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All Journal International Journal of Artificial Intelligence Research JTERA (Jurnal Teknologi Rekayasa)
Agung, Muhammad Zakuan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PERANCANGAN DISASTER RECOVERY PLAN SISTEM INFORMASI AKADEMIK DENGAN PENDEKATAN KERANGKA KERJA NIST 800-34 Agung, Muhammad Zakuan
JTERA (Jurnal Teknologi Rekayasa) Vol 4, No 2: December 2019
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (701.965 KB) | DOI: 10.31544/jtera.v4.i2.2019.157-166

Abstract

Politeknik Negeri Sriwijaya telah memiliki Sistem Informasi Akademik yang terintegrasi bernama SISAK POLSRI. Terdapat 8 (delapan) sub sistem di dalamnya yang meliputi Sistem Informasi Akademik (SIAK), Sistem Informasi Bimbingan Akademik (SIBA), Learning Management System Politeknik Negeri Sriwijaya (LMS Polsri), E-Complaint Politeknik Negeri Sriwijaya, E-Library Politeknik Negeri Sriwijaya, Sistem Informasi Kepegawaian (SIMPEG), Sistem Informasi Alumni dan Tracer Study (SIAT), dan Sistem Pendaftaran dan Pendataan  Mahasiswa Baru (E-Regist). SISAK POLSRI merupakan hal yang vital dalam keberlangsungan operasional Politeknik Negeri Sriwijaya, sehingga diperlukan suatu upaya preventif. Salah satu upaya yang dapat dilakukan adalah dengan merancang dokumen Disaster Recovery Plan yang bertujuan untuk menjaga keberlangsungan sistem, ketika sistem telah terkena dampak ancaman. Tahapan dalam perancangan Disaster Recovery Plan dengan pendekatan kerangka kerja NIST 800-34 yang diinisiasi oleh Risk Assessment, Business Impact Analysis dan Strategy Recovery. Hasil dari penelitian ini berupa dokumen  Disaster Recovery Plan terhadap 9 ancaman dan 8 sub sistem SISAK POLSRI.
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Co-Authors Ade Silvia Handayani Afifah, Luthfia Asriyadi Asriyadi Emilia Hesti Kaila, Afifah Syifah M. Ardiansyah Rosita, Ella suzan zefi