bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Social Assistance Recipient Model in CangkringVillage Using the Naïve Bayes Algorithm
Authors: Rotika  , Nana Suarna  , Irfan Ali  , Dendy Indriya Efendi
Published: February 2025

Social assistance is one of the methods used by the government to help the underprivileged. Cangkring Village is a village in Cirebon Regency that has inaccurate data on recipients of social assistance or underprivileged people. The Naive Bayes algorithm is one of the most effective techniques in machine learning for classifying data, in determining the eligibility of recipients of social assistance. The method works with a probabilistic approach to analyze data efficiently and accurately, can group data based on attributes and produce high accuracy. The problem in Cangkring Village, namely the accuracy of data on recipients of social assistance, is still a problem that requires special attention. This inaccuracy not only reduces the effectiveness of social assistance programs but also creates injustice for people in need. Invalid and inappropriate data causes the distribution of social assistance to be suboptimal. The purpose of this study is to optimize the accuracy model of social security recipients using the Naive Bayes algorithm, which can help improve the accuracy in determining eligible recipients.The method used in the study is secondary data processing taken from social assistance recipient data in Cangkring Village. This process includes data preprocessing stages, training and testing data distribution, and implementation or application of the Naive Bayes algorithm to perform classification. The results of the study show that the Naive Bayes algorithm is able to increase the accuracy of the classification of social assistance recipients with an accuracy rate of 90%, compared to the conventional method used previously. This study contributes to providing a more efficient and targeted method in selecting social assistance recipients, so that it can improve the social assistance distribution system in the future. Thus, the Naive Bayes algorithm can be an effective method for data-based decision making in the context of social policy.
Book Detection System At Bogor Library Using Teachable Machine
Authors: Eka Kusuma Pratama  , Mohamad Ridwan Apriyadi
Published: February 2025

This research aims to develop a book detection system at the Bogor Library using Teachable Machine technology, focusing on improving efficiency in automatically searching and identifying books. The system is designed to replace the barcode-based search method, which is considered less flexible, especially since users often experience difficulties in returning books to their original places after reading. Through the application of machine learning, users can detect books based on their cover images with high accuracy, without needing to adjust the barcode’s position. This research involves collecting book data by capturing images from various angles to train the machine learning model. The developed model was tested under various conditions, with results showing detection accuracy above 80%, meeting the research targets. The application was developed using Flutter, with an interface designed to facilitate users in accessing book scanning and search features. The test results show that the system can detect books with high accuracy and provide information about the location of books in the library, such as shelf numbers and floors. This system is expected to improve the efficiency of book management in the library and assist users in finding and returning books to the correct location.
Information System Audit on Employee Claim Application System Using COBIT 5 Framework
Authors: Syaifur Rahmatullah Abdul Rojak  , Irmawati
Published: February 2025

PT ABC President Indonesia is a manufacturing company engaged in consumer goods, producing instant noodle and beverages packaged in ready-to-drink bottles with green tea and milk tea as the main ingredients. Sales promotions tailored to customer needs are expected to help achieve the company's goal of obtaining the best profits. Sales promotions such as giving rewards or discounts to customers with applicable terms and conditions. In the business process, the company has implemented information systems, one of which is the employee claim application menu advance, a system used for submitting payment claims for reward purchases. In implementation, advance submission from the user department is connected to the accounting and treasury departments. However, there are still shortcomings in the submission input process because supporting documents are still sent manually outside the system. There are no definite provisions regarding the completeness of the required documents to expedite the advance submission process, and there is no report menu that can be generated from the system. Based on this, an information system audit of the employee claim application system is needed using the COBIT 5 framework as the compatible method. Based on the results of the research, the current capability process values are as follows: DSS01 at 3.53, which is at level 4; DSS02 at 3.58, which is at level 4; MEA01 at 3.76, which is at level 4; and MEA02 at 3.40, which is at level 3. Therefore, the researcher provides recommendations for the selected processes so that the vision, mission, and objectives can be achieved according to the expected capability levels.
Implementation of Gamification in Data Processing with Statistical Visualization for using Google Cloud for Monitoring Performance and Improvement Evaluation of Personnel at PT. Surveyor Indonesia
Authors: Fajri Arvandi  , Pariyadi
Published: February 2025

