bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical and Experimental Validation of Homogenization Techniques for Bubble Deck Slabs
Authors: Garbowski Tomasz , Staszak Natalia , Kostrzewski Wojciech , Szymczak-Graczyk Anna
Published: February 2025

This study investigates the deflection behaviour of Bubble Deck slabs using numerical and experimental approaches. Two techniques—numerical homogenization and 3D cross-sectional integration—are applied to derive equivalent properties for simplified finite element models. A scaled slab specimen (1020×2040×60 mm) with a reinforcement mesh of Ø4 bars spaced at 30 mm (top and bottom) and plastic spheres (Ø40 mm, spaced at 60 mm) is tested under self-weight and a mid-span linear load. The slab, simply supported on two shorter edges, is modelled both in full 3D and using simplified 2D model with homogenized parameters. Experimental deflections are compared with numerical and analytical/theoretical predictions to validate the proposed techniques, demonstrating their effectiveness in simplifying structural analyses while maintaining accuracy.
Comparison of Soil Moisture Data From In-situ Measurements with Geological Data
Authors: Jaeckel Julian , Pawlik Marcin Piotr , Bernsdorf Bodo , Rudolph Tobias
Published: February 2025

As part of the MUSE project aimed at optimizing post-mining dewatering through multi-sensor geomonitoring, additional analyses were conducted based on soil samples. The aim of these analyses was to develop retention curves, which help determine critical soil physical parameters and soil moisture levels. Due to the absence of pressure pot analyses, grain size analysis was used to identify these critical parameters, allowing comparison with other data levels. In conclusion, the project results presented, that the methods used demonstrated a high degree of consistency with official soil data (maps and tables) in the working area, making them suitable for future studies on soil moisture, potentially eliminating the need for time-consuming laboratory analyses. The Soil Map 1:50,000 (BK50) data can serve as a benchmark for estimating soil moisture values in large-scale investigations.
UJI AKTIVITAS ANTIBAKTERI KOMBINASI EKSTRAK KULIT BUAH ALPUKAT DAN KULIT BUAH MELON TERHADAP Staphylococcus Aureus DAN Propiniobacterium Acnes
Authors: Wangka Sudirman , Firmansyah Firmansyah , Ajheng Tendri
Published: February 2025

Kulit Buah Alpukat (Persea americana Mill) dan Kulit Buah Melon (Cucumis sativus L.) merupakan salah satu tanaman yang memeliki senyawa berkhasiati salah satunya sebagai antibakteri. Penelitian ini bertujuan untuk mengetahui aktivitas antibakteri dan konsentrasi berapa pada perbandingan kombinasi ekstrak kulit buah alpukat dan kulit buah melon terhadap Staphylococcus aureus dan Propionibacterium acnes. Kulit buah alpukat dan kulit buah melon diekstraksi secara maserasi dengan pelarut etanol 96%. Ekstrak dikombinasikan dalam tiga rasio berbeda dan aktivitas antibakterinya diuji menggunakan metode difusi cakram, Dimana kertas cakram dijenuhkan kedalam masing-masing kombinasi suspensi ekstrak etanol kulit buah alpukat (Persea americana Mill) ekstrak kulit buah melon (Cucumis sativus L.) dengan perbandingan 1:3 , 2:2 dan 3:1, Na CMC sebagai kontrol negatif dan sebagai kontrol positif menggunakan kloramfenikol. Setelah itu kertas cakram diletakkan pada permukaan media nautrient agar (NA) yang telah diinokulasi dengan suspensi bakteri uji Staphylococcus aureus dan Propionibacterium acnes, kemudian diinkubasikan selama 1x24 jam pada suhu 35oC. Setelah itu di amati zona bening di sekitar kertas cakram untuk menunjukkan ada tidaknya zona hambat yang terbentuk. Diameter zona bening. Hasil penelitian menunjukkan bahwa ekstrak kombinasi menunjukkan berbagai tingkat aktivitas antibakteri terhadap kedua galur bakteri, dengan aktivitas tertinggi diamati pada kombinasi rasio 2:2. Aktivitas antibakteri diklasifikasikan sebagai sedang, dengan diameter zona penghambatan berkisar antara 10,31 mm hingga 13,14 mm untuk Propionibacterium acnes dan 10,59 mm hingga 12,48 mm untuk Staphylococcus aureus. Hasil ini menunjukkan bahwa pada kombinasi eksytak kulit buah alpokat dan buah melon yang paling baik memberikan aktivitas antibakteri adalah perbandingan 2:2. Dengan demikian ekstrak kombinasi kulit alpukat dan kulit melon mungkin merupakan agen antibakteri alami yang potensial untuk pengobatan jerawat dan infeksi kulit lainnya.
PENGEMBANGAN DAN VALIDASI MODEL HYBRID MACHINE LEARNING UNTUK DIAGNOSIS AWAL DEPRESI
Authors: Aziz Firman , Abasa Sustrin
Published: February 2025

