bluuebunny/crossref_metadata_2025 huggingface dataset:
Individualization of physical load in preactivation training and targeted direction of educational-training session in football
Authors: Voitsekhovich Alexey
Published: February 2025

The article examines the options for algorithms for calculating physical load in football during the use of various targeted training devices in preactivation training, taking into account an individualized approach.
The purpose of the study is to develop and justify a methodology for individual educational-training sessions of a preliminary preactivation direction for qualified football players, based on their morphofunctional characteristics.
Research methods and organization. The research methods applied were theoretical analysis and summarization of literary sources; survey (questionnaire); design; testing; bioimpedancemetry; functional movement assessment (FMS); pedagogical experiment; methods of mathematical statistics. The research was conducted in four stages from 2020 to 2023 with the university's football team "Inter-Lesgaft".
Research results and conclusions. The significance of applying modern diagnostic methods for assessing the functional state in the training process of qualified football players is confirmed by the need for an objective evaluation of their functional state, which allows for the individualization of the training process in terms of preparation for targeted loads. A structure and content for preliminary preactivation training using various types of simulators have been developed. It has been proven that the application of the developed methodology contributes to the reduction of imbalances in body composition and the functions of the musculoskeletal system, as well as the stabilization of speed-strength performance indicators.
Introduction to landscape ecology with R
Authors: Nowosad Jakub
Published: February 2025

Maximilian H.K. Hesselbarth and I gave the Introduction to landscape ecology with R workshop during IALE-North America 2020 Annual Meeting. You can find the workshop abstract, slides, and recordings below. Abstract R is a free, open-source programming language created as an environment for statistical computing and visualization. The advantages of using R include its flexibility, ease of collaboration, and focus on reproducibility.
How much our maps are distorted?
Authors: Nowosad Jakub
Published: February 2025

Last week Neil Kaye tweeted: This, of course, provoked me to ask: is it reproduclible? And more specifically, can it be reproduced in the open source statistical programming language, R? This post, building on experiences working on the open source and reproducible book project Geocomputation with R, answers both questions with a definitive YES.
How to measure spatial diversity and segregation?
Authors: Nowosad Jakub
Published: February 2025

The

raceland

package implements a computational framework for a pattern-based, zoneless analysis and visualization of (ethno)racial topography. The main concept in this package is a racial landscape (RL). It consists of many large and small patches (racial enclaves) formed by adjacent raster grid cells having the same race categories.
Information theory provides a consistent framework for the analysis of spatial patterns
Authors: Nowosad Jakub
Published: February 2025

Quantitative assessment of spatial patterns has been a keen interest of generations of spatial scientists and practitioners using spatial data. This post describes Information Theory-based metrics allowing for numerical description of spatial patterns. Each example is accompanied by an R code allowing for reproducing these results and encouraging to try these metrics on different data.
Finding similar local landscapes
Authors: Nowosad Jakub
Published: February 2025

Blog posts in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis: GeoPAT 2: Software for Pattern-Based Spatial and Temporal Analysis Pattern-based Spatial Analysis - core ideas Finding similar local landscapes Quantifying temporal change of landscape pattern Pattern-based regionalization Moving beyond pattern-based analysis: Additional applications of GeoPAT 2 This is a third blog post in the series
The Role of Strategic Evaluation and Control in SME Performance: Lessons from Empirical Evidence in Kabwe District
Authors: Yanda Ndila L , Mwange Austin , Moose John , Mwaanga Clement
Published: February 2025

This study investigates the role of strategic evaluation and control (SEC) in the performance of small and medium enterprises (SMEs) in Kabwe District, Zambia. Based on the Resource-Based Theory (RBT) and Dynamic Capabilities Theory (DCT), the research explores how SMEs assess and adjust their strategies over time to align with internal and external environmental changes. A mixed-methods approach was employed, collecting quantitative data from 202 SME owners and managers through structured questionnaires and qualitative data via semi-structured interviews. The findings reveal that strategic evaluation and control positively affect SME performance, particularly in terms of sales, profits, and customer satisfaction. However, qualitative findings indicate inconsistencies and a lack of structured frameworks in implementing evaluation and control processes. The study recommends enhanced training, collaboration, and access to financial resources to improve the strategy evaluation and control mechanisms within SMEs in Kabwe District.
REVOLUCIJA U RAZVOJU SOFTVERA: UTICAJ VEŠTAČKE INTELIGENCIJE NA PRODUKTIVNOST I KVALITET PROGRAMIRANJA
Authors: Cetina Miloš
Published: February 2025