Efficient data management is a crucial requirement for large companies like PT. Surveyor Indonesia to enhance performance and make strategic decisions. This research aims to develop an interactive web-based system with a gamification concept, utilizing technologies such as Laravel, MySQL, and Google Cloud Compute Engine. The system is designed to visualize personnel performance data, provide statistical information, and motivate employees through the implementation of a leveling and points system. By leveraging Google Cloud, the developed system is capable of offering high scalability, flexibility, and ease of access. This research is expected to significantly contribute to the operational data management at PT. Surveyor Indonesia and serve as a model for implementing similar systems for other needs.
Clustering Analysis of Administrative Service Types Using K-Means (Study Case: Village bojongsalam)
Authors: Wafiq Azizah  , Ade Irma Purnamasari  , Agus Bahtiar  , Kaslani
Published: February 2025

Advances in information technology present significant opportunities for the improvement of public services, especially in relation to the administrative functions of Bojongsalam Village. Reliance on traditional methods often leads to inefficiencies and inaccuracies in administrative processes. This research uses the K-Means algorithm to categorize administrative service data based on service type, document number, printing date, and accompanying remarks. Utilizing the Knowledge Discovery in Databases (KDD) framework, the analysis includes data selection, pre-processing, transformation, and clustering analysis conducted through RapidMiner software. The dataset consisted of 718 administrative records that had undergone a rigorous cleaning process, including attribute normalization. The analysis resulted in an optimal Davies-Bouldin Index (DBI) value of -0.498 at K = 4, with each cluster representing a different service utilization pattern. The issuance of Family Cards (KK) and Birth Certificates showed higher demand compared to other available services. This classification promotes workload optimization, fair resource allocation, and formulation of effective operational strategies. The application of the K-Means algorithm demonstrated its effectiveness in data clustering and made a significant contribution to technology-based administrative management. The findings lay a basic framework for addressing the needs of the community in a timely manner.
Implementation of Logistic Regression Algorithm in Predicting Tsunami Potential on Earthquake Data Parameters
Authors: Sofian Wira Hadi  , Ibnu Alfarobi  , Irmawati
Published: February 2025

This study presents the evaluation and testing of a logistic regression model for predicting earthquake-related features, including earthquake depth, magnitude, and tsunami potential. The model achieved high accuracy in predicting earthquake depth categories (99.82%) and earthquake magnitude (99.84%), but faced challenges with low recall for tsunami prediction (50%) due to class imbalance. Evaluation results showed that the model struggled to predict tsunami occurrence accurately, as the dataset contained a disproportionate number of 'no tsunami' instances. Despite these limitations, the model displayed high accuracy for earthquake depth and magnitude predictions. The testing phase revealed a series of prediction errors, particularly for the tsunami category, influenced by the imbalance in training data. The results emphasize the need for improved handling of imbalanced datasets and the potential for exploring other machine learning algorithms and techniques for better performance in multiclass classification problems. Future research could further refine these models by incorporating additional criteria and exploring other earthquake and tsunami prediction methodologies.
Geographic Information System for Data Collection of Cooperatives and Small Medium Enterprises in East Sumba
Authors: Sasqia Adinda Amin  , Fajar Hariadi2s  , Erwianta Gustial Radjah
Published: February 2025

Cooperatives and Small and Medium Enterprises (SMEs) are important pillars in the regional economy, with the Cooperatives and SMEs Office playing a crucial role in its development. East Sumba Regency has great potential, but the lack of comprehensive spatial data hinders the making of appropriate policies. This research aims to develop a Geographic Information System (GIS) for data collection of Cooperatives and SMEs in East Sumba Regency. The research method used is waterfall, with data collection through interviews, observations, and documentation. The results are in the form of maps and information on the distribution of Cooperatives and SMEs. It is hoped that this GIS can assist the Agency and Regional Governments in formulating policies that are right on target, as well as supporting training and funding. The positive impacts of GIS include improved data accuracy and quality, transparency, and accountability in the development of Cooperatives and SMEs. The System Usability Scale (SUS) method provides a positive picture of user satisfaction, with an average score of 90 indicating the "acceptable" category and an "excellent" rating. This score reflects the effectiveness and ease of use of the system. Overall, this geographic information system has succeeded in supporting the efficient data collection of Cooperatives and SMEs.
Use of Natural Language Processing in Social Media Text Analysis
Authors: Badry Ali Mustofa  , Wawan Laksito Yuly Saptomo
Published: February 2025