Depresi adalah gangguan mental yang umum dan dapat mengurangi kualitas hidup secara signifikan. Diagnosis dini sangat penting untuk mencegah dampak buruk depresi, namun sering kali terlambat karena keterbatasan sumber daya dan kesadaran. Penelitian ini bertujuan untuk mengembangkan model hybrid machine learning untuk diagnosis dini depresi, menggunakan dataset yang berisi 42 pertanyaan terkait gejala depresi. Model hybrid ini menggabungkan tiga algoritma: Random Forest (RF), Support Vector Machine (SVM), dan Naive Bayes (NB), dengan penyesuaian bobot untuk meningkatkan akurasi prediksi. Hasil eksperimen menunjukkan bahwa model individual RF, SVM, dan NB memiliki akurasi masing-masing 91%, 88%, dan 86%, sementara model hybrid yang menggabungkan ketiga algoritma dengan bobot RF: 0.5, SVM: 0.3, dan NB: 0.2 menghasilkan akurasi 93%, precision 93%, recall 92%, F1-Score 93%, dan AUC 0.95. Hasil ini menunjukkan bahwa model hybrid dapat memberikan akurasi yang lebih baik dibandingkan dengan model individual, serta memiliki potensi untuk digunakan dalam aplikasi berbasis teknologi untuk memberikan peringatan dini mengenai depresi. Penelitian ini juga mencatat pentingnya kualitas dataset dalam menentukan performa model dan mendorong penelitian lebih lanjut untuk menguji model dengan data real-time dan dataset yang lebih beragam.
Impact of Home Isolation on the Mental Health of Children and their Parents During COVID-19 Pandemic Prevention and Control in China
Authors: Zhang Xianlai , Wang Bingsong , Tao Hong , Lin Bokai , Wang Bo
Published: February 2025

Background: The mental health status of children and their parents was significantly impacted duringthe COVID-19 pandemic, particularly during periods of home isolation. This study aims to assess theCOVID-19 prevention and control measures, and psychological health status of children and their parentsduring the home isolation period in China. Methods: The study sample comprised 201 children aged4-16 years and their parents, divided into two groups, 101 children designated as the experimentalgroup, who had experienced home isolation in the Wuhu area from April 2022 to April 2023. The controlgroup had 100 children and their parents who did not experience home isolation. The Achenbach ChildBehavior Checklist (CBCL) was used to assess the psychological status of the selected children, and theSymptom Checklist 90 (SCL-90) was used to evaluate the psychological health status of their parents.Results: Children who experienced home isolation had significantly higher CBCL scores (mean: 34.31)compared to those who did not (mean: 9.75), indicating that home isolation may exacerbate psychologicalconditions in children. Parents who experienced home isolation also had significantly higher SCL-90scores (mean: 130.68) than those who did not (mean: 100.67), suggesting a negative impact on thepsychological health status of parents. Secondary outcome analyses revealed that parents of childrenwho experienced home isolation had higher factor coefficients in somatization, obsessive-compulsivesymptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, psychoticism, andsleep-eating subscales. Children in the experimental group also performed worse in social competence,interpersonal relationships, and academic performance as revealed in statistically significant differences,compared to those in the control group. Conclusion: Home isolation during the COVID-19 pandemic hasa negative impact on the psychological health status of children and their parents, with consistent effectsacross different age groups and genders, and more pronounced impacts observed in females and olderchildren above 10 years. These findings emphasize the need to consider the overall psychological healthneeds of families when formulating public health policies and providing psychological support services.
Design of a Web-Based Ordering Information System for the Legendary Ketan Restaurant in Tegal City
Authors: Maulana Ikbal , Mutiara Handayani Ujianti
Published: February 2025

The owner of Ketan Legendaris Restaurant in Tegal City also operates two other businesses, Mie Ayam Jhaya Baroe and Cafe Kopi PDKT, all of which are located within the same area. This setup often leads to challenges during peak times, such as unorganized order queues, accumulated orders, and delayed deliveries. Additionally, the restaurant's ordering system remains manual, relying on conventional record-keeping methods, which results in inefficient service and prolonged waiting times for customers.
This research aims to design a web-based ordering information system to enhance the efficiency and effectiveness of the ordering process at Ketan Legendaris Restaurant. Data collection methods include direct observation, interviews, and library research. The analysis phase is conducted through several stages: surveying the existing system, analyzing survey findings, identifying information needs, and determining system requirements.
The system design adopts the Waterfall model, utilizing UML diagrams as a tool for system modeling. MySQL is employed as the database, while the CodeIgniter framework is used to develop the web-based system using PHP. The research outputs a prototype of a web-based ordering information system designed to address the existing issues and improve the restaurant's operational performance. This system is expected to streamline the ordering process, making it faster, more organized, and accurate, thereby enhancing customer satisfaction and supporting the overall efficiency of the restaurant operations.
Design of a Web-Based Inventory Management System For Toko Fajar Mandiri Tani In Karangmulya, Suradadi
Authors: Maulidi Muhammad Akwan , Aries Setyani Wahyu Prasetyawati
Published: February 2025

