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All Journal Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Bulletin of Social Informatics Theory and Application
Dendi Triadi
Universitas Bina Darma
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Social Sciences

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PELATIHAN PENGGUNAAN DAN PEMANFAATAN SISTEM INFORMASI MOBILE UNTUK OBJEK WISATA KOTA LAHAT Edi Surya Negara; Dedy Syamsuar; Ria Andryani; Dendi Triadi; Yepi Kusmeta; Mery Sintia
Jurnal Pengabdian Masyarakat Information Technology Vol 1 No 1 (2022): JPM ITech - Maret 2022
Publisher : Teknik Informatika dan Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.639 KB) | DOI: 10.33557/jpm_itech.v1i1.1639

Abstract

Lahat memiliki berbagai pilihan objek wisata yang terletak di Propinsi Sumatera Selatan. Akan tetapi yang menjadi permasalahan adalah sulitnya mengetahui informasi serta letak dari objek wisata yang terdapat di Kota Lahat. Wisatawan yang berkunjung ke Lahat harus selalu bertanya ke masyarakat untuk memperoleh informasi tentang objek wisata di Lahat. Oleh karena itu, kegiatan Pengabdian ini bertujuan untuk memperkenalkan serta melatih staf Dinas Pariwisata Kota Lahat tentang Sistem Informasi Objek Wisata Kota Lahat berbasis mobile. Adapun Sistem Informasi ini merupakan hasil penelitian dari Tim Pengabdian, yang mana diterapkan berbasis mobile agar nanti pengguna atau wisatawan dapat menggunakannya serta memperoleh informasi berkaitan dengan objek wisata Lahat dimanapun berada selama terkoneksi internet. Pada kegiatan pengabdian ini, dilakukan pelatihan secara langsung terhadap staf Dinas Pariwisata Kota Lahat agar dapat mengelola ataupun memproses Sistem Informasi berbasis mobile untuk Objek Wisata Kota Lahat.
Topic modeling using latent dirichlet allocation (LDA) on twitter data with Indonesia keyword Edi Surya Negara; Dendi Triadi
Bulletin of Social Informatics Theory and Application Vol. 5 No. 2 (2021)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v5i2.455

Abstract

Digital transformation causes an increase in the volume of information in the form of text such as news. On social media, a lot of news is uploaded in such a fast time and one of them is Twitter. Twitter is a social media service that has served many users, making it one of the social media that has very large data. From this very large data, it can be used as a news source for online news web. However, with the many topics extracted from Twitter data, the incoming data has a variety of topics which causes difficulties in identifying the topics from the data set taken and will require a lot of time if it has to be done manually by humans. Meanwhile, the data is potentially needed to provide information as quickly as possible. This study aims to classify topics on data taken from Twitter automatically so that it can make a classification on the news taken, can be more effective and efficient and does not take as much time as done manually by humans. The research was conducted using the Latent Dirichlet Allocation (LDA) method. News documents that will be classified are Indonesian news documents and will be classified into topics to be determined. The results of the research using topic modeling using the LDA method concluded that the number of topics formed from 9094 tweet data was 10 topics.
Co-Authors Andryani, Ria Dedy Syamsuar Edi Surya Negara Mery Sintia Yepi Kusmeta