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All Journal MEDIA ELEKTRIKA Jurnal Teknologi Informasi dan Ilmu Komputer Teknika: Jurnal Sains dan Teknologi Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal Rekayasa Mesin Sistemasi: Jurnal Sistem Informasi JTERA (Jurnal Teknologi Rekayasa) Jurnal Sisfokom (Sistem Informasi dan Komputer) KOMPUTIKA - Jurnal Sistem Komputer Jurnal Mnemonic International Journal of Engineering, Technology and Natural Sciences (IJETS) Devotion: Journal of Research and Community Service Fidelity : Jurnal Teknik Elektro International Journal Engineering and Applied Technology (IJEAT) Medika Trada Journal of Artificial Intelligence and Digital Business Journal of Community Service in Science and Engineering Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia
Muchtar Ali Setyo Yudono
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Sultan Ageng Tirtayasa
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Desain Prototipe Teropong Fundus 3D Berbasis Smartphone dengan Lensa 20D untuk Deteksi Retina Katarak Ridha, Fabrobi Fazlur; Yudono, Muchtar Ali Setyo; Mardiyana, Dani; Al-Ghozi, Faturrohman; Maulana, Aldi
JTERA (Jurnal Teknologi Rekayasa) Vol 9, No 2: December 2024
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v9.i2.2024.1-10

Abstract

Katarak merupakan penyebab utama kebutaan di Indonesia, khususnya di daerah terpencil dengan akses terbatas terhadap layanan kesehatan mata. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi prototipe teropong fundus 3D berbasis smartphone yang mudah digunakan dan terjangkau guna meningkatkan akses pemeriksaan mata di wilayah tersebut. Desain perangkat mempertimbangkan aspek ergonomi, ukuran yang sesuai, serta kualitas citra yang dapat mendukung diagnosis katarak secara efektif. Evaluasi melibatkan dua dokter spesialis mata dan sejumlah pasien untuk menguji efektivitas, kenyamanan, dan kualitas perangkat yang dihasilkan. Hasil penelitian menunjukkan bahwa prototipe ini memiliki kualitas citra yang memadai untuk mendukung diagnosis, meskipun masih diperlukan peningkatan pada beberapa aspek teknis. Perangkat dinilai ergonomis, mudah dioperasikan, dan memiliki akurasi yang baik dalam mengklasifikasikan tingkat keparahan katarak. Selain itu, responden mengindikasikan bahwa perangkat ini dapat meningkatkan efisiensi dalam pemeriksaan klinis, terutama di daerah dengan keterbatasan akses kesehatan. Area perbaikan yang diidentifikasi meliputi kekokohan alat, peningkatan kualitas gambar, serta sistem pencahayaan untuk memastikan kejelasan citra yang optimal. Tingkat penerimaan pasien terhadap perangkat ini juga cukup tinggi, meskipun terdapat keluhan terkait instruksi penggunaan yang kurang jelas dan kualitas citra yang buram dalam kondisi tertentu. Secara keseluruhan, prototipe teropong fundus 3D berbasis smartphone ini memiliki potensi besar untuk diadopsi dalam praktik klinis, dengan beberapa perbaikan yang diperlukan untuk memastikan kualitas dan keandalan yang lebih optimal.
Sistem Otentikasi Biometrik Multimodal Berbasis Fitur Tekstur Garis Telapak dan Pembuluh Darah Punggung Tangan dengan Metode BPNN Yudono, Muchtar Ali Setyo; Nugraha, Adi; Franata, Nauval; Ramadhani, Ahmad; Erlindriyani, Ratu Verlaili; Zulfiqar, Danial; M.Syam, Fajar; Futri, Dila Aura; Fauzan, Akmal Nuur
Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Vol 13, No 2 (2024): Edisi Desember 2024
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/setrum.v13i2.29589

