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All Journal Applied Information Technology and Computer Science (AICOMS)
Muhammad Radja Juang Jamemiko
Universitas Multi Data Palembang
Computer Science & IT

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Analisis Sentimen Publik terhadap Isu Pembuatan CBDC di Indonesia Menggunakan IndoBERT Muhammad Radja Juang Jamemiko; Joseph Eduard Uly Loni; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/srtytf27

Abstract

Perkembangan teknologi finansial mendorong munculnya inovasi sistem pembayaran digital, salah satunya melalui pengembangan Central Bank Digital Currency (CBDC) atau Rupiah Digital oleh Bank Indonesia. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap isu pembuatan CBDC di Indonesia berdasarkan opini masyarakat pada platform media sosial X. Penelitian menerapkan pendekatan Natural Language Processing menggunakan model Deep Learning berbasis Transformer, yaitu IndoBERT, untuk melakukan klasifikasi sentimen secara otomatis. Data tweet yang telah dikumpulkan melalui proses crawling kemudian melalui tahapan pre-processing, tokenisasi, serta klasifikasi ke dalam tiga kategori sentimen, yaitu positif, netral, dan negatif. Selain itu, penelitian juga melakukan visualisasi distribusi sentimen dan pemetaan kata dominan menggunakan wordcloud untuk mengidentifikasi fokus pembahasan masyarakat terkait CBDC ataupun Rupiah Digital. Hasil penelitian menunjukkan bahwa sentimen netral mendominasi diskusi publik sebanyak 61,01%, diikuti oleh sentimen negatif 29,11% dan positif 9,87%. Temuan ini mengindikasikan bahwa masyarakat masih berada pada tahap pengamatan dan diskusi terhadap implementasi CBDC, namun tetap terdapat kekhawatiran terkait aspek keamanan, privasi, dan kontrol sistem keuangan digital.
Perbandingan Algoritma Greedy dan Dynamic Programming Pada Optimasi Playlist Spotify Untuk Jogging Fadhel Muhammad; Muhammad Radja Juang Jamemiko; Yohannes Yohannes
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/1htfcz49

Abstract

Spotify provides audio metadata that can be utilized to support physical activities such as jogging. This study compares the performance of Greedy and Dynamic Programming algorithms for Spotify playlist optimization modeled as a 0/1 Knapsack Problem. Song duration is treated as weight, while a score derived from popularity and energy is used as value. The dataset was obtained from Spotify Wrapped 2025 Top 50 Songs and Spotify All-Time Top 100 Songs, resulting in 31 candidate songs after preprocessing and filtering. Experiments were conducted on playlist durations of 30, 45, 60, 75, and 90 minutes. The results show that Dynamic Programming consistently achieved higher total scores than Greedy across all scenarios. For the 60-minute playlist, Dynamic Programming obtained a total score of 1897 compared to 1894 achieved by Greedy. However, Greedy required a lower execution time (4.244 ms) than Dynamic Programming (16.196 ms). The average optimality gap between the two methods was 1.89%, indicating that Greedy produced solutions that were close to the optimal solutions generated by Dynamic Programming while requiring less computation time.
Co-Authors Fadhel Muhammad Joseph Eduard Uly Loni Muhammad Rizky Pribadi Yohannes, Yohannes