The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword “Brimob” with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%.
Penelitian ini bertujuan untuk mengetahui pengaruh kualitas pelayanan, nilai pelanggan, dan kepercayaan terhadap kepuasan pelanggan pada Fixpay. Fixpay adalah sebuah platform Mobile Payment yang dapat melakukan beragam jenis pembayaran dan pembelian secara online dari smartphone..Penelitian ini menggunakan pendekatan kuantitatif dengan metode asosiatif. Data yang digunakan menggunakan data primer berupa kuesioner yang diperoleh melalui google form. Pengambilan sampel menggunakan teknik non random sampling sehingga diperoleh sampel penelitian sebanyak 100 responden. Hasil penelitian menunjukkan bahwa kualitas pelayanan, nilai pelanggan, dan kepercayaan berpengaruh signifikan terhadap kepuasan pelanggan Fixpay baik secara parsial maupun simultan. Disarankan kepada pihak perusahaan untuk terus meningkatkan kepuasan pelanggan, seperti dengan membuat mudah aplikasi Fixpay untuk dioperasionalisasikan, mudah mengakses aplikasi, dan meningkatkan nilai kegunaan dari aplikasi Fixpay. Pengolahan data dalam penelitian ini menggunakan Structural Equation Modeling (SEM) dengan Partial Least Square (PLS).
Nowadays, computer networks are widely used to exchange valuable and confidential data information between servers to computers or cellular devices. Access to user control and use of software or hardware as a firewall often experience security problems. Unauthorized access to information through computer networks continues to occur and tends to increase. This study examines the attack detection mechanism by using three data mining algorithms based on particle swarm optimization (PSO), namely PSO-K Nearest Neighbor, PSO-Random Forest, and PSO-Decision Tree in the Canadian Institute for Cybersecurity Dataset (CICIDS2017). The initial experiment showed that the approach using the PSO-RF method was able to produce the highest accuracy of attack detection. Accuracy values generated using the PSO-RF algorithm with a combination of the number of trees and maximal depth = 20 in the CICIDS2017 dataset are intact higher than other proposed algorithms. The highest accuracy of attack detection in the CICIDS2017 dataset is intact, which is 99.76%. In the CICIDS2017 dataset 50% Benign and 50% Attack it turns out that the PSO-RF algorithm with a combination of the number of trees and maximal depth = 20 also gets the highest accuracy value of 99.67%.
Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, “transform case”, “tokenize”, “token filters by length”, “n-gram”, “stemming” were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.
Situs media sosial Twitter adalah tempat di mana pengguna Internet di seluruh dunia dapat bertukar perspektif tentang diskusi terkini. Salah satunya sepak bola, olahraga ini merupakan hobi yang digandrungi oleh seluruh penjuru dunia, termasuk warga Malang, dengan kecintaan mereka terhadap olahraga tersebut mereka menamakan dirinya Aremania yaitu suporter tim Arema Malang, namun terjadi peristiwa kelam. Kanjuruhan di Stadion Malang pada 01/10/2022, memunculkan pandangan berbeda dari semua akun pengguna Twitter, yang menyebabkan peningkatan tweet dan menjadi trending topik saat itu. Untuk mengembangkan perspektif yang berbeda berdasarkan apa yang membawa keuntungan dan kerugian di komunitas, diterapkan prosedur untuk mengklasifikasikan perspektif positif atau negatif pengguna Twitter melalui analisis sentimen dengan pengklasifikasi Naïve Bayes. Analisis sentimen dilakukan dengan mengindeks tweet pengguna Twitter dengan tagar UsutTuntasTragediKanjuruhan, mengambil (crawling) data 1.500 tweet yang ada sebagai kumpulan data (dataset), setelah itu data untuk diproses diidentifikasi (labeling) untuk langkah selanjutnya yaitu tahap Preprocessing data yang terdiri dari Annotation Removal, Remove Hashtag, Transformation Remove Url, Regexp, Indonesian Steaming, Indonesian Stopword Removal dipadukan dengan operator Smote Upsampling. Pembuatan Confusion Matrix yang menunjukkan hasil akhir analisis berjalan dengan baik yaitu nilai accuracy 77,67%, nilai precision sebesar 77,19%, nilai recall sebesar 78,50%, dan nilai AUC 0.820 (good classification).
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