Social media generates enormous volumes of text data, creating both opportunities and challenges for analysis. Natural Language Processing (NLP) enables in-depth analysis of public opinion, identification of trends and language patterns from social media texts. However, texts from social media often face problems with informal language, slang, and spelling errors. This research discusses the application of NLP techniques, such as sentiment analysis, tokenization, and text classification, and compares classical machine learning models (Naive Bayes and SVM) with deep learning models (BERT). Results show deep learning-based models excel at understanding informal language contexts, producing more accurate analysis. This study makes an important contribution in the development of AI-based applications for social media analysis.
Improving the Voter List Clustering Model Fixed(DPT) using the K-Means Algorithm in Girinata Village
Authors: Rizki Aldi  , Nana Suarna2  , Irfan Ali  , Dendy Indriya Efendi
Published: February 2025

Elections are one of the pillars of democracy that require accurate voter data to ensure transparency and fairness. The Permanent Voter List (DPT) is a crucial element in supporting the smooth running of elections, but there are often data validity problems such as duplicate data, voter location errors, or voter data that does not meet the requirements. This research focuses on the application of the K-Means algorithm to increase the accuracy and validity of the DPT at TPS 05, Girinata Village. The problem formulation in this research includes the accuracy level of the DPT, the effectiveness of the K-Means algorithm in identifying inaccuracies, as well as factors that influence the accuracy of voter data. This research aims to analyze the accuracy level of the DPT, evaluate the effectiveness of the K-Means algorithm in grouping data, and identify factors contributing to the validity of the DPT. The analysis results show that the K-Means algorithm succeeded in grouping voter data with good quality, with a Davies-Bouldin Index (DBI) value of 0.389, which indicates clearly defined clusters. The main factors that influence clustering are age, distance to TPS, and location (RT and TPS). This research shows that the K-Means algorithm can be used to detect inaccuracies in voter data, such as data that does not match the TPS location or age that does not meet the requirements as a voter. With these results, the K-Means algorithm makes a significant contribution to validating voter data, thereby supporting a more transparent and accountable election process.
Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan
Authors: Robi  , Nana Suarna  , Irfan Ali  , Dendy Indriya Efendi
Published: February 2025

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.
Educational Media Introducing Computer Devices Based on Augmented Reality for Elementary Schools
Authors: Hafizh Maulana , Ilham Faisal  , Arief Budiman
Published: February 2025

Early mastery of technology is a crucial step in preparing young generations to face the challenges of the digital era. Therefore, introducing computer devices to elementary school students is essential. This study aims to develop an educational media based on Augmented Reality (AR) as an interactive learning tool for introducing computer devices to elementary school students. This educational media is designed to facilitate a more engaging and immersive learning process by visualizing computer devices in three dimensions and allowing students to interact with virtual objects. Through the use of AR, students can learn about computer components such as monitors, keyboards, mice, and CPUs in a more interactive and enjoyable way. Trials conducted in several elementary schools students indicate that the use of this media improves students' understanding of computer device concepts compared to conventional teaching methods. Therefore, this AR-based educational media is expected to serve as an innovative alternative in information technology education at the elementary school level.
Application of the K-Means Method for Disease Clustering in Medical Records at Puskesmas Jatiwangi
Authors: Maharani Putri Firly , Astuti Rini , Prihartono Willy , Ryan Hamonangan
Published: February 2025

Medical record data is often underutilized, limiting opportunities to analyze disease distribution patterns. At Puskesmas Jatiwangi, such data is primarily used for documentation purposes, providing minimal insights to support data-driven decision-making. This study applies the K-Means Clustering method to group disease types based on specific similarities, enabling better identification of disease distribution patterns.
The study utilized 556 patient medical records from October 2024, including attributes such as age, gender, and disease diagnosis. The analysis followed the Knowledge Discovery in Databases (KDD) process, which involves data selection, preprocessing, data transformation, K-Means algorithm implementation, and clustering evaluation using the Davies-Bouldin Index (DBI). Testing was conducted with varying k values from 2 to 10 to determine the optimal number of clusters.
The results indicated that the best DBI value of 0.847 was achieved at k = 2, forming two main clusters. The first cluster represented common diseases such as acute respiratory infections (ARIs) and toothaches, while the second cluster included specific conditions like sciatica and acute lymphadenitis. This study demonstrates that the K-Means method is effective for clustering medical record data, providing valuable insights into disease distribution patterns and aiding in the development of targeted health policies.
 
