KEHIDUPAN ISTRI BEKERJA DI LINGKUNGAN MASYARAKAT ISLAM: SUATU TINJAUAN TEORI FUNGSIONALISME STRUKTURAL ROBERT K. MERTON DI DESA SENDANGREJO, KECAMATAN DANDER, KABUPATEN BOJONEGORO

Di dalam skripsi ini ada satu rumusan masalah yang hendak dikaji, yaitu: Bagaimana kehidupan istri bekerja di lingkungan masyarakat Islam di Desa Sendangrejo Kecamatan Dander Kabupaten Bojonegoro. Untuk menjawab permasalahan tersebut, peneliti menggunakan metode penelitian kaulitatif. Metode ini di pilih agar di peroleh data penelitian yang bersifat mendalam dan menyeluruh mengenai kehidupan istri an bekerja di lingkungan masyarakat Islam yang berada Di Desa Sendangrejo, yang nantinya data yang di peroleh kemudian akan di sajikan dengan menggunakan teori Fungsionalisme struktural Robert K. Merton. Melalui pendekatan ini diharapkan skripsi ini mampu memberikan kesimpulan tentang wujud dari kehidupan perempuan pekerja di lingkungan masyarakat Islam, sehingga nantinya bisa memperkaya khazanah ilmu pengetahuan khususnya terkait dengan sosiologi. Dari hasil penelitian ini ditemukan bahwa (1) Kehidupan istri yang bekerja di lingkungan masyarakat Islam di Desa Sendangrejo, Kecamatan Dander, Kabupaten Bojonegoro, mereka sebagai perempuan, menyandang status ganda, atau peran ganda. Yaitu selain sebagai ibu rumah tangga, juga sebagai pencari nafkah tambahan untuk keluarga mereka. Mereka menjalani kehidupan sehari-hari dengan berbagai aktivitas dan kegiatan. Mereka banyak menghabiskan waktu untuk bergelut dengan pekerjaan atau mencari uang, dan tidak ketinggalan mereka juga tetap menjalankan kewajiban dan pekerjaannya sebagai ibu rumah tangga. Mereka tinggal di lingkungan masyarakat Islam, akan tetapi mereka tetap memperoleh kebebasan untuk bekerja, dengan catatan tetap pada koridor-koridor aturan Islam. Perempuan Islam atau seorang istri mempunyai hak untuk mengembangkan diri dengan berbagai cara, termasuk dengan cara bekerja. Dengan bekerja bisa meningkatkan pengalaman, juga menambah ilmu. (2) Perempuan atau ibu rumah tangga banyak yang bekerja di daerah ini dikarenakan berbagai faktor dan alasan, diantaranya ingin menambah penghasilan keluarga, mengisi waktu luang, meraih cita-cita, dan sebagainya

SENTIMENT ANALYSIS OF COMMENTS ON GOOGLE PLAY STORE, TWITTER AND YOUTUBE TO THE MYPERTAMINA APPLICATION WITH SUPPORT VECTOR MACHINE

Application is an important requirement in a business because it makes work more efficient thereby increasing the results of the company, pertamina as a supplier of fuel oil (BBM) in Indonesia provides the latest innovations by launching the mypertamina application for purchasing BBM which raises public opinion, and conveys its aspirations in social media. Text mining is a way to group community comments because text mining has an analysis that focuses on analyzing a comment that is extracted into information. The purpose of this study was to determine public sentiment towards the use of mypertamina by classifying comments using the Support Vector Machine (SVM) algorithm and finding the best kernel among linear, polynomial and RBF. In this study, data was taken from three social media, namely Google Play Store with 18.000 data, Twitter with 20.000 data and YouTube with 6.400 data with a total of 44.400 data. Sentiment is carried out by giving positive and negative classes, the accuracy obtained from sentiment is carried out for Google Play Store data of 95%, Twitter 76% and YouTube 99% and it is known that the best svm kernel in this study is the RBF kernel which outperforms the linear and polynomial kernels

IMPLEMENTATION OF THE NAIVE BAYES CLASSIFIER ALGORITHM FOR CLASSIFICATION OF COMMUNITY SENTIMENT ABOUT DEPRESSION ON YOUTUBE

Depression is a disease that knows no age, gender and social status. WHO states that more than 264 million people suffer from depression, people with depression will continue to grow if public knowledge about mental health is still low, especially in Indonesia. This can be known from the way the community responds to a case. This study aims to determine public sentiment towards people with depression by classifying comments using the Niave Bayes Classifier (NBC) algorithm and adding the Term Frequency-inverse Document Frequency (TF-IDF) method as a feature extraction method. Sentiment used as data is obtained from YouTube comments on several news media accounts such as tvOneNews, Kompas TV, Tribunnews, Official iNews, VIVACOID, CNN Indonesia and Tribun Jateng, so that 4783 data are obtained with training data of 3826 and 957 testing data. This sentiment was analyzed by giving three classes, namely positive, neutral and negative. The results of the sentiment analysis were dominated by positive sentiment of 93.31%, followed by negative comments of 6.68% while neutral sentiment was 0%, and the accuracy of the NBC Algorithm was 84.11%

APPLICATION OF NAÏVE BAYES CLASSIFIER ALGORITHM IN DETERMINING THE LEVEL OF CUSTOMER SATISFACTION WITH RUMBAI POST OFFICE SERVICES

Technological advances make service and delivery of goods grow rapidly. Coupled with people's changing shopping habits by shopping online, shipping companies are increasingly needed. PT. POS Indonesia is the first expedition company in Indonesia. Currently PT. POS Indonesia has opened many POS Office branches in every region in Indonesia, one of which is the Rumbai POS Office located in Pekanbaru City. To continue to maintain the company while competing with other expeditions, the Rumbai POS Office must continue to maintain its customers by improving the quality of service. Survey analysis can be done to determine the extent of customer satisfaction with the services provided. To find out the level of customer satisfaction, you can use the classification method. Naive Bayes is a popular and effective machine learning algorithm for classification problems. The study used datasets sourced from the results of questionnaire distribution to customers of the Rumbai POS Office. The questionnaire used 14 indicators derived from the Community Satisfaction Index set by the Ministry of Agriculturein 2004. The classification resulted in a Satisfied class of 16 data with a percentage of 84.2% and a Dissatisfied class of 3 data with a percentage of 15.8%, it can be concluded that the service at the Rumbai POS Office is good. From the classification results, it is proven that the Naïve Bayes algorithm is able to predict well the level of customer satisfaction with an accuracy value of 94.74%, precision of 100%, and recall of 94.12%. The results of this research can later be used as information for the Rumbai POS Office to be able to improve service quality