Razvoj softvera prolazi kroz suštinsku transformaciju zahvaljujući integraciji veštačke inteligencije (AI). Ova teza istražuje kako VI redefiniše softversko inženjerstvo kroz automatizaciju kodiranja, optimizaciju razvoja, poboljšanje testiranja i unapređenje održavanja softverskih sistema. Analizira se uticaj mašinskog učenja, generativnih modela i inteligentnih alata na produktivnost programera, kvalitet softverskih proizvoda i efikasnost razvojnih procesa. Posebna pažnja posvećena je etičkim izazovima, sigurnosnim rizicima i promenama u tradicionalnim ulogama unutar softverske indus- trije. Na osnovu analize najnovijih tehnologija i studija slučaja, rad zaključuje da VI ne samo da ubrzava razvoj softvera već i postavlja temelje za novu paradigmu u ovoj oblasti.
Analysis of Groundwater Temperature in Urban Conditions - the Case of Wrocław, Poland
Authors: Hajnrych Monika , Blachowski Jan , Worsa-Kozak Magdalena
Published: February 2025

With increasing urbanization, it is becoming important to study the impact of human activity and climate change on the underground environment, including groundwater temperatures. The subsurface urban heat island (SubUHI) is one of the effects of these changes, which consists in increasing the temperature of soil and groundwater in urban areas. This article analyses groundwater temperatures in Wrocław in 2022–2024. The research was conducted at 19 measurement points. The average groundwater temperature was 12.7°C, with values from 8.9°C to 25.4°C. The highest temperatures were recorded in the city center, and the lowest on its western outskirts. Spatial analysis showed higher temperatures in the city center, related to the influence of underground infrastructure and artificial surfaces. At a depth of 15 meters, the influence of external factors on groundwater temperature disappeared. The results indicate the need for further research on local factors influencing groundwater temperature, which may be important for water resource management in cities.
Wound-Healing Effect of Ethyl Acetate Subfraction Ointment from Meniran Leaves (Phyllathus niruri L.) at 5% Concentration on White Male Rats
Authors: Tobat Sanubari Rela , Wahyuni Fatma Sri , Yenny Satya Wydya , MYH Etriyel , Annisa Annisa , Wardi Epi Supri , Fendri Sandra Tri Juli
Published: February 2025

A study has been conducted on the healing effect of excision wounds using subfraction ointment of ethyl acetate of meniran leaves (Phyllathus niruri L.) with a concentration of 5% on the percentage of wound healing area, epithelialization time, and hydroxyproline levels in male white rats. This study consisted of 3 groups of rats based on ointment grouping, and each group consisted of 15 rats; from 15 rats, they were grouped again into 3 groups based on the day of taking the results of the wound healing parameters, namely days (5, 10 and 15); each group consisted of 5 rats, where group 1 was control with the administration of ointment base, group 2 was given (T® ointment) as a comparison, and group 3 was treated with the administration of the test preparation topically with subfraction ethyl acetate concentration of 5%. Each group was observed and measured for the percentage of wound healing area, epithelialization time, and hydroxyproline levels on days 5, 10, and 15. The results of data analysis using one-way ANOVA and two-way ANOVA followed by Duncan's test (SPSS 23.0) showed a significant difference between the control group and the treatment group in terms of the percentage of wound healing area, epithelialization time and hydroxyproline levels (p<0.05), so it can be concluded that the preparation of ethyl acetate subfraction ointment of meniran leaves with a concentration of 5% has an effect as an excision wound therapy.
PENENTUAN AKTIVITAS ANTIINFLAMASI SEDIAAN EMULGEL MINYAK NILAM (POGOSTEMON CABLIN) TERHADAP MENCIT JANTAN (MUS MUSCULUS)
Authors: Farmasi Muliana , Abasa Sustrin
Published: February 2025

Minyak Nilam (Pogostemon cablin) mengandung senyawa patchouli alkohol yang memiliki efek sebagai antiinflamasi. Pemberian minyak Nilam secara topical dapat memberikan efek antiinflamasi pada kulit punggung Mencit jantan. Penelitian ini bertujuan untuk mengetahui aktivitas antiinflamasi minyak Nilam yang di formulasikan kedalam bentuk sediaan emulgel terhadap hewan uji Mencit Jantan dan apakah sediaan emulgel minyak Nilam memenuhi syarak mutu fisik sediaan. Penelitian ini dilakuan dengan tiga formula dengan konsentrasi Minyak Nilam masing-masing FI 10%, FII 12.5%, FIII 15%. Berdasarkan pengujian stabilistas fisik sediaan dari parameter organoleptik, viskositas, homogenitas, tipe emulsi, pH, daya lekat dan cycling test, minyak Nilam dapat diformulasikan kedalam bentu sediaan dengan konsentrasi 10% dan 12.5% karena stabill sebelum dan sesudah cycling test. Hasil pengujian statistik ANOVA dengan SPSS dilakukan uji normalitas dan homogenitas menuujkan bahwa emulgel minyak Nilam tidak menunjukan perbedaan signifikan meskipun rata-ratanya berbeda jauh dalam mengatasi radang pada kaki mencit (sig>0,05), namun jika dilihat dari persen daya inflamasi maka dapat ditarik kesimpulan bahwa emulgel minyak Nilam dengan konsentrai 12.5 % dengan daya inflamasi sebesar 96.87% di menit ke 300 memiliki daya inflamasi yang paling besar dan memenuhi syarat uji mutu fisik sediaan.
Prediction of the Population of Kapuas Hulu District Based on Gender Using the Backpropagation Method
Authors: Siregar Alda Cendikia , Sucipto  , Ilham Gunawan
Published: February 2025

