bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of a Web-Based Computer Sales Information System at Tend Komputer
Authors: Veri Rizqiyanto  , Mutiara Handayani Ujianti
Published: February 2025

The rapid advancement of technology has significantly influenced trading systems and facilitated business operations such as offering, purchasing, and selling goods and services via the internet. Alongside the development of computer technology, the internet has emerged as an effective solution to address various challenges in commerce. Business transactions can now be conducted electronically without requiring physical contact or face-to-face meetings. In the realm of online business, e-commerce plays a pivotal role in enhancing the efficiency and effectiveness of electronic business activities, offering numerous benefits for both companies and consumers. The presence of e-commerce is expected to improve services by providing comprehensive information about the offered products.This study employed data collection methods including observation, interviews, and library research. Data analysis was conducted through four stages: system survey, survey findings analysis, information needs identification, and system requirements specification. The system design utilized the Waterfall model with Unified Modeling Language (UML) tools. Furthermore, the MySQL database was applied to facilitate the processing of goods data for sales, ensuring swift, accurate, and efficient marketing efforts.
The outcome of this web-based sales information system design is anticipated to enhance operational efficiency in the sales process. This system provides an integrated platform to streamline product management and improve customer access to information. Additionally, the system contributes to optimizing sales processes by reducing manual workloads and increasing data management accuracy. The implementation of this system is expected to support the effectiveness of sales activities at TEND Komputer while offering a competitive edge in an increasingly dynamic market.
Accuracy in Sentiment Analysis of the by.U Application Using Naïve Bayes and SMOTE Techniques
Authors: Athhar Hafizha Luthfi  , Ahmad Faqih  , Gifthera Dwilestari
Published: February 2025

Imbalanced data is a significant challenge in sentiment analysis, as it often impacts the performance of machine learning models. This study applies the Naïve Bayes algorithm, enhanced with the Synthetic Minority Oversampling Technique (SMOTE), to address class imbalance in user reviews of the by.U application. Using the Knowledge Discovery in Databases (KDD) framework, the research involves data selection, preprocessing (text cleaning, normalization, stemming), transformation using TF-IDF, and train-test data splitting. SMOTE is applied to the training data to improve minority class representation, while Naïve Bayes performs sentiment classification. Model evaluation using cross-validation demonstrates that SMOTE increases accuracy from 84.42% to 85.83%. These results underscore the effectiveness of integrating SMOTE with Naïve Bayes in addressing imbalanced data, offering meaningful insights into user sentiment and aiding the development of improved features for the by.U application.
Development of Web and Android Based Employee Attendance Monitoring Application
Authors: Pratiwi Heny , Fitriani Nur , Junirianto Eko , Sa'ad Muhammad Ibnu
Published: February 2025

This research was conducted to develop an Android-based employee attendance monitoring system that can assist the Department of Manpower and Transmigration of East Kalimantan Province in monitoring employee attendance, recapitulating employee attendance, and timely submission of attendance reports.
The objective of this research is to simplify employee attendance monitoring and expedite the recapitulation of employee attendance lists at the Department of Manpower and Transmigration of East Kalimantan Province.
The system development method used is the prototype model. This method consists of five stages: Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, and Deployment Delivery & Feedback.
The result of this research is a web-based information system for Administrators and Direct Supervisors to process data and monitor employee attendance, and an Android-based system for employees to record their check-in and check-out times. In the Android-based system, employees can also input attendance with various remarks such as early leave, absence, sick leave, personal leave, business trips, and external duties. The blackbox testing in this research shows that the system functions as expected, and the betabox testing results in a score of 89.60%.
Using the Apriori Algorithm to Identify Purchase Patterns for Enhancing Sales in Personal Shopper Services
Authors: Fadilah Euis , Ahmad Faqih  , Sandy Eka Permana
Published: February 2025

This research aims to explore the application of the Apriori algorithm in identifying purchasing patterns in the drop-off service industry in order to increase sales. Drop-off services often face challenges in designing effective marketing strategies due to limited understanding of customer purchasing behavior. In this study, the Apriori algorithm is applied to uncover recurring purchase patterns among customers, which are then used to develop more efficient marketing strategies. Customer transaction data is analyzed to find associations that reflect their purchasing preferences. The results show that the application of the Apriori algorithm successfully identifies patterns that can improve marketing strategies and, ultimately, increase sales. This research emphasizes the importance of applying data mining techniques to improve the performance of delivery services.
Comparative Analysis of Demand Forecasting Accuracy in Sajiku Seasoned Flour Product with Software POM-QM
Authors: Fadilah Artanti Rahmania  , Nur Rahmawati
Published: February 2025

