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All Journal Proceeding SENDI_U Jurnal Informasi dan Teknologi
Ardhana Januar Mahardhani, Ardhana
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Computer Science & IT Education

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PENGEMBANGAN MASYARAKAT PESISIR DI KABUPATEN TULUNGAGUNG Januar Mahardhani, Ardhana
Proceeding SENDI_U 2015: SEMINAR NASIONAL MULTI DISIPLIN DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (72.979 KB)

Abstract

Keberadaan Kabupaten Tulungagung yang berbatasan langsung dengan Samudera Indonesia, menjadikan lautmerupakan bagian penting yang perlu dikembangkan. Keberadaan masyarakat yang ada di sekitar pantai semakinmemerlukan perhatian khusus manakala terjadi gap yang berlebih antara masyarakat pesisir dan perkotaan. Tulisanini merupakan hasil penelitian yang akan menguraikan permasalahan masyarakat pesisir yang ada di KabupatenTulungagung, melihat bagaimana keadaan yang ada dan apa saja peran yang telah diambil oleh pemerintah daerahmengatasi permasalahan yang ada. Desain penelitian menggunakan kualitatif dengan metode pengumpulan dataadalah observasi, wawancara, serta dokumentasi. Lokasi penelitian adalah di Desa Panggungkalak, KecamatanPucanglaban, Kabupaten Tulungagung. Hasil penelitian diketahui jika masyarakat Desa Panggungkalak KecamatanPucanglaban yang ada di sekitar pesisir hidup dengan sangat terbatas, pemerintah daerah tidak begitu mengambilperannya dalam rangka memberikan kekuatan kepada masyarakat guna mengembangkan ekonomi lokal.Kata kunci : Pengembangan, Masyarakat, Pesisir.
Application of Naive Bayes Classifier Method to Analyze Social Media User Sentiment Towards the Presidential Election Phase Dharta, Firdaus Yuni; Januar Mahardhani, Ardhana; Rachmawati Yahya, Sitti; Dirsa, Andika; M. Usulu, Elvira
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i1.494

Abstract

This research aims to analyze the sentiment of social media users towards the election. The author collected data in this research through a literature study and observation. The author uses a classification method with the Naïve Bayes Classifier Algorithm and Support Vector Machine to analyze sentiment results. Next, this research extracts word assessment features using TextBlob, which changes text into positive or negative classes. Based on the research results, after going through the text preprocessing stage of more than 15,000 tweets, 11,000 clean tweets were obtained, which were then labelled using the text blob library in Python. The labelling results show that 4,000 tweets are positive, and the rest are harmful, indicating that most social media users' sentiment towards the election is positive. Words that often appear in the positive class express support and confidence in implementing elections that are considered honest and fair. On the other hand, words in the negative class reflect negative sentiment towards implementing elections, which are considered unsuccessful and time-consuming. The Naïve Bayes method provides accuracy, precision, and recall values of 85%, 80%, and 75%. In the Support Vector Machine method, testing is carried out with three kernels (linear, RBF, and poly), where the poly kernel with the best parameter values C is ten and degree is 1 produces the highest accuracy, precision, and recall of 90%, 90%, and 85%, respectively.
Co-Authors Dirsa, Andika Firdaus Yuni Dharta M. Usulu, Elvira Rachmawati Yahya, Sitti