Article Per Year (5 Year)
p-Index From 2021 - 2026
4.417
P-Index
This Author published in this journals
All Journal Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) Prosiding Semnastek Panrita Abdi - Jurnal Pengabdian pada Masyarakat Jurnal Akuntansi Jurnal Komputasi Jurnal Teknoinfo JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Telematics and Information Technology (TELEFORTECH) Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Komputasi
Damayanti, Damayanti
Universitas Teknokrat Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
SENTIMENT ANALYSIS OF PUBLIC OPINION ON SOCIAL MEDIA X TOWARDS ETHNIC ROHINGYA IN INDONESIA Ayu Amalia; Damayanti Damayanti
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 3 (2024): Juni 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i3.3079

Abstract

Abstract: Rohingya refugees continue to arrive in Aceh by sea by boat. Based on data from the United Nations High Commissioner for Refugees (UNCHR), as of December 10 2023, 1,543 Rohingya refugees had landed in Aceh since mid-November 2023. The increasing number of refugees arriving has caused resistance from local residents. This rejection was the result of the bad experiences of Acehnese people with Rohingya refugees. The main problem of this research is that analyzing public opinion on Rohingya ethnicity in Indonesia is still done manually by looking at tweets one by one. The solution to overcome this is to analyze opinions using data crawling with the Naïve Bayes algorithm. The purpose of this research is to determine public opinion on Twitter media regarding refugee refugees. The method used in this research is the Naïve Bayes algorithm method. The results of the research show that the Naïve Bayes algorithm can classify public opinion sentiment on Twitter social media towards the Rohingya Ethnic in Indonesia into positive sentiment. and negative with a total accuracy of 70%. So, the words "Rohingya Ethnicity in Indonesia" tend to be accepted by the X community with the arrival of Rohingya refugees in Indonesia.            Keywords: rohingya; twitter; naïve bayes; opinion  Abstrak: Pengungsi Rohingya terus berdatangan ke Aceh melalui jalur laut dengan menggunakan perahu. Berdasarkan hasil data United Nations High Commissioner for Refugees (UNCHR), per 10 Desember 2023 sebanyak 1.543 pengungsi Rohingya datang ke dalam wilayah Aceh. Meningkatnya jumlah pengungsi yang datang menimbulkan perlawanan dari warga setempat. Penolakan ini imbas dari pengalaman buruk warga Aceh terhadap pengungsi Rohingya. Permasalahan utama penelitian ini yaitu menganalisis opini publik terhadap Etnis Rohingya di Indonesia masih secara manual dengan melihat tweet satu persatu. Solusi mengatasi hal tersebut maka analisis opini menggunakan crawling data dengan algoritma Naïve Bayes. Tujuan penelitian ini untuk mengetahui opini publik pada media twitter terkait pengungsi Rohingya. Metode yang digunakan dalam penelitian ini yaitu metode algoritma Naïve Bayes. Hasil penelitian menunjukan bahwa pada algoritma Naïve Bayes dapat mengklasifikasikan sentimen dengan total akurasi 70%. Maka “Etnis Rohingya di Indonesia” cenderung dapat diterima oleh masyarakat X dengan datangnya pengungsi Rohingya di Indonesia. Kata kunci: rohingnya; twitter; naïve bayes; opini 
APLIKASI PERMAINAN SEBAGAI MEDIA PEMBELAJARAN PETA DAN BUDAYA SUMATERA UNTUK SISWA SEKOLAH DASAR Dyah Ayu Megawaty; Damayanti Damayanti; Zakaria Sani Assubhi; Maulana Aziz Assuja
Jurnal Komputasi Vol. 9 No. 1 (2021)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i1.2779