Keywords: K-Means, Medical Records, Clustering, Davies-Bouldin Index, Puskesmas Jatiwangi
Artificial Neural Network for Determining the Freshness of Nile Tilapia Based on Gill Images Using the LVQ (Learning Vector Quantization) Method
Authors: Mustopa  , Barry Caesar Octadi  , Rachmat Wahid Saleh Insani
Published: February 2025

Determining the freshness of Nile tilapia is essential in the fisheries industry to ensure quality and safety. This study develops an Artificial Neural Network (ANN) system to classify Nile tilapia freshness based on gill images using the Learning Vector Quantization (LVQ) method, known for its effective data clustering. The process involves collecting fresh and non-fresh gill images, extracting features like color, texture, and shape, and training the ANN with the LVQ algorithm. The system achieved an accuracy of 86.67% and an F1 Score of 88.74%, demonstrating its effectiveness in identifying freshness patterns. This approach provides a reliable solution for assessing fish quality, reducing the risk of consuming non-fresh fish, and contributes to ANN-based advancements in the fisheries sector
Data Collection Service Information System Population-Based Web in Lambanapu Village
Authors: Janward Bunga Kale  , Yustina Rada  , Desy Asnatd Sitaniapessy
Published: February 2025

The rapid development of information technology has a major impact on the government sector, especially in public services. In Lambanapu Village, population data collection is still done conventionally using paper forms, which causes a number of problems such as inefficient processes, risk of data loss, and data that is not always accurate. To overcome these problems, this research aims to develop a website-based population data collection information system that can simplify the data collection process, improve data accuracy, and provide better service to the community. The implemented features include data collection of newcomers, address changes, deaths, births, permanent residents, and total population. The collected data stored digitally and can be accessed by village officials. The development method used is System Development Life Cycle (SDLC) with Waterfall model, which allows a structured approach from requirement analysis to implementation. The system was built using PHP and MySQL. The SUS test results show an average value of 70%, which gives a grade of C and falls into the "Good" category based on the standard grade scale of the SUS method, indicating this system is quite effective in meeting user needs.
Agile-Based Academic Information System Development for Efficient Assessment at Al-Ma'rifah Boarding School
Authors: Ayu Ningrat  , Rini Astuti  , Willy Prihartono  , Ruli Herdiana
Published: February 2025

Advances in information technology provide great opportunities to increase the efficiency of managing academic data in educational institutions, including the Al-Ma'rifah Islamic Boarding School, which has been managing data manually and facing various obstacles such as recording errors, limited data access, and time inefficiency. This research develops an Academic Information System (AIS) based on the CodeIgniter framework with Agile methodology, which allows iterative system development by involving users to ensure needs are met. This system is designed to include student data management features, lesson schedules, UTS and UAS scores, as well as academic reports, using an MVC structure to increase speed, scalability and ease of use. Research data was obtained through observation and interviews with admin and teachers, supported by internal Islamic boarding school documents. The results show that Agile-based AIS is able to increase data management efficiency by up to 70%, reduce recording errors by 60%, and speed up data processing by up to 50%. This system also makes it easier to access academic information, increases transparency, and supports more structured data management. With intuitive navigation and integrated features, this system has received positive responses from users, becoming an effective modern solution to support academic management at Al-Ma'rifah Islamic Boarding School.
Implementation of Motion Sensors and Buzzers on Robots to Detect Object Movement
Authors: Wahyu Rizki Ananda  , Abdul Jabbar Lubis  , Ummul Khair
Published: February 2025

Technological advances in this modern era have brought many significant changes in various fields, including security and surveillance. One innovation that stands out is the use of robots to detect object movement. This research aims to implement motion sensors and buzzers on robots to detect the movement of objects around them. This system uses a Passive Infrared (PIR) sensor to detect changes in infrared radiation produced by object movement, and a buzzer as an auditory warning device when movement is detected. In addition, the robot is designed to operate automatically by identifying movement in certain areas. This research also evaluates the effectiveness of the system in detecting objects in various environmental conditions, such as light intensity and sensor detection angle. The results of this implementation show that the PIR sensor is able to detect object movement with good accuracy within a radius of 3 - 4 meters, while the buzzer succeeds in providing a fast and clear auditory response whenever movement occurs. This system can be further integrated with other components for the development of security robots or other smart robots.
K-Means Cluster as a Reading Interest Analysis Tool in the North Sumatra Provincial Library
Authors: Adam Amarullah Lubis  , Tengku Mohd Diansyah
Published: February 2025