Rediction is a branch of science used to estimate future events based on historical data. One of the effective methods currently developing is the Backpropagation Artificial Neural Network. This study aims to determine prediction results, the developed model, and its accuracy in forecasting the population of Kapuas Hulu district by gender using the Backpropagation method. The resulting model has an architecture of 2-5-2, with 2 neurons in the input layer, 5 in the hidden layer, and 2 in the output layer. The model uses a learning rate of 0.8, an error tolerance of 0.00001, and 8000 epochs. Predictions for one year after the last dataset year (2024) estimated 138,756 males and 131,434 females, achieving an accuracy of 99.38%. Model validation using the k-fold cross-validation method with 4-folds showed the best accuracy of 99.38% in the first fold. This indicates that the Backpropagation model is highly reliable and effective for predicting population data based on gender.
Developing of A Learning Content Recommendation System Using Collaborative Filtering Based on User Rating
Authors: Piantari Erna , Muhammad Fadjrin Diraja , Prabawa Harsa Wara
Published: February 2025

The advancement of artificial technology has paved the way for personalized learning experiences through adaptive systems which could be built by developing a recommendation system. In education filed, a variety of learning material recommendation systems that employ user filtering algorithms has prompted a lot attention as well. These systems aim to enhance the learning journey by offering tailored learning content suggestions based on individual preferences. This research explores the design of recommendation of learning content system, focusing on user filtering algorithms to analyze user preferences. By leveraging techniques such as collaborative filtering and user-based filtering, the system can accurately predict and recommend relevant learning materials to users based on others rating. The system continuously refines itself in an effort to increase user satisfaction and recommendation accuracy, which will eventually contribute to more efficient and engaging learning experiences.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm
Authors: Lestari Anjar Ayuning , Ahmad Faqih  , Gifthera Dwilestari
Published: February 2025

Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
Sentiment analysis to classify TikTok Shop Users on Twitter with Naïve Bayes Classifier Algorithm
Authors: Lestari Ayu , Ade Irma Purnamasari  , Agus Bahtiar  , Edi Tohidi
Published: February 2025

Advances in information technology have facilitated the use of social media as an e-commerce platform, with TikTok Shop enabling in-person transactions. This research addresses the gap in understanding user perceptions of TikTok Shop through sentiment analysis on Twitter. Sentiment classification is performed using the Naïve Bayes Classifier algorithm. The dataset consists of 1,907 Indonesian tweets, collected from January 2023 to July 2024, and processed using RapidMiner in the Knowledge Discovery in Database (KDD) framework. The preprocessing stages include data cleaning, normalization, tokenization, stopword removal, and stemming. To overcome data imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. The model achieved 93.98% accuracy, with balanced precision and recall for positive, neutral, and negative sentiments. The sentiment distribution among TikTok Shop users on Twitter was 35.5% positive, 35.5% negative, and 29.0% neutral. This research provides insights into consumer behavior on social media and emphasizes the importance of sentiment analysis to increase user engagement and understand market perception. This research is expected to provide information to platform developers and businesses looking to improve TikTok
FP-Growth Algorithm for Association Model Optimization in Household Sales Data
Authors: Zulfa Hana Aqliyah  , Rudi Kurniawan  , Tati Suprapti
Published: February 2025

This research aims to find the value of support and confidence parameters needed so that associations between products can be identified and get the value of support, confidence, lift for the association rules found, and identify products that have the highest support value in frequent itemsets. The method used is Knowledge Discovery in Databases (KDD) with the stages of data collection, data pre-processing, data transformation, data mining, dan interpretation and evaluation. Sales transaction data was collected from January 1 to September 30, 2024, focusing on support and confidence values. The results showed that the association was successfully found with a parameter value of support 0.02 and confidence 0.5. In the association found, the products SWEAT BRONZE PANTS MINI M5 and SWEAT BRONZE PANTS MINI L5 have a support value of 0.004, confidence of 0.073, and lift of 1.421. These values indicate that although the frequency of this association is low, its strength exceeds that of a random association, which can be used in marketing strategies like product bundling.The product “SENSI PEREKAT S20” has the highest support of 0.149 (14.9%. The findings provide insight into the use of data mining algorithms to design data-driven marketing strategies and more efficient inventory management.
Optimization of Social Assistance Recipient Determination using Gradient Boosting Algorithm
Authors: Windi Herlita Vidila  , Rudi Kurniawan  , Saeful Anwar
Published: February 2025

