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All Journal Jurnal Media Teknik Elektro dan Komputer Jurnal Pembina
Cahyani, Anisa Ninda
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Other

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Pemanfaatan Media Digital untuk Edukasi Pengelolaan Sampah pada SDIT Ashabul Kahfi Pohan, Aisyah Fadilla Suffi; Cahyani, Anisa Ninda; Yahya, Rafly Auliya; Siregar, Reza Abdillah
Jurnal Pembina Vol. 1 No. 1 (2025): Mei 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/pembina.v1i1.25

Abstract

Sosialisasi pemilahan, pengelolaan, dan pengenalan sampah merupakan upaya untuk menumbuhkan kepedulian terhadap lingkungan pada Siswa Kelas 1 SDIT Ashabul Kahfi dengan menggunakan media digital. Melalui pendidikan tentang pemilahan dan pengelolaan sampah termasuk kesadaran tentang limbah elektronik diharapkan siswa kelas 1 dapat memahami pentingnya melindungi lingkungan. Kegiatan ini dilaksanakan di SDIT Ashabul Kahfi dengan menggunakan teknik interaktif dengan melakukan presentasi materi, permainan edukatif, video pembelajaran, dan latihan memilah sampah. Meskipun Green Computing umumnya dikaitkan dengan teknologi canggih dalam kegiatan ini teknologi diterapkan melalui penggunaan media digital yang aman namun efektif dalam menyampaikan pesan pendidikan kepada anak-anak. Menurut kuesioner hampir 80% siswa kelas 1 SDIT Ashabul Kahfi percaya bahwa materi pelajaran jelas dan mudah dipahami. Selain itu 85% siswa menyatakan bahwa pelatihan keterampilan praktis sangat menyenangkan dan membantu mereka memahami betapa pentingnya memperhatikan kondisi lingkungan. Hasil penelitian ini menunjukkan bahwa sosialisasi melalui penggunaan media digital dan teknologi c dapat meningkatkan kesadaran dan keterlibatan masyarakat dalam isu-isu lingkungan. Penggunaan media digital secara interaktif dan dinamis juga berkontribusi pada penerapan prinsip Green Computing di bidang pendidikan. Kegiatan sosialisasi secara jelas berkontribusi pada pengembangan kesadaran lingkungan pada siswa kelas 1 SDIT Ashabul Kahfi melalui penggunaan teknologi yang menghormati dan mendidik.
Analisis Sentimen Pengguna Aplikasi MyASN di Twitter Menggunakan Algoritma Support Vector Machine Zalfa, Amirah Nafiah; Cahyani, Anisa Ninda
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i2.132

Abstract

This study aims to analyze user sentiment toward the MyASN application based on public discussions on X. A total of 1059 tweets were collected through a web scraping process using the Tweet-Harvest tool on Google Colab, with data obtained from tweets containing keywords and hashtags related to MyASN and BKN. The collected data were preprocessed through case folding, tokenizing, normalization, stopword removal, and stemming, then represented using the TF-IDF weighting scheme. Sentiment labels were assigned into three categories: positive, negative, and neutral. The classification process employed the Support Vector Machine (SVM) algorithm, with data divided into 80% training data and 20% testing data. The experimental results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 98.1% with a precision value of 0.983, a recall of 0.981, and an F1-score of 0.982. Evaluation based on sentiment class shows that in the negative class, SVM produced a precision of 1.000, a recall of 0.977, and an F1-score of 0.989. In the neutral class, it achieved a precision of 0.929, a recall of 1.000, and an F1-score of 0.963, while in the positive class, SVM achieved a precision of 0.885, a recall of 1.000, and an F1-score of 0.939. These results show that the indicates that the implemented SVM model demonstrates strong reliability in handling text-based sentiment classification, particularly in datasets with imbalanced sentiment distributions. Overall, the results demonstrate that SVM is effective in capturing user sentiment patterns and can provide meaningful insights for evaluating and improving the MyASN application service.
Co-Authors Pohan, Aisyah Fadilla Suffi Siregar, Reza Abdillah Yahya, Rafly Auliya Zalfa, Amirah Nafiah