This study addresses the problem of mismatched stock records and actual inventory levels in the warehouse of Toko Fajar Mandiri Tani, which often disrupts operations and customer service. To solve this, the study used data collection methods such as direct observations, interviews with staff, and literature review to build a strong foundation for the system design.
The analysis of the current manual system identified its strengths and weaknesses, which became the basis for developing a new solution. The result is a web-based inventory management system that helps record, monitor, and report stock levels in real-time.
The system implementation showed significant benefits, including improved accuracy in inventory data, reduced risks of overstocking or understocking, and better stock order recommendations. Additionally, the automated inventory reporting feature supports faster and more informed decision-making. This system not only records stock but also streamlines the overall operational management of the store. The findings of this study offer a practical technological solution to help small and medium-sized businesses manage their inventory effectively.
Operational Data Analysis and Visualization of PT XYZ Using Business Intelligence Approach with Microsoft Power BI
Authors: M. Frizky Feri Setiawan  , Yekti Condro Winursito
Published: February 2025

The study aims to analyze and visualize operational data at PT XYZ, a furniture manufacturing company, utilizing Business Intelligence methods with Microsoft Power BI. A systematic approach was employed, encompassing data import, transformation, cleaning, and visualization, to develop an interactive dashboard that enhances decision-making. Key findings indicate that certain production sections consistently met or exceeded their targets, while others revealed opportunities for improvement. Insights into service wage distribution, standard time requirements, and target realizations were derived from the dashboard. The research identified sections with high service wages and highlighted areas with elevated standard times, suggesting a need for efficiency enhancements. Recommendations include focusing on underperforming sections and optimizing operations to reduce service wages. The study concludes that the developed dashboard supports data-driven decision-making, ultimately contributing to improved operational performance within the companys.
Improving Regional Clustering Based on Tuberculosis Cases using the K-Means Algorithm of the Cirebon City Health Office
Authors: Wilda Rusmiati Rahayu  , Purnamasari Ade Irma , Bahtiar Agus , Kaslani
Published: February 2025

Tuberculosis (TB) is a highly infectious disease prevalent in Indonesia, including Cirebon City. This study utilizes the K-Means algorithm to optimize the clustering of areas based on TB case data from the Cirebon Health Office. By analyzing the number of cases, population density, and other factors, the study aims to identify regional clusters with similar TB case characteristics. The research employed Rapid Miner software and the Knowledge Discovery Database (KDD) methodology. The K-Means analysis categorized the study area into two clusters. Cluster_0, representing 20 areas, had lower TB risk, characterized by higher population density, smaller geographic size, and fewer TB cases. Cluster_1, representing two areas, exhibited higher TB risk, marked by lower population density, larger area, and more TB cases. The clustering quality was evaluated using the Davies-Bouldin Index (DBI), which yielded an optimal value of 0.189 at K=2K = 2. Additionally, the Avg within Centroid Performance Vector Analysis supported the clustering validity the clusters with  value of 19851032.925.The results demonstrate that this clustering approach effectively identifies TB risk areas, aiding targeted interventions. The findings provide the Cirebon Health Office with a framework for better resource allocation, focusing intensive programs in high-risk regions and preventive measures in low-risk areas.
Improving the School Type Clustering Model on the Foundation Using the K-Means Algorithm (Case Study: Kebon Kelapa Al-Ma'rifah, Cirebon Regency)
Authors: Hanifah Nur Aulia  , Martanto  , Arif Rinaldi Dikananda  , Mulyawan
Published: February 2025

This study aims to improve the  school type grouping model at the Kebon Kelapa Al-Ma'rifah Foundation, Cirebon Regency, using the K-Means algorithm. Data-based grouping is very important in supporting efficient education management, especially in environments that have various types of schools such as Madrasah Aliyah (MA), Vocational High School (SMK), Madrasah Tsanawiyah (MTs), and Madrasah Ibtidaiyah (MI). The data used comes from the New Student Registration (PPDB) dataset for the 2023–2024 school year, with demographic attributes such as name, place of birth, gender, and time of school entry. The evaluation of clustering quality was carried out using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results show that the optimal number of clusters is K=5 with the lowest DBI value of 0.201, which results in compact and well-separated clusters. The implementation of the K-Means algorithm helps the foundation understand the distribution pattern of students based on attributes such as gender, region, and entry time. This research provides practical benefits, including more targeted resource allocation, improved quality of education, and efficiency in school management. In addition, this research contributes to the development of data mining models in the education sector and opens up opportunities for the exploration of additional attributes such as academic achievement and socioeconomic conditions. Further research is suggested to use alternative algorithms such as K-Medoids or DBSCAN.
Support Vector Regression to Improve Ethereum Price Prediction for Trading Strategies
Authors: Muhamad Abdul Fatah  , Martanto  , Dikananda Arif Rinaldi , Rifai Ahmad
Published: February 2025

