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All Journal Jurnal Ilmiah KOMPUTASI Multitek Indonesia : Jurnal Ilmiah
Ibnu Tsalis Assalam
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Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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Perbandingan Metode Pembobotan ROC, AHP, dan CRITIC dalam Menentukan Prioritas Kriteria Studi Kasus Penentuan Mahasiswa Berprestasi Nafiatul Fadlilah; Ulla Delfana Rosiani; Ibnu Tsalis Assalam; Khosyi Nasywa Imanda; Agung, Muhammad Helmi Permana
Jurnal Ilmiah Komputasi Vol. 23 No. 2 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 2, Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.2.3546

Abstract

Pendidikan memiliki peran kunci dalam membentuk sumber daya manusia unggul. Mahasiswa berprestasi menjadi salah satu hasil proses pendidikan yang berkualitas. Penentuan mahasiswa berprestasi melalui sistem pendukung keputusan akan memudahkan instansi perguruan tinggi. Pada penelitian ini kriteria yang digunakan meliputi nilai prestasi, nilai akhir setiap mata kuliah dalam satu semester, dan ketidakhadiran. Penentuan bobot kriteria perlu dilakukan secara cermat karena berdampak pada hasil akhir perangkingan kandidat. Hal tersebut menimbulkan keraguan dalam menentukan prioritas kriteria. Dengan demikian, pemilihan metode pembobotan kriteria yang sesuai dengan studi kasus penentuan mahasiswa berprestasi menjadi penting dilakukan agar hasil akhir perangkingan tidak merugikan pihak manapun serta dapat mengatasi kebingungan dalam menentukan prioritas kriteria. Metode ROC, AHP, dan CRITIC dibandingkan untuk menemukan metode pembobotan yang paling sesuai dengan studi kasus. Hasil penelitian menunjukkan bahwa metode CRITIC objektif dalam melakukan pembobotan karena prioritas kriteria ditentukan berdasarkan korelasi antar kriteria. CRITIC diusulkan karena hasil pembobotannya tidak dipengaruhi subjektivitas pengambil keputusan melainkan bergantung pada seberapa kuat hubungan antar kriteria dari sebaran data sehingga secara otomatis akan terbentuk urgensitas kriteria.
Automated Synthesis Of Product Return Recommendations Via Groq And Large Language Models Dian Hanifudin Subhi; usman nurhasan; Ibnu Tsalis Assalam
MULTITEK INDONESIA Vol 20 No 1 (2026): July (On Progress)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13659

Abstract

Industrial economic resilience depends on the efficiency of after-sales service provisioning, which is often hindered by semantic ambiguity in customer reports and latency constraints of conventional computing infrastructures. This study examines the integration of a Language Processing Unit (LPU) with a Large Language Model (LLM) under a Deterministic Reasoning Architecture (DRA) framework to address these limitations. Experiments were conducted on a heterogeneous dataset (N = 27.500) consisting of operational service records from PT Rekaindo Global Jasa and a Southeast Asian manufacturing entity over the period 2021–2025. Semantic complexity analysis based on Shannon Entropy indicates that the Repair category exhibits the highest information density (5.2 bits), corresponding to an increased risk of logical failure. Performance benchmarking demonstrates that the proposed LPU-based architecture achieves deterministic inference with a Risk Priority Number (RPN) of 42 significantly lower than stochastic GPU-based baselines (RPN > 120). Predictive integrity evaluation yields an AUC–ROC of 0.988 and an inter-rater agreement of 0.81 (Fleiss Kappa), indicating substantial alignment between automated recommendations and expert assessments. Economic robustness is validated through Monte Carlo simulations, showing a 94.2 % probability of achieving Return on Investment within 20 months, even under high-volatility scenarios. Furthermore, the framework complies with ISO/IEC 42001:2023 and the EU AI Act, achieving a Fairness Ratio above 0.94. Overall, the results demonstrate that the LPU–LLM synergy enables fast, reliable, and responsible generative AI deployment in industrial settings.
Co-Authors Agung, Muhammad Helmi Permana Dian Hanifudin Subhi Fadlilah, Afi Khosyi Nasywa Imanda Ulla Delfana Rosiani Usman Nurhasan