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All Journal Media Jurnal Informatika Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Khen Dedes
Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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BERT Sentimen: Fine-Tuning Multibahasa untuk Ulasan Bahasa Indonesia Khen Dedes; Fatimatuzzahra; Mas'ud Hermansyah; Akas Bagus Setiawan; Reza Putra Pradana; Annisa Fitri Maghfiroh Harvyanti
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.585

Abstract

Penelitian ini mengevaluasi pengaruh teknik augmentasi dan fine‑tuning terhadap kinerja model BERT multibahasa pada tugas klasifikasi sentimen ulasan film berbahasa Indonesia. Dataset awal terdiri dari 1.200 ulasan; 80% digunakan untuk pelatihan dan validasi (n = 960) dan 20% untuk pengujian (n = 240). Data pelatihan diperluas melalui augmentasi menjadi 2.880 sampel sintetis untuk keperluan fine‑tuning. Model kemudian di‑fine‑tune pada korpus yang diperluas dan dievaluasi menggunakan metrik akurasi, precision, recall, dan F1. Pada set pengujian diperoleh akurasi 82,5%, precision untuk kelas positif 76,0%, recall 95,0%, dan F1‑score 84,44%. Matriks kebingungan menunjukkan TP = 114, FN = 6, FP = 36, dan TN = 84, yang mengindikasikan sensitivitas tinggi terhadap ulasan positif namun terdapat proporsi false positive yang relatif besar. Temuan ini mengindikasikan bahwa augmentasi meningkatkan kemampuan model dalam menangkap sinyal positif (tingginya recall), namun memerlukan penyesuaian lebih lanjut untuk mengurangi kesalahan prediksi positif (meningkatkan precision). Secara keseluruhan, hasil penelitian menyediakan bukti bahwa BERT multibahasa mampu menangani tugas sentimen berbahasa Indonesia dengan performa memadai apabila didukung strategi augmentasi dan prosedur validasi yang tepat.
A Stock Demand Forecasting for MSME E-Commerce Using LSTM and Facebook Prophet: A Comparative Study Fatimatuzzahra Fatimatuzzahra; Akmal Amilunizar; Khen Dedes; Helyatin Nisyak; Nadzirotul Fitriyah
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6365

Abstract

Manual sales processes and stock management in micro, small, and medium enterprise (MSME) settings often lead to limited market reach and inefficient inventory management. The purpose of this research is to solve these operational problems by designing and developing a web-based e-commerce system equipped with an integrated monthly stock demand forecasting module to enhance the competitiveness of MSMEs in the roster industry. The system was developed following the waterfall methodology and it has a decoupled architecture to separate the artificial intelligence computational workloads from the core application. Two time series forecasting models Long Short-Term Memory (LSTM) and Facebook Prophet were applied and compared for forecasting stock requirements from intermittent, zero-inflated demand patterns of historical sales data. System functionality was validated using User Acceptance Testing and the forecasting accuracy was measured using Root Mean Square Error (RMSE) and Weighted Mean Absolute Percentage Error (WMAPE). The performance evaluation showed that the unscaled LSTM model outperformed the linear additive regression method of Facebook Prophet in terms of lower physical volume deviation and consistent operational error in the course of the evaluation period. The developed platform provides a reliable data-driven decision support for inventory management. The incorporation of forecasting using neural networks in the e-commerce system has reduced the risk of stockouts, expanded the market, and proved the increase in the business competitiveness of artisan MSMEs.
Co-Authors Akmal Amilunizar Annisa Fitri Maghfiroh Harvyanti Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Mas'ud Hermansyah Nadzirotul Fitriyah Reza Putra Pradana Setiawan, Akas Bagus