Predicting erratic assets like Ethereum is difficult in the dynamic cryptocurrency market. This study uses an enhanced Support Vector Regression (SVR) algorithm to create a daily price prediction model for Ethereum. Yahoo Finance provided the data, which was preprocessed to include missing value cleaning, normalization, and feature extraction of Moving Average (MA) and Exponential Moving Average (EMA). The data was collected between August 4, 2019 and August 4, 2024. An ideal combination was obtained by parameter optimization with GridSearchCV: gamma scale, linear kernel, epsilon of 1, and C of 100. The model performed well, as evidenced by its R2 of 0.9985 and MSE of 2137.97. The model's reliability in predicting Ethereum's price movement patterns was validated via prediction graphs. A 30-day forecast indicated a stable trend, with prices slightly decreasing from $2921.31 on January 1, 2025, to $2919.83 on January 31, 2025. These results highlight the importance of data preprocessing and parameter optimization in enhancing SVR model performance.
The Effect of SMOTE Application on Support Vector Machine Performance in Sentiment Classification on Imbalanced Datasets
Authors: Andriyani Dini , Ahmad Faqih  , Sandy Eka Permana
Published: February 2025

This research explores the effect of applying Synthetic Minority Oversampling Technique (SMOTE) on the performance of Support Vector Machine (SVM) algorithm in sentiment classification on imbalanced datasets. Public review data was collected from social media platform X (formerly Twitter) regarding the Free Lunch Program, with a total of 2,368 reviews automatically labeled using the BERT model into three categories: positive, negative, and neutral. Sentiment imbalance in the dataset was addressed by applying SMOTE to generate synthetic data on minority classes. The research method follows the stages of Knowledge Discovery in Databases (KDD), including data selection, preprocessing, labeling, transformation using TF-IDF, SVM model training, and performance evaluation. The experimental results show that the application of SMOTE successfully improves the accuracy of the SVM model by 12.48%, from 71.41% to 83.89%. Other evaluation metrics, such as precision, recall, and F1-score, also showed significant improvement from 0.69, 0.71, and 0.68 to 0.84, respectively. These findings confirm that SMOTE is effective in overcoming data imbalance, resulting in a more accurate and reliable sentiment classification model. This research contributes to the application of sentiment analysis in data-driven public policy evaluation.
The Impact of Principal Component Analysis Dimensionality Reduction on Sentiment Classification Performance Using Support Vector Machine
Authors: Fajria Azzahra Moudy , Faqih Ahmad , Dwilestari Gifthera
Published: February 2025

This study investigates the application of Principal Component Analysis (PCA) to enhance sentiment classification performance using the Support Vector Machine (SVM) algorithm. User reviews of the ChatGPT application from the Play Store were collected, preprocessed, and analyzed to identify the sentiment within the text (positive, negative, or neutral). The research follows the Knowledge Discovery in Databases (KDD) framework, starting with data selection, preprocessing, transformation, and applying PCA for dimensionality reduction. PCA was used to reduce the complexity of the high-dimensional text data, improving SVM's efficiency in sentiment classification. Evaluation results show that applying PCA led to an improvement in model performance, with accuracy increasing from 72.65% to 73.20%, precision from 71.58% to 72.24%, recall from 71.77% to 72.66%, and F1-score from 71.56% to 72.32%. Although the improvements were modest, the findings demonstrate that PCA effectively simplifies complex datasets and enhances SVM performance in sentiment classification, offering benefits in processing high-dimensional text data.
Naïve Bayes Optimization by Implementing Genetic Algorithm in Sentiment Analysis of BCA Mobile Reviews
Authors: Rizqy Muhammad Enricco , Faqih Ahmad , Dwilestari Gifthera
Published: February 2025

The development of the digital era has encouraged the adoption of mobile banking applications that facilitate banking transactions, including the BCA Mobile application which is simple but still adheres to a slightly outdated, user-friendly appearance but to provide the best service, it is necessary to evaluate the various problems that arise through review analysis. This study aims to conduct sentiment analysis of BCA Mobile application reviews taken from the Google Play Store, with data totaling 1,200 reviews scraping results using Google Collaboratory python programming language, to categorize negative and positive reviews used manual labeling for more accurate results, the Naïve Bayes approach is used in classifying positive and negative category reviews due to the ability of this algorithm to handle text data. However, the weakness of Naïve Bayes which is sensitive to irrelevant features can cause a decrease in accuracy. This research implements Genetic algorithm to improve the performance of Naïve Bayes. The results showed that the application of Genetic algorithm successfully increased the accuracy, precision of Naïve Bayes classification 95%, precision 92% to accuracy 98%, precision 99%, which proved the effectiveness of Genetic algorithm in optimizing the model and improving the quality of sentiment analysis.
Design and Development of the Sidajaya Tourism Village Information and Promotion Media
Authors: Rahayu Slamet , Tri Herdiawan Apandi  , Taufan Abdurrachman  , Nurfitria Khoirunisa  , Adi Firmansyah  , Asep Saepuloh
Published: February 2025

The potential of Sidajaya Tourism Village, which is not yet known to many people, has hampered the development of this tourist village. Information about the Sidajaya tourist village is still difficult to access, making people reluctant to look for the information they need, so knowledge about it is limited. Therefore, a system is needed to help the public access information about the Sidajaya tourist village. This research aims to build a website-based Sidajaya Tourism Village Information System as a medium for information and promotion. This system was built using the PHP programming language and MySQL database, with data collection methods through observation and interviews. The system development stage includes analysis, design, implementation, and testing. The result of developing this system is a website that allows the public to search for information about the Sidajaya tourist village easily and supports more efficient promotion and management to increase tourist visits.
Design of Student Attendance Information System at MTS Assalafiyah, Tegal City
Authors: Silvia Berliana Ghany  , Muhammad Fadlullah
Published: February 2025

