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All Journal Jurnal Ekonomi, Bisnis dan Manajemen DBESTI: Journal of Digital Business and Technology Innovation
Chasanah, Nur Indah
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Computer Science & IT Economics, Econometrics & Finance Library & Information Science

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Pembuatan Bisnis Ekstrakurikuler Desain UI/UX Berbasis Figma dengan Penerapan Business Model Canvas Chasanah, Nur Indah; Meliala, Rajhaga Jevanya; Bintang, Syah; Kuntari, Wien
Jurnal Ekonomi, Bisnis dan Manajemen Vol. 3 No. 4 (2024): Desember : Jurnal Ekonomi, Bisnis dan Manajemen (EBISMEN)
Publisher : FEB Universitas Maritim Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/ebismen.v3i4.2732

Abstract

Penelitian ini mengkaji pentingnya keterampilan desain antarmuka pengguna (UI) dan pengalaman pengguna (UX) bagi siswa SMP, SMA, dan SMK dalam menghadapi tuntutan industri digital. Tujuan penelitian adalah untuk mengevaluasi efektivitas program pembelajaran Academy Figma dalam meningkatkan kompetensi desain siswa. Metode yang digunakan adalah pendekatan deskriptif kualitatif, yang meliputi riset pasar dan analisis kebutuhan untuk merancang kurikulum yang sesuai. Hasil penelitian menunjukkan bahwa program ini berhasil meningkatkan pemahaman siswa tentang prinsip dasar desain UI/UX serta keterampilan praktis dalam menggunakan Figma. Kesimpulan dari penelitian ini menegaskan bahwa Academy Figma berperan penting dalam menjembatani kesenjangan antara kurikulum pendidikan formal dan kebutuhan industri, serta mempersiapkan siswa dengan keterampilan yang relevan untuk bersaing di pasar kerja global. Program ini diharapkan dapat menjadi model bagi inisiatif pendidikan lainnya dalam bidang desain digital.
Analisis Sentimen Tagar #KaburAjaDulu Pilihan Migrasi ke Jepang pada Platform X dengan NLP Meliala, Rajhaga Jevanya; Chasanah, Nur Indah; Manik, Jonser Steven Rajali; Pasya, Thoriq Muhammad; Lestari, Humannisa Rubina
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 1 (2025): Mei, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i1.1756

Abstract

The hashtag #KaburAjaDulu, which went viral on platform X, reflects the concerns of Indonesian society—particularly among younger generations—regarding domestic social and economic pressures, as well as an increasing interest in migrating to Japan. This phenomenon illustrates the complexity of digital public opinion, yet few studies have specifically compared the effectiveness of different sentiment analysis algorithms within this context. Therefore, this study aims to analyze and compare public sentiment toward the #KaburAjaDulu hashtag, particularly about migration to Japan, using a Natural Language Processing (NLP) approach with three sentiment analysis algorithms: VADER, TextBlob, and BERT. A total of 1000 tweets were collected using scraping techniques, and after preprocessing, 967 tweets were included in the analysis. Sentiments were categorized into three classes: positive, negative, and neutral. The results show that VADER and TextBlob tend to classify tweets as neutral or positive, while BERT reveals a dominant negative sentiment of 52.3%. These findings suggest that BERT is more sensitive to context and implicit sentiment in the informal Indonesian language. This study highlights the importance of selecting appropriate algorithms for social media sentiment analysis and contributes to a deeper understanding of digital migration aspirations within Indonesian society.
Co-Authors Bintang, Syah Kuntari, Wien Lestari, Humannisa Rubina Manik, Jonser Steven Rajali Meliala, Rajhaga Jevanya Pasya, Thoriq Muhammad