Abstract

Kebutuhan akan sistem otentikasi yang aman terus meningkat seiring perkembangan teknologi digital. Sistem autentikasi biometrik berbasis ciri khas manusia, seperti pengenalan wajah dan sidik jari, menawarkan solusi namun masih rentan terhadap manipulasi. Penelitian ini mengembangkan sistem autentikasi biometrik multimodal dengan menggabungkan fitur garis telapak tangan dan pola pembuluh darah punggung tangan, memanfaatkan Backpropagation Neural Network (BPNN) untuk klasifikasi dan Tapis Gabor untuk ekstraksi ciri. Sistem ini dirancang untuk meningkatkan akurasi, sensitivitas, dan spesifisitas autentikasi. Pada data pelatihan, sistem mencapai akurasi sebesar 98,1%, sensitivitas 100% pada kelas tertentu, dan spesifisitas optimal. Sementara pada data pengujian, akurasi tercatat sebesar 91%, dengan sensitivitas tertinggi mencapai 95% dan spesifisitas tetap tinggi pada kelas tertentu. Hasil ini menunjukkan bahwa sistem autentikasi multimodal berbasis BPNN dan Tapis Gabor dapat diandalkan dalam aplikasi keamanan digital, karena mampu memanfaatkan dua sumber biometrik berbeda untuk meningkatkan keakuratan dan daya tahan terhadap teknik pemalsuan.
Ultrasonic Sensor for Measurement of Water Flow Rate in Horizontal Pipes Using Segment Area Suryana, Anang; Yudono, Muchtar Ali Setyo
Fidelity : Jurnal Teknik Elektro Vol 5 No 1 (2023): Edisi Januari 2023
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v5i1.143

Abstract

Measurement of flow rate in wastewater pipes is still challenging to be done in real-time. The main challenge in wastewater measurement is the non-homogeneity of wastewater due to the presence of solid waste material. This becomes a hindrance when using mechanical flow measurement or direct contact between the fluid and the measuring device. Therefore, the solution is to perform non-contact flow measurement between the fluid and the measuring device. In this study, a flow sensor was developed for a horizontal pipe using the cross-sectional area measurement method on a horizontal pipe measured by an ultrasonic sensor as the fluid level measuring device. The ultrasonic sensor can measure the height of the fluid level, allowing the flow velocity and cross-sectional area of the horizontal pipe to be determined. The basis of this measurement is that any flowing fluid in a pipe that experiences a change in velocity will also experience a change in volume or fluid level. This measurement is in accordance with the continuity equation. From the calibration results, an error of 1.7% was obtained for the height measurement from the ultrasonic sensor compared to the ruler used as a height calibration tool. Meanwhile, the error in the flow velocity measurement from the ultrasonic sensor compared to the calibration results using the tracker software was 4.2%. The error in volume measurement from the ultrasonic sensor compared to the standard measuring tool, a 5-liter beaker glass, was 0.8%. As a conclusion, flow rate measurement using the ultrasonic sensor with the cross-sectional area measurement method can be used to measure the flow rate in a horizontal pipe with a diameter of 11 cm.
Water Level Classification for Early Flood Detection Using KNN Method Akbar, Jiwa; Yudono, Muchtar Ali Setyo
Fidelity : Jurnal Teknik Elektro Vol 6 No 2 (2024): Edition for May 2024
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v6i2.227

Abstract

Floods occur when water levels exceed normal limits, causing rivers to overflow and inundate low-lying areas. Early warning systems for flood disasters are crucial to mitigate the damage caused, such as loss of life and property. A flood classification system can be developed by utilizing water level data from the Department of Water Resources to predict the likelihood of flooding using the K-Nearest Neighbors (KNN) algorithm. This study aims to determine the flood status classification based on water levels using the KNN method in the Ciliwung River. The research data were obtained from the DKI Jakarta open data site, consisting of 564 samples. The study evaluated K values ranging from 1 to 10. The average accuracy across all K scenarios was 99%, with the best K value being 1, which provided 100% accuracy, sensitivity, and specificity. These results indicate that the KNN method is effective in classifying flood status based on water level data, making it a reliable tool for early warning systems. This system is expected to help reduce the negative impacts of floods by providing accurate and timely information to the public and authorities. This research makes a significant contribution to the development of disaster mitigation technology, particularly in flood risk management in urban areas.
Implementation of the Internet of Things in Centrifuge Calibrators Kuspranoto, Abdul Haris; Alfatih, Muhammad Fa'iz; Yudono, Muchtar Ali Setyo
Fidelity : Jurnal Teknik Elektro Vol 6 No 3 (2024): Edition for September 2024
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v6i3.240

