1,720,993 research outputs found
Implementation Of The C4.5 Algorithm For Recruitment Of E-Sports Team Members
Full text linkFenomena E-Sport in an increasingly fast-paced world, creating a global culture ranging from international E-Sport tournaments to the birth of management that houses the competing E-Sport teams to be the best. E-Sport teams need players or gamers who have high skills, but it is not only the skill level of a player that determines the success of an E-Sport team in a tournament, there are other factors that determine the success of an E-Sport team, where factors this can be used in determining the decision to recruit players or gamers to become members of the E-Sport team. Decision support systems (DSS) is one of the systems that can be relied upon as a method to assist an organization or E-sports team management in assisting the decision-making process. One method that can be used in DSS is to use the Decision Tree C4.5 Algorithm. The solution technique is to use entropy and information gain for the expansion of decision trees. C4.5 algorithm is a decision tree-based method. In the C4.5 algorithm, the selection of attributes is done using Gain, Ratio, by finding the Entropy value. C4.5 algorithm can provide effective results in supporting a decisio
Algoritma Triple Exponential Smoothing Untuk Prediksi Trend Turis Pariwisata Jatim Park Batu Saat Pandemi Covid-19
Full text linkThe level of tourism visits in 2021 both local and foreign to Indonesian tourism has decreased drastically. The COVID-19 pandemic is one of the causes of this loss. In the last 1 year, the level of tourism has dropped dramatically due to this pandemic. The impact on a country is an economic recession, Singapore is a country that is experiencing a severe recession of up to -40%, a country is a country that also depends, one of which is on tourism. Jatim Park Batu is a tourism learning park and family recreation area in Batu, East Java. Jatim Park is a well-known tourism object in East Java. The uncertainty of the number of tourists each month affects the operational management of Jatim Park in making every decision, both technical and strategic decisions. The researcher proposes to use the Triple Exponential Smoothing algorithm, the Holt Winters model, where this algorithm is classified as a prediction algorithm that can consider trend and seasonal factors. The method of measuring accuracy uses the MAPE (Mean Absolute Percetage Error) method. Tests were carried out by initiating the alpha beta gamma parameter 30 times and obtained an average of 9%.Tingkat kunjungan pariwisata ditahun 2021 baik lokal maupun mancanegara terhadap pariwisata Indonesia mengalami penurunan drastis. Pandemi COVID-19 menjadi salah satu sebab dari adanya kerugian tersebut. Dalam 1 tahun terakhir ini, tingkat pariwisata menurun drastis dikarenakan pandemi ini. Dampak terhadap sebuah negara adalah resesi ekonomi, Singapura adalah negara yang mengalami resesi cukup parah hingga -40%, negara adalah negara yang juga bergantung salah satunya pada pariwisata. Jatim Park Batu adalah sebuah pariwisata taman belajar dan tempat rekreasi keluarga di Batu, Jawa Timur. Jatim Park merupakan tergolong pariwisata yang terkenal di Jawa Timur. Ketidakpastian jumlah turis tiap bulannya mempengaruhi manajemen operasional Jatim Park dalam melakukan setiap pengambilan keputusan, baik keputusan yang bersifat teknis maupun strategis. Peneliti mengusulkan untuk menggunakan algoritma Triple Exponential Smoothing, model Holt Winters, dimana algoritma ini adalah tergolong algoritma prediksi yang dapat mempertimbangkan faktor trend dan musiman. Metode pengukuran akurasi menggunakan metode (Mean Absolute Percetage Error) MAPE. Pengujian dilakukan dengan inisiasi parameter alfa beta gamma sebanyak 30 kali dan didapatkan rata "“ rata sebesar 9%
DDoS Penerapan Random Forest dan Adaboost untuk Klasifikasi Serangan DDoS
No full textDi antara berbagai jenis serangan di bidang Teknologi Informasi, serangan DDOS adalah salah satu ancamanterbesar bagi situs internet dan menimbulkan risiko yang menghancurkan keamanan sistem komputer, terutamakarena potensi dampaknya. Oleh karena itu mengapa penelitian di bidang ini berkembang pesat, dengan parapeneliti yang berfokus pada cara-cara baru untuk mengatasi deteksi dan pencegahan intrusi. Machine learningdan Artificial Intelligent adalah beberapa tambahan terbaru dalam daftar teknologi yang diteliti untukmelakukan klasifikasi deteksi intrusi. Studi ini mengeksplorasi perilaku dan penerapan dataset DDoS untukpembelajaran mesin dalam konteks deteksi intrusi. Alur dalam penelitian ini, pertama adalah mengumpulkandataset DDoS mentah dari sumber yang memiliki reputasi baik. Setelah data diperoleh, kumpulan data akhirdibuat untuk pemodelan. Manajemen data melibatkan pembersihan data, transformasi tipe data dan pertukarandata pada pengumpulan data. Proses seleksi disertai dengan model. Dua algoritma terpisah, random danadaboost, digunakan untuk melatih model dengan dataset. Model divalidasi dan dilatih ulang dengan k-foldcross. Model tersebut akhirnya dievaluasi menggunakan data yang tidak terlihat. Hasilnya ditentukan olehberbagai ukuran keluaran. Dalam percobaan, dataset DDoS digunakan: CICDDoS_2019 Performa deteksiintrusi set data ini dianalisis menggunakan dua model pembelajaran mesin. Dataset dibagi dalam rasio 80:20untuk pelatihan model, validasi dan pengujian. Model pembelajaran mesin dipilih secara sistematis dan hatihatiuntuk memastikan bahwa eksperimen dilakukan dengan cara yang tepat. Hasilnya dianalisis menggunakansekumpulan metrik performa, termasuk akurasi, presisi, recall, f-measure, dan waktu komputasi
PENGEMBANGAN FITUR REKOGNISI KEGIATAN DENGAN METODE SCRUM
Full text linkActivity recognition from independent activities can be done according to learning outcomes. The recognition process uses Google form with submission stages, checking stages, and decision-making stages. The problems at checking stage are the conversion calculation process takes a long time and difficulty in finding data. The solution to solve is develop an activity recognition features. Development is carried out using the scrum method containing stages of user story, product backlog, sprint planning, daily scrum, sprint review, and sprint retrospective. The features produced are according to needs
Penerapan Random Forest dan Adaboost untuk Klasifikasi Serangan DDoS
