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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal INKOM TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics IJAIT (International Journal of Applied Information Technology) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Abdi Insani Madani : Indonesian Journal of Civil Society JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Charity : Jurnal Pengabdian Masyarakat JURNAL ILMIAH GLOBAL EDUCATION Prosiding Konferensi Nasional PKM-CSR Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Applied Science eProceedings of Engineering Abdibaraya: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL Journal of Applied Engineering and Social Science Proceeding of Community Service and Engagement
Sugondo Hadiyoso
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Shadowing and Mobility Effect on Proactive and Reactive Routing Protocol in MANET ISTIKMAL, ISTIKMAL; HADIYOSO, SUGONDO; IRAWATI, INDRARINI DYAH; IRAWAN, ARIF INDRA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 4: Published October 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i4.966

Abstract

ABSTRAKPada makalah ini, kami membandingkan protokol routing proaktif Optimized Link State Routing (OLSR) dan protokol routing reaktif Ad-hoc on-demand distance vector (AODV) dan Dynamic Source Routing (DSR) pada model propagasi Shadowing dan pergerakan pada mobile adhoc network (MANET). Terdapat dua skenario pengujian, yaitu dampak dari kecepatan pergerakan pengguna dan variasi waktu jeda pengguna dengan mobilitas Random Way Point (RWP). Hasil simulasi menunjukkan bahwa semakin besar kecepatan maka kinerja throughput akan menurun, sedangkan delay dan Normalized Routing Load (NRL) akan meningkat. Semakin lama waktu jeda, semakin lama topologi berubah, akibatnya parameter throughput meningkat sedangkan delay end-to-end dan NRL menurun. OLSR proaktif menunjukkan kinerja terbaik dibandingkan dengan protokol lain berdasarkan parameter throughput, sedangkan AODV reaktif mengungguli penundaan end-to-end dan parameter NRL.Kata kunci: Protokol perutean, shadowing, Mobility, MANET ABSTRACTIn this paper, we compared the proactive routing protocols Optimized Link State Routing (OLSR) and the reactive routing protocols Ad-hoc on-demand distance vector (AODV) and Dynamic Source Routing (DSR) in shadowing propagation and mobility model on mobile adhoc network (MANET). There are two test scenarios, such as the impact of user movement velocity and variations in user pause time with Random Way Point (RWP) mobility. In user velocity testing, the greater the speed, the throughput performance will decrease, while the delay and Normalized Routing Load (NRL) will increase. The pause time test describes the dynamics of changing network topology. The longer the pause time, the longer the topology changes, as a result, the throughput parameter increases while the end-to-end delay and NRL decrease. The proactive OLSR shows the best performance compared to other protocols based on throughput parameters, while the reactive AODV outperforms for end-to-end delay and NRL parameters.Keywords: routing protocol, shadowing, Mobility, MANET
Perbandingan Ekstraksi Fitur dan Proses Matching pada Autentikasi Sidik Jari Manusia PRASASTI, ANGGUNMEKA LUHUR; IRAWAN, BUDHI; FAJRI, SETIO EKA; RENDIKA, ANANDA; HADIYOSO, SUGONDO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 1: Published January 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i1.95

Abstract

ABSTRAK Sidik jari merupakan biometrik yang sering digunakan dalam teknologi autentikasi. Terdapat banyak metode yang bisa digunakan untuk membuat sistem klasifikasi sidik jari. Maximum Curvature Points (MCP) umumnya digunakan untuk ekstraksi citra pembuluh darah jari yang juga digunakan sebagai autentikasi. Pada penelitian ini akan diuji performansi dari metode MCP jika dibandingkan dengan metode yang umum digunakan pada proses pengenalan sidik jari, yaitu Hit and Miss Transform (HMT). Perbedaan domain, yaitu spasial pada Normalized Cross Correlation (NCC) dan frekuensi pada Phase Correlation (PC) dalam proses matching ternyata juga mempengaruhi performansi sistem. Hasilnya menunjukkan bahwa penggunakaan metode MHTNCC memiliki tingkat akurasi yang lebih baik dalam pengenalan sidik jari yaitu 92% untuk pengenalan ibu jari dan 98% untuk pengenalan jari telunjuk, dibandingkan dengan menggunakan metode MCP-PC yang hanya memiliki tingkat akurasi sebesar 88% untuk pengenalan ibu jari dan 92% untuk pengenalan jari telunjuk. Kata kunci: sidik jari, MCP, HMT, phase correlation, normalized cross correlation ABSTRACTFingerprint is one of the biometric systems that are often used in an authentication technology. There are many methods that can be used to develop fingerprint’s classification system. Maximum Curvature Points (MCP) are generally used for finger vein image extraction which is also used as authentication. MCP performance will be compared to common method in finger print recognition, Hit and Miss Transform (HMT). Using different domains, spatial in Normalized Cross Correlation (NCC) and frequency in Phase Correlation (PC) affect the system performance. The results show that the application of HMT-NCC more accurate in terms of finger print’s recognition, 92% in accuracy for thumb recognition and 98% accuracy for index finger recognition, while MCP-PC is only reach 88% in accuracy for thumb recognition and 92% accuracy for index finger recognition. Keywords: fingerprint, MCP, HMT, phase correlation, normalized cross correlation
Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network HARIYANI, YULI SUN; HADIYOSO, SUGONDO; SIADARI, THOMHERT SUPRAPTO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 2: Published May 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i2.443

