Analysis sentiment about islamophobia when Christchurch attack on social media

Islamophobia is formed by "Islam" with "-phobia" which means "fear of Islam". This shows the view of Islam as "other" and can threaten Western culture. The recent horrific terror attack that took place at the Christchurch mosque in New Zealand, is the result of allowing an attitude of hatred towards Islam in the West. Twitter is social media that allows users send real-time messages and can be used for sentiment analysis because it has a large amount of data. The lexical based method using VADER is used for automatic labeling of crawling data from Twitter. And then compare Supervised Machine Learning Naïve Bayes and SVM algorithm. Addition of SMOTE for Imbalanced Data. As result, SVM with SMOTE is proven the highest performance value and short processing time

SURVEI KEPUASAN KADER POSYANDU TULIP TERHADAP PENGGUNAAN APLIKASI SIPOS UNTUK PENDATAAN DAN PELAPORAN SECARA DIGITAL

Pemerintah Desa Cipenjo sangat aktif mengadakan kegiatan - kegiatan untuk warga dan masyarakat sekitar, tidak hanya itu para kader - kader ibu PKK dan Aktifis Posyandu juga turut berpartisipasi dalam kegiatan ini. Saat ini Pemerintah Desa berharap agar masyarakat dibekali dengan kemampuan mengoperasikan Microsoft, hal ini bermanfaat untuk membuat segala kegiatan yang berlangsung menjadi automation. Pengabdian ini bertujuan memberi kontribusi pada ibu kader PKK dan Posyandu agar dapat mengoperasikan komputer guna mendukung kegiatan - kegiatan dan pencatatan yang dibuat di Pemerintah Desa.Setelah mengikuti pelatihan, peserta pelatihan terbukti lebih percaya diri untuk berbicara di depan public karena mereka lebih memahami cara mengoperasikan Microsof

Evaluasi Perkuliahan Menggunakan Metode Framework Pieces pada Universitas Mercubuana

Mercubuana University (UMB) is a private university that was getting Higher Education Accreditation (Akreditasi Institusi Perguruan Tinggi - AIPT) with an A in 2016. UMB has implemented E-learning since 2009 in all faculties. E-learning users, such as students and professors, can use an E-Learning Mercubuana University for 24 (twenty four) hours with a range of facilities as well as the use of Single Sign On for login. Facilities at E-Learning are the Dashboard, Course Overview, Forum, Quiz, Assignment, and others. In this research, The questionnaire was designed using the PIECES framework modified and distributed to 23 lectures and 23 students related to college the first semester of 2016-2017 with a sample random sampling method. The result of Implementation of online courses at the University Mercubuana Agree that there is value in Performance (67%), Information (61.7%), Economic (79.37%), Control (61.61%), and Service (61.97%). As for the assessment in the implementation Eficiency to E-Learning is Strongly Agree (80.81%)

Implementasi Perbandingan Deteksi Tepi Pada Citra Digital Menggunakan Metode Roberst, Sobel, Prewitt dan Canny

The field of digital image processing, such as segmentation, has become a widely discussed topic. Segmentation aims to divide the image into parts or regions so that there is no overlap with similar characteristics, such as color, shape, texture, and intensity. The segmentation process is generally divided into three groups of segmentation, including segmentation based on classification (classification based segmentation), segmentation based on edges (edge based segmentation), and segmentation based on region (region based segmentation). Edge detection is a systematic process used to detect pixels in digital images that are not fixed or always changing their brightness level in a line or curve. The purpose of this study is to compare edge detection methods using image objects. This research was conducted using the method of Robert, Prewitt, Sobel and Canny to detect the number of white pixels in each image. The tool used in this research is Simulink Matlab, where the parameters of each algorithm will be compared. Then the total number of white pixels is calculated from each edge detection method

Model Prediksi Kualitas Udara dengan Support Vector Machines dengan Optimasi Hyperparameter GridSearch CV

Air pollution continues to increase in Jakarta. The city ranks 12th in the world as the capital of a country with high levels of pollution. The Jakarta Environmental Service requires processing air quality data generated by the Air Quality Monitoring Station in order to produce valuable information as a decision-making tool. This data processing can be processed with data mining techniques to seek new knowledge from the database so as to find valid, useful and easy-to-learn patterns. The SVM data mining classification model is proposed in this study. Our contribution in this research is to create a classification model with SVM with new techniques, namely improvements in data processing to perform hyperparameter tuning. We saw that previous researchers only pursued high accuracy scores. In contrast to previous studies, we used the gridsearch cv hyperparameter optimization technique on the SVM classification model. The kernel polynomial with 2 degrees is the best parameter recommendation from the grid search cv technique. The accuracy before optimization is 73,31%, while after optimization is 94,8%. This shows an increase in accuracy of 3.2% after applying the grid search cv method to the classification of air quality monitoring using the SVM model Pencemaran udara terus meningkat di Jakarta. Kota ini menempati urutan ke 12 di dunia sebagai ibukota negara dengan tingkat polusi tinggi. Dinas Lingkungan Hidup Jakarta memerlukan pengolahan data-data kualitas udara yang dihasilkan oleh Stasiun Pemantauan Kualitas Udara agar menghasilkan informasi berharga sebagai alat pengambil keputusan. Pengolahan data ini dapat diproses dengan teknik data mining untuk mencari pengetahuan baru dari basis data sehingga menemukan pola-pola yang valid, bermanfaat dan dapat dipelajari dengan mudah. Model klasifikasi data mining SVM diusulkan dalam penelitian ini. Kontribusi kami dalam penelitian ini adalah membuat model klasifikasi dengan SVM dengan teknik baru yaitu perbaikan dalam pemrosesan data hingga melakukan hyperparameter tuning. Kami melihat para peneliti sebelumnya hanya mengejar nilai akurasi yang tinggi. Berbeda dengan penelitian sebelumnya, kami menggunakan teknik optimasi hiperparameter gridsearch cv pada model klasifikasi SVM. Polinomial kernel dengan 2 derajat merupakan rekomendasi parameter terbaik dari teknik grid search cv. Akurasi sebelum optimasi adalah 73,31%, sedangkan setelah optimasi adalah 94,8%. Hal ini menunjukkan peningkatan akurasi sebesar 21,5% setelah menerapkan metode grid search cv pada klasifikasi pemantauan kualitas udara menggunakan model SVM

