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All Journal Teknika: Jurnal Sains dan Teknologi Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Journal of Embedded Systems, Security and Intelligent Systems
Muhammad Rizki Fadillah
Jurusan Teknik Elektro, Fakultas Teknik, Universitas Sultan Ageng Tirtayasa
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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ANALISIS APLIKASI SISTEM INFORMASI BERBASIS WEB SNIPE-IT UNTUK MANAJEMEN INVENTARIS DI PUSAT TEKNOLOGI INFORMASI DAN KOMUNIKASI – BPPT Alief Sadikin; Muhammad Sadikin; Muhammad Rizki Fadillah
Jurnal Teknika Vol 14, No 1 (2018): Edisi Juni 2018
Publisher : Faculty of Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/tjst.v14i1.5860

Abstract

Pemulihan Voltage Sag Menggunakan Dynamic Voltage Restorer Pada Sistem Distribusi Dengan Metode Artificial Neural Network Fadil Muhammad; Rezha Andriyanto; Anggoro Suryo Pramudyo
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Vol 10, No 2 (2021): Edisi Desember 2021
Publisher : Fakultas Teknik Elektro - Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/setrum.v10i2.13094

Abstract

Permasalahan kualitas daya listrik yang sering terjadi pada sistem distribusi adalah voltage sag. Voltage sag pada sistem distribusi umumnya diakibatkan oleh gangguan hubung singkat. Kualitas daya listrik dikatakan baik jika memenuhi persyaratan yang diantaranya adalah daya yang memiliki tegangan dengan nilai mendekati 1 pu. Untuk mengurangi dampak dari kualitas daya yang buruk, maka digunakanlah perangkat kompensasi yang disebut Dynamic Voltage Restorer (DVR). DVR adalah perangkat daya berbasis elektronik yang terhubung seri pada saluran tenaga. Pada penelitian ini akan disimulasikan pemulihan voltage sag yang terjadi akibat gangguan hubung singkat satu fasa ke tanah, dua fasa ke tanah, tiga fasa ke tanah, dan antar fasa menggunakan DVR dengan kontrol Artificial Neural Network (ANN). Pada saluran distribusi tiga fasa dengan menggunakan pemodelan Dynamic Voltage Restorer dengan kontrol ANN yang telah dibuat. Hasil simulasi menunjukkan bahwa DVR dengan kontrol ANN mampu memulihkan tegangan hingga 0.9912 dengan THD 2,26% pada durasi 0,2 detik sampai 0,6 detik.
Vehicle Detection Counting using YOLO and DeepSORT on Edge Device Rafli; Siswo Wardoyo; Rocky Alfanz; Rian Fahrizal; Fadil Muhammad; Imamul Muttakin
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9482

Abstract

Vehicle counting is a crucial method used in traffic management. Computer vision can be employed for efficient detection and classification techniques for vehicle objects. This paper reports on a simultaneous process of vehicle classification and counting implemented on NVIDIA Jetson Nano. The use of YOLOv5 overcomes computational load issues in edge computing deployments, whereas its combination with the DeepSORT tracker algorithm enhances the accuracy of vehicle detection and counting in various directions. A total of 18200 images are used to train the detectors that are designed to target local vehicles. The average accuracy of the model for detecting cars, motorcycles, buses, and trucks is 72.1%, 21.56%, 70%, and 25.63%, respectively. Real-time tests obtained an overall average vehicle counting accuracy of 49.95%.
Implementasi Artificial Intelligence untuk Non-Destructive Testing: Ulasan Open Access Imamul Muttakin; Ceri Ahendyarti; Fadil Muhammad; Masjudin Masjudin; Ratu Verlaili Erlindriyani; Nauval Franata; Muchtar Ali Setyo Yudono; Agusutrisno Agusutrisno; Rian Fahrizal; Alief Maulana
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Vol 15, No 1 (2026): Edisi Juni 2026
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/setrum.v15i1.40021

Abstract

Reviu ini mengkaji kemajuan terkini dalam pengujian dan evaluasi non-destruktif terintegrasi kecerdasan buatan (AI-NDT) di berbagai bidang seperti kualitas pangan, infrastruktur, manufaktur, sistem energi, ilmu kayu, dan pertanian. Literatur menunjukkan pergeseran dari inspeksi konvensional menuju sistem cerdas yang mampu melakukan pemantauan real-time, deteksi cacat, aesmen kualitas, penilaian kuantitatif, dan pemeliharaan prediktif. Modalitas sensing utama meliputi ultrasonik, akustik, elektromagnetik, gelombang mikro, pencitraan, spektroskopi, bioimpedansi, dan teknologi hidung/lidah elektronik, yang umumnya dikombinasikan dengan model machine learning dan deep learning. Meskipun telah terjadi kemajuan yang signifikan, tantangan tetap ada dalam hal ketersediaan dataset, generalisasi model, pergeseran sensor, interpretasi, standardisasi, dan penerapan di lapangan. Penelitian di masa mendatang harus menekankan fusi sensor multimodal, explainable AI dan AI berbasis fisika, edge computing, dataset benchmark, dan validasi jangka panjang. Secara keseluruhan, AI-NDT muncul sebagai teknologi sentral untuk NDE 4.0 dan sistem inspeksi cerdas.
Pengaruh Medan Magnet Terhadap Morfologi Permukaan Lapisan Tipis yang Difabrikasi Melalui Elektrodeposisi - Mini Review Agusutrisno Marlis Nurut; Ceri Ahendyarti; Fadil Muhammad; Masjudin Masjudin; Dina Estining Tyas Lufianawati; Nauval Franata; Muchtar Ali Setyo Yudono; Imamul Muttakin
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Vol 14, No 2 (2025): Edisi Desember 2025
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/setrum.v14i2.36529

Abstract

This mini-review summarizes recent advances in understanding how external magnetic fields influence the surface roughness of thin film produced by electrodeposition. The application of magnetic fields during deposition introduces magnetohydrodynamic (MHD) effects that modify ion transport, nucleation, and lapisan growth behavior. Studies across Fe-, Co-, and Ni-based systems demonstrate that parallel magnetic fields promote smoother, denser, and more uniform deposits by enhancing ionic convection and stabilizing the diffusion layer, whereas perpendicular fields frequently induce columnar or rougher surfaces due to localized microvortices and stress accumulation. The extent of roughness reduction depends nonlinearly on magnetic field intensity—moderate fields (0.3–1.0 T) typically yield optimal smoothing, while excessive fields (>1.5 T) can cause turbulence and morphological instability. Quantitative analyses reveal reductions in RMS roughness of 40–80% under appropriate field conditions, accompanied by refined grain structure and improved lapisan compactness. Overall, magnetic-field-assisted electrodeposition provides a controllable and scalable approach for tailoring thin film morphology, bridging the performance gap between electrochemical and vacuum-based deposition techniques. Its implementation offers new opportunities for developing high-quality film for spintronic devices, magnetic sensors, and microelectronic applications
Co-Authors Agusutrisno Agusutrisno Agusutrisno Marlis Nurut Alief Maulana Alief Sadikin Anggoro Suryo Pramudyo Ceri Ahendyarti Dina Estining Tyas Lufianawati Fahrizal, Rian Franata, Nauval Imamul Muttakin Masjudin Masjudin Muchtar Ali Setyo Yudono Muhammad Sadikin Nauval Franata Rafli Ratu Verlaili Erlindriyani Rezha Andriyanto Rocky Alfanz Siswo Wardoyo