<p><em>Vaksinasi </em><em>telah mulai</em><em> dilakukan pemerintah Indonesia per tanggal 13 Januari 2021 menjadi momen yang penting. Vaksinasi menjadi pilihan pemerintah Indonesia untuk menekan grafik penularan virus Covid-19. Indonesia telah menyiapkan dosis vaksin sebesar 371 juta vaksin corona. Namun, isu tentang vaksin juga beredar luas di masyarakat. Rumor tentang vaksin yang belum aman dan kurangnya sosialisasi menimbulkan pro dan kontra terhadap vaksinasi ini. Maka dari itu mencari tahu opini masyarakat tentang vaksinasi ini menjadi opsi untuk menentukan sentimen masyarakat terhadap vaksin.</em></p><p><em>Media sosial menjadi salah satu sumber opini masyarakat dalam menuangkan pendapatnya. Pengolahan opini tersebut dapat menjadi sebuah alternatif untuk menentukan respon publik terhadap suatu peristiwa tertentu. Menurut HootSuit Indonesia tahun 2021 Youtube menjadi social media dengan pengguna terbanyak. Dengan pertimbangan tersebut, Youtube menjadi sebuah pilihan untuk menjadi sumber dataset.</em></p><p><em>Selanjutnya dataset </em><em>itu</em><em> diolah dan </em><em>diproses dengan</em><em> metode Support Vector Machine data yang diolah tersebut akan dijadikan model klasifikasi untuk melakukan pengklasifikasian teks dari komentar Youtube dengan topik bahasan “vaksin covid-19”. Hasil dari pengklasifikasian tersebut diharapkan dapat menjadi informasi maupun masukan kepada pihak tertentu untuk dijadikan pertimbangan.</em></p>
Along with the development of the Covid-19 pandemic, many responses and news were shared through social media. The new Covid-19 vaccination promoted by the government has raised pros and cons from the public. Public resistance to covid-19 vaccination will lead to a higher fatality rate. This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM). This research aims to study the public response to the acceptance of the vaccination program. The research result can be used to determine the direction of government policy. Data collection was taken via Twitter in the year 2021. The data then undergoes the preprocessing methods. Afterward, the data is processed using SVM classification. Finally, the result is evaluated by a confusion matrix. The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative. The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy.
This research aims to cluster mall visitors. This is motivated by the mall's income which has decreased since the pandemic. Later from these several clusters we can find out the characteristics of the mall's visitors. Those characteristics will be used later to increase the income from the mall. In this research, we use a dataset from Kaggle named Pengunjung_mall in CSV format which will later be processed using Python language on Jupiter Notebooks using the K-Means method. To ensure how accurate the K-Means method is, optimization is carried out using the PSO (Particle Swarm Optimization) method. After performing clustering and optimization using Jupyter Notebook, the results will then be evaluated with DBI (Davies Bouldin Index) in Microsoft Excel to find out how well the Clustering is generated. The Clustering results obtained are used as a reference to determine the characteristics of mall visitors which is one strategy to increase Mall profits. As a result, we have succeeded in dividing mall customers into 5 clusters based on their annual earned income and expense scores. The cluster has been optimized with PSO and has succeeded in increasing the cluster resulting from the K-Means method which is proven by the Davies Bouldin Index method. This research has concluded that customers who have high income levels and have high spending scores are the targets with the highest priority level for malls.
According to the Minister of Education and Culture of the Republic of Indonesia's regulations from 2014, one of the essential elements in implementing higher education is the student's study duration. Higher education institutions will use early graduation prediction as a guide when developing policy. According to XYZ University data, the student study period is Grade Point Average (GPA), Gender, and Age are all aspects to consider. Using a dataset of 8491 data, the Prediction of Early Graduation of Students based on XYZ University data was examined by this study, particularly in the information systems and informatics study program. The aim is to find significant features and compare three prediction models: Artificial Neural Networks (ANN), K-Nearest Neighbor (K-NN) method, and Support Vector Machines (SVM). The Challenge in the development of a prediction model is imbalanced data. The Synthetic Minority Oversampling Technique (SMOTE) handles the class imbalance problem. Next, the machine learning models are trained and then compared. Prediction results increase. The best test accuracy value is on ANN with a data Imbalance of 62.5% to 70.5% after using SMOTE, compared to the accuracy test on the K-NN method with SMOTE 69.3%, while the SVM method increased to 69.8%. The most significant increase in recall value to 71.3% occurred in the ANN.
Tracer Study is a mandatory aspect of accreditation assessment in Indonesia. The Indonesian Ministry of Education requires all Indonesia Universities to anually report graduate tracer study reports to the government. Tracer study is also needed by the University in evaluating the success of learning that has been applied to the curriculum. One of the things that need to be evaluated is the level of absorption of graduates into the working industry, so a machine learning model is needed to assist the University Officials in evaluating and understanding the character of its graduates, so that it can help determine curriculum policies. In this research, the researcher focuses on making a reliable machine learning model with a tracer study dataset format that has been determined by the Government of Indonesia. The dataset was obtained from the tracer study of Amikom University. In this study, SVM will be tested with several variants of the algorithm to handle imbalanced data. The study compared SMOTE, SMOTE-ENN, and SMOTE-Tomek combined with SVM to detect the employability of graduates. The test was carried out with K-Fold Cross Validation, with the highest accuracy and precision results produced by SMOTE-ENN SVM model by value of 0.96 and 0.89.
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