bluuebunny/crossref_metadata_2025 huggingface dataset:
Analisis Perbandingan Pembelajaran Online Dan Offline Terhadap Mahasiswa UIN SUSKA Riau Menggunakan Naive Bayes
Authors: Asiah Nur , Maita Idria , Novita Rice , Saputra Eki , Marsal Arif
Published: July 2024

Online lectures have become a common method used in education to deliver course materials to students. However, the exucution of Online lectures is not always smooth and often faces various challenges. One of the main issues is the limited internet access frequently experienced by students, particularly in regions where internet connectivity is limied, making it difficult for them to parcipate in lecturesnseamlessly. Addtionally, some students encounter difficulties in time management and self-motivation for independent learning. This research aims to analyst the conditions and issues that arise during the implementation of Online lectures and compare them with the traditional Offline lecture delivery at UIN SUSKA Riau. The Naïve Bayes algorithm is applied for the analysis, with a focus on Accuracy, Precision, Sensitivity, and specificity. The findings and analysis using this algorithm demonstrate a remarkable accuracy rate of 66,67%, precision rate of 70%, sensitivity rate of 77,78% and specificity rate of 50%. By looking at the results obtained, the Naïve Bayes method was successfully used in analyzing comparisons of Online and Offline learning for students of UIN SUSKA Riau.
Sistem Pendukung Keputusan Penerapan Metode EDAS Dalam Menyeleksi Konten Youtube Terbaik Untuk Anak Usia Dini
Authors: Salmon Salmon , Adytia Pitrasacha , Fahmi Muhammad
Published: July 2024

Youtube is an online video platform that has become one of the most popular and widely used websites in the world. YouTube's main goal is to provide facilities for content creators to upload the videos they create, interact with their audiences and provide users with a better and innovative video viewing experience, with features such as video browsing tailored to the user's interests and improved video quality. YouTube content is a term that refers to videos or other material uploaded to the YouTube platform, which is currently one of the largest websites in the world for sharing videos. In selecting Youtube content for early childhood, there are several criteria, namely: entertaining, not pornographic elements, increasing insight, not being violent, being creative. The results showed that the EDAS (Evaluation Based on Distance From Average Solution) method can provide an objective and accurate assessment by considering various relevant criteria. So as to produce YouTube content Omar and Hana ranked first (A2) with a score obtained 0.084 is YouTube content that is appropriate for children at an early age.
Implementasi Aplikasi Berbasis Mobile menggunakan Framework React Native dan Algoritma C4.5 untuk Rekomendasi Kelayakan Penerima Bantuan Yatim Impian Indonesia
Authors: Kosim Mochammad Alvian , Aji Setiawan Restu , Darwis Muhammad
Published: July 2024

One of the programs of Yatim Impian Indonesia, a foundation that focuses on social and education, is sharing carts for MSMEs (Gedor UMKM). Cart beneficiaries are selected based on certain qualifications so that they are considered eligible. However, in its implementation, there are problems in determining the eligibility of beneficiaries quickly. One of them is that the selection criteria have not been structured. To overcome the difficulties in determining the eligibility of beneficiaries quickly, a classification model is needed. This research aims to create a cross-platform mobile application that has the function of classifying the eligibility of beneficiaries by utilizing the C4.5 decision tree algorithm. This algorithm can provide recommendations to the foundation regarding whether the beneficiary is eligible to get a cart, based on the performance of the decision tree in the C4.5 algorithm. Furthermore, the classification model obtained from the decision tree is implemented in a mobile application to meet the needs of foundation administrators who are on duty in the field in conducting surveys. Using K-Fold Cross Validation Testing, the results of the C4.5 algorithm classification model show 81% accuracy in determining the eligibility of beneficiaries, and the results of the mobile application built with the React Native Framework which have been tested with Black Box testing show that all functional parts of the mobile application have run well.
Analisis Perbandingan Algoritma SVM, Naïve Bayes, dan Perceptron untuk Analisis Sentimen Ulasan Produk Tokopedia
Authors: Aulia Muhammad , Hermawan Arief
Published: July 2024