The Attendance Information System is a modern solution for recording student attendance in schools, replacing conventional manual attendance lists. Student attendance tracking comprises two main aspects: attendance timekeeping and working timekeeping. With technological advancements, particularly in computing, educational institutions can leverage these tools to enhance operational efficiency. This study aims to design and develop a web-based Student Attendance Information System at MTs Assalafiyah Kota Tegal. The system is designed to streamline attendance management, making it faster, more structured, and resource-efficient.
The research employs the waterfall development method, consisting of several stages: requirements analysis, system design, coding, testing, and implementation for end-users. These stages are carried out sequentially to ensure that the system meets the needs of all stakeholders, including students, teachers, and school administrators. Using a web-based system supported by online networks, attendance processes can be conducted in real-time and with greater transparency.
The results of this study are expected to significantly contribute to administrative efficiency in schools, improve the accuracy of student attendance data, and simplify the management of attendance records in educational settings. Additionally, this system supports digitalization efforts in education, particularly in school administration.
The Improvement of Indonesian Film Genre Clustering Model Using the K-Means Algorithm in Film Production Decision-Making
Authors: Wiratriyana  , Martanto  , Arif Rinaldi Dikananda  , Mulyawan
Published: February 2025

The Indonesian film industry is expanding rapidly, but understanding audience preferences remains a significant challenge for producers. This study aims to cluster Indonesian films by genre and synopsis using the K-Means algorithm to aid in marketing strategies and content development. The dataset comprises 1,271 Indonesian film entries, including attributes like release year, genre, synopsis, and user ratings. The research follows the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, transformation, clustering with K-Means, and evaluation using the Elbow method to identify the optimal number of clusters. The results show that the K-Means algorithm successfully grouped the films into three clusters: drama, horror, and others. The analysis indicates that drama films dominate the high-rating cluster, while horror films are more commonly found in the low-rating category. The use of Principal Component Analysis (PCA) in the visualization aids in interpreting the clustering results, providing a clearer view of the data distribution. These findings highlight the potential for improving film production strategies by aligning content with audience preferences. By understanding genre patterns and ratings, producers can make more informed decisions in marketing and content development.
Web-Based Geographic Information System for Toddlers Nutritional Status in Rindi District
Authors: Meychristian V. Ama Lay  , Fajar Hariadi  , Hawu Yogia Pradana Uly
Published: February 2025

Sistem Informasi Geografis (SIG) merupakan teknologi yang memudahkan visualisasi dan analisis data spasial, berguna dalam berbagai bidang termasuk kesehatan. Salah satu penerapannya adalah pemantauan status gizi balita, dimana masalah gizi dapat berdampak serius terhadap pertumbuhan balita. Di Kecamatan Rindi, Sumba Timur terdapat 200 kasus balita dengan masalah gizi, namun pemantauan gizi masih dilakukan dengan menggunakan peta manual yang tidak dimutakhirkan secara berkala. Hal ini menghambat efektivitas program intervensi gizi karena proses pemantauan data dilakukan secara manual dan kurang optimal. Penelitian ini bertujuan untuk mengembangkan SIG berbasis web untuk memetakan status gizi balita di Kecamatan Rindi. Sistem dirancang dengan menggunakan metode waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Metode ini dipilih untuk memastikan bahwa pengembangan sistem dilakukan secara terstruktur dan menyeluruh. SIG yang dihasilkan diharapkan mampu memberikan informasi yang akurat dan real time, sehingga memudahkan dalam mengidentifikasi desa-desa yang memiliki masalah gizi. Hasil pengujian Black Box menunjukkan bahwa seluruh menu pada sistem berfungsi dengan baik dan dari hasil pengujian SUS diperoleh nilai akhir sebesar 78,25 yang menunjukkan bahwa penilaian Status Gizi Balita SIG di Kecamatan Rindi adalah Cukup dengan kategori penilaian Baik.
Enhancing Election Staff Selection through Decision Tree-Based Classification
Authors: Rizal Rayyan Firdaus  , Nana Suarna  , Irfan Ali  , Ahmad Rifai
Published: February 2025

The selection of competent election committee members is a critical aspect in ensuring the success of a fair and transparent election process. However, the subjective nature of this selection process necessitates a data-driven approach to optimize the selection of officials who meet the required competency criteria. This research aims to classify the competencies of prospective election committee members using the Decision Tree algorithm based on demographic data and technological attributes of the population. The study employs the Knowledge Discovery in Databases (KDD) methodology, which includes the stages of data selection, preprocessing, transformation, data mining, and evaluation. In this process, data collected through various attributes are processed to build a classification model. The Decision Tree algorithm is applied to extract patterns from the data, resulting in a decision tree that can classify individuals into different competency classes based on existing features. The research findings indicate that the Decision Tree algorithm effectively classifies respondents into several competency classes that represent varying levels of skills and interest in the election process. The model shows that Class 4 is the dominant class, indicating that most respondents have moderate competency in technological skills and interest in elections. Class 3 represents individuals with higher technological skills but moderate interest, while Classes 2 and 1 represent individuals with varying combinations of interest and skills. This study demonstrates that using the Decision Tree algorithm in the KDD process is highly effective in objectively classifying the competencies of prospective election committee members. By analyzing the interactions among relevant attributes, the model provides insights that can improve the accuracy of election official selection. This data-driven approach can be adapted to other contexts requiring competency classification, offering broader benefits for various criteria-based selection systems.
Automatic Waste Type Detection Using YOLO for Waste Management Efficiency
Authors: Alfattah Atalarais  , Kana Saputra S  , Hermawan Syahputra  , Said Iskandar Al Idrus  , Insan Taufik
Published: February 2025