Indonesia's growing wheat flour consumption requires precise demand forecasting to optimize supply chain management. This study evaluates the forecasting accuracy of Sajiku seasoned flour demand using three methods: Single Exponential Smoothing, Moving Average, and Linear Regression. Data processing and forecasting error calculations were performed using POM-QM software. The analysis reveals that the Linear Regression method yields the lowest forecasting error, making it the most reliable approach for predicting future demand. This study emphasizes the importance of selecting suitable forecasting techniques to improve the accuracy of demand predictions, which can enhance customer satisfaction and contribute to the long-term sustainability of businesses. The findings underscore the significance of accurate demand planning in maintaining a well-balanced supply chain and addressing market fluctuations effectively.
3D Application Development with Blender and Roblox Integration: A Case Study of the North Sumatera State Museum
Authors: Malik Fajri Maulana , Said Iskandar Al Idrus  , Yulita Molliq Rangkuti  , Kana Saputra S  , Debi Yandra Niska
Published: February 2025

This research explores the potential of Metaverse and Immersive Space technologies to enhance virtual tourism experiences at the North Sumatra State Museum through the integration of Blender and Roblox Studio. The main focus is on developing complex and interactive metaverse content, as well as implementing an adaptive visit counting system. The methodology involves developing a 3D application using Blender for modeling and Roblox Studio for the virtual environment. Key results include the addition of Virtual Reality (VR) features, expansion of the virtual museum collection, and a continuous evaluation system based on user feedback. In conclusion, the integration of Blender and Roblox Studio proves effective in creating immersive virtual museum experiences, opening new opportunities in utilizing Metaverse technology to increase museum accessibility and offering innovative solutions for preserving and promoting cultural heritage through digital platforms.
Quality Control Analysis on Animal Feed Storage to Minimize Defect Using Six Sigma Method in PT XYZ
Authors: Asmaul Husna  , Dira Ernawati
Published: February 2025

As the industrial world develops, companies are required to produce quality products to attract consumer interest in order to survive in the midst of intense competition in the industrial world, including companies in the animal feed industry. Therefore, quality control is an activity that must be carried out so that the products produced have good quality. At PT XYZ, there are defects in animal feed storage such as expired, ticky, tilted plots, and leaky sacks. To handle these problems, an analysis is carried out using the Six Sigma method with the DMAIC (Define, Measure, Analyze, Improve, and Control) stages. Based on company data from January to October 2024, the defects amounted to 1.459 sacks with the most types of defects in the form of leaky sacks with a percentage of 83%. The DPMO value obtained shows that there is a possibility of 59.726 defects for a million production with a sigma level value of 3,125. This value shows that the quality is still not consistent and maximum, so improvements need to be made to improve quality and sigma level. Thus it is expected that the company can become a competitive company and can compete well in the industrial world.
Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique
Authors: Abi Fajar Ahmad Fauzi  , Ahmad Faqih  , Kaslani
Published: February 2025

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Naive Bayes Algorithm to Enhance Sentiment Analysis of Coursera Application Reviews on Google Play Store
Authors: Masdarul Rizqi  , Martanto  , Arif Rinaldi Dikanda  , Dede Rohman
Published: February 2025

Coursera is an online learning platform that provides various courses and certifications. This study aims to analyze user perceptions of the Coursera application after the reviews are translated into Indonesian, identify factors that influence positive and negative sentiment, and activate the effectiveness of the Naive Bayes algorithm in classifying review sentiment. The method used is Knowledge Discovery in Databases (KDD), with stages of data collection, preprocessing, and sentiment analysis using Naive Bayes. The results of the study show that the translation of reviews does not change the essence of user perception. Analysis of key words reveals positive experiences such as "kursus", "berguna", and "terima kasih", as well as criticism related to application performance. Factors such as price, content, and user experience play an important role in positive sentiment, while technical issues are the main cause of negative sentiment. The Naive Bayes model shows high accuracy with an accuracy value of 83.62%, precision of 83.34%, recall of 87%, and F1-score of 85.2%. These results indicate that the Naive Bayes algorithm is effective in analyzing sentiment of Coursera application user reviews. Further research is recommended to explore other algorithms or expand the analysis by considering additional factors that can influence user sentiment
Implementation of Bridge Filtering to Prevent DHCP Starvation Attack (Case Study: SD Inpres Papindung)
Authors: Yosua Nggaba Patimay  , Fajar Hariadi  , Raynesta Mikaela Indri Malo
Published: February 2025

