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All Journal Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat (JURDIMAS)
Anik Sri Widowati
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences

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PELATIHAN ECOMMERCE DAN MANAJEMEN KEUANGAN UNTUK UKM KERAJINAN DIDESA KARANG TENGAH anggit Dwi Hartanto; Eny Nurnilawati; Anik Sri Widowati; Mulia Sulistiyono
JURDIMAS : Jurnal Pengabdian Masyrakat Universitas DIPA Makassar Vol 1 No 1 (2022): Jurnal Pengabdian pada Masyarakat 2022-1
Publisher : JURDIMAS : Jurnal Pengabdian Masyrakat Universitas DIPA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jurdimas.v1i1.1117

Abstract

Desa karang tengah adalah desa yang sedang berkembang menjadi desa wisata dan terdapat banyak sekali usaha mikro salah satunya adalah usaha kerajinan warang kakeris.Kerajinan keris merupakan kerajinan yang potensial untuk go-international jika promosi dan pemasaran dilakukan denganbaik dan benar. Saat ini proses promosi dengan menggunakan media blog dan facebook yang mempunyaifitur sistem terbatas. Berdasar pada permasalahan yang muncul akan disolusi dengan kegiatan yang dimulaidenganmenganalisis metode pemasaran yang sedang berjalan, membangun sistem informasi promosi danpemasaran, melakukan pelatihan penggunaan dan manajemen sistem pemasaran serta melakukan pelatihanbidangmanajemenkeuangan. Luaran dari kegiatan ini adalah sistem informasi promosi dan pemasaran untuk usaha mikrokerajinan warangka keris di desa karang tengah kecamatan imogiri kabupaten bantul.Luaran lainnyaadalahpeningkatanpemahamandalammenggunakansisteminformasitersebut. Hasil akhir dari kegiatan ini secara umum dapat meningkatkan pertumbuhan ekonomi Usaha mikro. Dan secara khusus dapat meningkatkan kesejahteraan dengan penggunaan teknologi informasi dalam pemasaran.
AMIKOM-RECSYS: Enhancing Movie Recommender System using Large Language Model (ChatGpt), Deep Learning and Probabilistic Matrix Factorization Hanafi Hanafi; Anik Sri Widowati; Sri Ngudi Wahyuni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.897

Abstract

E-commerce has become one of the most widely used digital applications globally, enabling personalized product discovery and purchasing. To support these services, recommender systems are essential, offering item suggestions based on user preferences. Most recommender systems rely on machine learning algorithms to estimate user-item relevance scores, often utilizing product ratings. However, a persistent challenge in this domain is the issue of data sparsity, where only a small fraction of users provides explicit ratings, leading to reduced accuracy in recommendation results. In this study, we introduce a novel hybrid recommendation algorithm, named AMIKOM-RECSYS, designed to address the sparsity problem and enhance rating prediction. Our model integrates three main components included a Large Language Model (LLM) using ChatGPT, a Transformer-based encoder (BERT), and Probabilistic Matrix Factorization (PMF). The LLM generates descriptive information about movies based on specific prompts, which is then passed to BERT to encode the content into meaningful 2D vector representations. These enriched embeddings are subsequently utilized by the PMF algorithm to predict missing user-item ratings. We evaluate the proposed model on two benchmark datasets, ML-1M and ML-10M using Root Mean Squared Error (RMSE) as the evaluation metric. The AMIKOM-RECSYS model achieved RMSE values of 0.8681 on ML-1M and 0.7791 on ML-10M under a 50:50 data split, outperforming several baseline models including CNN-PMF, LSTM-PMF, and Attention-PMF. These results highlight the effectiveness of integrating LLM and Transformer-based contextual understanding into matrix factorization frameworks. In future work, we plan to extend this framework by incorporating other matrix factorization techniques such as Singular Value Decomposition (SVD) and integrating additional sources of user information, including social media activity, to further improve recommendation performance.
Co-Authors Anggit Dwi Hartanto Eny Nurnilawati Hanafi Hanafi Mulia Sulistiyono Sri Ngudi Wahyuni, Sri Ngudi