The management of waste in Indonesia is currently suboptimal, with only 66.24% being effectively managed, leaving 33.76% unmanaged. This highlights a significant challenge in waste management, primarily due to a lack of understanding in selecting appropriate waste types. Advances in deep learning and computer vision offer promising solutions to this issue. This study employs the YOLOv8l model, a well-regarded deep learning model for object detection, to develop an automated waste type detection system integrated with trash bins. The dataset comprises 2800 images across four classes, each containing 700 images, and is split with an 80:10:5 ratio for training, validation, and testing. Evaluation on test data yields a mean Average Precision (mAP) of 96.8%, indicating robust model performance in object detection. The model's accuracy is further validated with a score of 89.98%. Real-time testing conducted at Merdeka Park, Binjai, demonstrates the system's capability to detect waste with varying confidence levels, consistently above the 0.5 threshold. The highest confidence was observed in bottle detection at 0.94, and the lowest in cans at 0.64, underscoring the system's reliability across different detection scenarios within a 30cm range.
Analysis of Fatigue Levels Using Multiple Linear Regression Methods on PT XYZ Workers
Authors: Audiansyah Agni Nirvana  , Rizqi Novita Sari
Published: February 2025

PT XYZ is a company engaged in warehousing. In every process, there are various activities that can cause fatigue in workers. Currently, researchers want to analyze the effect of body condition, material, and warehouse conditions on the fatigue level of warehouse workers. To do this, companies can use multiple linear regression tests with SPSS software. In the multiple linear regression test that has been carried out, it is known that the body condition and material factors do not have a strong influence on the level of fatigue that occurs. This is because the Pvalue >0.05. While the warehouse condition factor has a strong influence on the level of fatigue, H0 is rejected because it has a Pvalue of 0.015 <0.05. From the results of the anova test conducted, it can be seen that the three existing factors simultaneously affect the fatigue level of PT XYZ workers. This is because the significance value in the anova test is 0.004; smaller than the value of 0.005. The benefit of this research is that PT XYZ can determine the right solution in order to reduce the level of fatigue so that warehouse productivity increases.
Use of K-Means Algorithm in Model Improvement Production Data Grouping for Determination Convection Production Strategy
Authors: Ica Ica Pandia , Martanto  , Arif Rinaldi Dikananda  , Dede Rohman
Published: February 2025

This research was conducted to implement the K-Means Clustering algorithm in grouping convection production data to support the improvement of efficiency and effectiveness of production strategies. The data used is PT ABC's production data, which consists of important attributes, such as Production ID, Production Date, Product Name, Product Type, Color, Size, Raw Materials, and Order. The research method follows the stages of Knowledge Discovery in Database (KDD), which includes selection, preprocessing, transformation, data mining, and evaluation, so as to ensure that the data processed is relevant and ready to be analyzed. The grouping process is carried out using the K-Means algorithm, which groups data based on attribute similarity by determining the optimal number of clusters. The evaluation of the clustering results was carried out using the Silhouette Score and Davies-Bouldin Index metrics, where the results showed values that represented good cluster quality. A high Silhouette Score indicates that the data in the cluster has good uniformity, while a low Davies-Bouldin Index indicates a clear distance between clusters. The results of the grouping produce three main clusters that illustrate different production patterns, such as clusters with high, medium, and low order quantities. This analysis of the cluster provides important insights in supporting strategic decision-making, such as prioritizing resource management in high-order clusters and evaluating production efficiency in low-order clusters. This research is in line with previous literature that shows that the K-Means algorithm can be used effectively in big data grouping to support strategic planning. The practical contribution of this research is to help convection companies in understanding production patterns, so that production strategies can be designed more efficiently, responsively, and directionally. For further research, it is recommended to add new variables, such as production costs or work duration, as well as test other clustering algorithms to obtain more comprehensive results.
Sales Data Classterization Analysis Using K-Means Method for Marketing Strategy Development
Authors: Mifta Almaripat  , Ahmad Faqih  , Ade Rizki Rinaldy
Published: February 2025