The impact of internet cut off or disrupted at SD Inpres Papindung, the teachers will have difficulty finding teaching materials and may have to return to traditional methods that are less effective. Updating Dapodik data will also be disrupted, which could affect school administration and evaluation. The unavailability of an internet connection does occur due to technical reasons or due to intentional elements, one of which is the use of Yersinia software. Yersinia is a piece of software that is commonly used to attack available networks by sending fake MAC addresses continuously so that the IP address on the DHCP server runs out, so that the client does not get an IP address which causes the client unable to access the internet. This attack are called DHCP Starvation Attack. This can be prevented using Bridge Filtering method. Bridge Filtering can filter only recognized MAC addresses to transmit data or can request an IP address from the DHCP server, while MAC addresses that are not recognized or foreign devices will not be allowed to request from that port or will not receive an IP address from the DHCP server.
The aim of implementing Bridge Filtering is to measure the creativity of the Bridge Filtering method in preventing DHCP Starvation Attack attacks so that clients who want to connect to the network can access the internet and improve network quality at SD Inpres Papindung.The results show that the Bridge Filtering method effective in preventing DHCP Starvation Attacks and improves network quality at SD Inpres Papindung which is shown by an increase of throughput, before implementation it was 794.66 Kbps and after implementation it was 1283.70 Kbps, there was a decrease of delay from 6.89 ms before implementation to 4.06 ms after implementation. There was also a decrease in jitter before implementation 10.56 ms and after implementation 6.05 ms, but caused an increase in packet loss which was 0.30% before implementation and after implementation increased to 0.79%. Of the four variables, all of them remain at the same level except for the throughput variable, where there is a change from the fair category to the good category, so that it has an impact on the quality of the internet network at SD Inpres Papindung after implementing Bridge Filtering which is stated to be better than before implementing Bridge Filtering on the internet network at SD Inpres Papindung.
Strategies for Reducing Defect Rates in Commercial Feed Production Using the RCA, FMEA, and 5W+1H Methods in the Feed Industry at PT XYZ
Authors: Reina Anindita Putri  , Akmal Suryadi
Published: February 2025

The high level of defects in feed production, especially related to particle size (particle out of standard) is the main focus of this research because it has a significant impact on the quality of the final product and customer satisfaction. To overcome these problems, this research uses the Root Cause Analysis (RCA) method to identify the root causes of the problem, Failure Mode and Effect Analysis (FMEA) to determine risk priorities based on the Risk Priority Number (RPN) value, and the 5W+1H approach to provide implementable improvement proposals for the factors that cause defects. The results show that the dominant defect is caused by the clogged boiler duct factor with an RPN value of 200 which is the top priority in improvement efforts. The proposed improvement recommendations include conducting routine inspection and cleaning of boiler lines, as well as creating a checklist and cleaning schedule to ensure boiler lines remain clean and free from blockages. The implementation of this strategy is expected to reduce the level of defects so as to improve the efficiency of the production process and support the company's competitiveness in the animal feed industry.
Improvement of Students' Academic Achievement Classification Model Through the Analytical Hierarchy Process Algorithm in Elementary School Burujul Kulon III
Authors: Candra Candra Rahmawati , Martanto  , Arif Rinaldi Dikananda  , Dede Rohman
Published: February 2025