The rapid growth of the online market in Indonesia has changed the business landscape. Tokopedia, one of the leading E-commerce platforms, serves millions of users with a variety of products. In the fierce E-commerce competition, understanding customer reviews is very important. However, performing review analysis manually is a complex and time-consuming task. Sentiment analysis is needed to understand customer preferences, improve service quality, and maintain Tokopedia's competitiveness in the competitive E-commerce market. This study carried out a comparison between three algorithms, Support Vector Machine, Perceptron, and Multinomial Naïve Bayes to evaluate and determine the most effective and accurate algorithm in conducting sentiment analysis of product reviews on Tokopedia. The results of research using 2000 Tokopedia product review data show that Multinomial Naïve Bayes has the highest level of accuracy, reaching 84.00% and precision of 96.00%. Support Vector Machines has an accuracy rate of 80.00% and a precision value of 95.00%. Meanwhile, Perceptron provides 81.00% accuracy and 95.00% precision. Evaluation using the confusion matrix also indicates that Multinomial Naïve Bayes provides superior results with a truth level of 1011 for positive sentiment labels and 860 for negative sentiment labels. This research provides valuable insights regarding sentiment analysis of product reviews on Tokopedia, and the results can be a reference for further research exploring more innovative sentiment analysis methods or the application of technology to increase the efficiency of sentiment analysis in the context of E-commerce.
Peningkatan Algoritma C4.5 Berbasis PSO Pada Penyakit Kanker Payudara
Authors: Nurcahyo Rudi , Fanani Ahmad Zainul , Affandy Affandy , Aziz Mochammad Ilham
Published: July 2024

Onenof the diseases innthe world that causes deathnin women isncancer. Cancernis a diseasencaused by uncontrolled enlargement of abnormal organs in the body. Cancer diagnosis is made using anthropometric data from routine blood analysis. The data used is the Breast Cancer Coimbra Data Set obtained from the UCI Machine Learning Repository. The C4.5 method is andecision treenalgorithm that is often used in the classification process. The selection of the right features, as well as the selectionnof the right method to overcome the class imbalance in the classification process cannimprove the performancenof the C4.5 algorithm. confusion matrix can benused in the Test to determine Classification accuracy. In this research, the application of PSO as a feature organization.
Kombinasi Metode Multi-Attribute Utility Theory dan Pivot Pairwise Relative Criteria Importance Assessment Dalam Penentuan Lulusan Terbaik
Authors: Maharani Ainaya Qintan , Ardiansah Temi
Published: July 2024

The determination of the best graduates at SMK YPT Pringsewu is determined based on the average score, achievement, and report card score. By using this assessment, the school takes a long time with a large number of prospective graduates to complete and select all prospective graduates and determine the best graduates. So it requires a recommendation system in determining the best graduates, one of the recommendation systems for determining the best graduates is a decision support system. With the decision support system for determining the best graduates, it will speed up the process of determining the best graduates because it uses a computerized system. This study aims to develop a decision support system that applies the MAUT method and the PIPRECIA method in determining the best graduation in school and develop an objective system or approach to assess and rank the best graduates based on relevant criteria and preferences set by researchers or related parties. PIPRECIA and MAUT methods are used to overcome complexities in multi-criteria decision making and help select the best graduates effectively. The results of determining the best graduate students using the MAUT method and PIPRECIA recommended Mareta Amelia as the best graduate student in school because the final ranking results got Rank 1 with a total score of 0.642. The overall results for testing using the blackbox testing model with results are 100% in accordance with the functionality of the system for the determination of the best graduates.
Pengelompokan Status Stunting Pada Anak Menggunakan Metode K-Means Clustering
Authors: Tinendung Intan Saleha , Zufria Ilka
Published: July 2024

Stunting is a physical health disorder caused by a deficiency or imbalance of nutrients necessary for the growth and development of children. This article examines the problem of stunting in children in Kerajaan sub-district, with a focus on the Sukaramai village of Pakpak Bharat district. In 2021, Pakpak Bharat District saw an increase in the number of toddlers in North Sumatra, who were diagnosed with stunting, reaching 21.25%. This occurs due to socio-economic factors and socio-cultural background which have a lot to do with diet and nutrition. According to the Indonesian Toddler Nutrition Statutes (SSGBI), in 2019, the stunting rate in Indonesia increased to 27.7%. The impact of stunting on children includes physical growth disorders, delayed brain development, and the risk of chronic disease in adulthood. Recognizing the urgency of this problem, the government has taken various steps, including the designation of funds for stunt prevention programs. This study uses the K-Means Clustering method to group stunting status in children into three categories: normal, stunting, and rapid growth. Therefore, a method is needed to group stunting status in children, namely using the Clustering method with the K-Means algorithm. The aim is to assist the government in adopting appropriate policies related to reducing the prevalence of stunting in children based on the status and problems of each cluster. The data used primarily comes from the Sukaramai village. The results of the research showed that around 30% of the 101 children studied experienced stunting in the Kerajaan sub-district, including 0 total, 43 children with normal status, 1 total, 31 children with stunting, and 2 total children. 27 children who had not yet developed rapidly did not start early his umur. This study makes an important contribution to the management of data on the nutritional status of children in the remote area and can be a reference for future research. By applying the K-Means Clustelring algorithm, this study helps understand stunting patterns and design more targeted solutions.
Performance Comparison of k-Nearest Neighbor Algorithm with Various k Values and Distance Metrics for Malware Detection
Authors: Rafrastara Fauzi Adi , Supriyanto Catur , Amiral Afinzaki , Amalia Syafira Rosa , Al Fahreza Muhammad Daffa , Ahmed Foez
Published: July 2024