This study aims to apply the K-Means algorithm in analyzing students' reading interests in regional libraries in North Sumatra. As library collections and information sources increase, understanding readers' preferences has become a key element in improving the quality of library services and collections. The K-Means algorithm was chosen because of its ability to group data based on similar characteristics, thus providing deeper insight into students' reading interest patterns. The data used in this study were obtained from the visitor information database per library, which includes the number of visits, types of books frequently borrowed, duration of visits, and categories of the most popular reading materials. The data were processed through several stages, namely preprocessing to clean and align the data, clustering using the K-Means algorithm, and cluster analysis. The analysis revealed that students can be grouped into several categories based on their reading interest patterns, such as groups with high interest in scientific literature, groups that predominantly read entertainment and fiction books, and groups with low levels of reading interest. Furthermore, the results of this study also show that students' reading interests fluctuate over time. The results of this study have practical implications for library managers in developing more targeted strategies, such as literacy programs, procuring books that suit visitors' needs, and optimizing data-based services. Therefore, this study is expected to help efforts to increase the number of visits and the level of user satisfaction.
YOLOv8 Algorithm to Improve the Sign Language Letter Detection System Model
Authors: Madyanto  , Rudi Kurniawan  , Yudhistira Arie Wijaya
Published: February 2025

Sign language is the language used by someone with a deaf disability, their limitations in socializing certainly need a tool to help them socialize. Therefore, by utilizing the YOLOv8 model, this study seeks to improve the accuracy and efficiency of visual-based sign language detection, especially the Indonesian Sign System (SIBI) alphabet. The purpose of this study is to evaluate the performance  of the YOLOv8  model in detecting sign language to identify the SIBI alphabet. The method used includes training and testing  the YOLOv8 model  on a dataset consisting of videos and photos using a laptop camera. Each video is converted into an image and taken each frame of the video, then preprocessing, data transformation and data mining are carried out  using YOLOv8 to generate bounding boxes, labels, and confidence scores of detected objects. With a complexity of 168 layers and more than 11 million parameters, the model is able to consistently detect sign language with an average inference rate of 4.6 ms per image. The detection results for each letter class showed a very high success rate, especially on letters such as D, F, N, O, and Q which achieved an accuracy of up to 96%. Overall, 22 out of 26 letters showed "very good" detection results (above 90%), while 4 letters (H, M, T, Z) had "quite good" detection results (86%-89%).
Application of Decison Tree Algorithm to Improve Student Learning Pattern Classification Model at Sempoa Sip Perjuangan
Authors: Aulia Agustina Sri Maharani  , Nana Suarna  , Irfan Ali  , Dodi Solihudin
Published: February 2025

This study aims to analyze the relationship patterns between attributes in the dataset using the Decision Tree model on the RapidMiner Studio platform. This model is used to identify various factors that affect the performance of participants in solving visual and auditory-based questions. The research stages include data preprocessing, model building, and performance evaluation based on accuracy, precision, and recall metrics. The results show that the Decision Tree is able to intuitively divide the data with attributes such as gender and test-taking method as the main factors. The final node of the decision tree provides a prediction of the number of questions that can be solved correctly. Model evaluation showed good accuracy, although there were indications of overfitting that required pruning. This research supports previous literature that highlights the influence of individual characteristics and learning methods on participant performance. The results can be used to design more personalized and effective learning strategies. Further studies are recommended to use larger datasets and other machine learning algorithms for comparison.
Design of Sales Purchasing and Inventory Information System at Kopi Akhir Zaman
Authors: Chintya Mahdiyyah Akbar Santoso  , Latiffah Rabbani  , Sembiring Salsalina , Hita
Published: February 2025

The development of information technology is currently very influential in the business world to improve performance, both in trade and service businesses. Kopi Akhir Zaman is one of the cafes in the city of Medan. The menu available includes various types of drinks such as Americano coffee, long black, Japanese black, coffee latte, double espresso, non-coffee versions such as red velvet, black oreo, taro, lychee milk. This cafe also sells food such as fried rice, instant noodles, fried bread, French fries and many other types of food. The increase in the Kopi Akhir Zaman business has made it difficult for cafe owners to serve orders every day, as well as in managing inventory and estimating purchases of goods from suppliers. This study was conducted to analyze and design an information system that can help cafe owners manage sales, purchasing and inventory transactions. The method used in this research to design this system is the System Development Life Cycle (SDLC). With a design that is designed in the form of a simple and user-friendly display design that makes it easy for users to carry out the sales, purchasing and inventory processes. The results of this study can be useful as recommendations as a basis for developing a proposed system.
Applying Sticky and Violation Shutdown Mode on Security Port Switch for Security Wan Infrastructure Network
Authors: Ritonga Ikhsan , Ilham Faisal  , Tengku Mohd.Diansyah
Published: February 2025

