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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) Medika Teknika : Jurnal Teknik Elektromedik Indonesia 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 Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal of Community Service in Science and Engineering Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Advance Sustainable Science, Engineering and Technology (ASSET)
Muchtar Ali Setyo Yudono
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Sultan Ageng Tirtayasa
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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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Ear Biometric Identification based on Gabor Filters using Backpropagation Neural Networks Kumaran, Ivano; Yudono, Muchtar Ali Setyo; Sujjada, Alun
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4573

Abstract

The development of reliable security systems is crucial for protecting personal information and access control. Ear biometrics, which utilizes the unique structure of the ear, is a promising method for human identification due to its resistance to forgery. This research aims to design and test an ear biometric identification system using images of the right ear without accessories from five men, totaling 224 images. The preprocessing steps include resizing the images, converting them to grayscale, and applying Gaussian filters. Image segmentation is performed using Canny edge detection, followed by morphological operations such as dilation and hole filling. Features of the ear images are extracted using Gabor filters, and classification is carried out using Backpropagation Neural Networks. The system achieved an average success rate of 88.8% across five testing scenarios, with the highest accuracy of 94% in the first and fifth scenarios. Sensitivity for classes 1, 2, 3, 4, and 5 was 98%, 74%, 92%, 96%, and 82%, respectively. Specificity reached 100% for classes 1 and 3, and 94%, 97.5%, and 94.5% for classes 2, 4, and 5. Based on the results of accuracy, sensitivity, and specificity testing, the ear biometric system using Gabor feature extraction and Backpropagation Neural Network classification demonstrates good performance and potential for security applications.
Classification of Beef, Goat, and Pork using GLCM Texture-Based Backpropagation Neural Network Saraswati, Irma; Fahrizal, Rian; Fauzan, Anugrah Nuur; Yudono, Muchtar Ali Setyo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4715

Abstract

Identifying different types of meat is crucial for preventing fraudulent activities and improving food safety. This research aims to create a classification system for various meat types (beef, goat, and pork) using the Gray Level Co-occurrence Matrix (GLCM) for extracting texture features, followed by classification through a Backpropagation Neural Network (BPNN). The methodology utilizes 60 images of beef, goat, and pork, achieving a remarkable accuracy of 100% in the training phase, which highlights the model's capability to effectively recognize patterns. However, when tested with new data, the system exhibits a sensitivity of 90% and a specificity of 95%, with some misclassifications occurring between goat and beef due to their similar textures. The findings of this study suggest that GLCM is an effective tool for deriving relevant statistical parameters necessary for classification. This research makes a significant contribution to developing a meat identification system that safeguards consumers and promotes awareness of food safety issues. The results are anticipated to provide a solid foundation for advancing meat type recognition and practical applications in the marketplace, ultimately boosting public trust in the meat products they purchase.
Backpropagation Design for Authenticating Blood Vessel Patterns of the Back of the Hand Using GLRLM Syam, Fajar M; Yudono, Muchtar Ali Setyo; Sujjada, Alun
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4109

Abstract

Digital security is a critical aspect in the current era of information technology, where access to personal devices and data is often the main target by irresponsible parties. Traditional identification methods such as passwords and PINs are starting to show limitations in addressing increasingly complex security challenges.. The dorsal hand veins offer certain advantages that make them an attractive option for biometric recognition systems because the dorsal hand vein pattern tends to be stable over time, unaffected by external factors such as changes in weather or hygiene. This research aims to develop a system that can identify the blood vessels of the back of the hand as a biometric sign. The approach used involves extracting GLRLM features and applying the Back Propagation Neural Network identification method. The main goal is to achieve a higher level of accuracy than previous studies in the same domain. The identification process involves several stages, starting from image reception, image pre-processing, segmentation, feature extraction, identification, to obtaining images resulting from blood vessel identification. Test results show that the system developed achieved an average success rate of 82.52% based on five different test scenarios. The fourth scenario was proven to provide the highest test accuracy results, namely 87%.
ANALISIS INSPEKSI LEVEL 2 TERHADAP KELAYAKAN OPERASI LIGHTNING ARRESTER DI GI CIANJUR DM, Dwigian Netha Putra; Yudono, Muchtar Ali Setyo; Tambunan, Handrea Bernando
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5223