Malware could evolve and spread very quickly. By these capabilities, malware becomes a threat to anyone who uses a computer, both offline and online. Therefore, research on malware detection is still a hot topic today, due to the need to protect devices or systems from the dangers posed by malware, such as loss/damage of data, data theft, account hacking, and the intrusion of hackers who can control the entire system. Malware has evolved from traditional (monomorphic) to modern forms (polymorphic, metamorphic, and oligomorphic). Conventional antivirus systems cannot detect modern types of viruses effectively, as they constantly change their fingerprints each time they replicate and propagate. With this evolution, a machine learning-based malware detection system is needed to replace the existence of signature-based. Machine learning-based antivirus or malware detection systems detect malware by performing dynamic analysis, not static analysis as used by traditional ones. This research discusses malware detection using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features is reduced using the Information Gain feature selection method. The performance of kNN with Information Gain will then be measured using the evaluation metrics Accuracy and F1-Score. To get the best score, some adjustments are made to the kNN algorithm, where 3 distance measurement methods will be compared to obtain the best performance along with the variations in the k values of kNN. The distance measurement methods compared are Euclidean, Manhattan, and Chebyshev, while the variations of k values compared are 3, 5, 7, and 9. The result is, kNN with the Manhattan distance measurement method, k = 3, and using information gain features selection method (reduction until 32 features remain) has the highest Accuracy and F1-Score, which is 97.0%.
Komparasi Teknik Feature Selection Dalam Klasifikasi Serangan IoT Menggunakan Algoritma Decision Tree
Authors: Setiawan Dicky , Nugraha Adhitya , Luthfiarta Ardytha
Published: July 2024

Presence of Internet of Things (IoT) has revolutionized how we interact with the world on our daily life by enabling various devices to connect the internet and transmit data. However, the increasingly widespread use of IoT technology also brings serious threats to cyber security and increases the number of IoT attacks. The need for robust classification models is becoming increasingly clear to anticipate these problems. This research focuses on developing an IoT attack classification model by comparing feature selection techniques that utilize data from the CIC IoT Dataset 2023. This research faces challenges such as data imbalance and the complexity of handling various features. To overcome these challenges, this research uses random undersampling techniques to balance the data and utilizes various feature selection methods, including filter based, wrapper based, and embedded based. Apart from that, this research also tries to use a decision tree algorithm. The findings reveal that the application of wrapper based techniques as feature selection together with a decision tree algorithm produces the highest accuracy of 87.32% in classifying IoT attack types. This emphasizes that the use of techniques and algorithms that are still rarely used can provide fairly good accuracy results.
Implementasi Metode CRISP DM dan Algoritma Decision Tree Untuk Strategi Produksi Kerajinan Tangan pada UMKM A
Authors: Asyraf Haekal , Prasetya Machmudin Eka
Published: July 2024

In industry 4.0, the utilization of information technology and data analysis is very important in helping business decision making. The use of data mining and the Decision Tree algorithm in analyzing data can help to classify products that best suit customer preferences for Micro, Small and Medium Enterprises (MSMEs) handicraft products. In this research we use the Cross Industry Standard Process for Data Mining methodology to analyze data in classifying the types of craft products produced by MSME A to determine production strategy that suits market demand after the Covid-19 pandemic. Covid-19 influenced MSME A as a handicraft producer to temporarily stop production due to decreased demand and produce a special order only. Changes in consumer behavior due to the Covid-19 pandemic mean that MSME A must determine the right production strategy so that products are sold and can reuse the capital. We succeeded in building a fairly effective model with CRISP-DM and the Decision Tree algorithm which has an accuracy rate of 74.2%. This model found as many as 21 types of products that were still selling well during the pandemic, making it useful for MSME A for making production decisions based on market conditions during the Covid-19 pandemic.
Pengembangan Aplikasi Android Menggunakan REST API dengan Metode Waterfall Untuk Peningkatan Aksesibilitas Situs Repositori
Authors: Setiawan Muhhamad Ajun , Avianto Donny
Published: July 2024