This study aims to improve the classification model of students' academic achievement at SD Negeri Burujul Kulon III using the Analytical Hierarchy Process (AHP) algorithm. This method is applied to analyze various student assessment criteria, including knowledge, skills, spiritual attitudes, and social attitudes, in order to create an objective and systematic evaluation system. The Knowledge Discovery in Databases (KDD) approach is used to ensure structured data management, including the stages of data collection, selection, transformation, analysis, and evaluation. The population of this study was all students of SD Negeri Burujul Kulon III with a sample size taken using the Stratified Random Sampling method, which ensures accurate representation of all grade levels. Data were collected through documentation studies, including exam result reports, skill assessments, and student behavior records observed by teachers. The analysis was carried out by applying the AHP algorithm to determine the priority weight of each assessment criterion through pairwise comparisons. The weights obtained are used to calculate the final grade which is the basis for classifying student achievement. The results of the study indicate that the AHP algorithm is able to produce a more accurate and relevant classification model to identify students with superior achievement, which is not only based on academic exam results but also includes student skills and attitudes. The resulting system provides significant benefits in academic decision-making, such as awarding outstanding students, identifying students who need special attention, and developing more effective learning strategies. This research also contributes to the development of data-based technology for educational evaluation, and can be an important reference for other educational institutions that want to improve the quality of evaluation, learning effectiveness, and student data management comprehensively, systematically, and sustainably in the future.
Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe
Authors: Widisa Adi Kumara  , Rini Astuti  , Willy Prihartono  , Tati Suprapti
Published: February 2025

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.
Development of Educational Game for Introduction Animal Types Using the ADDIE Method Smart Apps Creator In Improving Knowledge Students
Authors: Artoti Azzahra Rizky , Martanto  , Dikananda Arif Rinaldi , Mulyawan
Published: February 2025

The development of technology in education opens up opportunities for innovation to create interactive learning media, especially for early childhood. This research aims to develop educational games based on Smart Apps Creator using the ADDIE method to introduce animal species to Al-Washliyah kindergarten students. The method used is ADDIE, consisting of five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. in this study conducted validity, reliability, normality, homogeneity, and anova tests to measure the effectiveness of this learning media. The results showed that this animal species recognition educational game succeeded in improving student understanding with an average score before the use of learning media of 59.2% increasing to 87.73% after using learning media. Validity and reliability tests show that this learning media meets the criteria of effective, easy-to-use, and interesting learning media.
Implementation of Hazard Identification, Risk Assessment, and Determining Control (Hiradc) System for Hazard Risk Management in Mining Work Environment at PT XYZ
Authors: Sadewa  , Hafid Syaifullah
Published: February 2025

The mining industry is known as a high-risk sector, which often faces challenges in identifying the causes of work accidents. This research focuses on the implementation of the Hazard Identification, Risk Assessment, and Determining Control (HIRADC) system at PT XYZ, especially at the gold mine. The aim of this research is to improve work safety through hazard identification and effective risk management. The method used includes observation and descriptive analysis of secondary data from the Occupational Health and Safety (K3) department. The research results show that there are 9 potential dangers, with 6 of them in the extreme risk category and the other three in the high category. Through the implementation of the 50 proposed control measures, the risk level was successfully reduced to a safer category. These findings emphasize the importance of implementing HIRADC to reduce work accidents and support operational sustainability in the mining industry.
Application of the K-Means Algorithm in Enhancing the Clustering Model for Job Seekers in Cirebon City
Authors: Laylatunna'imah  , Martanto  , Arif Rinaldi Dikananda  , Ahmad Rifa’i
Published: February 2025

The development of information technology opens up opportunities to improve the efficiency of job search through the grouping of job seekers based on specific characteristics. This study uses the K-Means algorithm to analyze data on job seekers in Cirebon City for 2018–2022, focusing on education level and gender. The stages of the research include (1) data selection, (2) data preprocessing, (3) transformation of attributes into numerical format, (4) data grouping using RapidMiner, and (5) evaluation of clustering results using the Davies-Bouldin Index (DBI). The results showed that the optimal number of clusters was two (K=2), with a DBI value of 0.608 which indicates good cluster separation. The first cluster consists of job seekers with a higher level of education, while the second cluster has a lower level of education. Gender did not show a significant influence. These findings provide strategic insights for governments and companies in developing data-driven policies, such as more effective training or recruitment programs. The K-Means algorithm has proven its potential in supporting strategic decision-making in workforce management and being adaptable to other regions.
Analysis of Passenger Satisfaction Levels Using the K-Means Cluster and Hierarchial Cluster Methods in Purabaya Sidoarjo Type A Terminal Services
Authors: Zahra Khania Putri  , Akmal Suryadi
Published: February 2025

