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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Prosiding SNATIF SMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics IJFAC (Indonesian Journal of Fundamental and Applied Chemistry) JETT (Jurnal Elektro dan Telekomunikasi Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro CYCLOTRON Jurnal Teknologi Sistem Informasi dan Aplikasi JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Qua Teknika bit-Tech Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) International Journal of Research in Vocational Studies (IJRVOCAS) Jurnal Minfo Polgan (JMP) BEES: Bulletin of Electrical and Electronics Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Polimesin semanTIK Smatika Jurnal : STIKI Informatika Jurnal
Ahmad Taqwa
Politeknik Negeri Sriwijaya
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Real-Time Student Attendance Recognition Using a Centroid Based MTCNN–ArcFace Framework Suci Putri Widyani; Suroso Suroso; Ahmad Taqwa
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.35448

Abstract

Student attendance remains susceptible to proxy attendance and operational inefficiencies, while many face recognition systems are evaluated only on benchmark datasets and rarely investigate identity representation strategies under real-world educational conditions. This study evaluates a hybrid MTCNN–ArcFace framework incorporating centroid-based identity representation, application-specific threshold calibration, and real-time validation for automated student attendance. A quantitative experimental design was conducted using a locally collected dataset from a secondary school. MTCNN was employed for face detection and alignment, whereas ArcFace with a ResNet-50 backbone generated facial embeddings that were aggregated into centroid templates for identity matching. The framework was assessed through offline performance evaluation and operational deployment. The proposed approach achieved 92.86% accuracy, 97.22% precision, 92.86% recall, and a 93.49% F1-score, with a 4.76% false acceptance rate and 2.38% false rejection rate. In addition, centroid representation reduced template storage requirements and supported efficient real-time recognition using limited enrollment samples. These findings demonstrate that centroid-based identity representation enhances the practicality of deep face recognition for educational attendance systems by improving computational efficiency while maintaining reliable recognition performance in authentic school environments.
Experimental evaluation of micro-inverters and string inverters for PV array performance under partial shading Muhammad Rayyan Harahap; Ahmad Taqwa; RD. Kusumanto
Jurnal Polimesin Vol 24, No 4 (2026): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i4.9505

Abstract

Solar photovoltaic (PV) systems are increasingly adopted as renewable energy sources, but their performance is often compromised by partial shading, which reduces efficiency and output. The choice of inverter topology string inverters versus micro-inverters plays a critical role in mitigating shading losses and improving overall system performance. This study aims to experimentally evaluate the performance of micro-inverters compared to string inverters under varying partial shading conditions, focusing on electrical efficiency and economic feasibility. The experiment was conducted in Palembang using four 100 Wp polycrystalline PV panels installed at a 15° tilt and north-facing azimuth. AC power output was measured under four shading levels (0%, 5%, 10%, and 15%) using irradiance meters and wattmeters over 10-hour daily measurement periods. Performance ratio, fill factor, and efficiency were calculated, and Break-Even Point (BEP) analysis was performed to assess economic viability. Micro-inverters achieved 22.98% efficiency compared to 15.96% for string inverters in unshaded conditions. Both systems performed similarly (14.57% vs. 14.40%) at 5% shading. However, at 10% and 15% shading, micro-inverters significantly outperformed string inverters, with efficiencies of 10.40% and 9.87% compared to 4.38% and 1.35%, respectively. BEP analysis revealed that while string inverters reached payback faster under minimal shading, micro-inverters were more economically advantageous when shading exceeded 10%, with string inverter BEP extending to 25.9 years under 15% shading. These results demonstrate that micro-inverters provide greater resilience to partial shading and can offer improved technical and economic performance under shaded operating conditions.
SawitScan: Aplikasi Android untuk Deteksi Kematangan dan Prioritas Panen Kelapa Sawit Berbasis YOLO26 dan Metode SAW Juriawan Raja Saputra; Irma Salamah; Ahmad Taqwa
Jurnal Minfo Polgan Vol. 15 No. 3 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i3.16447

Abstract

ABSTRAK Penentuan kematangan tandan buah segar (TBS) kelapa sawit di lapangan masih banyak dilakukan secara manual, sehingga bersifat subjektif dan rawan menimbulkan kesalahan panen, sementara penentuan area yang diprioritaskan untuk dipanen lebih dahulu juga belum dilakukan secara sistematis. Penelitian ini mengembangkan dan mengevaluasi SawitScan, aplikasi Android yang menggabungkan deteksi objek YOLO26 untuk klasifikasi kematangan (ripe, underripe, unripe) secara on-device melalui kamera smartphone, pencatatan koordinat GPS otomatis tiap deteksi, dan sistem pendukung keputusan Simple Additive Weighting (SAW) untuk menghitung skor prioritas panen tiap klaster lokasi. Model YOLO26n dilatih pada dataset gabungan 1.124 citra karena arsitekturnya yang ringan dan desain end-to-end tanpa Non-Maximum Suppression yang sesuai untuk implementasi mobile, dengan akurasi kompetitif (precision 89,8%, recall 87,3%, mAP@0.5 92,1%). Evaluasi independen model akhir yang ter-embed pada aplikasi (TFLite) pada data uji menghasilkan mAP@0.5 0,748 dan F1-score 0,743. Sistem SAW memeringkat area panen berdasarkan kombinasi jumlah deteksi ripe, underripe, dan unripe per klaster, dengan bobot 0,6, 0,3, dan 0,1 sesuai urgensi panen. Hasil penelitian menunjukkan deteksi objek on-device, pemetaan berbasis GPS, dan sistem pendukung keputusan dapat diterapkan praktis pada perangkat mobile untuk mendukung perencanaan panen kelapa sawit yang lebih objektif dan efisien. Penelitian selanjutnya dapat diarahkan pada perluasan dataset ke berbagai lokasi perkebunan serta validasi hasil pemeringkatan SAW terhadap data realisasi panen aktual di lapangan. Kata Kunci: Deteksi Objek; Kematangan Kelapa Sawit; Simple Additive Weighting; Sistem Pendukung Keputusan; YOLO26
Co-Authors Abdul Rakhman Abdurrachman Faiz Ade Silvia Handayani Adewasti Adewasti Adewasti Aditia Prathama Aida Syarif Aknes Tasia Pratama Alpha Rabbi Sugara Altrucia Pinem Amperawan Amperawan Bayu Sanjaya Ciksadan Ciksadan Dimas Andreansyah Dina Hervita Eliza Fitri Ramadanti Evelina, Evelina Fatahul Arifin Felisia Talitha Aprilia Hafizh Ulwan Handava Wardana Hesti, Emilia Ihsan Mustaqiim Indri Ariyanti Irma Salamah Isa, Indra Griha Tofik Juriawan Raja Saputra Lindawati Lindawati Lindawati Lusi Risky Faradila M. Ardiansyah Martinus Mujur Rose Masayu Anisah Mila Mila Mohammad Fadhli Muhammad Gian Azzra Ramadhan Muhammad Rayyan Harahap Nadia Miranda Narita Tria Almirah RD Kusumanto RD. Kusumanto Rizkia Meyliana Rosita Efriyanti Rusdianasari Rusdianasari Sabilal Rasyad Shaffa Shaffa Sholihin Sholihin Siti Fatima Sopian Soim, Sopian Suci Putri Widyani Suroso Suroso Suroso Syanno Revy Widia Salsabila Yanita Fibriliyanti Yohandi Bow