The repository system offered by the campus to students to increase students' interest in reading, in fact, is still low in use even in an academic environment. This happens because the repository system offered is less convenient for students to use as a literacy tool. The result of the research is an application that is expected to be able to improve accessibility to repositories. A data filter feature and a new display update for the repository system will be added by the author in the implementation. Users of this service can filter their search results depending on the field of study, research specialization, and research period. Usability testing on the created application has shown that many students will start to be interested in using the repository system as their literacy material. This test used the System Usability Scale methodology, where the users of the program - in this case, university students - were given a questionnaire. According to the results obtained in the questionnaire, the usability value was quite high at 75.63 percent. The conclusion is that the repository application developed with the addition of this filtering feature can increase literacy interest in students.
Prediksi Retensi Pengguna Baru Shopee Menggunakan Machine Learning
Authors: Mustafa Wahyu Fajrin , Hidayat Syarif , Fudholi Dhomas Hatta
Published: July 2024

Shopee has evolved into one of the leading e-commerce platforms connecting sellers with consumers. However, the challenge of keeping users active and engaged on the platform has become increasingly complex. User retention, the ability of a platform to sustain and enhance user presence, is a key factor in the long-term success of an e-commerce platform. Understanding the factors influencing users' decisions to remain active or cease interactions with the platform involves analyzing various variables, including user behavior, preferences, shopping experiences, and interactions with the platform. This research is designed to develop an effective user retention prediction model using data from new Shopee users. By analyzing the data and applying machine learning techniques using Logistic Regression, Decision Tree, Gaussian Naive Bayes, Random Forest, KNN (K-Nearest Neighbors), MLP (Multi-Layer Perceptron), AdaBoost, and XGBoost methods, this study predicts user retention within a 14-day period after registration on Shopee. The results of this research indicate that the Random Forest model performs the best with an Accuracy value of 0.733677, Precision of 0.702161, Recall of 0.811626, and F1-Score of 0.752936. Cross-validation values demonstrate the model's consistency with an Accuracy of 0.727626, Precision of 0.698143, Recall of 0.801884, and F1-Score of 0.746328. The Random Forest model becomes a model with a high recall value, indicating good sensitivity in identifying users who retain. Consequently, the results of this research provide valuable insights for Shopee in developing retention strategies for new users, which is an important aspect in the growth and sustainability of the e-commerce business.
Analisa Algoritma K-Means Untuk Menentukan Strategi Marketing
Authors: Geni Bias Yulisa , Kurnia Okto , Hayati Nova , Thoriq Muhammad , Manurung Kiki Hariani
Published: July 2024

This research explored the application of the K-Means algorithm in the marketing field to increase the effectiveness of ABC Cosmetic Store marketing strategies. Sales data can be processed using data mining to be used as decision making at ABC Cosmetic Stores. One of the techniques in data mining is Clustering, which is used to categorize data. The K Means algorithm is used to identify hidden patterns in the data. By using bodylotion sales data, this research aims to classify consumers into several groups. The data group in question is sales data that is of great interest to consumers and data that is of little interest to consumers. The results of clustering using k=2 show that cluster 1 consists of 5 products with product transactions sold being 1295 products. In this case, it shows that cluster 1 is a group of product data whose quantity sold has increased and can provide profits at the ABC Cosmetic Store. Meanwhile, cluster 2 has 1 data with 214 product transactions sold and is grouped as data with products that are less popular with consumers so there is no need to increase the stock available in the warehouse by ABC Cosmetic Shop. The results of this research show that K Means-based customer segmentation can increase personalization in marketing communications and increase the efficiency of marketing resource allocation. This study provides new insights into how data mining techniques can be involved in marketing strategies to determine product availability for the future.
Implementation Graph Sampling and Aggregation (GraphSAGE) Method for Job Recommendation System
Authors: Wijaya Dewa Made , Wiharja Kemas Rahmat Saleh
Published: July 2024

Finding job is currently a challenge, especially for final-year students. Career Development Centre (CDC) is a service that is provided by a university for its students. However, a more sophisticated system is needed that not only provides job information but provides job recommendations based on their interests, skills, and experience. Developing a GraphSAGE-based job recommendation system can help provide suitable jobs according to user preferences. GraphSAGE works by embedding nodes or feature vectors at each node or node in a graph. GraphSAGE aggregates information from neighbouring nodes and propagates that information using different model layers. By combining the feature information of each node, the resulting representation can be richer in information and also more accurate. The development of the GraphSAGE system uses a dataset from the "Job Recommendation Challenge" from Kaggle which consists of 3 data, namely job data, user dataset, and applicant dataset. This study also uses GAT to provide a value or weight for each node before GraphSAGE process the graph. Based on experimental results, this GraphSAGE model has an accuracy value of 97.5% and this value is 13% greater than its comparison, namely FNN (Feedforward Neural Network) commonly used at tabular dataset. This comparison helps us know that which the best model we have to use to the dataset. The model also tested on the Movie dataset, Food dataset, and Epinions dataset.
Analisis Topic-Modelling Menggunakan Latent Dirichlet Allocation (LDA) Pada Ulasan Sosial Media Youtube
Authors: Alpiana Vika , Salam Abu , Alzami Farrikh , Rizqa Ifan , Aqmala Diana
Published: July 2024

