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Designing a Modern Web Interface with Vue.js and Tailwind for University Information System Perdana, Fhadiel Putra; Supratman, Edi; Saputra, Dhimas Rosanto
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5409

The development of responsive, efficient, and maintainable web applications is a critical challenge in modern web development. This study presents the design and development of a tracer study information system for Bina Darma University using Vue.js and Tailwind CSS. The system focuses on three key components: responsive web design, pagination for managing large datasets, and reusable components implemented with Vue.js slots. By integrating Tailwind CSS, the system achieves a highly adaptive interface optimized for various devices, ensuring a seamless user experience. The implementation of Vue.js-based pagination significantly improves the management of alumni questionnaire histories in the admin interface, enhancing navigation and performance. Additionally, the use of Django pagination complements this by efficiently handling server-side data management, allowing for smooth transitions between pages and reducing load times for large datasets. This dual approach to pagination ensures that both client-side and server-side data handling are optimized for performance. Furthermore, the use of Vue.js slots for component reuse allows for tailored functionalities across different interfaces, reducing redundancy and improving code maintainability. The results demonstrate that adopting modern frameworks like Vue.js and Tailwind CSS, alongside robust backend solutions like Django, can streamline development, reduce effort, and enhance application efficiency. This study provides a scalable solution that can be adapted for similar academic systems, offering insights into best practices for contemporary web application development.

Peran Big Data Dan Artificial Intelligence Dalam Optimalisasi Pengawasan Pajak Corly, Fery; Ariana, Sunda; Trisninawati, Trisninawati; Fitriasuri, Fitriasuri; Perdana, Bosya; Saputra, Dhimas Rosanto
MBIA Vol. 23 No. 3 (2024): Management, Business, and Accounting (MBIA)
Publisher : Direktorat Riset dan Pengabdian kepada Masyarakat Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/cw2tyd15

The integration of Big Data and Artificial Intelligence (AI) into tax supervision has significantly improved anomaly detection and compliance monitoring. This study examines the application of these technologies across 12 countries, highlighting their influence on modern tax governance. The findings reveal that AI-driven models enhance the accuracy of tax anomaly detection by 40–78% in countries with a digital maturity index above 0.65 but show limited improvements of only 12–15% in contexts with fragmented infrastructure. Critical success factors include the availability of well-labeled historical datasets, the analytical capacity of human resources in interpreting machine learning outputs, and regulatory frameworks that support algorithmic audits. A hybrid federated learning model combined with blockchain was found to boost detection accuracy while reducing central computing requirements by 35%. This research extends the Technology–Organization–Environment (TOE) framework by underscoring the pivotal role of data governance in digital economy taxation. Strategic recommendations include the development of integrated tax data lakes, enhancing AI literacy among tax authorities, and establishing legal frameworks to ensure algorithmic transparency and accountability. Implementing these strategies is projected to increase national tax compliance rates by 25–40% over the next five years while mitigating risks associated with the digital divide. Keywords: Big Data, Artificial Intelligence, Tax Compliance. Abstrak Kemajuan teknologi digital telah membawa perubahan signifikan dalam sistem perpajakan global, khususnya melalui pemanfaatan Big Data dan kecerdasan buatan (AI) dalam deteksi anomali dan prediksi risiko wajib pajak. Penelitian ini menganalisis implementasi teknologi tersebut di 12 negara dengan fokus pada efektivitas, tantangan, serta faktor keberhasilan utama. Hasil penelitian menunjukkan bahwa penerapan Big Data dan AI mampu meningkatkan efektivitas pengawasan pajak sebesar 40-78% pada negara dengan indeks kematangan digital di atas 0,65, tetapi hanya 12-15% di negara dengan infrastruktur terfragmentasi. Faktor utama yang memengaruhi keberhasilan implementasi teknologi ini meliputi ketersediaan dataset historis yang berkualitas, kapasitas analitik sumber daya manusia, serta kerangka regulasi yang mendukung audit berbasis algoritma. Model hybrid federated learning dengan integrasi blockchain terbukti meningkatkan akurasi deteksi anomali pajak sekaligus mengurangi kebutuhan komputasi sentral sebesar 35%. Temuan ini memperkuat teori adaptasi teknologi organisasi dengan menambahkan dimensi data governance sebagai variabel krusial dalam implementasi teknologi pajak. Rekomendasi strategis diajukan untuk otoritas pajak, pembuat kebijakan, serta penelitian lanjutan guna meningkatkan kepatuhan pajak nasional secara berkelanjutan. Kata Kunci: Big Data, Kecerdasan Buatan, Kepatuhan Pajak.