Abstract

ABSTRAKPenyakit Coronavirus-2019 atau Covid-19 telah menjadi pandemi global dan menjadi masalah utama yang harus segera dikendalikan. Salah satu cara yang dapat dilakukan adalah memutus rantai penyebaran virus tersebut dengan melakukan deteksi dan melalukan karantina. Pencitraan X-Ray dapat dijadikan alternatif dalam mempelajari Covid-19. X-Ray dianggap mampu menggambarkan kondisi paru-paru pada pasien Covid-19 dan dapat menjadi alat bantu diagnosa klinis. Pada penelitian ini, kami mengusulkan pendekatan deep learning berbasis residual deep network untuk deteksi Covid-19 melalui citra chest X-Ray. Evaluasi yang dilakukan untuk mengetahui performa metode yang diusulkan berupa precision, recall, F1, dan accuracy. Hasil eksperimen menunjukkan bahwa usulan metode ini memberikan precision, recall, F1 dan accuracy masing-masing 0,98, 0,95, 0,97 dan 99%. Pada masa mendatang, studi ini diharapkan dapat divalidasi dan kemudian digunakan untuk melengkapi diagnosa klinis oleh dokter.Kata kunci: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network ABSTRACTCoronavirus-2019 or Covid-19 disease has become a global pandemic and is a major problem that must be stopped immediately. One of the ways that can be done to stop its spreading is to break the spreading chain of the virus by detecting and doing quarantine. X-Ray imaging can be used as an alternative in detecting Covid-19. X-Ray is considered able to describe the condition of the lungs for Covid-19 suspected patients and can be a supporting tool for clinical diagnosis. In this study, we propose a residual based deep learning approach for Covid-19 detection using chest X-Ray images. Evaluation is carried out to determine the performance of the proposed method in the form of precision, recall, F1 and accuracy. Experiments results show that our proposed method provides precision, recall, F1 and accuracy respectively 0.98, 0.95, 0.97 and 99%. In the future, this study is expected to be validated and then used to support clinical diagnoses by doctors.Keywords: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network
Klasifikasi Sinyal EKG menggunakan Ciri Statistik dan Parameter Hjorth dengan SVM dan k-NN WIJAYANTO, INUNG; HUMAIRANI, ANNISA; RIZAL, ACHMAD; HADIYOSO, SUGONDO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.132

Abstract

ABSTRAKSinyal elektrokardiogram (EKG) dapat dianalisis dengan memperhatikan bentuk, durasi, dan irama. Pada penelitian ini, dikembangkan sebuah metode ekstraksi ciri sinyal EKG dengan menggunakan parameter Hjorth dan ciri statistik. Kedua parameter tersebut diaplikasikan untuk mengekstrak ciri-ciri dari rekaman suara sinyal EKG. Terdapat tiga kondisi rekaman sinyal EKG yang menjadi masukan dari sistem, kondisi normal, atrial fibrillation (AF), dan congestive heart failure (CHF). Set ciri rekaman EKG yang didapatkan kemudian diklasifikasikan dengan menggunakan metode support vector machine (SVM) dan k-Nearest Neighbor (k-NN) untuk dibandingkan performansinya. Hasil pengujian menggunakan semua ciri sebagai prediktor menunjukkan bahwa usulan sistem mampu memberikan akurasi sebesar 100%. Sementara itu pada skenario reduksi ciri dimana hanya dua ciri yaitu skewness dan complexity, performansi sistem tidak berkurang. Komparasi dengan beberapa studi sebelumnya menunjukkan bahwa usulan metode lebih unggul dalam hal akurasi deteksi dan jumlah ciri yang digunakan.Kata kunci: EKG, atrial fibrillation, congestive heart failure, Hjorth, SVM, k-NN ABSTRACTAn electrocardiogram (ECG) signal can be analyzed by paying attention to its shape, duration, and rhythm. In this study, feature extraction for ECG signals is applied using the Hjorth parameter and statistical characteristics. These two parameters are applied to extract the characteristics of the ECG signal sound recording. There are three conditions of ECG signal recording that are used as input for the system. They are normal conditions, atrial fibrillation (AF), and congestive heart failure (CHF). The set of ECG recording features are classified using the support vector machine (SVM) and k-Nearest Neighbor (k-NN) methods. The test results using all features show that the proposed system can achieve 100% of accuracy. On the other hand, by reducing the feature using only skewness and complexity, the system’s performance is not reduced. Comparative studies with several previous studies show that the proposed method is superior in detection accuracy and the number of features used.Keywords: ECG, atrial fibrillation, congestive heart failure, Hjorth, SVM, k-NN
Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma AULIA, SUCI; HADIYOSO, SUGONDO; RAMADAN, DADAN NUR
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 3, No 1: Published January - June 2015
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v3i1.75