This research explores the role of Micro, Small, and Medium Enterprises (MSMEs) in the Indonesian economy, focusing on sales and marketing challenges in the era of social media, especially YouTube. With millions of individuals using this platform to share product insights, reviews, and experiences, MSMEs need to receive relevant feedback. This study applies text mining, particularly the topic modeling analysis method with Latent Dirichlet Allocation (LDA), to analyze user comments on MSME videos, with an emphasis on Lumpia Gang Lombok Semarang on YouTube. Through the application of LDA, the identification of ten main topics is conducted, with the highest coherence value reaching 0.414027. The visualization of the intertopic distance map provides an understanding of the relationships between topics and dominant words. Comment analysis provides valuable insights into user preferences and perceptions of products, supporting MSMEs in understanding customer satisfaction and enhancing value for those enterprises. These findings also affirm the effectiveness of YouTube as a relevant data source for understanding public preferences for MSME products. This research details text processing methods, including extraction, cleaning, tokenization, normalization, removal of stopwords, and stemming. With this approach, the research not only provides insights into topic analysis in the context of social media but also makes a valuable contribution to the development and marketing of MSMEs through a better understanding of social media data, especially on the YouTube platform.
Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection
Authors: Andrean Muhammad Niko , Shidik Guruh Fajar , Naufal Muhammad , Zami Farrikh Al , Winarno Sri , Azies Harun Al , Putra Permana Langgeng Wicaksono Ellwid
Published: July 2024

Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.
Decision Support System for Selecting Peer-to-Peer Lending Applications using ARAS and Rank Sum Approaches
Authors: Diana Elviza
Published: July 2024

The development of information technology has brought major changes to the world of finance, especially with the emergence of new business models such as Peer-to-Peer (P2P) lending. Even though the potential for high profits can be obtained through P2P lending platforms, the challenges faced by investors in choosing the right investment application are increasingly complex. With so many choices in P2P lending applications, decision-makers must be careful in making their choice. This creates problems if the decisions made are incorrect, resulting in financial losses. So, the aim of this research is to build a decision support system for choosing a Peer-to-Peer lending application by applying a combination of the Additive Ratio Assessment (ARAS) method and Rank Sum weighting to make it easier for users to determine their choice. Based on the case study, the utility value obtained is highest to lowest, namely: Amartha Microfinance (A3) got a score of 0.9034, Asetku (A4) got a score of 0.8954, KoinWorks (A5) got a score of 0.8640, Investree (A2) got a score of 0.8484, and Danamas Lender (A1) got a score of 0.8080. Besides that, the usability test got an average score of 88.75%, which means the system is appropriate for its use and function. The resulting decision support system has features that make it easier for decision makers to determine P2P lending applications because the system developed can display alternative rankings.
Perbandingan Resident Set Size dan Virtual Memory Size Algoritma Machine Learning dalam Analisis Sentimen
Authors: Yudhanegara Reza Ardiansyah , Hana Nisrina Aliya , Mahfiridho Syahrizal Yonanda , Kardian Aqwam Rosadi
Published: July 2024

In the rapidly advancing era of digital transformation, where textual data abounds from various online sources such as social media, forums, and product reviews, sentiment analysis has become a critical component in understanding public opinions and consumer behavior. Sentiment analysis employs machine learning, natural language processing, and computational linguistics to comprehend the feelings and opinions of others. The machine learning algorithms investigated in this paper include K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Naive Bayes, ID3, and C4.5. The sentiment analysis process requires significant computational resources to handle the complexity and scale of data. This research aims to examine the differences in resource usage among these algorithms and determine which algorithm is best suited for sentiment analysis in this context. The research methodology employed is quantitative, focusing on the collection and numerical analysis of datasets. Testing is conducted using the Anaconda Library in the Python programming language to measure the usage of Resident Set Size (RSS), Virtual Memory Size (VMS), execution time, and the accuracy of each algorithm. The test results indicate that the Support Vector Machine (SVM) algorithm with an accuracy rate of 96% and the Naive Bayes algorithm with an accuracy rate of 97% are the best choices for use in the context of sentiment analysis. When considering the context of Resident Set Size (RSS) and Virtual Memory Size (VMS) usage in a single execution, ID3 is the algorithm with the smallest resource usage, with an accuracy rate of 92%. The average resources used by ID3 are 8.318.566,4 bytes for Resident Set Size (RSS) and 7.965.900,8 bytes for Virtual Memory Size (VMS) with an execution time of 2,619 seconds.
Prototipe Alat Pendeteksi Detak Jantung, Saturasi Oksigen, dan Suhu Tubuh Berbasis Arduino Mega Menggunakan Fuzzy Sugeno
Authors: Aurum Annisa Dwita , Fitriani Endah
Published: July 2024

