The relocation state capital of Indonesia raises various responses, especially from the Indonesian people. The discussion related to these issues is very interesting to study, how are the positive and negative sentiments of the Indonesian towards the government's decision. This study aims to analyze the sentiments of the Indonesian people regarding the relocation state capital of Indonesia, including the chosen name of Nusantara on Twitter. In this study, a comparison of 3 algorithms is used, namely the Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbor (KNN) algorithms. From this study, the results obtained are 1,141 positive comments, while negative sentiments are 591 comments. This shows that the Indonesian people have a positive opinion towards the new capital city of Indonesia. In the classification and model testing phase, 10-fold cross validation is used. From these tests, the SVM algorithm obtained an accuracy value of 85.71%, the Naïve Bayes algorithm obtained an accuracy value of 76.70%, the KNN algorithm obtained an accuracy value of 52.74%. This study shows that the SVM algorithm can work better than the Naïve Bayes algorithm and KNN. The accuracy value for the KNN algorithm obtains a low value, this is because the KNN algorithm is sensitive to features that are less relevant.
Along with the passage of sales through the marketplace. The courier service industry has also experienced an increase. There are many choices of courier services to choose from, each courier service has different qualities depending on how the service provided by the courier service to customers. To find out the quality of courier services, this research was carried out, this research was conducted on courier service applications on Playstore, usually the best application predicate is for applications with a high number of downloads and ratings, while comments from users must also be considered to get results which is relevant. In this study, the Naïve Bayes algorithm is used because it has a high level of accuracy to determine the best courier service application. Based on the research that has been done, the highest accuracy value is obtained by the JNT application of 100% but has a positive sentiment of 50 reviews. The two JNE applications with an accuracy value of 98% with a positive sentiment of 46 reviews, the three ninjaxpres applications with an accuracy value of 97.87% with a positive sentiment of 49 reviews, the fourth by the Sicepat application with an accuracy value of 97.85% with a positive sentiment of 47 reviews and finally by the Idexpress application with an accuracy value of 94% with a positive sentiment of 49 reviews.
ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57.6% towards ChatGPT, while the negative sentiment reached 42.4%. The resulting classification model had an accuracy of 80%, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development.
<p><em>Abstract</em></p><p><em>Sentiment analysis of twitter tweets from the Indonesian people can be used as one of the parameters to be a support for the government in evaluating decision making and policies in the future. This study aims to find out the sentiments of Indonesian people's tweets on Twitter about the Information and Electronic Transaction Law. The data material used in this study uses a query on the Information and Electronic Transaction Law, Hate Speech, Defamation, Online Fraud, and Data Theft. The test is carried out by calculating accuracy, precision, recall and F1-score, using a variety of training data and test data. The highest accuracy results were obtained from the composition of 90% training data and 10% test data with an accuracy value of 84% with an average precision of 84%, recall 65%, f1-score 71% for each sentiment class.</em></p><p><em>Keywords: Sentiment Analysis, Support Vector Machine Algorithm, Community Tweet</em></p><p>Abstrak</p><p>Analisis Sentimen cuitan twitter dari masyarakat Indonesia dapat dijadikan sebagai salah satu parameter untuk menjadi penunjang bagi pemerintah dalam mengevaluasi pengambilan keputusan dan kebijakan di masa yang akan datang. Penelitian ini bertujuan untuk mengetahui sentimen dari cuitan masyarakat Indonesia di twitter seputar Undang-Undang Informasi dan Transaksi Elektronik. Bahan data yang digunakan dalam penelitian ini menggunakan <em>query</em> Undang-Undang Informasi dan Transaksi Elektronik, Ujaran Kebencian, pencemaran nama baik, Penipuan <em>Online</em>, dan Pencurian data. Pengujian dilakukan dengan perhitungan akurasi, <em>precision</em>, <em>recall </em>dan<em> </em>F1-<em>score</em>, dengan menggunakan variatif data latih dan data uji. Hasil akurasi tertinggi didapatkan dari komposisi data latih 90% dan data uji 10% dengan nilai akurasi 84% dengan rata-rata <em>precision</em> 84%, <em>recall</em> 65%, <em>f1-score</em> 71% tiap kelas sentimen.</p><p><strong><em>Kata Kunci</em></strong><em>: Analisis Sentimen, Algoritma Support Vector Machine,</em> Cuitan Masyarakat</p>
Pengabdian kepada masyarakat merupakan salah satu unsur Tri Dharma Perguruan Tinggi yang rutin dijalankan oleh Universitas Nusa Putra setiap tahunnya dalam kegiatan Kuliah Kerja Nyata (KKN).Mahasiswa berkolaborasi dengan dosen pembimbing terjun secara langsung ke lokasi KKN untuk membantu memenuhi kekurangan yang ada di sebuah desa.Desa Sukamanah Kecamatan Gegerbitung Kabupaten Sukabumi merupakan daerah paling selatan Sukabumi yang berbatasan langsung dengan Kabupaten Cianjur.Secara Topografi Desa Sukamanah termasuk kategori daerah perbukitan dengan akses jalan yang kurang baik. Hal ini menyebabkan terhambatnya pelayanan kantor desa kepada masyarakat dalam hal administrasi kependudukan. Selain itu sistem konvensional penyuratan yang diterapkan menyebabkan sering terjadinya surat tercecer dan hilang. Maka dari itu pengabdian kepada masyarakat ini bertujuan untuk membuat sebuah sistem administrasi surat untuk kantor Desa Sukamanah guna mempermudah para pegawai untuk melakukan layanan administrasi surat-menyurat kepada masyarakat seperti surat masuk, surat keluar, surat pindah, surat kematian, dsb. Sistem ini dibangun menggunakan bahasa pemrograman PHP dan sistem basis data MySQL dengan metode pengambangunan sistem Waterfall.
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