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PENGEMBANGAN APLIKASI PEMANDU WISATA GUCI BERBASIS MOBILE MELALUI PRINSIP USER-CENTERED DESIGN (UCD) Muhammad Fikri Hidayattullah; Dwi Intan Af’idah; Sharfina Febbi Handayani; Putri Ajeng Imamah
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.3694

Abstract

Pariwisata memainkan peran krusial dalam ekonomi Indonesia, dengan pertumbuhan sektor ini mencapai 7,2% per tahun, melebihi rata-rata dunia sebesar 4,7%. Objek Wisata Guci di Kabupaten Tegal, Jawa Tengah, menarik perhatian sebagai destinasi potensial. Namun, kesulitan wisatawan dalam mendapatkan informasi fasilitas umum di tempat tersebut menjadi tantangan. Penelitian ini mengembangkan aplikasi pemandu wisata "Guci Explore" berbasis Android, menerapkan metode User-Centered Design (UCD) untuk meningkatkan pengalaman pengguna. Dengan melibatkan pemandu wisata digital, aplikasi ini memberikan informasi akurat dan terkini untuk mengatasi kebingungan wisatawan. Pengujian menunjukkan kepuasan pengguna mencapai skor 81, memperkuat kategori Grade Scale "A" dan Acceptability Ranges "Acceptable". Aplikasi ini diharapkan dapat meningkatkan efisiensi perjalanan, memberikan manfaat ekonomi lokal, dan mendukung pertumbuhan sektor pariwisata Indonesia.
Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website Muhammad Rizki; Muhammad Fikri Hidayattullah; Dwi Intan Af'idah
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1908

Abstract

The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.