Health is a highly sought-after aspect for all living beings on Earth. By health checkups can prevent health disturbances that may develop into diseases. Various health checks include heart rate, oxygen saturation, and body temperature checks. These checks can be conducted independently at home using various health monitoring devices available in the market. However, these health devices only display numerical values without providing detailed information about the results, requiring users to seek further information. With the development technological and sensors, three health parameters can be combined into a single detection device capable of storing results. This detection device is designed using an Arduino Mega microcontroller with the Fuzzy Logic Sugeno classification method. Fuzzy input is obtained from the MAX30102 sensor for heart rate and oxygen saturation, as well as the MLX90614 sensor for body temperature. The detection results are displayed based on their classifications along with recommendations and can be stored on the device using a MicroSD card and sent to a Telegram application. The device operates on batteries for increased practicality.This research an accuracy of 96.11% for the MAX30102 sensor and 98.83% for the MLX90614 sensor. The Fuzzy Sugeno method was successfully implemented in the device and met expectations, producing outputs consistent with calculations. The data storage process using the SDCard Module was successful with a delay of 6.17 seconds, allowing review previous results. The battery testing indicated that the device can operate for up to 4 hours.
Sentiment Analysis of Reviews on Lazada Apps using Naïve Bayes Algorithm
Authors: Nurdiyansa Zhafran Afif , Berlilana Berlilana
Published: July 2024

Lazada app reviews on the Google Play Store become useful information if processed properly. Existing or new users can analyze app reviews to get information that can be used to evaluate the service. The activity of analyzing app reviews is not enough just to look at the number of stars, it is necessary to look at the entire content of the review comments to be able to know the purpose of the review. A sentiment analysis system is a system used to automatically analyze reviews to obtain information including sentiment information that is part of online reviews. This time the data will be classified using the Naive Bayes method. A total of 1,000 user reviews of the Lazada app were collected to form a dataset. The purpose of this study was to conduct sentiment analysis of Lazada app reviews on Google Play Store using Naive Bayes algorithm. This stage of research involves data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage, there are 6 stages, namely Cleaning, Case Factoring, Word Normalization, Tokenization, Hyphen Removal, and Base Word Formation. The TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighing. The data will be grouped into two categories, namely negative and positive. Next, the data will be evaluated using accuracy parameters. The test results showed an accuracy value of 84%, then for the grouping of negative and positive reviews, it was found that Lazada application reviews tended to be negative.
Integrating LightGBM and XGBoost for Software Defect Classification Problem
Authors: Airlangga Gregorius
Published: July 2024

Software defect classification is a crucial process in quality assurance, pivotal for the development of reliable software systems. This paper presents an innovative approach that synergizes traditional software complexity metrics with advanced machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost), to enhance the accuracy and efficiency of software defect classification. Leveraging a dataset characterized by McCabe's and Halstead's metrics, this study embarks on meticulous data preprocessing, feature engineering, and hyperparameter optimization to train and evaluate the proposed models. The LightGBM and XGBoost models are fine-tuned through the Optuna framework, aiming to maximize the ROC-AUC score as a measure of classification performance. The results indicate that both models perform robustly, with XGBoost demonstrating a slight superiority in predictive capability. The integration of machine learning with traditional complexity metrics not only enhances the defect classification process but also provides deeper insights into the factors influencing software quality. The findings suggest that such hybrid approaches can significantly contribute to the predictive analytics tools available to software engineers and quality assurance professionals. This research contributes to the field by offering a comprehensive methodological framework and empirical evidence for the effectiveness of combining machine learning algorithms with traditional software complexity metrics in software defect classification.
Analisis Perbandingan Optimizer pada Pelatihan Model Convolutional Neural Network untuk Kasus Klasifikasi Hewan Primata
Authors: Solihat Sinta , Widodo Suprih , Sari Dian Permata
Published: July 2024