Pengembangan Strategi Pemasaran Berbasis Algoritma Backtracking untuk Meningkatkan Engagement dan Penjualan pada Butik Islami Saputra, Dhimas Rosanto; Sutabri, Tata
TEKNIKA Vol. 19 No. 2 (2025): Teknika Mei 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15522565

Peningkatan daya saing di industri butik Islami yang berkembang pesat memerlukan strategi pemasaran inovatif yang adaptif terhadap preferensi pelanggan yang dinamis. Penelitian ini bertujuan merancang strategi pemasaran optimal berbasis algoritma backtracking untuk memaksimalkan engagement pelanggan dan volume penjualan. Metode yang diterapkan melibatkan penggunaan algoritma backtracking guna menganalisis kombinasi strategi pemasaran multichannel—seperti kampanye media sosial, program diskon bertingkat, dan event kolaboratif offline—dengan mempertimbangkan data preferensi pelanggan yang beragam. Algoritma ini mengevaluasi seluruh kemungkinan kombinasi strategi secara sistematis, kemudian mengeliminasi opsi suboptimal untuk menentukan konfigurasi terbaik berdasarkan kriteria biaya dan efektivitas. Hasil eksperimen menunjukkan bahwa implementasi strategi berbasis backtracking berhasil meningkatkan engagement pelanggan sebesar 30% dan penjualan sebesar 25% dalam kurun 3 bulan, dibandingkan dengan pendekatan konvensional. Temuan ini menegaskan bahwa integrasi algoritma backtracking mampu menyediakan solusi personalisasi yang efisien, bahkan dalam skala sumber daya terbatas. Penelitian ini memberikan dua kontribusi utama: (1) kerangka kerja pemasaran berbasis komputasi yang dapat diadaptasi oleh UMKM fashion bernuansa Islami, dan (2) validasi empiris tentang potensi optimasi algoritmik dalam strategi pemasaran multichannel. Implikasi praktisnya mencakup rekomendasi penerapan model decision-making berbasis data untuk meningkatkan akurasi penetapan strategi pemasaran di sektor ritel niche.

Designing a Modern Web Interface with Vue.js and Tailwind for University Information System Perdana, Fhadiel Putra; Supratman, Edi; Saputra, Dhimas Rosanto
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5409

The development of responsive, efficient, and maintainable web applications is a critical challenge in modern web development. This study presents the design and development of a tracer study information system for Bina Darma University using Vue.js and Tailwind CSS. The system focuses on three key components: responsive web design, pagination for managing large datasets, and reusable components implemented with Vue.js slots. By integrating Tailwind CSS, the system achieves a highly adaptive interface optimized for various devices, ensuring a seamless user experience. The implementation of Vue.js-based pagination significantly improves the management of alumni questionnaire histories in the admin interface, enhancing navigation and performance. Additionally, the use of Django pagination complements this by efficiently handling server-side data management, allowing for smooth transitions between pages and reducing load times for large datasets. This dual approach to pagination ensures that both client-side and server-side data handling are optimized for performance. Furthermore, the use of Vue.js slots for component reuse allows for tailored functionalities across different interfaces, reducing redundancy and improving code maintainability. The results demonstrate that adopting modern frameworks like Vue.js and Tailwind CSS, alongside robust backend solutions like Django, can streamline development, reduce effort, and enhance application efficiency. This study provides a scalable solution that can be adapted for similar academic systems, offering insights into best practices for contemporary web application development.

Filter Bubble Phenomenon on Instagram and Its Impact on Teenagers Lifestyle and Social Interaction Misnawati, Desy; Perdana, Bosya; Ariana, Sunda; Damayanti, Novita; Saputra, Dhimas Rosanto
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 3 (2025): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i3.692

This research aims to analyze the phenomenon filter bubble on Instagram and its impact on lifestyle changes and social interactions of teenagers aged 16-20 years in Palembang. Instagram, as a visual-based social media platform, uses Artificial Intelligence (AI) algorithms to customize and filter content according to user preferences, which can influence user thought patterns, attitudes and behavior. In this research, a qualitative approach was used with literature study methods and content analysis. Data was collected through in-depth interviews with teenagers in Palembang who actively use Instagram. The research results show that filter bubble influencing teenagers in adopting lifestyle trends, especially in terms of appearance and beauty, such as the use of makeup which often appears in Instagram feeds. In addition, this phenomenon also contributes to the formation of homogeneous social groups, where teenagers are more likely to interact with individuals who have similar interests, while different views tend to be ignored. Based on Information Integration Theory, this research explains that information received via social media, which is filtered by the Instagram algorithm, shapes teenagers attitudes and behavior. The conclusion of this research is that the phenomenon filter bubble has a significant impact on the development of teenagers lifestyles and social interactions, which has the potential to narrow their horizons to various perspectives. Therefore, better digital literacy is needed to help teenagers be more critical in consuming information on social media.

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