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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer dan Informasi Jurnal Simetris MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Journal of Telematics and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics TEKNOLOGIA TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Journal of Informatics and Computing JPMB Parahita Scientific Journal of Informatics
Ariana Yunita
Department Of Computer Science, Universitas Pertamina, Jakarta
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing

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Be Wise With Your Waste: Penyuluhan Sampah Dan Evaluasi Usability Aplikasi Pengelolaan Sampah Untuk Siswa Sekolah Menengah Atas Di Jakarta Selatan Suharti, Suharti; Yunita, Ariana; Tasmi, Tasmi; Adharis, Azis; Mayangsari, Tirta Rona; Ratri, Paramita Jaya; Berghuis, Nila Tanyela; Muttaqin, Muttaqin; Sofiyah, Evi Siti; Fuqaha, Hafizh; Istadewi, Berliani; Afiq, Muhammad; Sakinah, Nanda; Fauziyah, Andanda Reza
Jurnal Pengabdian Masyarakat Bakti Parahita Vol. 4 No. 02 (2023): Jurnal Pengabdian Masyarakat Bakti Parahita
Publisher : Universitas Binawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54771/jpmbp.v4i02.1193

Abstract

Indonesia merupakan penyumbang sampah plastik terbesar ketiga di dunia pada tahun 2020, dimana DKI Jakarta merupakan salah satu kota penyumbang sampah terbesar di Indonesia. Untuk menangani sampah plastik diperlukan kerjasama dari berbagai pihak, baik dari masyarakat, akademisi, pemerintah dan organisasi. Tanpa kontribusi publik, penanganan sampah terutama sampah plastik akan sulit. Tujuan kegiatan ini adalah untuk meningkatkan kesadaran masyarakat untuk memilah dan menangani sampah, sebagai upaya untuk meningkatkan kontribusi publik. Pada kegiatan ini, salah satu aplikasi pengelolaan sampah diperkenalkan dan dilakukan evaluasi usability untuk mengetahui seberapa mudah digunakan aplikasi tersebut. Kegiatan dilakukan pada 50 siswa salah satu Sekolah Menengah Negeri Atas Negeri (SMAN) di Jakarta Selatan. Hasil dari kegiatan ini menunjukkan bahwa siswa-siswa berminat untuk menggunakan aplikasi pengelolaan sampah, tetapi masih mengalami kesulitan dalam menggunakan aplikasi pengelolaan sampah. Selain itu, sebagai tindak lanjut dari penyuluhan ini, tempat sampah untuk memisahkan sampah plastik juga diletakkan di sekolah tersebut sebagai tindak lanjut dari penyuluhan ini.
Identifying Relevant Messages from Citizens in a Social Media Platform for Natural Disasters in Indonesia Using Histogram Gradient Boosting and Self-Training Classifier Yunita, Ariana; Ramadhan, Zhafran; Angelia Regina Dwi Kartika; Irawan, Ade
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.2287

Abstract

Purpose: This research aims to develop a classification model using histogram-based gradient boosting to identify relevant contextual tweets about disasters. This model can then be used for subsequent data cleaning stages. Methods: This study uses a semi-supervised approach to develop a classification model using histogram-based gradient boosting. The model is trained to identify and remove irrelevant tweets that are related to disasters and gathered from Twitter. Optimization techniques, such as the AdaBoost classifier, calibrated classifier, and self-training classifier, are used to enhance the model's performance. The goal is to accurately recognize and categorize relevant tweets for additional data analysis and decision-making. Result: The classification model that has been developed has achieved a high F1-score of 93.07%, which indicates its effectiveness in filtering disaster-related tweets that are relevant. This highlights the potential of the model to enable more precise aid distribution and faster decision-making in disaster response efforts. The successful implementation of the model also demonstrates its usefulness in utilizing social media data to enhance disaster management practices. Novelty: This research contributes to the analysis of social media through machine learning algorithms. By utilizing social media, specifically Twitter, as a valuable resource for disaster response efforts, this study tackles challenges related to data collection and analysis in disaster management. The classification of relevant tweets into different types of natural disasters offers opportunities to enhance stakeholder decision-making processes in disaster scenarios.
Co-Authors Ade Irawan Ade Irawan Ade Irawan Adharis, Azis Afiq, Muhammad Aldri Frinaldi Angelia Regina Dwi Kartika Aulia Rahma Annisa Berghuis, Nila Tanyela Chastine Fatichah Erwin Setiawan Erwin Setiawan Erwin Setiawan, Erwin Fauziyah, Andanda Reza Fuqaha, Hafizh Galang Amanda Dwi Pamungkas Herminarto Nugroho Herminarto Nugroho Istadewi, Berliani Jayawarsa, A.A. Ketut Koyimatu, Muhamad Mayangsari, Tirta Rona Nugroho, Herminarto Ramadhan, Zhafran Randi Farmana Putra Ratri, Paramita Jaya Sakinah, Nanda Sara Florensia Telaumbanua Shelma Alifia Sofiyah, Evi Siti Susanty, Meredita Tasmi Tasmi Tasmi Tasmi, Tasmi Teguh Aryo Nugroho Umi Laily Yuhana, Umi Laily Wahyu Kunto Wibowo