Network security is a crucial aspect of managing information technology infrastructure. The risk of network threats, such as data theft and operational disruptions, increases as access points proliferate. One technique to mitigate these risks is port security. This study aims to implement and evaluate the effectiveness of switch port security in enhancing security within a Wide Area Network (WAN) infrastructure. The port security feature is configured using sticky mode and violation shutdown, allowing the recognition and authorization of legitimate devices based on their Media Access Control (MAC) addresses. A simulation using Cisco Packet Tracer was conducted to test this approach, where port security was applied to prevent unauthorized access. The results show that switch port security with sticky mode and violation shutdown effectively identifies and blocks unauthorized devices. When a device with an unknown MAC address attempts to access the network, the switch automatically disables the corresponding port, ensuring that only authorized devices can connect. The process of recovering disabled ports also proved to be quick and straightforward for network administrators. In conclusion, the implementation of switch port security in WAN infrastructure significantly strengthens network security, protects data from unauthorized access, and ensures optimal network availability and performance.
Random Forest Algorithm to Improve the Classification Model for Recipients of Direct Cash Assistance in Danalampah Village, Pancalang District, Kuningan Regency
Authors: Harsadino Rio , Rudi Kurniawan  , Saeful Anwar
Published: February 2025

Data classification plays a crucial role in machine learning, particularly in supporting data-driven decision-making processes. Data imbalance between majority and minority classes often leads to model bias toward the majority class, reducing accuracy in detecting the minority class. In this study, recipients of direct cash assistance (BLT) represent the minority class, whose numbers are significantly fewer than non-recipients. Therefore, specific strategies are required to ensure more accurate and fair classification results. This study aims to analyze the impact of data imbalance on the performance of the BLT classification model, evaluate the model's performance using accuracy, precision, and recall metrics, and explore the effectiveness of data balancing techniques in improving the model's sensitivity to the minority class. The Random Forest algorithm serves as the primary method for building the classification model. This algorithm works by constructing numerous decision trees based on randomly selected data and features, then combining predictions through majority voting. Additionally, data balancing is implemented through manual reduction in the majority class, resulting in a dataset with a more proportional ratio (1:2), comprising 66 records: 22 BLT recipients and 44 non-recipients. The results demonstrate that the Random Forest algorithm applied to the original dataset achieves high accuracy (99.59%), but the recall for BLT recipients is 0%, indicating bias toward the majority class. After data balancing, accuracy slightly decreases to 98.33%, while recall significantly improves to 66.67%, and precision reaches 65.83%. These findings indicate that data balancing successfully enhances the model's sensitivity to the minority class without significantly compromising accuracy. This study concludes that the integration of data balancing techniques and the Random Forest algorithm improves the fairness and representation of classification outcomes. These findings offer practical contributions to the application of social data analysis, particularly in classifying social assistance recipients, to support more targeted decision-making processes
System Usability Scale in Optimizing the Usability Value of the Tutoring and Private Lessons Ayo Pintar Application in Cirebon City
Authors: Muhammad Iqbal Nugraha  , Rudi Kurniawan  , Tati Suprapti
Published: February 2025

This study aims to evaluate the usability level of the Ayo Pintar website using the System Usability Scale (SUS) method. This research falls under the category of action research, where the usability test results are further analyzed to provide improvement recommendations that can enhance the quality of user experience. The evaluation was conducted through an SUS questionnaire consisting of 10 statements, using a Likert scale as the measurement tool. The findings indicate that the Ayo Pintar website has a good usability level, with a score of 87.8. However, the improvement recommendations derived from data analysis are expected to further optimize the website's usability, providing greater benefits to users and supporting the goal of flexible educational services.
Web-based GKS Papindung Congregation Data Management Information System
Authors: Stepanus Tamu Ama  , Rambu Yetti Kalaway  , Desy Asnath Sitaniapesy
Published: February 2025

Sumba Christian Church (GKS) Jemaat Papindung which is one of the churches in Pandawai District, Mauliru Village, East Sumba Regency, which was established on September 07, 2009, which at that time was still a branch of GKS Mauliru, which was later divided in 2021, becoming GKS Papindung until now. The Sumba Christian Church (GKS) Papindung congregation faces obstacles in the congregation data management system, which was previously recorded manually using the congregation's master book. The loss of the congregation data book in 2021 caused difficulties in re-recording, given the large number of congregants, namely 490 people. The main problems faced are difficulties in searching and grouping data, inconsistency in recording, and the risk of data loss or damage. This research aims to design and develop a web-based congregation data management information system to improve the efficiency and accuracy of data recording. This system was developed using the Waterfall method with PHP, MySQL, and HTML technologies. Testing is done using the Black Box method to ensure the accuracy of system functionality. The test results show that this system can assist the church secretary in managing congregational data more effectively, as well as facilitate the pastor in viewing and exporting congregational data as needed.
Application of the K-Nearest Neighbor (KNN) Algorithm in Machine Learning to Predict the Selection of Undergraduate Study Programs Based on New KIP Lecture Students
Authors: Prahmana I Gusti , Adek Maulidya  , Kristina A. Sitepu  , Razaq Habibi
Published: February 2025