Purabaya Type A Terminal is one of the terminals that provides public transportation facilities in the form of bus transportation ranging from city buses, intra-provincial intercity buses, and inter-city inter-provincial buses. This study aims to analyze the level of passenger satisfaction with the service at the terminal and group each variable into a homogeneous group. The research methods used are the K-means Cluster and Hierarchial Cluster methods. The data from the questionnaire will be grouped into several groups that have relatively homogeneous properties using the help of SPSS software. From the results of the study, the output of Non-Hierarchial clusters was obtained in the form of Initial Cluster Centers, Iteration History, Final Cluster Centers, ANOVA, and Number of Cased In Each Cluster. Meanwhile, the output of the Hierarchial cluster is in the form of Case Processing Summary and Dendogram Using Average Linkage. Through analysis using the cluster method, all variables were obtained including in cluster 1 and none were included in cluster 2, the iterations carried out were 2 times and the valid data value was 100 data with a missing value of 0.
Simple Additive Weighting Method for Improving Decision Support Systems Laptop Selection
Authors: Ika Riantika  , Martanto  , Arif Rifaldi Dikananda  , Ahmad Rifai
Published: February 2025

The development of information technology significantly benefits various activities, particularly for students, by facilitating access to information and supporting academic tasks. However, students majoring in Information Technology often face challenges in selecting a suitable laptop due to the wide range of options with varying specifications and prices. This study aims to develop a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to assist in choosing the best laptop. The SAW method was selected for its ability to evaluate multiple criteria through a weighting process. The study utilizes five main criteria: price, processor, RAM, storage type, and storage capacity. Data were collected through interviews and observations at the "IComp" laptop store. The analysis process involves matrix normalization and preference value calculation to determine recommendations. The DSS recommends the best laptop based on the highest preference score: Lenovo IP Flex 5 (0.78), followed by Lenovo IP3 (0.77) and HP Pav14 (0.76). The results indicate that these laptops offer an optimal balance between performance and price. The web-based sy stem designed accelerates the evaluation process, enhances objectivity, and improves user accessibility. The implementation of the SAW method proves effective and accurate in determining the best laptop, particularly in scenarios combining cost and benefit criteria. The system successfully meets the needs of Information Technology students by providing relevant and reliable results. This study successfully develops a DSS using the SAW method for selecting the best laptop. The system designed is effective and reliable for multi-criteria decision-making. Future research can integrate real-time data and broader user surveys to improve result generalization, making it applicable to other product selection contexts.
Implementation of MobileNet V3 In Classifying Butterfly Species with Android and Cloud Based Application Development
Authors: Ihsan Zulfahmi  , Said Iskandar Al Idrus  , Hermawan Syahputra  , Insan Taufik  , Kana Saputra S
Published: February 2025

This research aimed to develop an Android application capable of classifying butterfly species using cloud computing and deep learning technologies. MobileNetV3-Large, a Convolutional Neural Network (CNN) architecture, was employed to process and classify six butterfly species. The dataset was divided into two ratios, 70:30 and 80:20, for training and testing. Evaluation results indicated that the optimal model was achieved with an 80:20 ratio, yielding an accuracy of 94% and precision, recall, and F1-Score values exceeding 90% for each species class. Google Cloud Platform (GCP) was utilized to manage and run the model using the Cloud Run service, enabling the application to function efficiently even with limited resources on Android devices. The application incorporates an encyclopedia of species and a camera scanning feature, making it a valuable educational tool
Earthquake Simulation Design Using Virtual Reality at the East Java Province Environmental Agency Building
Authors: Dirama Romeo , Rizqi Novita Sari
Published: February 2025

This study presents the development of a virtual reality-based simulation aimed at improving earthquake disaster preparedness at the East Java Provincial Environmental Service Building. The simulation introduces proper emergency response procedures, realistic earthquake scenarios, and comprehensive knowledge of building layouts, evacuation route signs, and designated assembly points. The research methodology involved direct observations and interviews with employees to gather detailed information about existing preparedness measures. The simulation was developed through stages of problem identification, data collection, object modeling using Blender 3D, and integration into a cohesive system via Unity. The final implementation resulted in a dynamic, interactive, and educational simulation that closely mirrors real-life emergency situations. Testing results indicate that the simulation operates effectively, providing immersive experiences and facilitating user interaction. This tool is expected to enhance the understanding of emergency procedures for employees, particularly new staff and visitors, while fostering a safer, disaster-prepared work environment. The study underscores the potential of virtual reality technology as a strategic approach to disaster risk reduction and education.
Website Based Digital Branding Strategy for Increase Sales of Gunung Puntang Coffee In Mekarjaya, Bandung Regency
Authors: Juliyanti  , Rini Astuti  , Willy Prihartono
Published: February 2025