Abstract

ABSTRAKPenelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe) berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet  pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All.Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet. ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe) based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with parameters One agains all .Keywords: Diabetic Retinopathy, KNN, SVM, Wavelet.
Automatic Leukocytes Classification using Deep Convolutional Neural Network HADIYOSO, SUGONDO; AULIA, SUCI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 1: Published January 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i1.195

Abstract

ABSTRAKSel darah putih atau leukosit adalah salah satu bagian darah yang bertanggung jawab untuk sistem kekebalan tubuh. Penghitungan setiap jenis leukosit merupakan hal yang krusial untuk menentukan status kesehatan. Sel darah dihitung menggunakan hematology analyzer. Namun, perangkat ini hanya tersedia di laboratorium klinik pusat atau rumah sakit. Saat ini masih banyak clinician yang melakukan perhitungan manual dengan memperkirakan jumlah leukosit menggunakan mikroskop. Hal ini berpotensi menimbulkan kesalahan perhitungan yang tinggi. Oleh karena itu, penelitian ini mengusulkan suatu sistem yang dapat mengklasifikasikan jenis-jenis leukosit. Metode convolutional neural network (CNN) dengan arsitektur VGG-19 digunakan dalam klasifikasi leukosit. Beberapa skenario pengujian dengan mengubah parameter epoch dan ukuran batch diterapkan untuk mendapatkan akurasi terbaik. Hasil simulasi model pembelajaran yang digunakan dapat menghasilkan akurasi hingga 100% untuk mengklasifikasikan neutrofil, eosinofil, monosit, dan limfosit. Hasil ini dicapai dengan menggunakan pengoptimal Adam, Epoch=5 dan batch size=60.Kata kunci: leukosit, klasifikasi, CNN, VGG-16 ABSTRACTWhite blood cells or leukocytes are one of the blood components responsible for the body's immune system. Counting each type of leukocyte is a crucial thing to determine the health status. Blood cells were counted using a hematology analyzer. However, this device is only available in central clinical laboratories or hospitals. Currently, there are still many clinicians doing manual calculations by estimating the number of leukocytes using a microscope. This has the potential to generate high errors in calculations. Therefore, this study proposes a system that can classify the types of leukocytes. The convolutional neural network (CNN) method with VGG-19 architecture was employed in leukocyte classification. Several test scenarios by changing the epoch and batch size parameters were applied to obtain the best accuracy. The results of the simulation of the learning model used can generate accuracy up to 100% for classifying neutrophils, eosinophils, monocytes, and lymphocytes. This result was achieved using Adam optimizer, epoch=5 and batch size=60.Keywords: leukocyte, classification, CNN, VGG-16
IoT-Based Early Detection of Cardiovascular Disease with Ankle Brachial Index Measurement for Right and Left Body Simultaneously DEWI, ERVIN MASITA; SETIAWAN, AWAN WAHYU; HADIYOSO, SUGONDO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 4: Published October 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.1032

