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Sunggono, Nova Teguh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics

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Analisis Kinerja ESP32-CAM Dalam Mendeteksi Objek Herwandi, Aditya; Ramadhan, Alfian Aditya; Sunggono, Nova Teguh; Ferawati, Ferawati
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2296

Abstract

Deteksi objek merupakan salah satu komponen kunci dalam sistem visi komputer yang banyak digunakan pada berbagai aplikasi seperti pengawasan, otomasi, dan sistem berbasis Internet of Things (IoT). Namun, penerapan algoritma deteksi berbasis deep learning umumnya memerlukan sumber daya komputasi tinggi yang tidak sesuai untuk perangkat berbiaya rendah seperti ESP32-CAM. Penelitian ini bertujuan untuk menganalisis kinerja ESP32-CAM dalam mendeteksi objek menggunakan algoritma YOLOv3, serta mengkaji pengaruh berbagai kondisi lingkungan terhadap hasil deteksi. Studi ini menggunakan pendekatan eksperimental dengan lima skenario pengujian yang mencakup variabel pencahayaan (siang dan malam), gerakan kamera (bergerak dan tidak bergerak), resolusi gambar (800×600 dan 1280×1024 piksel), serta ukuran objek (kecil, sedang, dan besar). Evaluasi dilakukan dengan mengukur tingkat akurasi deteksi, waktu pemrosesan per frame, serta kejernihan citra pada masing-masing skenario. Hasil pengujian menunjukkan bahwa ESP32-CAM mampu mendeteksi objek dengan akurasi tertinggi sebesar 73% pada kondisi siang hari dengan pencahayaan optimal dan kamera statis. Akurasi menurun secara signifikan dalam kondisi gelap, saat kamera bergerak, serta ketika objek berukuran kecil. Penelitian ini menegaskan bahwa keberhasilan deteksi sangat dipengaruhi oleh pengaturan lingkungan dan karakteristik objek. Meskipun ESP32-CAM memiliki keterbatasan dalam daya komputasi dan memori, hasil penelitian ini membuktikan bahwa perangkat ini tetap layak digunakan untuk sistem deteksi objek sederhana dalam lingkungan terkendali. Penelitian ini memberikan kontribusi praktis dalam pengembangan sistem deteksi visual berbasis perangkat ringan, dan menawarkan rekomendasi teknis untuk meningkatkan performa melalui pengaturan lingkungan dan pemilihan parameter yang tepat.
Implementation of Weighted Product Method for Teacher Selection at an Islamic Boarding School Rezagustini, Nurafni; Adisti, Andara; Royadi, Dedi; Sunggono, Nova Teguh
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2736

Abstract

Service teachers play a crucial role in Islamic boarding schools by imparting the knowledge and values they acquired during their studies. At Al-Kamil Islamic Boarding School, the selection of these service teachers is conducted annually to ensure only the most competent and ethically grounded candidates are chosen. Traditionally, this selection has been performed manually, which introduces several challenges, including subjectivity, inefficiency, and inconsistencies in evaluation. These limitations can compromise the fairness and transparency of the process, potentially leading to biased outcomes and reduced institutional credibility. To address these issues, this study proposes the implementation of a web-based Decision Support System (DSS) utilizing the Weighted Product (WP) method to support the teacher selection process. The WP method is particularly effective in multi-criteria decision-making, as it applies multiplicative weights to criterion scores, enabling a comprehensive and balanced evaluation of each candidate. In this application, 14 candidates are assessed based on eight criteria: morality, academic performance, craftsmanship, Quran memorization, achievements, understanding of classical Islamic texts, language skills, and computer literacy. The system calculates a preference score for each candidate using vector S and vector V normalization processes. The top three candidates identified are Arizkia Aulia (V = 0.07377), Zaskia Faliza (V = 0.07279), and Zulfa Nisa (V = 0.07231). The integration of this WP-based DSS enhances the objectivity, fairness, and efficiency of decision-making. Furthermore, it provides a replicable and scalable framework for educational institutions seeking to implement structured and data-driven staff selection processes.
Co-Authors Adisti, Andara Ferawati Ferawati Herwandi, Aditya Ramadhan, Alfian Aditya Rezagustini, Nurafni Royadi, Dedi Sidik, Achmad Sidiq, Yusuf Alimun Siregar, Maichel Cristiano Siti Maisaroh Stianingsih, Lilis Sudesta, Arga