Abstract

The implementation of Internet of Things (IoT) technology in centrifuge calibrators brings innovation by enhancing accuracy and efficiency in medical device calibration. This research develops an IoT-based centrifuge calibrator capable of storing calibration data digitally and providing real-time access, using the E18-D80NK sensor for rotational motion detection, which is more cost-effective than conventional laser modules. Measurement results demonstrate high accuracy across various RPM settings (1000, 1500, 2000, 2500, and 3000 RPM), with minimal error and stability within acceptable tolerance limits. The key advantage of IoT implementation lies in efficient calibration data management, enabling real-time access and integration with other systems for effective analysis and monitoring of calibration outcomes. Overall, IoT technology in centrifuge calibration not only enhances healthcare service quality with reliable results but also supports efficient and transparent equipment management in medical settings, offering long-term benefits to healthcare facilities and patients.
Design of Lightning Strike Hazard Zone Detection System for Direct Lightning Strike Prevention in Open Area Nugraha, Adi; Al Bantani, Rahmat Ato'ullah Gumilang; Grahito; Yudono, Muchtar Ali Setyo; Suryana, Anang
Fidelity : Jurnal Teknik Elektro Vol 6 No 3 (2024): Edition for September 2024
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v6i3.247

Abstract

Lightning is a dangerous natural phenomenon due to its high-voltage electrical energy, which can cause significant damage. The primary hazard of lightning is direct strikes, potentially leading to severe burns, organ failure, and even death. Additionally, lightning can trigger fires, disrupt power grids, and cause explosions. To mitigate these risks, this research focuses on designing a lightning detection system using the "rolling sphere" method. The lightning strike zone is modeled using CorelDRAW software, aiding users in visualizing areas prone to lightning strikes. The device is assembled with electronic components designed to detect lightning based on the method, considering parameters such as peak current, current steepness, charge, and impulse force. Once assembled, the system undergoes testing in an open area at the Faculty of Engineering, Sultan Ageng Tirtayasa University, to verify its functionality and accuracy. Testing is conducted at night to minimize weather-related disturbances, such as Cumulonimbus clouds. The results confirm that the device operates as expected, successfully identifying areas at risk of lightning strikes. This system is anticipated to enhance safety and infrastructure protection in lightning-prone regions.
Designing a GaN-Based 1x2 Optical Power Splitter Using Rectangular Waveguide Coupling Franata, Nauval; Ali Setyo Yudono, Muchtar; Ramadhani, Ahmad; Verlaili Erlindriyani, Ratu
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 6 No 2 (2024): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v6i2.415

Abstract

The need for compact and efficient optical power splitters is becoming increasingly urgent due to the growing demand for integrated photonic devices, which are essential in fields like optical interconnects and sensing. This study addresses the issue by focusing on the design and analysis of a GaN based 1x2 optical power splitter that employs rectangular waveguide coupling. The primary aim of the research is to evaluate how the number of rectangular waveguide couplings affects the performance of the power splitter, particularly in terms of splitting angle and excess loss. To achieve this, simulations were conducted using the finite-difference time-domain (FDTD) method. The design was tested with three configurations: three, five, and seven rectangular waveguides. The research design follows a structured simulation approach, where the FDTD method was employed to explore the impact of varying the number of rectangular waveguides. The process involves systematically altering the number of coupling sections and analyzing the resulting output. The 3D optical power distribution and the optical field intensity at the splitter output for each design were examined. Additionally, the excess loss distribution over the wavelength range of 1500 nm to 1600 nm was determined, demonstrating the potential of the proposed designs for optical communication applications. This data analysis enabled the researchers to evaluate the performance of each configuration. Notably, as the number of waveguides increased, the splitting angle became wider, but this was accompanied by a rise in excess loss. These findings demonstrate the potential of the proposed design for practical applications. Future studies could explore further optimization and real-world implementation.
Water Level Classification for Detect Flood Disaster Status Using KNN and SVM Akbar, Jiwa; Setyo Yudono, Muchtar Ali
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2166

Abstract

Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.
Perancangan Alat Pemantauan Berkelanjutan Kualitas Udara Dalam Ruangan Sholahudin, Sholahudin; Yudono, Muchtar Ali Setyo; Suryana, Anang
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.13413