In the digital era, utilizing sales data is very important to support strategic decision making. This research aims to overcome the problems faced by 9Doors Store in optimizing marketing strategies and stock management. The main problem faced is the lack of in-depth analysis of existing sales data, which results in difficulties in formulating appropriate marketing strategies and efficient stock management. For this reason, this research applies the K-Means Clustering method to group products based on customer purchasing behavior characteristics. The data used includes product categories, selling prices, initial stock, number of products sold, and total sales obtained from 9Doors Store during the period March to September 2024. The method used in this research is Data Mining approach with K-Means algorithm, which is implemented using RapidMiner software. The data analysis process goes through Knowledge Discovery in Databases (KDD) stages, including data collection, data cleaning (preprocessing), data transformation, and data mining using K-Means. Cluster evaluation is done using Davies-Bouldin Index (DBI) to assess the quality of clustering results. The results of this study show that the division of sales data into three clusters provides optimal results with the lowest DBI value (0.106), which indicates efficient clustering. This finding identifies products with high, medium, and low sales levels, which can be used to formulate more targeted marketing strategies. With these results, Toko 9Doors can improve stock management and design more effective promotions based on better customer segmentation.
Virtual Reality-Based Simulation Design for Hazardous and Toxic Waste Management in the Cement Industry
Authors: Denaldi Rananda Saputra  , Mega Cattleya PA Islami
Published: February 2025

Businesses with activities that impact the environment must conduct environmental protection and management for the waste and emissions resulting from their operations. Provisions regarding this matter are stated in Government Regulation Number 22 of 2021. Environmental management must be appropriate according to the type of waste produced, including hazardous and toxic waste. Hazardous and toxic waste has specific methods for storage and management, as detailed in the Regulation of the Minister of Environment and Forestry Number 6 of 2021. However, many companies still violate these regulations and must understand the proper storage techniques for hazardous and toxic waste. Therefore, with the advancement of current technology, we can create interactive technology for education and awareness, one of which is virtual reality-based simulation. Hence, the researcher designed a virtual reality-based simulation for hazardous and toxic waste management in the manufacturing industry.
Apllication of Six Sigma and FMEA Methods to Improve The Quality of Laminated Tube Packaging
Authors: Hafizh Hazmi Al Fauzi  , Rizqi Novita Sari
Published: February 2025

PT Lamipak Primula Indonesia is a company that produces various packaging made of plastic. The main product of PT Lamipak Primula Indonesia is toothpaste laminated tube packaging. PT Lamipak Primula Indonesia wants to minimize defects in products to minimize waste, reduce costs, increase efficiency, and maximize profits. This can also increase customer confidence in the product. PT Lamipak Primula Indonesia improves the quality of its products by applying Six Sigma with the DMAIC method. In this study, DPMO of 15766 was obtained and then converted to a sigma level of 3.7 which shows that the sigma level is below the 6 sigma level. This shows the achievement of sigma that has not been consistent and still shows the need for quality improvement in the laminated tube packaging production process in order to achieve zero defects. Based on FMEA analysis, it shows that the most significant failure occurs in machine conditions that cause defective shoulders with a value RPN of 210. This failure is caused by the lack of regular machine inspection, by the machine not being supervised in realtime, not following the SOP and the lack of worker skills.
New Employee Selection System using WP and SAW Methods Based on Web at PT Lanang Agro Bersatu
Authors: Ria Sapitri  , Syarifah Putri Agustini Alkadri  , Putri Yuli Utami
Published: February 2025

Employees are valuable assets for a company, requiring careful selection based on educational background and experience to ensure proper placement and avoid issues. At PT Lanang Agro Bersatu, the selection process involves approximately 30 candidates monthly. This study developed a web-based employee selection system using the Weighted Product (WP) and Simple Additive Weighting (SAW) methods. The system aims to calculate weight values for criteria such as Education, Work Experience, Age, Health, GPA, Academic Tests, and Psychological Tests, providing accurate rankings to simplify decision-making. The top candidate, Khusnul Wasillah, achieved the highest preference value of 0.1563, calculated through combined SAW and WP methods. System testing using black box and equivalence partitioning methods showed 100% accuracy.
Usability Scale System Method on Convogenius Platform for MSME Business Optimization
Authors: Syaiful Imanudin  , Rudi Kurniawan  , Umi Hayati
Published: February 2025

Usability is a critical factor in the successful adoption of technology, particularly for AI-based platforms designed to support micro, small, and medium enterprises (MSMEs). Convogenius AI is developed to assist MSME operations, yet its effectiveness and user-friendliness must be evaluated. This study aims to assess the usability of the Convogenius AI platform using the System Usability Scale (SUS) method and identify areas requiring improvement. The research employs a SUS survey to measure aspects such as ease of use, functional integration, and the need for technical support. The findings reveal favorable SUS scores for ease of use (average 3.04) and user intention to repeatedly use the platform (average 3.05). However, deficiencies are noted in system complexity (average 2.96) and technical support requirements (average 2.95). Overall, Convogenius AI is accepted by MSME users but requires enhancements in interface design and consistency to improve user experience. These improvements can potentially increase user satisfaction and support the operational efficiency of MSMEs.
Analysis of Factors Causing Work Accidents in Steel Plate Production with FTA and PDCA Methods at PT. XYZ
Authors: M. H. N. Islamsyah  , Rizqi Novita Sari
Published: February 2025