Higher education plays a vital role in shaping the future of individuals and society. Choosing the right study program is an important decision for every student, because it will affect their career path and personal development. The KIP Lecture program is present as a government initiative to provide higher education opportunities to students from underprivileged families. However, with the many options of study programs available, new students often have difficulty in determining the study program that suits their interests and abilities. On the other hand, the data of new students that is quite complete and varied opens up opportunities to use machine learning technology in helping the study program selection process. The K-Nearest Neighbor (KNN) algorithm as one of the simple and easy-to-implement machine learning algorithms has the potential to provide more accurate recommendations for the selection of study programs based on student data at STMIK Kaputama. Therefore, this study focuses on analyzing the use of the KNN algorithm in machine learning to predict the selection of undergraduate study programs. This research aims to identify existing problems, evaluate the effectiveness of KNN in this context, and provide solutions that can be implemented to improve the study program selection process for new students who receive KIP Lecture. It is hoped that it can provide recommendations for the selection of study programs that are more accurate and relevant for new students who receive KIP Lecture at STMIK Kaputama. In addition, this solution can also increase the effectiveness of academic guidance and assist students in achieving better academic and career success.
Efektivitas Ekstrak Kulit Rambutan (Nephelium lappaceum) Terhadap Penurunan Kadar Low Density Lipoprotein
Authors: Nindhea Ferliza Yoan , Katarina Shinta , Dody Wibowo Aldinol , Farida Muhajir Nurlaili , Arisandi Desto
Published: February 2025

Dyslipidemia is a lipid component disorder characterized by increased levels of total cholesterol, Low-Density Lipoprotein (LDL), and triglycerides, as well as decreased levels of High-Density Lipoprotein (HDL). Rambutan is known to contain flavonoids, tannins, saponins, quercetin and alkaloids, which have the ability to lower blood cholesterol levels. The aim of the research was to determine the effect of giving rambutan peel extract to rats on reducing low density lipoprotein (LDL) cholesterol levels. This is a true experiment research with a pretest-posttest and control group design approach.  The research samples were 15 male rats aged 8-10 weeks and weighing 150-250 grams. The dose of rambutan fruit peel was given at 180 mg/kgBW/day for 14 days. LDL cholesterol levels were measured by a spectrophotometer using an enzymatic method. Data analysis used the paired t-test on STATA for Windows software version 12.0 with a confidence level of 95%. Average LDL cholesterol levels in rats before and after administration were 53 and 35 mg/dl, respectively. Giving rambutan skin extract at a dose of 180 mg/dl can reduce LDL cholesterol levels significantly (p < 0.001) by 18 mg/dl (33.9%) in rats
Hubungan Pengetahuan Tentang Anemia, Kepatuhan Konsumsi Tablet Fe, Dan Status Gizi Dengan Kejadian Anemia Pada Ibu Hamil Di Wilayah Kerja Puskesmas Batipuh II
Authors: Hidayah Afnas Nur , Widya Widya Francisca
Published: February 2025

Pregnancy is a condition where a woman is carrying a fetus in her womb which is the result of sperm cells and ovum cells. Anemia often occurs, especially in pregnant women due to lack of iron (Fe) consumption. Data from the World Health Organization (WHO) in 2017, the prevalence of anemia is still high, namely globally the prevalence of anemia in pregnant women worldwide is 43.9%. The purpose of this study was to see how the relationship between knowledge about anemia, compliance with Fe Tablet Consumption, and Nutritional Status with the incidence of anemia in pregnant women. This study used a quantitative research method with a cross-sectional approach, sampling by purposive sampling. Data analysis using Univariate and Bivariate. The results of this study showed that 75% of pregnant women had good knowledge, 75% of pregnant women were compliant in consuming Fe tablets, 75% of pregnant women with normal nutritional status, and 75% of pregnant women did not experience anemia. The conclusion is that there is a relationship between knowledge about anemia, fulfillment of Fe Tablet consumption, and Nutritional Status with the incidence of anemia in pregnant women in the Batipuh II Health Center area.
Community Participation Dynamics in the Land Registration Process in the Digital Era and the Challenges Faced
Authors: Robbani Muhammad Rizqi , Meidianto Muhammad Rizki , Nadhif Faza Fatkhun , Niravita Aprila , Fikri Muhammad Adymas Haikal
Published: February 2025