Classification is a way to group certain things, for example primate animals, based on the similarities and differences that exist in animals. This classification intends to identify similarities or characteristics in these animals. Image classification is the process of grouping an object in the form of an image into certain categories using the CNN algorithm, but the resulting accuracy level is not satisfactory. Therefore, this research aims to produce the right optimizer to be used in the CNN model. In this study, the data was collected using the web scrapping method, and the data source is Google Images, so the total amount of data obtained is 1631 images. The framework for completing this research is the AI Project Cycle, which includes problem scoping, data acquisition, data exploration, modelling, and evaluation. Based on the research results, the optimizer with the highest accuracy value is Adadelta, which has an accuracy value of 75%. Therefore, Adadelta is the right optimizer for primate classification in the CNN algorithm model.
Pemilihan Fitur Menggunakan Algoritma Chi-Square Dan Particle Swarm Optimization (PSO) Untuk Meningkatkan Kinerja Deep Neural Network Pada Deteksi Penyakit Diabetes
Authors: Santosa Wahyu Budi , Syukur Abdul , Purwanto Purwanto
Published: July 2024

Diabetes is a chronic disease caused by impaired glucose metabolism in the body. In type 1 diabetes, the body's immune system attacks and destroys the insulin-producing cells in the pancreas, so that the body is unable to produce sufficient amounts of insulin. In type 2 diabetes, the body is still able to produce insulin, but is unable to use it effectively. Insulin is a hormone that is useful for controlling glucose in blood cells. If glucose in the blood is not controlled it can cause a number of problems, such as heart disease, stroke, blindness, nerve damage and so on. So the research in this study formulated an increase in accuracy in the diabetes detection process using the Deep Neural Network (DNN) method which was enhanced with the chi-square and PSO methods through the attribute selection process. The results of testing the PIMA dataset with DNN obtained an accuracy value of 76.62% with an AUC value of 0.772. Meanwhile, testing using the DNN method for attribute selection using the Chi-square method and optimization with PSO obtained an accuracy value of 85.71% with an AUC value of 0.818. So it can be concluded from testing diabetes data using the Deep Neural Network method which adds Chi-square as a selection attribute and is optimized using PSO which is better when compared to the DNN method.
Data Mining Dalam Analisis Faktor Drop Out Mahasiswa Menerapkan Algoritma Decision Tree
Authors: Azhari Mulkan , Maulana Halim , Riza Ferdy
Published: July 2024

Graduation accuracy is one of the indicators used in assessing the suitability of undergraduate programs as a functional unit of higher education. Knowing the factors that influence student graduation time helps study programs and faculties make decisions to increase the number of students who graduate on time. The purpose of this research is to obtain an overview of the factors that influence students in the accuracy of completing the study time by using the Machine Learning algorithm, namely Decision Tree, which is expected to have high classification efficiency and good description so that it can increase the number of students graduating on time. The methods used to determine student dropout factors are Classification and Regression Tree (CART) and LightGBM. The data used is the data of undergraduate students of Universitas Muhammadiyah Sumatera Utara in 2019. The quality of classification can be read from the accuracy, sensitivity and specification values. The result using CART is 95.1% with the most influencing factors are GPA, faculty, lecture time and predicate while Lightgbm is 83% with the most influencing factors are GPA, gender, lecture time and faculty. Decision tree can be used to determine student dropout factors because of its high accuracy with GPA being the main factor.
Analisis Sentimen Pinjaman Online di Twitter dengan Metode Naive Bayes Classifier dan SVM
Authors: Arischo Ray Shandy , Damayanti Damayanti
Published: July 2024

Online loans are a form of financial service that occurs online or online, where online loans are available in applications or information technology. Online loans can also be a place to develop small and medium enterprises, because they provide easy access to loans and are also relatively safe. The social media platform twitter is one of the platforms that discusses illegal and legal online loans. Twitter has a trending topic feature that displays topics of conversation that are being discussed at a certain time. This research uses sentiment analysis which is useful as access to track public responses to an object of interest. In this study using a comparison of algorithms, namely naïve bayes classifier with support vector machine (SVM), where from the two methods will be sought who is better at analyzing data with which value of accuracy, precision, recall, f1-score is better. The data used in as many as 2725 tweets obtained through the crawling process with the python programming language and google collaboratory tools. Sentiment analysis is divided into 3 categories, namely positive, negative, and neutral, with data calculations divided into 70% training data and 20% test data. The naïve bayes classifier algorithm has an accuracy value of 55%, with a support of 404 data. Meanwhile, the support vector machine (SVM) accuracy is 77% with a support of 818 data. The results of the accuracy value of the SVM method are better than the naïve bayes classifier method in this study.
Penerapan Algoritma Hash Based Terhadap Penentuan Rule Asosiasi Transaksi Penjualan Sparepart Sepeda Motor
Authors: Salmon Salmon , Lailiyah Siti , Nursobah Nursobah , Andrea Reza
Published: July 2024