This research aims to classify social assistance recipients to ensure the accuracy of aid distribution by utilizing the Gradient Boosting algorithm on RapidMiner. The data used is data on residents who are categorized as receiving and not receiving social assistance in Cicadas village with a total dataset consisting of 670 entries with 18 attributes that will be divided equally between eligible and ineligible recipients. This research uses KDD (Knowledge Discover in Database) analysis which includes the stages of data selection, pre-processing, transformation, modeling, and interpretation of results. This research uses a quantitative approach, focusing on the distribution of datasets in a ratio of 70:30 with a stratified sampling technique for training and testing purposes. The experimental results show that the selected method is effective in classifying recipients by obtaining an accuracy of 91.67%, this accuracy result can be relied upon to support decision-making in social assistance distribution. The findings underscore the potential of machine learning in optimizing social welfare initiatives by improving target accuracy and ensuring aid reaches the rightful recipients.
Analysis the Level of Passenger Satisfaction with Community Services at Terminal Type A Purabaya using Service Quality Methods
Authors: Vanesha N.M Simanjuntak  , Dira Ernawati
Published: February 2025

Terminal Tipe A Purabaya is an institution providing various types of land transportation services under the Ministry of Transportation. The main issue faced by the Type A Purabaya Terminal lies in improving the quality of public service to meet expectations and enhance passenger satisfaction. The Service Quality (Servqual) method is employed, with validity and reliability testing conducted using SPSS software. Data analysis reveals a gap between the actual performance experienced by passengers and their expectations regarding services at the Type A Purabaya Terminal. Based on the calculations, it can be concluded that there is a discrepancy between service expectations and the actual performance at the terminal. The analysis identifies nine attributes with negative gap values, indicating that the services received by passengers remain unsatisfactory. The attributes requiring improvement include bus departure and arrival schedules, staff knowledge of routes and schedules, the accuracy of information provided by staff, facilities for the elderly and disabled, as well as various other aspects related to comfort, safety, and cleanliness of the facilities. Through the Service Quality analysis, the Type A Purabaya Terminal can prioritize service attributes that need enhancement and design effective improvement strategies. This will ensure continuous service improvements aligned with passenger expectations.
Improving Student Achievement Clustering Model Using K-Means Algorithm in Pasundan Majalaya Vocational School
Authors: Abdul mukhsyi Sopian , Irma Purnamaari Ade , Bahtiar Agus , Kaslani
Published: February 2025

This study analyzes and enhances the student achievement clustering model at SMK Pasundan Majalaya using the K-Means algorithm. The Knowledge Discovery in Databases (KDD) method and RapidMiner AI Studio 2024.1.0 were used to process data from 125 students based on 15 metrics, including academic scores and attendance rates. For group evaluation, the Elbow method and Davies-Bouldin Index (DBI) were employed. The results showed optimal clustering with 2 groups and a DBI value of 0.893. Analysis results revealed significant differences in characteristics between the two groups. Cluster_1 consists of 38 students and has lower score patterns (60-80), with attendance rates of 94-100%, and a positive correlation between attendance and academic achievement. On the other hand, Cluster_0 consists of 86 students and shows higher score patterns (67.5-87.5), with attendance rates of 80-100%, and demonstrates a positive correlation between attendance and academic achievement. Schools can use this clustering model to create learning approaches that are better suited to each student group.
Classification of Fetal Health Using the K-Nearest Neighbor Method and the Relieff Feature Selection Method
Authors: Anita  , Asrul Abdullah  , Syarifah Putri Agustini Alkadri
Published: February 2025

Understanding fetal health early can reduce risks to the pregnancy and the womb. Identifying correlations among factors influencing fetal well-being helps medical professionals clarify key impacts. Quantified relationships between features and labels also guide future research. This study focuses on three aspects: evaluating KNN model performance with and without ReliefF feature selection, analyzing the impact of feature removal, and assessing ReliefF's ability to identify critical features for fetal health classification.The research begins by framing fetal health classification as a supervised machine learning task using labeled datasets. A cardiotocographic dataset from the UCI Machine Learning Repository supports data collection. Initial analysis identifies data types and detects outliers, followed by preprocessing, feature selection, and KNN model training. Model testing uses metrics such as accuracy and recall. Results show the KNN model with ReliefF features achieves an accuracy of 0.896. Testing a pruned model by removing high-importance features slightly reduces accuracy to 0.866. These findings confirm ReliefF's effectiveness in identifying essential features and optimizing model efficiency without compromising quality. This study underscores ReliefF's role in improving KNN performance for fetal health classification.
Optimizing Naïve Bayes Algorithm Through Principal Component Analysis To Improve Dengue Fever Patient Classification Model
Authors: Santi Nurjulaiha  , Rudi Kurniawan  , Arif Rinaldi Dikananda  , Tati Suprapti
Published: February 2025