Abstract

Indoor air pollution is a significant threat because most human activities take place indoors. Several previous studies have proposed indoor air quality monitoring systems. Based on the literature reviewed, an opportunity identified from previous research is the change in the selection of the PM2.5monitoring sensor from GP2Y1010AUF, which is not specifically designed for PM2.5, to the ZH03B sensor, which is specifically designed for PM2.5measurement. The intersection of three air quality regulations (two national and one international) results in the measurement of four pollutants: PM2.5 (particulate matter 2.5), PM10 (particulate matter 10), NO2 (nitrogen dioxide), and CO (carbon monoxide). The system design is based on a literature review of the components, calculations, and hardware and software required. Detection data is stored on a memory card and Google Sheets, allowing users to view data history through a website published by Google Sheets. Additionally, the detection device's accuracy compared to other detectors is 93.55% for PM2.5, 93.13% for PM10, 97.10% for NO2, and 96.60% for CO.
Klasifikasi Katarak Berdasarkan Optic Disc Citra Fundus Smartphone: Perbandingan Ekstraksi Ciri Tekstur Dan Metode Neural Network Yudono, Muchtar Ali Setyo; Ridha, Fabrobi Fazlur; Mardiyana, Dani; Al-Ghozi, Faturrohman; Maulana, Aldi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129254