Abstract

Indonesia secara umum mempunyai banyak keanekaragaman budaya dari berbagai pulau. Pulau Sumatera termasuk dalam pulau-pulau terbesar di Indonesia. Seiring dengan perkembangan zaman, kebudayaan yang ada di Indonesia pada saat ini secara perlahan mulai terlupakan. Oleh karena itu tujuan dari penelitian ini adalah memberikan alternatif lain untuk mengenalkan pulau Sumatera pada pelajar sekolah dasar melalui aplikasi permainan sehingga siswa tidak mudah bosan dan tertarik untuk mengetahui sejarah masing-masing provinsi, rumah adat dan pakaian adatnya. Penelitian ini dikembangkan dengan menggunakan construct 2 dan metode pengembangan Multimedia Development Life Cycle. Hasil penelitian ini berupa aplikasi permainan pengenalan peta dan budaya Sumatera yang dapat dijalankan melalui smartphone berbasis android dengan disertakan latihan soal, untuk mengetahui sejauh mana kemampuan siswa mendalami materi yang didapat dari game ini. Aplikasi ini telah diuji kepada siswa sekolah dasar dengan rata-rata hasil 94% atau sangat baik.
Klasifikasi Risiko Bencana di Indonesia Menggunakan SVM dan Random Forest Erland Adhe Sharendra; Tri Widodo; Damayanti Damayanti; Okma Arnilia
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9818

Abstract

Indonesia is a country with a high level of disaster vulnerability, requiring effective methods to accurately classify disaster risk levels. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in disaster risk classification. The dataset used consists of disaster event data from 2019–2024, including disaster type, region, number of victims, and population density. Disaster risk levels were classified into three categories, namely low, medium, and high, based on the total impact calculated from the number of victims. The proposed method includes data preprocessing, normalization, and train-test data splitting. The results show that both models achieved high performance, where Random Forest obtained an accuracy of 95.66% and SVM achieved 95.28%, with ROC-AUC values of 0.9823 and 0.9769, respectively. Random Forest demonstrated slightly better performance with an accuracy difference of 0.38% and more consistent prediction results. The high performance indicates that the models were able to recognize the main patterns within the dataset, although the results were also influenced by the characteristics of the data used. Overall, Random Forest is more suitable for disaster risk classification on data with complex characteristics.
Co-Authors ., Rusliyawati aan setiawan Ade Dwi Putra Ade Dwi Putra Ade Putra Adi Sucipto, Adi Ady Candra Nugroho Agung Tri Prastowo Ahmad Ari Aldino Aldi Bagus Prasetyo Alfiah Alfiah Algho Niyu Behainksa Amdi Rizal Ana Loria Andi Nurkholis Andre Wijaya Anton Mahendra Anton Mahendra Arfinia Rahma Ari Sulistyawati Arief Budiman Ayesha Ziky Iftikhor Ayu Amalia Ayu Febriani Bahtiar Ayu Febriyani Bachtiar Ayu Ningtiara Berlintina Permatasari Chris Mario Clarisa Armanda Fernandis Creani Handayani Dani Saputra Dedi Darwis Desi Oktarina Devita Tri Yulianti Dian Novita Dian Widiarti Dimas Elang Setyoko Donaya Pasha Donaya Pasha Dyah Ayu Megawaty Erland Adhe Sharendra Erliyan Redi Susanto Erliyan Redi Susanto Erliyan Redy Susanto Fitrah Amalia Fitrah Amalia Rahmadianti Habib Rizky A.Siregar Heni Sulistiani Ida Nurwanti Imam Ahmad Joni Alfian Jupriyadi, Jupriyadi Leny Meilisa M Ghufroni An'ars M Ghufroni An’ars M. Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An’ars M. Tova M.Ghufroni An'ars Maulana Aziz Assuja Miswanto Miswanto Mohamad Ghazi Rio Muhammad Al Kahfi Masputra Muhammad Alfarizi Muhammad Bakri Muhammad Fitratullah Muhammad Yusuf Hernandez Nadia Aftirah Nirwana Hendrastuty Novia Agus Mila Okma Arnilia Pahlewi, Anita Diah Pangestu Agung Prabowo Prabowo Priliyani Hana Primawan Adrian Sitinja Putri Lestari Rifki Yudiantara Rifli Yanto Rohmat Indra Borman Rubiyah Rubiyah S. Samsugi Samuel Hutagalung Sefrizal Nata Prawira Setiawansyah Setiawansyah suaidah suaidah Tiara Azizul Andika Tri Widodo Tri Widodo Wahyudi, Agung Deni Warnars, Harco Leslie Hendric Spits Wisnu Mukti Dwiyudho Putro Wiwid Ayu Nisa Wulandia Wulandia Lestari Yeni Anggraini Yohanes Aji Pratama Yuri Rahmanto Zakaria Sani Assubhi Zurfah Muzrifah