Dengue fever is an infectious disease that has a significant impact on public health in tropical regions, including Indonesia. Early detection and proper classification of DHF patients is essential to reduce severity and mortality. For this reason, a method that can improve the accuracy in diagnosing this disease is needed. Principal Component Analysis (PCA) and Naïve Bayes (NB) are two commonly used techniques in medical data analysis. PCA is used to reduce the dimensionality of data to reduce complexity, while Naïve Bayes is used for classification of data based on probability. This study aims to optimize the use of PCA and Naïve Bayes in improving the accuracy of the dengue patient classification model. The method used in this study involves processing a medical dataset of dengue patients containing various clinically relevant attributes. The dataset was then processed using PCA to reduce dimensionality and identify key features that affect classification. Next, Naïve Bayes was applied to classify the data based on the selected features. This study compares the performance of classification models that use a combination of PCA and Naïve Bayes with models that only use Naïve Bayes without dimensionality reduction. The results show that the use of PCA in data processing significantly improves the accuracy of the classification model compared to the model that only uses Naïve Bayes. The combination of PCA and Naïve Bayes produces a more efficient model and has a higher accuracy rate in identifying patients with DHF risk. Thus, the application of PCA and Naïve Bayes in the classification of DHF patients can be an effective tool in assisting the medical diagnosis process, which in turn can reduce misdiagnosis and improve patient recovery rates. This research contributes to the development of artificial intelligence technology in the medical field, especially to improve the accuracy of dengue disease diagnosis, and serves as a basis for further research in the use of machine learning techniques in healthcare. This study analyzes the performance of the Naïve Bayes algorithm in classifying dengue fever patient data, by comparing models that use Principal Component Analysis (PCA) as a dimension reduction method and models that do not use it. The results show that the Naïve Bayes model without PCA has an accuracy of 49.96%, which is close to the random guess rate. This finding indicates that the model is less effective in recognizing patterns in the data. In contrast, the application of PCA successfully increased the model's accuracy to 50.03%
Implementation of Naive Bayes in Sentiment Analysis of CapCut App Reviews on the Play Store
Authors: Oka Alvianto  , Willy Prihartono  , Fathurrohman
Published: February 2025

The CapCut video editing application has gained significant popularity among mobile users. This study aims to analyze user sentiment towards CapCut reviews on the Play Store using the Naive Bayes algorithm. User reviews were collected and preprocessed to clean and prepare the text for analysis. The Naive Bayes algorithm was employed to classify the reviews into positive and negative sentiment categories. Findings indicate that the majority of user reviews are positive, highlighting features such as ease of use, attractive visual effects, and the ability to share videos on social media. However, negative reviews were also identified, primarily criticizing issues like bugs, intrusive advertisements, and limitations in specific features. This research provides valuable insights into user sentiment towards CapCut, which can be utilized by developers to enhance application quality and user experience.
Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales
Authors: Rohayati Eti , Martanto  , Arif Rinaldi Dikananda  , Dede Rohman
Published: February 2025

This study aims to assess the effectiveness of linear regression algorithm in predicting raw water demand by considering customer transaction data, raw water volume, and seasonal variables. The method used is Knowledge Discovery in Databases (KDD), including data selection, preprocessing, transformation, data mining, and result evaluation. The dataset is divided 80% for training and 20% for testing. The analysis results show that the linear regression model has a coefficient of determination (R²) of 0.77, which means that the model can explain 77% of the data variability. The prediction error value is low, with Mean Absolute Error (MAE) 0.06, Mean Squared Error (MSE) 0.01, and Root Mean Squared Error (RMSE) 0.08, indicating good accuracy. In the comparison between actual and predicted values, for actual data of 7,000 liters, the model predicts 7,984.70 liters. The variable number of customer transactions has the greatest influence on raw water demand, with a coefficient of 16,940.46, while seasonal factors have less influence. Based on these findings, it can be concluded that the linear regression algorithm is effective in predicting raw water demand, however further development is required to improve accuracy at extreme values, by adding variables or using more complex algorithms.
K-Means Algorithm to Improve Leaf Image Clustering Model for Rice Disease Early Detection
Authors: Gina Regiana  , Irma Purnamasari Ade , Bahtiar Agus , Tohidi Edi
Published: February 2025

