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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sains dan Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi JISICOM (Journal of Information System, Infomatics and Computing) Journal of Comprehensive Science
Lulu Chaerani Munggaran
Gunadarma University
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics

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Advantage Actor-Critic-based Test Case Prioritization for Early Detection of Operational Defects in AI Services Eko Agustina; Lulu Chaerani Munggaran
SISTEMASI Vol 15, No 10 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i10.6872

Abstract

API-based artificial intelligence (AI) services may experience semantic defects and runtime failures under high-load conditions, necessitating test case prioritization strategies that support the early detection of operational defects. This study aims to evaluate the effectiveness of adaptive test case prioritization using Advantage Actor-Critic (A2C) for AI services under high-load conditions. The experiments were conducted using intent classification services based on DistilBERT and ELECTRA-small, with a test suite derived from Banking77. A2C was compared with random prioritization and performance-based prioritization using full test suite prioritization over ten cycles and five runs, yielding 300 experimental units. Effectiveness was evaluated using Average Percentage of Faults Detected (APFD), Test to First Failure (TTF), and response time. For DistilBERT, A2C achieved the highest mean APFD of 0.883816 and the lowest TTF of 1.000. For ELECTRA-small, A2C achieved the highest mean APFD of 0.674268, with a TTF of 1.000, identical to that of performance-based prioritization. Performance-based prioritization yielded the lowest response times for both models: 3,760.845 ms for DistilBERT and 5,984.625 ms for ELECTRA-small. The Friedman tests indicated statistically significant differences across the three metrics for both models ([test statistics and p-values to be inserted]). Wilcoxon signed-rank tests with Holm correction showed that most method pairs differed significantly. Exceptions included TTF for A2C versus performance-based prioritization on ELECTRA-small, which produced identical results, and response time for A2C versus random prioritization, which did not differ significantly. These findings indicate that A2C's primary advantage lies in its ability to generate effective test case prioritization sequences for earlier operational defect detection rather than achieving the lowest response time.
Implementasi Metode Simple Additive Weighting pada Sistem Pendukung Keputusan Endorsement Melalui Instagram Hasnadhia Raufani; Lulu Chaerani Munggaran
Jurnal Sains dan Informatika Vol. 9 No. 2 (2023): Jurnal Sains dan Informatika
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v9i2.481

Abstract

Penelitian ini merancang Sistem Pendukung Keputusan (SPK) untuk menemukan pilihan influencer terbaik untuk tujuan endorsement untuk mendukung Glowbe Beauty. Kriteria yang digunakan untuk sistem adalah how-to-rate, average likes, followers, engagement rate, dan rate card, dan data diperoleh dari website Social Blade. Metode Simple Additive Weighting digunakan sistem untuk melakukan perhitungan dengan mengevaluasi skor dari setiap alternatif dengan mengalikan nilai yang diskalakan. Nilai yang diskalakan diberikan kepada keseluruhan atribut alternatif dengan bobot kepentingan relatif. Pengujian sistem digunakan dengan menggunakan metode Black Box Testing. Hasil perhitungan manual dan sistem akan dibandingkan untuk mengetahui keakuratan sistem. Hasil yang diperoleh dari perhitungan menunjukkan bahwa Dilla Jaidi memiliki skor tertinggi yaitu 0,7826 dan disimpulkan sebagai alternatif influencer pilihan terbaik untuk endorsement.
Co-Authors Akrom, Mohamad Akromudin Anindito Yoga Pratama Dela Harmilasari Eko Agustina Eko Rudi Suteja Esty Purnamasari Hasnadhia Raufani Herawan, Hery Ika Atman Satya Muhammad Khalif Fadhilah Rayhan Gusty Alif Sari Pranasti, Adira Setiawan, Foni Agus Sri Astuti Winarti Winarti Wulandari, Yestika Dian