Abstract

Indonesia memiliki kepadatan sambaran petir yang tinggi, sehingga petir menjadi salah satu penyebab gangguan pada sistem ketenagalistrikan. Salah satu upaya untuk menjaga keandalan sistem transmisi dari petir adalah dengan menggunakan Lightning Arrester (LA) di gardu induk. Penting untuk mengetahui dan mendeteksi sejak dini penurunan kinerja LA dikarenakan peralatan sistem ketenagalistrikan yang beroperasi tanpa henti. Hal ini dapat dilakukan dengan menganalisis hasil uji thermovisi dan leakage current measurement (LCM) yang termasuk dalam inspeksi level 2 (online maintenance). Penelitian ini bertujuan untuk menganalisis hubungan antara thermovisi dan LCM terhadap kelayakan operasi LA sebagai pengaman terhadap petir dengan objek penelitian yaitu LA pada Bay Sukaluyu 1 & 2 di Gardu Induk Cianjur. Dari hasil analisa didapatkan bahwa berdasarkan thermovisi dan LCM LA Bay Sukaluyu 1 phasa R dan T, serta LA Bay Sukaluyu 2 phasa R, S, dan T masuk kategori layak operasi dikarenakan tidak ditemukan adanya perbedaan warna mencolok pada thermovisi dan persentase kondisi LA masih dibawah 90%, sedangkan untuk LA Bay Sukaluyu 2 phasa S masuk dalam kategori tidak layak operasi dikarenakan ditemukan adanya perbedaan warna mencolok pada thermovisi dan persentase kondisi LA melebihi 90%.
ANALISIS KEGAGALAN KERJA RELAY AUTO RECLOSE PMT 7AB3 DI GISTET SAGULING Ramadhani Pratama, Mochammad Firdian; Yudono, Muchtar Ali Setyo; Tambunan, Handrea Bernando
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5224

Abstract

Sistem tenaga listrik meliputi pembangkit, transmisi (gardu induk dan saluran transmisi), serta distribusi. Gardu induk yang terhubung melalui saluran udara sering mengalami gangguan permanen atau temporer. Pada bay penghantar Bandung Selatan 2 di GISTET Saguling, terjadi gangguan temporer yang menyebabkan Pemutus Tenaga (PMT) 7A3 berhasil diaktifkan kembali oleh relay auto recloser, sedangkan PMT 7AB3 gagal beroperasi. Penelitian ini bertujuan untuk menginvestigasi gangguan pada bay penghantar Bandung Selatan 2 dan menganalisis kegagalan relay auto reclose PMT 7AB3. Analisis mencakup verifikasi kesesuaian antara pengaturan standar dan data setting pada relay serta pemeriksaan Programmable Scheme Logic (PSL). Troubleshooting dilakukan dengan memodifikasi program PSL, yaitu memutuskan hubungan masukan L5 AR BLK 7AB dari keluaran Block CB2 AR, sehingga fungsi AR Block tidak aktif. Langkah ini bertujuan untuk memastikan relay auto reclose dapat memberikan perintah agar PMT 7AB3 dapat diaktifkan kembali. Hasil analisis dan troubleshooting ini diharapkan dapat memperbaiki kinerja sistem proteksi dan memastikan kelancaran penyaluran energi listrik di masa mendatang.
Comparison of Gabor Filter Parameter Characteristics for Dorsal Hand Vein Authentication Using Artificial Neural Networks Putra, Wahyu Irwan; Yudono, Muchtar Ali Setyo; Sujjada, Alun
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1819

Abstract

The importance of digital security in today's technological era requires various innovations in creating a reliable security system for humans. Biometrics is an authentication method and the most effective system for performing personal recognition because biometrics have unique characteristics. Dorsal hand vein become biometrics for the individual recognition process in this study using feature extraction of gabor filters and neural network backpropagation to classify recognition into five classes of human individuals, which are expected to be able to provide a higher accuracy value when compared to research on the introduction of dorsal hand vein. This classification process has several stages, namely input image, image pre-processing, segmentation, feature extraction, and image classification. The test results show that the percentage of success based on the five test scenarios has an average value of 75%. In this study, the results of the greatest test accuracy in the fourth scenario were 91%.
Design and Performance Evaluation of an Arduino-Based Integrated Radiation and Environmental Monitoring System for X-Ray Laboratories Ahmad Ramadhani; Felycia; Ratu Verlaili Erlindriyani; Nauval Franata; Muchtar Ali Setyo Yudono; Dina Estining Tyas Lufianawati
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 2 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n2.3158

Abstract

Continuous monitoring of radiation exposure and environmental conditions is essential to ensure safety and operational efficiency in X-ray laboratories. However, commercially available systems are often expensive and less adaptable to smaller facilities. This study proposes the design and performance evaluation of a low-cost, Arduino-based integrated monitoring device for X-ray laboratory conditions. The system integrates a Geiger–Müller detector for radiation counting with environmental sensors for temperature, humidity, and light intensity (DHT11 and BH1750). Experimental testing was conducted to evaluate the accuracy of the timer, sensor performance, and radiation counting capabilities using background radiation and a Cs-137 source. The results demonstrate that the system operates reliably, with deviations in timer counting below 1%, acceptable sensor accuracy, and consistent radiation count rates between detectors. The prototype is capable of real-time data display via a serial monitor and an OLED 1306 screen. This study demonstrates that the proposed system provides an affordable and effective solution for laboratory radiation and environmental monitoring.
Application of LSTM and MODIS Satellite Imagery for Forecasting Oceanographic Dynamics and Identifying Potential Fishing Zones in the Sunda Strait Muta Ali Khalifa; Muchtar Ali Setyo Yudono; Nico Wantona Prabowo; Prakas Santoso; Farhan Rachmanto; Aditya Teguh Prasetia
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2815

