Analisis Sentimen Netizen Twitter terhadap Pemberitaan PPN Sembako dan Jasa Pendidikan dengan Pendekatan Social Network Analysis dan Naive Bayes Classifier

Pandemi covid-19 yang terjadi memberikan dampak di berbagai bidang kehidupan. Salah satu dampaknya penerimaan negara semakin tertekan hebat. Padahal di sisi lain negara dalam proses pemulihan ekonomi nasional (PEN) yang membutuhkan dana sangat besar. Sehingga pemerintah ingin menggenjot pendapatan negara dari pajak pertambahan nilai (PPN). Jika pemungutan PPN dapat dilakukan dengan seoptimal mungkin, maka akan meningkatkan penerimaan negara. Rencana tersebut mengakibatkan maraknya pemberitaan mengenai pengenaan PPN sembako dan jasa pendidikan di Indonesia. Pemberitaan tersebut secara otomatis memicu opini di masyarakat. Salah satu cara untuk melihat opini masyarakat adalah melalui media sosial Twitter. Penelitian ini bertujuan untuk mengkaji lebih dalam tentang network dan sentimen netizen Twitter tentang PPN Sembako dan jasa pendidikan. Hasil Social Network Analisis (SNA) menghasilkan 5 klaster dengan record ke-90 merupakan bottleneck node yaitu aktor utama penyebaran informasi antar klaster. Model Naive Bayes Classifier memberikan hasil Recall Accuracy bahwa untuk Accuracy Classified sebesar 74.865 persen sementara persentase untuk Incorrectly Classified Instance sebesar 25.135 persen. Hasil klasifikasi berdasarkan emosi terbentuk 5 ekspresi fear, sadness, surprise, joy, dan anger dan emosi kata yang paling banyak adalah emosi anger (amarah), artinya mayoritas respon masyarakat terhadap kebijakan pengenaan PPN sembako dan jasa pendidikan diidentifikasikan oleh R Studio sebagai wujud keamarahan

ANALISIS SENTIMEN TWITTER TERHADAP PERLINDUNGAN DATA PRIBADI DENGAN PENDEKATAN MACHINE LEARNING

As technology and information advances, the main defense and security aspects in the protection of personal data become very important. Protection of personal data is a human right that must be protected by the state. Data digitization is a demand and challenge in the advancement of information. Efforts in protecting personal data are basically carried out through legal certainty instruments in the form of regulations that regulate a system in order to realize a strong system in protecting cyber crime. Various regulations already exist in the legal system in Indonesia. Nevertheless, there are still cases of personal data leakage among Indonesians. The purpose of this study is to describe the condition of personal data protection in Indonesia and analyze cases of data leaks detected in Twitter tweets in the period July 1, 2021 to September 29, 2022. The study was conducted by using Twitter tweet scrapping techniques and classifying netizen responses based on positive, negative, and negative sentiments. neutral. Each sentiment is analyzed with wordcloud by finding what topics are often discussed by netizens on the protection of personal data. Furthermore, the classification evaluation is continued by looking at the accuracy of the machine learning classification algorithm, namely naive bayes and random forest. The results of the study stated that in the period from July 1, 2021 to September 29, 2022, the public's response to the protection of personal data was still negative. Which means that the data protection system in Indonesia is still not effective with the occurrence of various cases of data leakage. Based on the accuracy value, the Naive Bayes algorithm is very good at classifying tweets based on their sentiments, which is 99.84% compared to the random forest algorithm

Determinants of Poverty in East Java During The COVID-19 Pandemic

The global COVID-19 pandemic has infected million people in Indonesia. East Java has experienced Indonesia’s epicentre of positive COVID-19 cases. The economic disruption in East Java due to COVID-19 pandemic has led to increase the number of poor people. This study aims to examine the determinants of poverty during the pandemic outbreak. In this study, we employed multiple linear regression. The results reveals that simultaneously the cumulative number of COVID-19, unemployment rate, Gini Ratio, population density, human development index (HDI), and GRDP per capita affect the risk of poverty in East Java. Partially, the cumulative number of COVID-19, unemployment rate, population density, and HDI shows a significant effect to poverty. While the Gini ratio and GRDP per capita has an insignificant effect. The increase on cumulative number of COVID-19 cases is likely to increase the risk of poverty. Similarly, unemployment has a positive significant affect on poverty. The increase on unemployment rate tends to increase the number of poor people. Contrary, the HDI and population density have a negatively significant effect to poverty. The increase on HDI and population density tends to increase the number of poor people

Determinants of the Amount of Waste in East Java

Listed as one of the largest waste contributor provinces in Indonesia. The population of East Java in 2020 reached 39 million people, it is the second highest in Indonesia. The increasing number of people accompanied by an increase in income will increase people's consumption in an area and this will cause the increasing amount of waste. If this waste problem is not handled properly, it will have a domino effect as well as degrading the environment. This study wanted to determine the effect of population, real expenditure per capita per year and the number of waste banks on the amount of waste in 2020 in East Java Province. This study uses a comparison of OLS Regression and Robust Regression models. The criteria for selecting the best model use the smallest MAPE, RMSE, and RSE values and the largest R-square value. The results of the partial test and the simultaneous test show that the variables of population, real expenditure per capita per year and the number of waste banks significantly affect the variable amount of waste in East Java with the selected model is the Robust Regression model. The R-square value of the Robust Regression model in this study is 0.8909, meaning that the model's ability to explain the variability of the East Java waste amount data is 89.09 percent, and the rest is explained by other variables not included in the model.Tercatat sebagai salah satu provinsi penyumbang sampah terbesar di Indonesia. Jumlah penduduk Jawa Timur pada tahun 2020 mencapai 39 juta jiwa, tertinggi kedua di Indonesia. Peningkatan jumlah penduduk yang disertai dengan peningkatan pendapatan akan meningkatkan konsumsi masyarakat di suatu daerah dan hal ini akan menyebabkan jumlah sampah yang semakin meningkat. Jika masalah sampah ini tidak ditangani dengan baik, maka akan berdampak domino sekaligus merusak lingkungan. Penelitian ini ingin mengetahui pengaruh jumlah penduduk, pengeluaran riil per kapita per tahun dan jumlah bank sampah terhadap jumlah sampah tahun 2020 di Provinsi Jawa Timur. Penelitian ini menggunakan perbandingan model Regresi OLS dan Regresi Robust. Kriteria pemilihan model terbaik menggunakan nilai MAPE, RMSE, dan RSE terkecil dan nilai R-square terbesar. Hasil uji parsial dan uji simultan menunjukkan bahwa variabel jumlah penduduk, pengeluaran riil per kapita per tahun dan jumlah bank sampah berpengaruh signifikan terhadap variabel jumlah sampah di Jawa Timur dengan model yang dipilih adalah model Robust Regression. Nilai R-square model Robust Regression dalam penelitian ini adalah 0,8909, artinya kemampuan model dalam menjelaskan variabilitas data jumlah sampah Jawa Timur sebesar 89,09 persen, dan sisanya dijelaskan oleh variabel lain yang tidak termasuk dalam model

Saripati aljabar linear

viii, 217 p.; 21 c

Kompas teknik pengambilan sampel

x, 222 hlm. : ilus. ; tab. ; 21 cm

KOMPAS TEKNIK PENGAMBILAN SAMPEL/SM-19

236hlm;14x21c

Kompas Teknik Pengambilan Sampel

236 hlm.; 21 cm