The problem that occurs in increasing sales transactions is that the large number of sales transactions every day with many kinds of spare parts makes it difficult for sales to determine strategies for offering spare parts that are relevant and really needed by consumers. The large amount of transaction data to be analyzed is not possible to do manually. Therefore, a certain technique is needed that can carry out the association rule mining process quickly on quite large data. One technique that can be used for association rules is the Hash algorithm. Hash Based Algorithm Uses hashing techniques to filter out itemsets that are not important for generating the next itemset. Generating frequent itemsets in this research requires a minimum support value and a minimum confidence value. The minimum support value used in this research is the average frequency of spare parts sales. Development is carried out in the process of generating candidate rules using a hash table on transaction data. By using the Hash Based algorithm, the association rule mining process becomes faster and more efficient in memory usage. From the results of the association rules trial with a minimum support of 40% and a minimum confidence of 75%. 14 lists of association rules were produced that met the requirements. This list becomes a reference for sales in determining strategies for offering spare parts to consumers.
Implementasi Data Mining dengan Menerapkan Algoritma K-Means Clustering untuk Memberikan Rekomendasi Jurusan Kuliah Bagi Mahasiswa Baru
Authors: Arfyanti Ita , Bustomi Tommy , Haristyawan Ivan
Published: July 2024

At the tertiary level, a student studies in a field of expertise or major that suits his or her area of talent and interest. Choosing an inappropriate college major will have consequences for the future of the prospective new student. In choosing a major, a prospective new student should choose a major that suits his abilities, both academically and his talents. One way to overcome prospective students who are wrong in choosing this major is to use the K-Means Clustering method. The K-Means algorithm is part of clustering data mining which has the role of forming new groups based on cluster formation. The K-Means Clustering algorithm can solve the problem of recommending majors to prospective new students based on school grades. The results of applying the K-Means algorithm show that in Cluster 1 there are 6 prospective students, in Cluster 2 there are 11 prospective students and in Cluster 3 there are 3 prospective students.
Implementation of SVM, k-NN, and DT for Toxicity and Sentiment Classification of AWA Vlog Content in Wasur National Park
Authors: Singgalen Yerik Afrianto
Published: July 2024

This study delves into the response of viewers to video content focusing on Wasur National Park in Papua, Indonesia, with a particular emphasis on its implications for livelihood and ecology. The increasing popularity of online platforms such as YouTube has provided a medium for content creators to showcase natural landscapes and cultural heritage, potentially influencing viewers' perceptions and behaviors toward conservation efforts. Employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, this research systematically analyzes a specific video from the AWA channel, known for its documentaries on environmental and cultural topics. The methodology involves sentiment analysis to gauge viewers' emotional responses, toxicity assessment to identify harmful content, and thematic coding to categorize comments based on recurring themes. The analysis reveals that viewers engage with the content positively, expressing appreciation for the video's educational and visually compelling nature. Moreover, the study identifies various dimensions of toxicity within the dataset, including Toxicity (0.05364), Severe Toxicity (0.00629), Identity Attack (0.02250), Insult (0.03534), Profanity (0.03589), and Threat (0.01280). Furthermore, the performance of the Support Vector Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) is highlighted, demonstrating its effectiveness in classifying sentiment with an accuracy of 93.86%, precision of 100.00%, recall of 87.73%, f-measure of 93.44%, and an Area Under the Curve (AUC) value of 1.000. This research underscores the significance of balanced media portrayals in fostering positive attitudes toward environmental conservation and cultural preservation.
Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse
Authors: Ramadhani Bagus , Suryono Ryan Randy
Published: July 2024

Digital transformation makes the world change rapidly, especially in the development of metaverse technology. The development of metaverse technology has received positive and negative responses from the public, so it is necessary to analyze whether public opinion accepts the development of metaverse technology or vice versa. This research aims to analyze 6728 public comment data regarding the metaverse on social media X using a text mining approach. By comparing text mining algorithm models, this experiment seeks to find the best algorithm for metaverse sentiment analysis, thereby providing insight to industry players involved in metaverse development. This research experiment uses a comparison of two algorithms, namely Naïve Bayes and Logistic Regression. The comparison results for the Naïve Bayes algorithm have an accuracy value of 90% and Logistic Regression of 91%, but the precision, recall, and F1-Score results are low. This indicates that the machine predominantly learns positive sentiment because this sentiment has a majority label, namely 5799 positive sentiment data, while negative sentiment is a minority label with 795 data. To overcome the problem of unbalanced data (Imbalance) in this research, SMOTE optimization was used. The results of SMOTE optimization have a superior value in the Logistic Regression algorithm, the accuracy value of 95% has also increased in the confusion matrix, namely the precision value of 94%, recall of 93%, and F1-Score of 95%. Meanwhile, the Naïve Bayes algorithm has a smaller value, namely 91% accuracy, and the negative sentiment confusion matrix has increased to 87% precision, 97% recall, and 92% F1-Score, so the accuracy and confusion matrix values have better performance.