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All Journal Jati: Jurnal Akuntansi Terapan Indonesia Journal of Innovation Information Technology and Application (JINITA)
Rohmad Abidin
Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

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Perancangan Aplikasi Laporan Keuangan Berbasis Web Untuk Pelaku UMKM Sudati Nur Sarfiah; Ayunda Putri Nilasari; Retnosari Retnosari; Rohmad Abidin
Jati: Jurnal Akuntansi Terapan Indonesia JATI Vol 6, No 1: March 2023
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jati.v6i1.18034

Abstract

Penelitian ini bertujuan untuk merancang aplikasi penyusunan laporan keuangan berbasis web yang dapat diakses melalui smartphone dan komputer, sehingga memudahkan akses dan pengawasan terhadap keuangan UMKM. Penelitian ini menggunakan metode prototype, yang melalui empat (4) tahap yaitu 1. identifikasi kebutuhan dasar aplikasi, 2, perancangan dan membangun prototype aplikasi, 3. pengujian dan evaluasi prototype aplikasi dan 4. pengambilan kesimpulan, aplikasi penyusunan laporan keuangan berhasil dibuat dengan baik. Aplikasi memiliki desain menu sesuai kebutuhan UMKM yang mudah dipahami. Berdasarkan hasil uji coba dan evaluasi menunjukkan bahwa aplikasi penyusunan laporan keuangan telah berhasil mengolah data transaksi yang diinputkan dan menghasilkan laporan keuangan.
Robust DeBERTa-v3 Framework for Aspect-Based Sentiment Analysis with Extreme Class Imbalance Muhammad Rikzam Kamal; Sabrina Ahmad; Rohmad Abidin; Imam Prayogo Pujiono
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3296

Abstract

Aspect-Based Sentiment Analysis (ABSA) often operates under extreme class imbalance, causing Transformer-based models to favour majority classes while failing to detect minority sentiments disproportionately. This study proposes RoABSA, a robust DeBERTa-v3–based framework that integrates Hybrid Semantic Augmentation, combining Easy Data Augmentation and Back-Translation, with cost-sensitive optimization via Weighted Random Sampling and Focal Loss to enhance diversity representation and recalibrate gradient contributions. Evaluated on four SemEval benchmarks (Lap14, Res14, Res15, Res16), RoABSA achieves consistent state-of-the-art performance with Macro-F1 scores of 84.71, 91.65, 88.40, and 84.24, respectively, outperforming strong graph-based and Transformer-based baselines by margins of up to 10.35 points. Ablation results confirm that robustness emerges from the synergy between augmentation and cost-sensitive learning rather than any single component. At the same time, per-class analysis demonstrates substantial gains in detecting minority sentiments, including cases where baselines failed. These findings highlight that addressing long-tailed distributions in ABSA requires coordinated interventions at both data and optimization levels, and that RoABSA provides a practical, generalizable strategy for improving stability and fairness in sentiment classification.
Co-Authors Ayunda Putri Nilasari Imam Prayogo Pujiono Muhammad Rikzam Kamal Retnosari Retnosari Sabrina Ahmad Sudati Nur Sarfiah