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All Journal Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Prosiding Konferensi Nasional PKM-CSR
Shanty, Eryca Dhamma
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Nursing Public Health Social Sciences

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PERANCANGAN ANTARMUKA CHATBOT EDUKATIF UNTUK SISTEM TANYA JAWAB SDN KALIDERES 13 PETANG Shanty, Eryca Dhamma; Sugisandhea, Georgia; Mawardi, Viny Christanti
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 8 (2025): Penguatan Ekonomi Masyarakat Berbasis Ekologis untuk Mencapai Keberlanjutan Menuju Ind
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v8i0.2651

Abstract

The development of artificial intelligence (AI) has the potential to enhance the effectiveness of learning; however, limitations in infrastructure and the understanding of teachers and students remain obstacles to its implementation in elementary schools. SDN Kalideres 13 Petang, with 314 students from various socio-economic backgrounds, faces challenges in improving effective interactions between students and teachers. The goal of this community service activity is to introduce and assist in the implementation of the educational chatbot EduBuddy as an interactive medium in the learning process. EduBuddy is designed based on a Large Language Model (LLM) and Command R technology, allowing the chatbot to respond to students' questions automatically, quickly, and relevantly. The activities were conducted through socialization, training, and technical assistance for teachers, as well as guidance in using the chatbot for students. Teachers gained skills in integrating the chatbot into teaching activities, while students were directed to utilize it as an interactive learning medium. The results of the activity showed an increase in student engagement, expanded access to information, and significant support for teachers in delivering content. Evaluation was conducted through questionnaires and classroom observations, which indicated positive acceptance from both teachers and students. It is hoped that the success of this activity can serve as a model for digital transformation in elementary education and encourage the broader application of AI technology in the world of education.
ANALISA SENTIMEN ULASAN APLIKASI TRANSPORTASI MENGGUNAKAN METODE SVM DENGAN PENDEKATAN INSET LEXICON-BASED Shanty, Eryca Dhamma; Jayadi, Brandon Alexander; Marco; Mawardi, Viny Christanti
Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Vol. 9 No. 1 (2025): Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan
Publisher : Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/19y15a17

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap tiga aplikasi transportasi publik digital di Indonesia, yaitu Access by KAI, MyMRTJ, dan MitraDarat. Pendekatan yang digunakan dalam penelitian ini adalah kombinasi metode klasifikasi Support Vector Machine (SVM) dan pendekatan leksikal menggunakan kamus INSET Lexicon-Based. Data ulasan dikumpulkan melalui proses scraping dari Google Play Store dan kemudian melalui tahapan pre-processing, pelabelan sentimen, serta proses klasifikasi. Hasil penelitian menunjukkan bahwa mayoritas ulasan pengguna didominasi oleh sentimen positif. Access by KAI memperoleh ulasan positif terbanyak (446 ulasan), diikuti oleh MitraDarat (322 ulasan), dan MyMRTJ (286 ulasan). Model SVM memberikan hasil klasifikasi yang baik dengan akurasi tertinggi dicapai oleh MyMRTJ (87%), diikuti Access by KAI (84%), dan MitraDarat (82%). Selain itu, visualisasi word cloud berhasil menampilkan kata-kata dominan yang sering muncul dalam ulasan seperti "bagus", "mudah", dan "jalan", yang menunjukkan kepuasan pengguna terhadap layanan aplikasi. Pendekatan INSET Lexicon-Based terbukti efektif dalam mengenali polaritas kata dalam konteks lokal berbahasa Indonesia, serta meningkatkan akurasi pelabelan sebelum proses klasifikasi dilakukan. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan aplikasi transportasi digital berbasis data ulasan pengguna.
Co-Authors Jayadi, Brandon Alexander Marco Sugisandhea, Georgia Viny Christanti M