de terneras de carne en la Patagonia. concluyendo que al incluir un 20% de BCH se reduce el costo de la alimentación y se obtienen mejoresrendimientos.
Conducta alimentaria de bovinos en pastoreo restringido suplementados con ensilado en estrés calórico durante periodo de sequía
Authors: Mireles-Villanueva Mirelly Venecia , Anaya-Alvarado Dámaso Leonardo , Benavides-González Flaviano , Vázquez-Sauceda María de la Luz , Urbizu-González Ana Lucia , Reyna-Fuentes Jesús Humberto
Published: January 2025

Se evaluó la conducta alimentaria de bovinos macho en pastoreo restringido durante 15 días en periodo de sequía. Se evaluaron 16 animales (Cebú comercial), con peso vivo inicial de 320 ± 23 kg, pastoreando un área total de 25 ha-1. Se aplicó un diseño completamente aleatorizado con los tratamientos: Tratamiento 1 (T1): pastoreo diurno, 4 kg de heno de pasto estrella, harina de maíz (1kg/animal/día) y vinaza (½ litro/animal/día); Tratamiento 2 (T2): pastoreo diurno, 4kg MS de ensilado de moringa, CT.169, camote (ipomoea batata), probiótico (Vitafert®) como aditivo, vinaza (½ litro/animal/día) y harina de maíz (1 kg/animal /día). Se analizaron las actividades: pastoreo, rumia y otras actividades. Para evaluar el impacto del tipo de alimento sobre las variables de respuesta, se empleó un análisis de regresión lineal múltiple. Sin embargo, la suplementación con ensilado de moringa, CT-169, camote y probiótico no influyó bajo las condiciones analizadas en la conducta alimentaria de bovinos machos en periodo de sequía.