This research aims to develop a branding strategy through optimizing a website-based digital company profile to increase sales of Gunung Puntang coffee. Gunung Puntang coffee is a high-quality local product that requires a digital approach to marketing to reach a broader market and enhance competitiveness. In today's digital era, a website plays a crucial role as a promotional and informational medium, providing customers with easy access to product information and enabling online purchases. The research employs the Prototype method, consisting of problem identification, planning, requirements analysis, system design, implementation, and testing phases. Data collection was conducted through observation, interviews with the coffee business owner, and documentation studies related to business processes and branding strategies. The collected data serves as a basis for designing a website system that optimizes the company's profile and supports coffee sales transactions. The system development includes creating use case diagrams, activity diagrams, and system architecture designs to outline functional and non-functional requirements. The research outcome is a website functioning as a digital information medium for branding Gunung Puntang coffee products and supporting sales transactions. Key features include customer registration, product selection, quantity adjustment, payment methods, order confirmation, and order cancellation. Testing results indicate that the system operates effectively and meets user needs. This website enhances operational efficiency, expands market reach, and improves the shopping experience for customers. It serves as an effective medium for strengthening branding and marketing strategies in the digital era, ensuring the sustainability of local businesses in the global market. Regular evaluations and feature upgrades are recommended to maintain system relevance to customer needs and technological advancements..
Optimizing Email Spam Classification Using Naïve Bayes and Principal Component Analysis
Authors: Shinta Virgiana  , Rudi Kurniawan  , Tati Suprapti
Published: February 2025

In the ever-evolving digital era, email spam filtering is an important challenge to maintain the security and comfort of email services. The Naïve Bayes algorithm is widely used for spam email classification because of its ability to manage large data, although there are still limitations in terms of accuracy, precision and recall. This research aims to improve spam email classification performance by combining Naïve Bayes and Principal Component Analysis (PCA) to optimize model accuracy and explore optimal parameters in the reduction dimension. The research methodology goes through the Knowledge Discovery in Database (KDD) stages which include selection, preprocessing, transformation using PCA, development of a classification model using Naïve Bayes, and evaluation of model performance. The dataset used consists of emails categorized as spam and non-spam. The experimental results show that the combination of Naïve Bayes and PCA achieves the highest accuracy of 99.24% with 7 principal components. The fixed number of components approach shows better performance compared to preserving variance, emphasizing the importance of selecting appropriate PCA parameters in improving the effectiveness of model classification. This research shows that PCA not only reduces the complexity of the dataset but also increases the efficiency of the classification algorithm.
Designing the 112 Call Centre Service System in Sidoarjo Regency using the Object-Oriented Analysis and Design (OOAD) Method
Authors: Ningrum Dea Kusuma , Mohammad Dimas Ardiansyah  , Rizky Tri Aji Setiawan  , Alya Maytsa Ismawardi  , Anindo Saka Fitri
Published: February 2025

The 112 call centre in Sidoarjo district faces significant challenges, including a high number of false alarms, which account for 85% of all calls. This hampers the efficiency of the service, especially with the limited number of call takers and inefficient manual information flow. This research aims to develop a technology-based reporting system using an OOAD method (Analysis, Design, Build, Operate) approach to improve the reliability and efficiency of emergency services. The system includes E-KYC-based report verification features to filter false reports, real-time report status tracking, and integration between related parties such as the community, administration, and response units (police, ambulance, fire). The results show that the designed system is able to significantly reduce false reports, improve service response through report prioritisation, and provide community access to monitor reports independently. This approach not only overcomes operational constraints, but also provides more transparent and efficient emergency services. It is hoped that this system can become a standard model for modernising emergency services in the digital age.
K-Means Clustering Method to Make Credit Payment Groupinhg Efficient
Authors: Siti Nur Illah  , Nana Suarna  , Irfan Ali  , Dodi Solihudin
Published: February 2025