This research aims to improve the accuracy of rice leaf image clustering in early disease detection using the K-Means algorithm. The approach used involves the Knowledge Discovery in Databases (KDD) method, which includes data selection, pre-processing, data transformation, data mining, evaluation, and presentation of results. The dataset used consists of images of healthy leaves and leaves infected with diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut. The images are processed through grayscale conversion, noise removal, size adjustment, and data augmentation. The K-Means algorithm is applied to cluster image features based on visual similarity. Evaluation results using Silhouette Score showed that the best clustering was obtained at K=2 with a score of 0.8340, resulting in two main clusters separating healthy and infected images. This study concludes that the K-Means algorithm is able to improve the efficiency and accuracy of rice disease detection, so that it can assist farmers in taking early preventive measures and increase agricultural productivity. This implementation shows significant potential in the development of smart agriculture technology.
Implementation of GridSearchCV to Find the Best Hyperparameter Combination for Classification Model Algorithm in Predicting Water Potability
Authors: Kurniasih Aliyah , Previana Cantika Nur
Published: February 2025

Drinking water quality is an important factor in public health, so an accurate approach is needed to determine water potability. This research aims to create a water potability prediction model using machine learning methods, with a focus on model accuracy and testing. The dataset used includes various chemical parameters, as well as one radiological and acceptability parameter. In this study, various machine learning algorithms, such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression, were applied using GridSearchCV and their performance compared. Models were evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics, with cross-validation to ensure generalizability. The results showed that the Support Vector Machine algorithm provided the best performance with an accuracy of 70.43%, followed by Random Forest and Logistic Regression with accuracies of 70.12% and 62.20%, respectively. The Support Vector Machine-based model is able to provide reliable predictions and can be used as a tool to support decision-making in water quality management.
Design of A Website-Based Ordering Information System at PT. Mutiara Busana Indah
Authors: Nuraziza Novinda  , Galih Widyatmaja
Published: February 2025

The growing demand in the fashion industry has positioned Tegal Regency as one of the central hubs for garment production, offering a wide variety of fashion models and brands. PT. Mutiara Busana Indah, as a prominent player in this industry, faces challenges in managing customer orders efficiently, especially as order volumes continue to increase. This study aims to design and develop a web-based ordering information system that allows potential customers to place garment orders easily without the need to visit PT. Mutiara Busana Indah in person. Additionally, the system is designed to enhance the efficiency of recording customer order data, making the ordering process more organized and integrated. The data collection methods used in this research include interviews, involving direct question-and-answer sessions with relevant stakeholders to gather accurate information about the system requirements. Furthermore, a literature review was conducted by collecting references related to information systems, web-based technologies, and programming applications such as PHP. The insights obtained from these methods form the foundation for analyzing, designing, and implementing the proposed system.
The web-based ordering information system features key functionalities, including product data management, online order creation, and order status tracking for customers. From the company's perspective, the system facilitates more structured order management, including recording and storing customer data. Consequently, this system is expected to address the operational challenges faced by PT. Mutiara Busana Indah and contribute to improved customer satisfaction. This study demonstrates that implementing web-based technology in the ordering process yields positive impacts, not only in terms of accessibility for customers but also in enhancing the company's operational efficiency. With continuous system development tailored to evolving needs, the company can maintain its competitive edge in an increasingly dynamic market.
Internet of Things Monitoring and Automation Simulation Design Using Cisco Packet on Smart Office
Authors: Rikana Zain , Ummul Khair  , Rismayanti
Published: February 2025

The development of Internet of Things (IoT) technology has brought significant changes across various sectors, including office management. The use of IoT enables the integration of smart devices to improve operational efficiency and comfort. This study aims to design a monitoring and automation simulation in the context of a smart office using Cisco Packet Tracer as a network simulation tool. The system design involves integrating various IoT devices, such as temperature sensors, humidity sensors, lighting, and electrical device controllers, which can be monitored and managed automatically through a web-based application. The results of this simulation are expected to provide insights into the application of IoT technology in efficient and controlled smart office management, as well as enhance the understanding of IoT devices and network implementation using Cisco Packet Tracer. This simulation is hoped to serve as a foundation for the development of more advanced and efficient smart office systems in the future.
Design of Gelugur Acid (Garnicia Antroviridis) Dryer Using ESP32 Based on Internet of Things (IoT)
Authors: Satrio Luthfihan , Taufiq  , Asri  , Dahlan Abdullah  , Rosdiana
Published: February 2025

This research aims to design a system for gelugur tamarind dryer and test gelugur tamarind dryer using ESP32 microcontroller. This research uses an experimental method by using ESP32 as a data processing tool given by the DHT22 temperature sensor, then designing temperature and time controls, as well as designing mechanical and electronic devices for the gelugur tamarind dryer. Gelugur acid is dried to be used as a mixture of food or spices. In this tool, the temperature for drying can be adjusted as desired, each temperature set then gets different results. In this drying process is done for hours, in order to get optimal results. The results showed that the original weight of 500 grams of gelugur acid had been dried and sampled within 4 hours with a temperature of 60ºC that had been programmed, and when dry the weight of the acid was reduced to 140 grams if the percentage was 28%. The conclusion that the longer the drying, the less the weight of the acid that was originally wet. The presence of a temperature and time control system shows that this dryer can dry various raw materials, not limited to gelugur acid.
Implementation of Dijkstra Algorithm in Determining the Fastest Route for Goods Delivery
Authors: Ubaidillah Irganata Putra Mamba’ul Ulum  , Indyah Hartami Santi  , Mukh. Taofik Chulkamdi
Published: February 2025

