Claim Missing Document
Check
Articles

Found 13 Documents
Search

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.
DESIGN OPTIMIZATION OF GAS TRANSMISSION SYSTEM WITH DIFFERENTIAL EVOLUTION ALGORITHM Afdhal, Ahmad; Tasmi, Tasmi; Yunita, Ariana; Noegraha, Rangga Ganzar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1089-1098

Abstract

Gas distribution through a pipeline network is a highly complex process and requires significant financial investment. This network system consists of a source, pipe, the compressor and sink (consumer). The source is the node where the producer has the gas pressure that will be distributed, the pipe is used to connect the producer and the consumer. Between the pipes there is a compressor which functions to increase the pressure. This network system was created at a significant cost, so it is necessary to search for minimal costs, but consumer demand is still met. This research discusses the search for an optimal gas network with minimum costs. This minimum cost depends on several parameters i.e. the length and diameter of pipe, also the pressure on the compressors entry and exit points. There are many optimization methods, but one of the simple and easy to implement methods is the Differential Evolution Algorithm, so this method is used to determine the optimal solution to this problem. Researchers used seven DE variants based on mutation strategies, namely DE/rand/1, DE/best/1, DE/rand/2, DE/best/2, DE/current-to-best/1, DE/current-to- rand/1, and DE/rand-to-best/1. The seven variants have never been used in gas distribution networks by previous researchers. Therefore, the seven variants were compared, and the minimum solution was determined. The results show that the DE/best/2 variant is the variant that produces the minimum total costs compared to the other variants. DE/best/2 achieved the lowest annual operating cost at USD 13.99 million.