Credit payment management is one of the main challenges in the financial sector, especially in grouping customers based on risk and payment patterns. This study aims to evaluate the K-Means Clustering method in improving the efficiency of credit payment data clustering. The dataset used includes information on payment history, loan amount, tenor, and credit status from financial institutions. The research approach involves data processing stages, application of the K-Means algorithm, and evaluation of results using the Davies-Bouldin Index and Silhouette Score metrics. The results of the analysis show that the K-Means method is effective in identifying customer payment patterns and dividing them into three main clusters: high, medium, and low risk. In addition, this study found that determining the optimal number of clusters using the Elbow Method can improve the accuracy of the clustering results. The resulting model makes a significant contribution to credit risk management, helping financial institutions make strategic decisions related to credit policies and risk mitigation. This study offers practical implications, including increased operational efficiency and predictive ability against potential bad debts. Further studies are recommended to integrate this method with other algorithms to improve the performance of large-scale data analysis.
Web-Based Chatbot Development and User Satisfaction Analysis Using the Naive Bayes Method Through Online Questionnaires
Authors: Nurholis  , Willy Prihartono  , Fathurrohman
Published: February 2025

This study aims to develop a web-based chatbot using Natural Language Processing (NLP) technology and the Naive Bayes algorithm to enhance digital interaction quality. User satisfaction was evaluated through an online survey involving 202 university students, focusing on ease of use, response speed, and relevance. The research followed the CRISP-DM framework, including data preprocessing (case folding, tokenization, stopword removal, and stemming), text transformation using the TF-IDF method, and implementation of a Naive Bayes classification model. an F1-score of 84%. Sentiment analysis revealed predominantly positive feedback, reflecting user satisfaction with the chatbot’s ease of use and response accuracy. However, some limitations, such as insufficient contextual understanding, were identified. These findings provide valuable insights into NLP-based chatbot development to support effective digital interactions. The proposed chatbot demonstrates potential applications in customer service, education, and e-commerce, with future improvements suggested to enhance contextual comprehension and scalability.
Optimizing the Classification Model for Plant Medicine Supplies Using the Decision Tree Algorithm at the Anugrah Tani Shop, Brebes Regency
Authors: Saeful Amri  , Rudi Kurniawan  , Saeful Anwar
Published: February 2025

Retail businesses in the agricultural industry often face difficulties in estimating inventory needs, especially plant medicines which are important for protecting plants from pests and diseases. The lack of an accurate inventory prediction system can cause stock discrepancies, as happened at the Anugrah Tani Store, Brebes Regency, thereby disrupting operations and customer satisfaction. This research uses the Decision Tree classification technique to increase the accuracy of predicting the need for plant medicine supplies, with a clustering approach using the K-Means algorithm to determine the optimal K value through the Davies-Bouldin Index (DBI) calculation. A DBI value of -0.065 indicates good cluster quality with an optimal K of 2, where Cluster 0 has high inventory needs (1138 data) and Cluster 1 has low needs (4 data). The analysis results show that the accuracy level of the Decision Tree model is 98.25%, which is quite high. This model is not only able to predict inventory patterns accurately but also provides in-depth insights to support stock decision making. This research proves that the Decision Tree algorithm can help inventory management with a faster response to customer needs, while contributing to the development of machine learning-based classification models for the agricultural and retail sectors.
Utilizing Google Sites for Personal Branding Business
Authors: Ananda Amilus Sholikhah  , Evy Maya Stefany  , Diana Nurul Fajri  , Maimunah  , Nur Azizah Mawar Andini  , Rifqi Farhanul Ihsan  , Yogi Ade Chairudin
Published: February 2025

Personal branding telah menjadi aspek penting dalam dunia bisnis modern. Dengan meningkatnya persaingan, individu dan bisnis perlu membedakan diri melalui identitas yang kuat. Artikel ini membahas penggunaan Google Sites sebagai alat untuk membangun personal branding. Melalui kemudahan penggunaan dan integrasi dengan alat Google lainnya, Google Sites memungkinkan pengguna untuk membuat situs web yang profesional dan menarik. Penelitian ini menunjukkan bahwa penggunaan Google Sites dapat meningkatkan visibilitas dan kepercayaan audiens terhadap merek pribadi atau bisnis.
Design and Construction of a Web-Based Outpatient Data Management Information System (Case Study: UPTD Puskesmas Cikampek)
Authors: Ardiansyah Dian , Hayati Nung , Walim  , Yuliandari Dewi , Supriatin  , Lase Mareanus
Published: February 2025