Goods delivery is a critical component in business, especially for logistics industries such as J&T Express Gedog, Blitar, which serves areas in Kecamatan Kepanjen Kidul, Kanigoro, Nglegok, and Garum. During the delivery process, couriers often face difficulties in determining the fastest route, particularly for those who are not familiar with the delivery areas. These challenges often lead to getting lost and prolonged delivery times. This research aims to implement the Dijkstra algorithm to determine the fastest route for goods delivery. The road network is modeled as a weighted graph, where nodes represent delivery locations and edges represent the distances between nodes. The Dijkstra algorithm is then used to process this data and find the fastest route from the starting point to the destination. The testing results show that the algorithm provides the shortest route in 90% of the 10 delivery scenarios tested, with an average distance saving of 8.55%. Therefore, the Dijkstra algorithm proves to be effective in optimizing goods delivery routes, improving time and distance efficiency during the delivery process.
Design of A Decision Support System for Recipients of the Family Hope Program Assistance using the Topsis Method In Kambata Village, Bundung
Authors: Njurumana Riswan , Yustina Rada  , Hawu Yogia Pradana Uly
Published: February 2025

Poverty is still a problem in Indonesia, and the Family Hope Program (PKH) is one of the government's efforts to overcome it. This program provides conditional cash assistance to poor families registered in the Integrated Social Welfare Data (DTKS). Although PKH has succeeded in reducing the poverty rate, there are problems in determining the target of aid recipients, namely not on target in distributing aid. This can result in untargeted assistance and trigger a sense of injustice. Kambata Bundung Village is one of the villages with 159 PKH recipients (83 males and 76 females). To overcome this problem of not being on target, this study aims to design a decision support system (SPK) using the TOPSIS method. The TOPSIS method was chosen because of its ability to solve multi-criteria problems by considering conflicting criteria. This SPK is expected to help PKH companions in this research is expected to help the village government in ensuring that PKH assistance is on target and beneficial for poor families who really need it.
The success rate of black box testing has worked according to the expected specifications with a 100% success rate. No errors or bugs were found in this test, so the system can be declared ready to use.
From the results of the black-box and SUS tests, the design of the decision support system for the recipients of the Family Hope Program using the TOPSIS method, it can be concluded that the system built has met the needs of the system, which can be seen in the results of the black-box test where all the menus made are successfully accessed. This decision support system not only meets technical needs, but also provides a good user experience, as reflected in the final SUS calculation score of 86.5 which is in the excellent category.
Improvement of User Sentiment Classification Model for the Indomaret Poinku Application Using the Naïve Bayes Method
Authors: Sofyan Hidayat  , Nining Rahaningsih  , Raditya Danar Dana  , Mulyawan
Published: February 2025

The Indomaret Poinku application provides a platform for users to give reviews related to products and services. With the increasing number of user reviews, an effective method is needed to automatically analyze opinions. This research aims to improve the sentiment analysis model on the Indomaret Poinku application using the Naive Bayes algorithm. The selection of this algorithm is based on its simplicity and effectiveness in text classification. To improve the model's performance, this research also applies preprocessing techniques such as cleaning, case folding, tokenizing, normalization, stopword removal, and stemming, as well as the feature selection technique Information Gain. The research method involves stages of collecting review data from the Google Play Store, manually labeling the data, and analyzing the data using TF-IDF numerical representation. The Multinomial Naive Bayes model was trained and tested using evaluation metrics such as accuracy, precision, recall, and F1-score. The evaluation results show that the developed model is capable of achieving an accuracy of 75.5%, a precision of 78%, a recall of 75%, with an average F1-score of 70.4%. Further analysis shows that features such as "good" and "great" have a significant influence in sentiment classification. The results of this study reveal that the enhancement of the Naive Bayes model through feature selection and optimization of the Preprocessing process is capable of improving sentiment classification accuracy. These findings contribute to application developers in understanding user opinions, which can be used to improve the quality of services and products.
Analysis of Internal Control System of Cash Receipts and Expenditures of Muhammadiyah Development Islamic Vocational School Tana Toraja
Authors: Sangkala Masnawaty
Published: February 2025

