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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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Jaringan Syaraf Tiruan Perambatan Balik untuk Klasifikasi Covid-19 Berbasis Tekstur Menggunakan Orde Pertama Berdasarkan Citra Chest X-Ray Yudono, Muchtar Ali Setyo; Hamidi, Eki Ahmad Zaki; Jumadi, Jumadi; Kuspranoto, Abdul Haris; Sidik, Aryo De Wibowo Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022945663

Abstract

COVID-2019 pertama kali muncul di kota Wuhan, Cina pada Desember 2019, kemudian menyebar dengan cepat ke seluruh dunia dan menjadi pandemi. Pandemi COVID-19 telah menyebabkan dampak yang cukup fataluntukkesehatan masyaraka. Merupakan hal yang sangat penting untuk mendeteksi kasus positif sedini mungkin untuk pencegahan penyebaran lebih lanjut dari virus ini. Teknik tes paling umum yang saat ini digunakan untuk mendiagnosa COVID-19adalah reverse-transcriptase polymerase chain reaction (RT-PCR). Pencitraan radiologis dada seperti chest X-ray memiliki peran penting dalam diagnosis dinipenyakit ini. Karena sensitivitas RT-PCR rendah 60% -70%, bahkan jika hasil negatif diperoleh, gejala dapat dideteksi dengan pemeriksaan gambar radiologi pasien. Teknik kecerdasan buatanyang digabungkan dengan pencitraan radiologis dapat membantu untuk mendiagnosis COVID-19 dengan lebih cepat dan akurat.Proses klasifikasi pada penelitian ini terdapat beberapa tahapan yaitu pra-pengolahan, segmentasi, ekstraksi ciri, dan klasifikasi. Ekstraksi ciri yang digunakan adalah berdasarkan tekstur orde pertama dan klasifikasi yang digunakan adalah jaringan syaraf tiruan perambatan balik. Sistem klasifikasi pada penelitian ini menghasilkan rata-rata akurasi klasifikasi sebesar 94,17% untuk kelas normal dan 77,5% untuk COVID-19. Hasil akurasi tertinggi didapat pada skenario pertama dengan hasil akurasi sebesar 88,8%. Nilai rata-rata sensitivitas yang didapat pada penelitian ini sebesar 94,17% untuk kelas normal dan 76,67% untuk kelas COVID-19. Nilai rata-rata spesifisitas yang didapat pada penelitian ini sebesar 76,67% untuk kelas normal dan 94,17% untuk kelas COVID-19.AbstractCovid-2019 first appeared in Wuhan, China, in December 2019, then quickly spread throughout the world and became a pandemic. The Covid-19 pandemic has had a fatal impact on public health. It is crucial to detect positive cases as early as possible to prevent the further spread of this virus. The most common test technique currently used to diagnose Covid -19 is the reverse-transcriptase polymerase chain reaction (RT-PCR). Chest radiological imaging such as chest X-ray has a vital role in the early diagnosis of this disease. Due to the low RT-PCR sensitivity of 60%-70%, symptoms can be detected by examining the patient's radiological images even if a negative result is obtained. Artificial intelligence techniques combined with radiological imaging can help diagnose Covid -19 more quickly and accurately. The classification process in this study consists of several stages, namely pre-processing, segmentation, feature extraction, and classification. The feature extraction used is based on the first-order texture, and the classification used is a backpropagation neural network. The classification system in this study resulted in an average classification accuracy of 94.17% for the normal class and 77.5% for Covid -19. The highest accuracy results were obtained in the first scenario, with an accuracy of 88.8%. The average sensitivity value obtained in this study was 94.17% for the normal class and 76.67% for the Covid -19 class. The average specificity value obtained in this study was 76.67% for the normal class and 94.17% for the Covid -19 class.
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%.
Perancangan Sistem Pemantauan Suhu, Kelembaban, Asap, Kebakaran, Kecepatan Angin dan Arah Angin Berbasis SMS di Lahan Pertanian UNTIRTA Saraswati, Irma; Alimuddin, Alimuddin; Irwan, Sobriansyah; Yudono, Muchtar Ali Setyo
MEDIKA TRADA : Jurnal Teknik Elektomedik Polbitrada Vol 6 No 2 (2025): MEDIKA TRADA: Jurnal Teknik Elektromedik Polbitrada Vol 6 No 2 (2025)
Publisher : LPPM POLBITRADA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59485/jtemp.v6i2.159

Abstract

Farm fires are a major cause of agricultural loss, leading to the destruction of millions of hectares of farmland. In Indonesia alone, the financial losses due to farm fires were estimated at 221 trillion rupiah in 2015. This study proposes the development of an early fire detection and environmental monitoring system that integrates MQ-7 and photodiode sensors for smoke and fire detection, along with additional sensors for monitoring temperature, humidity, wind speed, and wind direction. The system utilizes Short Message Service (SMS) for communication, which was selected due to its low cost, extensive coverage, and ability to facilitate rapid responses during emergencies. Sensor testing revealed that the temperature and humidity sensors (DHT11) exhibited a temperature deviation of 0.37°C and a humidity error of 2 Percent. The photodiode sensor successfully detected fire at distances of up to 150 cm, especially at night, under low light intensity conditions (0-10 Lux). The GSM communication module showed an average response time of 2 seconds, with a deviation range of 1 to 4 seconds between the sending and receiving of SMS messages. The system demonstrated its effectiveness in detecting fire and monitoring environmental parameters, providing real-time alerts every 10 minutes via SMS. This system offers a reliable, cost-effective solution for early fire detection and continuous environmental monitoring, enabling timely intervention to mitigate farm fire risks.
Adaptive Traffic Signal System Utilizing YOLOv11 and Fuzzy Logic for Congestion Mitigation Permadi, Dio Damas; Yudono, Muchtar Ali Setyo; Kuspranoto, Abdul Haris; Rozandi, Ardin; Artiyasa, Marina; Mubarok, Alvin; Septiani, Dwi
SISTEMASI Vol 15, No 1 (2026): 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.v15i1.5865

Abstract

The increasing number of vehicles in urban and suburban areas has led to traffic congestion, resulting in longer travel times, higher exhaust emissions, and an increased risk of accidents. Conventional fixed-time traffic signal systems often fail to respond dynamically to changing traffic conditions, leading to inefficient vehicle queues. This study proposes the development of an adaptive traffic signal system that utilizes YOLOv11 and fuzzy logic to detect vehicle volume and adjust green light durations in real time. YOLOv11 is employed to detect vehicles in each lane, while fuzzy logic is used to regulate green signal durations based on the detected vehicle counts. Experimental results demonstrate a detection accuracy of 0.92 and a recall of 0.93. The green light duration varies from 80 seconds for low traffic volumes to 100 seconds for high traffic volumes. The traffic signal cycle is dynamically adjusted according to vehicle density, with a maximum total cycle time of 100 seconds. Overall, the proposed system is proven effective in reducing congestion and improving traffic management efficiency at intersections with high vehicle volumes.
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