The focus of this research is the design and development of an information system to manage outpatient data at the UPTD Cikampek Health Center which operates via the internet. The purpose of this system is to overcome the problem of inefficient manual recording at the cashier and to improve efficiency and accuracy in managing patient data. A prototype model is used in software development. This model includes steps such as needs analysis, system design, implementation, and testing. The results of this study are in the form of a web-based information system that includes login features, patient data management, payments, reports, and transaction history. It is expected that this system can help exchange information online throughout the scope of the Health Center, accelerate the distribution of reports, and facilitate the management of patient data. Therefore, this study offers an innovative and practical solution to managing health data at first-level health care facilities.
K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City
Authors: Aida Safitri  , Ade Irma Purnamasari  , Agus Bahtiar  , Edi Tohidi
Published: February 2025

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.
Optimization of Machine Learning Models for Jiwa Garuda in Predicting Geothermal Well Flow Rates
Authors: Pasaribu Aldo
Published: February 2025

The accurate prediction of geothermal well flow rates is critical for optimizing resource utilization and ensuring sustainable energy production. This study focuses on the optimization of machine learning models, termed "Jiwa Garuda," specifically designed for geothermal applications. The research aims to develop a robust predictive framework by leveraging advanced machine learning techniques to model complex thermodynamic and fluid dynamic behaviors within geothermal reservoirs. The outcomes of this research provide actionable insights for geothermal field operators, including predictive capabilities for well flow rates under varying operational scenarios. Furthermore, the Jiwa Garuda model offers potential scalability to other geothermal sites, contributing to the broader adoption of machine learning in sustainable energy development.
Optimizing Grocery Sales Data Grouping Using the Fuzzy C-Means Algorithm: Case Study of Nafhan Mart Store
Authors: Nafhan Khairuddin Fathin  , Rudi Kurniawan  , Saeful Anwar
Published: February 2025

The sale of staple food products at Nafhanmart Store, Cirebon Regency, includes essential household items such as rice, cooking oil, sugar, and flour, which maintain stable demand as basic necessities. This study focuses on improving sales clustering models at Nafhanmart using the Fuzzy C-Means (FCM) algorithm, a prominent method in data mining. Key factors influencing sales include price, sales volume, demand, and remaining stock. Accurate clustering analysis is vital for strategic inventory management and profit maximization. The research applies the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, FCM implementation, and evaluation using the Davies-Bouldin Index (DBI). Attributes analyzed include price, sales volume, demand, and remaining stock. The FCM algorithm clusters data based on patterns, with DBI evaluating clustering quality and determining optimal clusters. Data analysis and visualization were conducted using RapidMiner. Results show that the FCM algorithm achieves optimal clustering quality with a DBI score of 0.452 for two clusters, outperforming three clusters (DBI 0.474) and four clusters (DBI 0.536). Price and demand are identified as critical factors influencing clustering outcomes. These findings enhance the clustering model, offering actionable insights for inventory management and sales strategy, while showcasing the FCM algorithm's adaptability for other SMEs to support data-driven decision-making.
Application of K-Means for Product Grouping Best Sellers at Planet Tire Jatibarang Branch
Authors: Risnawati  , Rini Astuti  , Willy Prihartono
Published: February 2025

This research aims to identify the best-selling products at Planet Tire Workshop Jatibarang Branch using the K-Means Clustering method. Understanding product sales patterns is important in designing effective marketing strategies and managing stock efficiently. This research uses sales transaction data for one year, including the number of sales, product types, and total transaction value. The analysis process includes data preprocessing, selection of relevant attributes, application of the K-Means algorithm, and validation of the optimal number of clusters with the Elbow method. As a result, products were grouped into three categories: high, medium, and low sales. The high sales cluster contributes significantly to revenue, while the medium sales cluster shows potential for improvement through promotion, and the low sales cluster requires further evaluation. This research helps management manage stock, prioritize promotions, and optimize resource allocation. However, the research has limitations as it has not considered external factors such as seasonal trends and promotions, and focuses on one branch. Development of the research in other branches can expand its benefits. The results of this study are expected to improve operational efficiency, support data-driven strategies, and enrich academic literature related to the application of K-Means in retail management and sales data analysis.