Penerapan Finite State Automata Pada Vending Machine Cangklong Rokok

Salah satu masalah kesehatan dunia disebabkan oleh rokok. Menurut Organisasi Kesehatan Dunia (WHO), ada 2,5 miliar perokok di dunia, dua pertiganya di negara berkembang. Pandemi Covid 19 menyebabkan penurunan daya beli masyarakat karena sebagian besar masyarakat mengurangi aktivitas di luar ruangan untuk menekan penyebaran Covid 19. Strategi yang dapat dilakukan untuk memasarkan cangklong rokok yaitu dengan memanfaatkan Vending Machine (VM). VM yaitu mesin otomatis ang berjalan secara independen dan dapat secara otomatis menyediakan transaksi yang memenuhi kebutuhan manusia, seperti pembelian. Dalam jenis FSA ini, terdapat mesin bahasa yang mengenali, menerima, dan menolak jenis mesin FSA Deterministic Finite Automata (DFA) dan Nondeterministic Finite Automata (NFA)

Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja

Absence is a problem for the company. Absenteeism is defined as a task that is assigned to an individual, but the individual cannot complete the task when he is not present. Absence from work is influenced by many factors, including mismatched working hours, job demand and other factors such as serious accidents / illness, low morale, poor working conditions, boredom, lack of supervision, personal problems, insufficient nutrition, transportation problems, stress, workload, and dissatisfaction. The purpose of this study is to predict absenteeism at work based on the Absenteeism at work dataset obtained from the UCI Machine Learning repository site using the Weka 3.8 application and the Naïve Bayes algorithm, Support Vector Machine (SVM), and Random Forest. In the results of the study, the Random Forest algorithm obtained the highest accuracy, precision, and recall values compared to the Naïve Bayes and SVM algorithms, which resulted in an accuracy value of 99.38%, 99.42% precision and a recall of 99.39%

EVALUASI PERKULIAHAN MENGGUNAKAN METODE FRAMEWORK PIECES PADA UNIVERSITAS MERCUBUANA

Mercubuana University (UMB) is a private university that was getting Higher Education Accreditation (Akreditasi Institusi Perguruan Tinggi - AIPT) with an A in 2016. UMB has implemented E-learning since 2009 in all faculties. E-learning users, such as students and professors, can use an E-Learning Mercubuana University for 24 (twenty four) hours with a range of facilities as well as the use of Single Sign-On for login. Facilities at E-Learning are the Dashboard, Course Overview, Forum, Quiz, Assignment, and others. In this research, The questionnaire was designed using the PIECES framework modified and distributed to 23 lectures and 23 students related to college the first semester of 2016-2017 with a sample random sampling method. The result of Implementation of online courses at the University Mercubuana Agree that there is value in Performance (67%), Information (61.7%), Economic (79.37%), Control (61.61%), and Service (61.97%). As for the assessment in the implementation Eficiency to E-Learning is Strongly Agree (80.81%)

SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY ON COVID-19 VACCINATION ON TWITTER SOCIAL MEDIA

In the process, data mining will extract valuable information by analyzing the existence of specific patterns or relationships from extensive data. One of the concerns of the new disease outbreak caused by the coronavirus (2019-nCoV) or commonly referred to as Covid-19, was officially designated as a global pandemic by the World Health Organization (WFO) on March 11, 2020. To break the transmission of Covid-19, the government carried out vaccinations for the Indonesian population. In the first period, the vaccination target will be for health workers with a total of 1.3 million people, public officers with 17.4 million people, and 21.5 million people. 19. The Data processed is only text data from Twitter application reviews that use Indonesian. Using the polarity of the Sentiment class Textblob, the sentiment class is positive, negative, and neutral. The data mining used is SVM, Naive Bayes, and Logistic Regression. As for this research in the form of knowledge of sentiment in the community towards vaccination activities, the results of this study get 43% positive sentiment, 40.8% negative, and 16.2% negative by testing the classification algorithm, Logistic Regression accuracy of 87%, SVM 86, 4%, and Naive Bayes, 40% of these results, can be seen that the Indonesian people have a positive sentiment towards the covid-19 vaccine