This study aims to determine the suitability of internal control in the cash receipt and cash disbursement system of SMK Pesantren Pembangunan Muhammadiyah Tana Toraja with the components of internal control according to experts, one of which is according to Widjajanto, the components tested in this study are the organizational structure that clearly separates functional responsibilities, the authority system and recording procedures, healthy work practices, employees whose quality is in accordance with their responsibilities. The variables of this study are: the internal control system of cash receipts and disbursements as a single variable using qualitative methods. The population of this study is the internal control system of cash receipts and disbursements of SMK Pesantren Pembangunan Muhammadiyah Tana Toraja, while the sample is the internal control system of cash receipts and disbursements of SMK Pesantren Pembangunan Muhammadiyah Tana Toraja for the last year of the 2020 period which was taken using data collection and data analysis techniques. Data collection was carried out by interviews (using Internal Control Questionnaires), observation and documentation. Data analysis was carried out using qualitative descriptive (ICQ and flowchart). The results of this study indicate that the internal control system for cash receipts and disbursements at SMK Pesantren Pembangunan Muhammadiyah Tana Toraja is not fully in accordance with the application of internal control elements contained in the theory that is guided by the Standard Operating Procedure (SOP) for Financial Management at SMK Pesantren Pembangunan Muhammadiyah Tana Toraja.
Development of SAVI Model Speaking Skills Textbook Introduction to Surabaya Typical Food for BIPA 2 Learners
Authors: Artadiyanti Ni Made Novi , Suyatno  , Yuniseffendri
Published: February 2025

This research aims to develop a speaking skills textbook based on the Somatic, Auditory, Visual, and Intellectual (SAVI) model with introduction materials for Surabaya typical foods for Indonesian learners for Foreign Speakers (BIPA) level 2. The development of this textbook is based on the needs of learners who need a contextual and interactive learning approach to improve speaking skills in authentic communicative situations. The research method used is the Research and Development (R&D) method with the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model. The results of the study show that this textbook is effective and interesting for BIPA learners. The validation of material experts and media experts showed the category of "very decent" with scores of 92% and 90%, respectively. Product trials in small and large groups also showed a significant improvement in learners' speaking skills, based on the results of the pre-test and post-test with an average increase of 35%. This textbook is designed to support learners in getting to know Indonesian culture, especially Surabaya's culinary specialties, through learning activities that involve movement, hearing, visual observation, and intellectual analysis. Thus, this textbook can be one of the innovative and relevant learning media for BIPA learners, as well as contributing to introducing Indonesian local culture to the international world. This study recommends the use of the SAVI model for other learning materials to create a more contextual and meaningful learning experience.
A Different Perspective on Cosmological and Quantum Phenomena That Involves the Temporal Multiverse and the Static Universe
Authors: Bartlett Rodney
Published: February 2025

In early January, an article titled “How a quantum innovation may quash the idea of the multiverse” appeared in New Scientist [1] It received a prompt response from German physicist Sabine Hossenfelder in the form of a video on You Tube [2]. The multiverse part got my attention. Suppose quantum gravity one day goes far beyond unifying quantum mechanics and general relativity. It might unite everything in space and time. Assuming the universe is everything that has existed or will exist, the multiverse could be temporal or all the things that happen at different zeptoseconds in this universe (a zeptosecond is the smallest unit of time ever measured and equals 10−21s or a trillionth of a billionth of a second). That quantum gravity from the far future could unify all the times in the multiverse with the one physical universe. This makes the multiverseobservable constantly (and kind of scientific). Viewing a zeptosecond as the latest step towards discovering the quantum (smallest amount) of time, this Article proposes a connection between quantum mechanics and cosmology’s holographic principle. This suggests the microscopic is united with the macroscopic when the holographic principle is combined with the precision of unrecognized quantum certainty. The micro-macro union means the holographic principle could not only be used to achieve physical quantum entanglement of particles or atoms but could also be used to unify the temporal multiverse with the physical universe. It even opens the door on quantum wave functions and particles being used to overcome otherwise inevitable phenomena like the Sun’s future red-giant phase and the 2nd law of thermodynamics’ eventual entropic decayof the entire cosmos. Vopson writes, “In an expanding universe, the entropy will always increase because more possible micro-states are being created via the expansion of the space itself/universe”[3]. Therefore, avoiding the cosmic demise due to entropy requires the universe to be static. A possible mathematical structure of such a universe is outlined. The article finishes with Vector-Tensor-Scalar Geometry that offers support for the proposed building blocks of space-time via the Hodge Conjecture, presents a feasible alternative method for formation of astronomical bodies, and submits a new picture of the Higgs boson and field.
Do we live in a multiverse, and are the extra universes timelike?
Authors: Bartlett Rodney
Published: February 2025

This Communication was inspired by the latest IPI lecture - given by Prof. Tejinder Singh from the Tata Institute of Fundamental Research, India. Immediately below is the title and abstract of his talk.