The identification of work accidents in the steel plate production process poses a serious risk to workers and disrupts operational activities. This research analyzes the factors causing work accidents at PT XYZ in the steel plate production process using the Fault Tree Analysis (FTA) method and the Plan-Do-Check-Act (PDCA) method. FTA is applied to identify and decipher the root causes of accidents, while PDCA serves as a framework for continuous improvement. Data were collected through accident reports, observations. The results showed that human error, including being pinched by a plate, hit by a ganco, and exposed to sparks were the main contributors to the occurrence of accidents. Through the PDCA cycle, several preventive measures were proposed, including enhancing worker training, improving equipment maintenance, and strengthening safety protocols. This study provides actionable insights to improve workplace safety and reduce the risk of accidents in the steel plate production process.
Implementation of the Naive Bayes Method in Sentiment Analysis of Spotify Application Reviews
Authors: Agung Triyono  , Ahmad Faqih  , Fathurrohman
Published: February 2025

This study focuses on sentiment analysis of Spotify application reviews on Google Play Store using the Naive Bayes algorithm. As a leading music streaming platform, Spotify receives diverse user feedback that reflects their experiences, complaints, and satisfaction. Sentiment analysis aids in understanding user opinions, enhancing services, and innovating features. The research involves collecting user reviews via web scraping, followed by preprocessing steps such as text cleaning, tokenization, normalization, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) method is employed to assign weights to words, highlighting their significance in reviews. The Naive Bayes algorithm categorizes sentiments into positive, negative, and neutral classes. Performance evaluation uses a confusion matrix to measure accuracy, precision, recall, and F1-score. Results indicate that Naive Bayes effectively classifies large volumes of unstructured data with high accuracy and efficiency. This study contributes practically by offering actionable insights to improve Spotify's services and theoretically by advancing sentiment analysis methodologies using machine learning. The findings highlight the algorithm's potential to understand user needs and address issues, reinforcing its value in text analytics for mobile applications.
Financial Information System at the Sumba Christian Church Web-Based Wainggai Congregation
Authors: Ndapa Otu Toni Yiwa , Hariadi Fajar , Sitaniapessy Desy Asnath
Published: February 2025

Financial information systems are information systems designed to provide users with information about cash flows throughout an organization. The procedures carried out in this system are a series of written documents or actions involving many people in a department to ensure consistent treatment. The Sumba Christian Church (GKS) Wainggai Congregation currently does not have information technology that can help to summarize church finances, so that errors often occur from the congregation when examining the treasurer. Because the calculation or addition of church finances every month is often wrong, so each income and expense must be recalculated from the beginning of the month. From certain problems, using information technology can help alleviate complaints or problems that often occur. Therefore, it is necessary to create an information system at the GKS Wainggai Congregation to help manage church finances so that errors do not occur again. This study uses the prototype method.The results of testing using the blackbox method show that this system can run according to its function without any errors. Meanwhile, from the results of the SUS test that has been carried out from the level of user satisfaction with the church's financial recording information system, the assessment given to 1o respondents resulted in a score of 78%. With acceptability ranges "Acceptable" and "High" ranges. The scale of grades is in the category of class "C". and on the "Good" Adjective ratings model. These results show that the financial recording information system can be accepted by its users.
Decision Support System for Selection of Achieving Students Using MetDecision Support System for Selection of Achieving Students Using Method Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Web Based
Authors: Isra Pebrianti  , Syarifah Putri Agustini Alkadri  , Asrul Abdullah
Published: February 2025

The selection of outstanding students identifies the best students based on grades and achievements to recommend them for college entrance. This process often encounters challenges due to numerous determining factors, leading to potential biases in decision-making. A Decision Support System (DSS) helps address this by utilizing data and decision models to resolve structured and unstructured problems. This study applies the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method, using criteria such as attendance, attitude scores, knowledge and skills component values, extracurricular/organizational involvement, and achievements. The DSS identified 40 outstanding students at SMA Negeri 1 Tayan Hulu, with the highest preference score of 0.0819 achieved by Indah Prasetyaning Tias. Functional testing was conducted using the black-box method with Equivalence Partitioning, and accuracy testing through MAPE showed a calculation accuracy rate of 2.79%.
Association Analysis of Printing and Photocopying Sales Data in Adzmi Art Shop Cirebon Uses the FP-Growth Algorithm
Authors: Suteja  , Rudi Kurniawan  , Yudhistira Arie Wijaya
Published: February 2025

In the digital era, transaction data analysis plays a crucial role in strategic decision-making, especially for SMEs such as Toko Adzmi Art in Cirebon Regency. This study aims to develop a sales data association model using the FP-Growth algorithm to identify product association patterns. Daily transaction data over a year were collected, processed through data cleaning, standardization, and transformation, and analyzed using RapidMiner software. Minimum support and confidence parameters were applied to evaluate the frequency and strength of product relationships. The results show that the combination of "Photocopy" and "Passport Photo" services has a confidence of 0.491 and a support of 0.061, with "Photocopy" as the most in-demand product (support 0.497). These findings open opportunities for bundling strategies and inventory optimization to enhance operational efficiency. This model provides an empirical foundation for SMEs to leverage data mining technology to improve competitiveness and customer satisfaction.