Abstract

This study integrates AQUA-MODIS satellite imagery with the Long Short-Term Memory (LSTM) model to forecast oceanographic dynamics and identify Potential Fishing Zones (PFZ) in the Sunda Strait. The dataset spanning from January 2014 to December 2024 was used for model training, while forecasts for January–August 2025 were validated using in-situ observations from six sampling stations. The model predicted sea surface temperature (SST), chlorophyll-a concentration, and ocean current speed, with SST reaching 31°C, chlorophyll-a at 3.5 mg/L, and peak current speeds of 0.4 m/s. The performance metrics for SST (MSE: 1.107, RMSE: 0.994, MAD: 0.794), chlorophyll-a (MSE: 1.609, RMSE: 1.011, MAD: 0.5739), and current speed (MSE: 0.0183, RMSE: 0.1223, MAD: 0.0959) confirmed model accuracy. The PFZ detection algorithm, based on SST, chlorophyll-a, and ocean current data, demonstrated strong spatial agreement with in-situ data, validated using metrics such as MSE and RMSE. This validation approach, employing direct in-situ comparison, supports effective fisheries management by identifying productive fishing areas under varying seasonal and climate conditions. These results underline the operational potential of the LSTM-based forecasting framework for adaptive fisheries decision-making in the Sunda Strait.
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.
Pengembangan Sistem Pendeteksi Kadar Asap Rokok Dalam Ruangan Berbasis MQ-135 Septiya Hanum Pratiwi; Muchtar Ali Setyo Yudono
MEDIKA TRADA Vol 6 No 1 (2025): MEDIKA TRADA (JTEMP) Vol 6 No 1 (2025)
Publisher : LPPM POLBITRADA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59485/jtemp.v6i1.111

Abstract

Cigarette smoke is one of the main sources of indoor air pollution that contains harmful chemical compounds such as carbon monoxide, ammonia, and other particles that can disrupt the human respiratory system. To monitor and detect unhealthy air conditions due to cigarette smoke, this study aims to develop an air quality detection system based on the Wemos D1 microcontroller with MQ-135 and DHT11 sensors. The system is designed to identify bad air parameters based on MQ-135 values above 400 ppm, temperatures above 30°C, and humidity below 40%. The sensor reading results are displayed in real-time on a 128x64 OLED screen and stored into the SD Card memory card. In addition, the buzzer will activate as a warning alarm when the air conditions are declared unhealthy. System development involves the integration of hardware and software, with a focus on accuracy, efficiency, and ease of use. It is hoped that this system can help users in effectively monitoring air quality and take preventive measures to reduce the risk of health problems due to indoor cigarette smoke exposure.
Co-Authors ABA, Muhammad Ulin Nuha Abdul Haris Kuspranoto Adhitia Erfina Adi Nugraha Adi Nugraha Adi Nugraha Adi Nugraha Aditya Teguh Prasetia Agusutrisno Agusutrisno Agusutrisno, Agusutrisno Ahmad Ramadhani Ajat Akbar, Jiwa Akhmad Afifuddin Al Bantani, Rahmat Ato'ullah Gumilang Al-Ghozi, Faturrohman Alfatih, Muhammad Fa'iz Alief Maulana Alimuddin Alimuddin Alun Sujjada Alvin Mubarok 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 Dina Estining Tyas Lufianawati 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 Fabrobi Fazlur Ridha Fahmi Fauzi Fahrizal, Rian Fajar M.Syam Fandi Sugih Farhan Rachmanto Fauzan, Akmal Nuur Fauzan, Anugrah Nuur Febriansyah Felycia Felycia, Felycia 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 Imamul Muttakin Irawati, Nur Bebi Ulfah Irma Saraswati Irma Saraswati Irvan Syah Riadi 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 Masjudin, Masjudin Maulana, Aldi Maulana, Alief Moch Rizky Muhammad Alif Alfaturisya Muhammad Rizki Fadillah Muhammad Syahrul Fauzi Muhammad, Fadil Muntasiroh, Laily Muta Ali Khalifa Muttakin, Imamul Narputo, Panji Nico Wantona Prabowo Odi Akhyarsi Otong, Muhamad Paikun Prakas Santoso Puspita, Rina Putra, Wahyu Irwan Ramadhani Pratama, Mochammad Firdian Ramadhani, Ahmad Ramadhani, Ahmad Ramadhani, Ahmad Ratu Verlaili Erlindriyani Rian Fahrizal Rian Maulana Yusup Rozandi, Ardin Saputri, Utamy Sukmayu Saraswati, Irma Saraswati, Irma Septiya Hanum Pratiwi Sholahudin Sholahudin Sholahudin, Sholahudin Sobriansyah Irwan 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