Full text linkAmong the different types of attacks in the field of Information Technology, DDOS attacks are one of the biggest threats to internet sites and pose a devastating risk to the security of computer systems, mainly due to their potential impact. Hence why research in this area is growing rapidly, with researchers focusing on new ways to address intrusion detection and prevention. Machine learning and Artificial Intelligence are some of the latest additions to the list of technologies studied to perform intrusion detection classification. This study explores the behavior and application of DDoS datasets for machine learning in the context of intrusion detection. The flow in this study, first is to collect raw DDoS datasets from reputable sources. After the data is obtained, the final data set is created for modeling. Data management involves data cleansing, data type transformation and data exchange on data collection. The selection process is accompanied by a model. Two separate algorithms, random and adaboost, are used to train a model with a dataset. The model is validated and retrained with a k-fold cross. The model was eventually evaluated using invisible data. The result is determined by various output sizes. In the experiment, DDoS datasets were used: CICDDoS_2019 The intrusion detection performance of this dataset was analyzed using two machine learning models. The dataset is divided in an 80:20 ratio for model training, validation and testing. Machine learning models are selected systematically and carefully to ensure that experiments are conducted in the right way. The results were analyzed using a set of performance metrics, including accuracy, precision, recall, f-measure, and compute tim
Comparative analysis of YOLOv8 techniques: OpenCV and coordinate attention weighting for distance perception in blind navigation systems
Get PDFBlindness is a very important issue to consider in research aimed at assisting vision. This condition requires further study to provide solutions for the blind. This study evaluates and compares the effectiveness of the you only look once v8 (YOLOv8) model integrated with OpenCV and the coordinate attention weighting (CAW) technique for distance estimation in a blind navigation system. Initially, YOLOv8 integrated with OpenCV produced less than optimal results, prompting further improvement efforts to surpass the performance of CAW. The goal is to enhance the accuracy and efficiency of distance perception without the need for additional sensors. The materials used include a variety of datasets annotated with distance information to train and evaluate the model. The methods employed include integrating YOLOv8 with OpenCV for baseline comparison and applying CAW to improve distance perception through enhanced feature attention. The results show that YOLOv8+OpenCV Improved achieves the lowest mean squared error (MSE) across the entire distance range: 0-1 m (0.44), 1-2 m (0.50), 2-3 m (0.58), 3-4 m (0.64), and 4-5 m (0.71). YOLOv8+CAW also outperforms YOLOv8+OpenCV original, demonstrating a notable enhancement in accuracy. The model achieves a detection accuracy of 95.7%, showcasing the effectiveness of computer vision techniques in supporting blind navigation systems, offering precise distance estimation capabilities and reducing the reliance on external sensors. The implications include improved real-time performance and accessibility for the blind, paving the way for more efficient and reliable navigation assistance technologies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Object Detection with YOLOv8 and Enhanced Distance Estimation Using OpenCV for Visually Impaired Accessibility
Full text linkAccessibility challenges for the visually impaired are getting more serious yearly. To address this issue, this study presents an advanced object detection system that utilizes YOLOv8, enhanced with OpenCV for distance estimation. The methodology involves data preparation with diverse scenarios to test system accuracy, including environments like busy streets and indoor settings. Precision, recall, and F1-score metrics evaluate performance under varying lighting conditions. Results show a decrease in performance during low-light conditions, emphasizing the need for adequate lighting for effective detection. The system also includes a real-time implementation with a panic button feature, allowing immediate activation of object detection and distance estimation processes. The results are translated into Indonesian using a translation service and converted to speech, making the information accessible to users. By integrating YOLOv8 and OpenCV, the research achieves an average object detection accuracy of 91% with a low error rate of about 3.6%. Rigorous testing and evaluation under various conditions ensure reliability and effectiveness. The implications of this research extend to real-time applications like navigation assistance for the visually impaired, highlighting the potential for improved quality of life and independence. Future work will focus on optimizing detection in low-light conditions, incorporating additional sensors like infrared cameras, and enhancing real-time text translation services for accurate information delivery to visually impaired users. Additionally, continuous training with diverse datasets will be conducted further to improve the robustness and accuracy of the detection system
Pengurangan Dimensi dengan Metode Linear Discriminant Analist (LDA)
Full text linkThe purpose of this study is to reduce the dimensions of the dataset that affect the prediction of breast cancer. The data used in research is very much data or is called high-dimensional data. The use of classification algorithms has weaknesses when used on high-dimensional data, so an appropriate method is needed to reduce the dimensions or variables used. There are several methods that can be used to reduce dimensions. In this study using the method of linear discriminant analysis (LDA). LDA is a supervised machine learning algorithm that is used to classify data into several classes, using a linear technique to determine the best set of linear variables to unify class data. LDA is used to reduce the dataset variables used by retaining information that is important for the classification process. The method used in this research is using LDA in data processing and then using a logistic regression model for the classification process. The conclusion obtained in this study is that LDA can overcome the problem of multiclass classification. The results obtained were 16 wrong cases out of a total of 455 cases so that the results obtained were 0.035% misclassification
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