This article aims to examine the dynamics of community participation in the land registration process in the digital era, as well as the challenges faced in optimizing citizen involvement. In the midst of developments in digital technology, land registration has great potential to become more transparent and efficient. However, although information technology can facilitate access to information and speed up the registration process, there are a number of challenges that need to be overcome. These challenges include the digital divide, low technological literacy among the community, and a lack of understanding of land rights. This research uses qualitative methods to find out people's experiences in participating in the land registration process and find ways to make people more active.  It is hoped that the results of this research can provide advice to the government and related parties so that people can participate more easily and everyone feels involved.
The Impact of Organizational Behavior on Member Performance in the Sociology Education Student Association at Makassar State University
Authors: Dipoatmodjo Tenri Sayu Puspitaningsih
Published: February 2025

This study aims to determine whether organizational behavior will affect organizational performance and whether there is no influence of organizational behavior on organizational performance. This research uses descriptive qualitative method. By conducting interviews with two members of the association who have different positions, namely the treasurer and staff of the organization's association. Based on the results of the interviews that have been conducted, it shows that within the Sociology Education Student Association organization, Makassar State University, there is an influence of organizational behavior that has had an impact on organizational performance. . This impact depends on individual behavior in the organization because it is the individual who will run the organization to achieve the desired goals. Therefore in the organization must have positive individual behavior in carrying out organizational goals. it can be concluded based on the conclusions above according to the interviews of the two members, namely organizational behavior is important for every organization to encourage positive behavior and build a work climate that supports achieving long-term success.
Exploring the Role of Organizational Behavior in Shaping Effective Marketing Strategies for Business Financial Enhancement
Authors: Guterres Aida dos Santos , Pangilinan Angelyn M.
Published: February 2025

This study explores the critical role of organizational behavior and marketing strategies in enhancing a company's financial performance within the context of increasing business competition. Organizational behavior is essential for aligning individual and group actions with organizational goals, which directly influences business efficiency and effectiveness. Marketing management, on the other hand, is a key process that involves identifying market targets, analyzing consumer needs, and implementing strategies that satisfy these needs, ultimately driving profitability. In the digital era, businesses face heightened competition, necessitating a strategic focus on customer satisfaction and brand positioning. This research analyzes the intersection of organizational behavior and marketing strategies, emphasizing the importance of a cohesive approach to organizational dynamics and market orientation. By integrating findings from recent studies, this paper highlights the urgency of adopting effective marketing strategies influenced by organizational behavior to improve financial outcomes and maintain competitive advantage in rapidly evolving markets.
Implementation of Entrepreneurship Education in Family Business Sustainability: A Narrative Study of Mojosari Klepon Traders
Authors: Alifah Luthfi Nur , Rahmawati  , Sudarmiatin  , Firmansyah Rizky
Published: February 2025

The family entrepreneurship education model is important to emphasize because it is a basic measure of the success or failure of the business sustainability process. The individual process acts as a recipient of education from his family, acting as a motivator, teacher, and at the same time a parent as it should be. All stages of the process are expected to create individual output that has the ability, knowledge, and competence to be an entrepreneur in order to prepare for the sustainability of the family business. This study aims to understand the effectiveness of the entrepreneurship education model and its implementation in the sustainability of the Mojosari klepon trader family business. The approach used is descriptive qualitative which includes data reduction, data presentation and conclusions. The results of the study indicate that parental involvement in providing entrepreneurship education to children has been carried out by providing motivation, support and encouragement in verbal form and involving children in the family business so that children understand business management, so that they understand the basics of how to manage a business when they will continue the leadership of the family business or establish an independent business.
The role of total quality management in improving administrative performance among employees at Umm Al-Qura University
Authors: Muhdar Wafaa , Allehyani Maha
Published: February 2025

This study aims to analyze the role of Total Quality Management (TQM) in enhancing administrative performance at Umm Al-Qura University. It examines the core principles of TQM, the challenges associated with its implementation, and the relationship between TQM application and administrative efficiency. The study adopts a descriptive-analytical approach by reviewing previous literature and analyzing the experiences of other universities. The findings indicate that implementing TQM principles positively contributes to improving administrative efficiency, reducing errors, and enhancing employee satisfaction. The study also highlights major challenges, including weak institutional awareness, limited resources, and employee resistance to change. It recommends fostering a culture of quality within the university, developing training programs for employees, improving organizational structures to support TQM implementation, and establishing clear evaluation criteria for continuous performance monitoring.