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All Journal Journal of Research and Publication Innovation AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan
Gustyanto Firgiawan
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Implementasi Metode You Only Look Once (YOLO) untuk Pendeteksi Objek dengan Tools OpenCV Gustyanto Firgiawan; Nazwa Lintang Seina; Perani Rosyani
AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Vol. 2 No. 2 (2024): Jurnal AI dan SPK : Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan
Publisher : CV. Shofanah Media Berkah

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Abstract

Deteksi objek adalah salah satu aplikasi utama dalam bidang computer vision. Pada penelitian ini akan dibahas mengenai implementasi metode YOLO (You Only Look Once) untuk pendeteksi objek secara real-time menggunakan OpenCV. YOLO dikenal karena kecepatannya dalam mendeteksi objek dalam gambar atau video. Implementasi ini menunjukkan keunggulan YOLO dalam deteksi objek yang cepat dan akurat.
Perancangan Sistem Monitoring Inventory Stock Product Berbasis Web Menggunakan Metode Prototype Dewi Putri Aulia; Gustyanto Firgiawan; ⁠Muhammad Al Hafizh Winarno; Muhammad Ramadien Rizky Darmawan; Yoga Aditya; Samsoni; Aprinia Handayani
Journal of Research and Publication Innovation Vol 2 No 3 (2024): JULY
Publisher : Journal of Research and Publication Innovation

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Laporan ini memaparkan perancangan sistem monitoring inventory stock product berbasis web dengan menggunakan metode prototype. Sistem ini dirancang untuk membantu PT. Plause Beauty dalam mengelola inventory produknya secara lebih efektif dan efisien. Metode prototype digunakan untuk mengembangkan sistem secara bertahap dan mendapatkan umpan balik dari pengguna. Hasil perancangan menunjukkan bahwa sistem ini dapat memudahkan proses pencatatan, pemantauan, dan pelaporan stok produk. Implementasi sistem diharapkan dapat meningkatkan produktivitas dan pengambilan keputusan di PT. Plause Beauty.
ESTIMASI PROPORSI STRES TINGGI PADA MAHASISWA Pasya Syamil Diyah; Andika Rachman; Pugoh Khavid Prayogo; Gustyanto Firgiawan; Perani Rosyani
Journal of Research and Publication Innovation Vol 3 No 4 (2025): OCTOBER
Publisher : Journal of Research and Publication Innovation

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This study applies a quantitative approach based on computational experiments to develop an accurate student stress level prediction model. The research design employs a cross-sectional method with data collection via online questionnaire instruments integrating multidimensional variables, including the Perceived Stress Scale (PSS-10) as the target variable, as well as the Pittsburgh Sleep Quality Index (PSQI) and Self-Compassion Scale (SCS) as key predictor features alongside academic variables. The main challenge of imbalanced data is addressed by applying the Synthetic Minority Over-sampling Technique (SMOTE) during the preprocessing stage to synthesize minority class samples and prevent majority bias. In the modeling phase, the Random Forest Classifier algorithm is utilized due to its superiority in handling complex non-linear relationships, and its performance is compared with Logistic Regression as a baseline model. Model validation is conducted using the 10-Fold Cross-Validation method to test data generalization. Performance evaluation focuses on Recall, Precision, and F1-Score metrics to ensure the model's sensitivity in effectively detecting high-stress cases as a clinically relevant early warning system.
Co-Authors Andika Rachman Aprinia Handayani Dewi Putri Aulia Muhammad Ramadien Rizky Darmawan Nazwa Lintang Seina Pasya Syamil Diyah Perani Rosyani Pugoh Khavid Prayogo Samsoni Yoga Aditya ⁠Muhammad Al Hafizh Winarno