Abstract

biaya tinggi pemeriksaan sering kali menjadi hambatan, terutama di Indonesia. Kamera fundus konvensional, meskipun efektif, memiliki harga yang mahal dan kurang portabel, membatasi aksesibilitas di daerah terpencil. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi katarak otomatis menggunakan kamera fundus berbasis smartphone, yang menawarkan solusi lebih ekonomis dan portabel dibandingkan perangkat konvensional. Penelitian ini mengevaluasi kinerja tiga algoritma jaringan syaraf tiruan yaitu, Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), dan Radial Basis Function Neural Network (RBFNN), dalam klasifikasi katarak. Metode penelitian meliputi pra-pengolahan citra, segmentasi optic disc, ekstraksi ciri tekstur menggunakan Gray-Level Co-occurrence Matrix (GLCM) dan Filter Gabor, serta klasifikasi tingkat keparahan katarak ke dalam empat kategori, yaitu retina normal, katarak ringan (mild), katarak sedang (medium), dan katarak berat (severe). Hasil pelatihan menunjukkan rerata nilai akurasi sistem sebesar 96,35%, dengan kinerja terbaik pada ekstraksi ciri GLCM menggunakan PNN (100%) dan Filter Gabor menggunakan PNN (96,88%). Sensitivitas tertinggi dalam pelatihan dicapai oleh metode GLCM dan PNN (100%) untuk kategori katarak normal dan berat. Pada pengujian, sistem mencapai nilai rerata akurasi sebesar 77,98%, dengan hasil terbaik pada ekstraksi ciri GLCM menggunakan PNN (89,29%). Sensitivitas tertinggi pada pengujian diperoleh dengan metode GLCM dan PNN (89,29%) untuk katarak ringan dan berat, sementara spesifisitas tertinggi dicapai oleh GLCM dan BPNN (95,24%) untuk katarak normal. Temuan ini menunjukkan bahwa sistem berbasis smartphone ini tidak hanya meningkatkan aksesibilitas diagnosis katarak di daerah terpencil tetapi juga memberikan akurasi yang kompetitif dengan solusi konvensional.   Absctract Eye health, particularly cataract diagnosis, is a crucial aspect of individual well-being. However, the high cost of examinations often poses a barrier, especially in Indonesia. Conventional fundus cameras, while effective, are expensive and less portable, limiting accessibility in remote areas. This research aims to develop an automatic cataract classification system using smartphone-based fundus cameras, offering a more cost-effective and portable solution compared to conventional devices. The study evaluates the performance of three neural network algorithms: Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), and Radial Basis Function Neural Network (RBFNN) for cataract classification. The research methodology includes image preprocessing, optic disc segmentation, texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM) and Gabor Filter, and classification of cataract severity into four categories: normal retina, mild cataract, medium cataract, and severe cataract. Training results show an average system accuracy of 96.35%, with the best performance on GLCM feature extraction using PNN (100%) and Gabor Filter using PNN (96.88%). The highest sensitivity in training was achieved by GLCM and PNN (100%) for normal and severe cataract categories. During testing, the system achieved an average accuracy of 77.98%, with the best results for GLCM feature extraction using PNN (89.29%). The highest sensitivity in testing was obtained with GLCM and PNN (89.29%) for mild and severe cataracts, while the highest specificity was achieved by GLCM and BPNN (95.24%) for normal cataracts. These findings indicate that the smartphone-based system not only enhances cataract diagnosis accessibility in remote areas but also provides competitive accuracy compared to conventional solutions.
Co-Authors Abdul Haris Kuspranoto Adhitia Erfina Adi Nugraha Adi Nugraha Adi Nugraha Adi Nugraha Agusutrisno, Agusutrisno Ajat Akbar, Jiwa Akhmad Afifuddin Al Bantani, Rahmat Ato'ullah Gumilang Al-Ghozi, Faturrohman Alfatih, Muhammad Fa'iz Alun Sujjada Alya Abdul Zabar Anang Suryana Andika Kurniawan Anggi Dwiyanto Anggy Pradifth Anggy Pradiftha Junfithrana Any Elvia Jakfar Arsal Adriana Yusuf Artiyasa, Marina Aryo De Wibowo Aryo de Wibowo Bayu Indrawan Budianto, Anwar Ceri Ahendyarti Danang Purwanto Dani Mardiyana Dede Ajudin Dede Sukmawan Diky Zakaria Dio Damas Permadi DM, Dwigian Netha Putra Dodi Iwan Sumarno Dwi Septiani Edwinanto Edwinanto Edwinanto Eko Susilo Budi Utomo Elok Setianingtyas Eneng Siti Anisa Nurhasanah Erlindriyani, Ratu Verlaili Erlindriyani, Ratu Verlaili Fahmi Fauzi Fajar M.Syam Fandi Sugih Fauzan, Akmal Nuur Fauzan, Anugrah Nuur Febriansyah Felycia, Felycia Franata, Nauval Franata, Nauval Franata, Nauval Futri, Dila Aura Grahito Hamid Hamidi, Eki Ahmad Zaki Handrea Bernando Tambunan Harurikson Lumbantobing Haryanto, Heri Haryanto, Heri Himawan, Ganda Idrus Firdaus Ilman Himawan Kusumah Ilyas Aminuddin Irawati, Nur Bebi Ulfah Irma Saraswati Irvan Syah Riadi Irwan, Sobriansyah Isep Tedi Jumadi Jumadi Kumaran, Ivano Kuspranoto, Abdul Haris Lazuardi Akmal Islami Lucia Kharisma, Ivana Lufianawati, Dina Estining Tyas Luluk Hermawati M.Syam, Fajar Mansyur, Mansyur Marina Artiyasa Marina Artiyasa Marina Artiyasa Masjudin, Masjudin Maulana, Aldi Maulana, Alief Moch Rizky Mubarok, Alvin Muhammad Alif Alfaturisya Muhammad Syahrul Fauzi Muhammad, Fadil Muntasiroh, Laily Muttakin, Imamul Narputo, Panji Odi Akhyarsi Otong, Muhamad Paikun Permadi, Dio Damas Pratiwi, Septiya Hanum Putra, Wahyu Irwan Ramadhani Pratama, Mochammad Firdian Ramadhani, Ahmad Ramadhani, Ahmad Ramadhani, Ahmad Rian Fahrizal Rian Maulana Yusup Ridha, Fabrobi Fazlur Rozandi, Ardin Saputri, Utamy Sukmayu Saraswati, Irma Saraswati, Irma Sholahudin Sholahudin Sholahudin, Sholahudin Sriwijaya, Sayid Bahri Sutisna, Muhamad Galuh Syam, Fajar M Verlaili Erlindriyani, Ratu Wahyu Dwi Nurhidayat Wahyu Irwan Putra Wiryadinata, Romi Yasser Arafat Yordanius Damey Yudha Putra Yufriana Imamulhak Zulfiqar, Danial