Abstract

ABSTRAKDeteksi dini penyakit kardiovaskular sangat diperlukan untuk mengurangi risiko kematian. Deteksi dini penyakit kardiovaskular dapat dilakukan dengan bermacammacam metode, salah satunya adalah menggunakan metode Ankle Brachial Indeks (ABI). Metode ini membandingkan tekanan darah antara sistole pada bagian tangan dan kaki secara bersamaan. Pada penelitian ini dibuatlah alat pengukur ABI yang dapat mengukur secara serempak antara bagian tubuh kanan dan kiri, yaitu merupakan pengembangan dari penelitian sebelumnya yang hanya dapat melakukan pengukuran pada satu sisi tubuh saja. Dengan pengukuran secara serempak, diharapkan hasil yang diperoleh lebih akurat dan lebih efektif. Hasil validasi dari alat ini setelah dibandingkan dengan sphygmomanometer memiliki akurasi sebesar 96.6%. Selain itu data riwayat pemeriksaan dapat disimpan dan diakses oleh pasien dan dokter melalui teknologi IoT.Kata kunci: deteksi dini, kardiovaskular, Ankle Brachial Indeks, IoT ABSTRACTEarly detection of Cardiovascular Disease (CVD) is needed to reduce the risk of death. Early detection of cardiovascular disease can be done using various methods, one of which is the Ankle Brachial Index (ABI) method. This method compares blood pressure between systoles on the hands and feet simultaneously. In this study, the ABI measuring instrument was made that could simultaneously measure the right and left parts of the body, a development from previous research that could only take measurements on one side of the body. With simultaneous measurements, the results will be more accurate and effective. The validation results of this tool, when compared with the sphygmomanometer, have an accuracy of 96.6%. Besides, patients and doctors can store and access examination history data through IoT platform.Keywords: early detection, cardiovascular, Ankle Brachial Indeks, IoT
Obstructive sleep apnea detection based on electrocardiogram signal using one-dimensional convolutional neural network Widadi, Rahmat; Rizal, Achmad; Hadiyoso, Sugondo; Fauzi, Hilman; Said, Ziani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4129-4137

Abstract

Obstructive sleep apnea (OSA) is a respiratory obstruction that occurs during sleep and is often known as snoring. OSA is often ignored even though it can cause cardiovascular problems. Early diagnosis is needed for prevention towards worse complications. OSA clinical diagnosis can use polysomnography (PSG) while the patient is sleeping. The PSG examination includes calculating total apnea plus hypopnea every hour during sleep. However, PSG examination tends to be high cost, takes a long time, and is impractical. Since OSA is related to breathing and heart activity, the electrocardiogram (ECG) examination is an alternative tool in OSA analysis. Therefore, this study proposes OSA detection on single lead ECG using one dimensional (1D)-convolutional neural network (CNN). The proposed CNN architecture consists of 4 convolutional layers, 4 pooling layers, 1 dropout layer, 1 flatten layers, 2 dropout layers, 1 dense layer with rectified linear unit (ReLU) activation function, and 1 dense layer with SoftMax activation function. The proposed method was then tested on the ECG sleep apnea dataset from PhysioNet. The proposed model produces an accuracy of 92.69% with the average pooling scenario. The proposed method is expected to help clinicians in diagnosing OSA based on ECG signals.
IMPLEMENTASI KONTAINER KANTOR UNTUK MENDUKUNG PROGRAM SINDULANG NETCONNECT SEBAGAI STASIUN WI-FI PUBLIK DAN RUANG KOMUNITAS DIGITAL Irawati, Indrarini Dyah; Hadiyoso, Sugondo; Munadi, Rendy; Hertiana, Sofia Naning; Istikmal, Istikmal
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 7 (2024): PKMCSR2024: Kolaborasi Hexahelix dalam Optimalisasi Potensi Pariwisata di Indonesia: A
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v7i0.2372

Abstract

The Sindulang Village community faces obstacles in internet access due to uneven network infrastructure. This challenge makes it difficult for residents to obtain the latest information which is a key factor in supporting economic progress, education, and community welfare in the area. These obstacles arise due to geographical conditions that are difficult to access and suboptimal regional structures, which cause significant obstacles in resource management and accessibility. The Telkom University, PT. Tigaresi Bangun Nusaperdana, and SMK Al Amah Sindulang community service teams together overcome these obstacles by building Sindulang NetConnect as a public Wifi station as an innovative answer to provide easy internet access while accelerating the digitalization process for the Sindulang Village Community. In addition, in order to develop the Teaching Factory (TeFa) at SMK Al Amah Sindulang, an office container was implemented as a means of producing a Wifi network that can be used by students to improve their skills and experience in the networking field. This implementation was carried out in 2 stages, where the first stage was the construction of an office container and public Wifi services and in the second year a digital community space would be developed. Based on the results of the questionnaire on 30 respondents, it showed that 93.3% strongly agreed with the implementation of office containers as public Wifi stations and TeFa facilities that provide benefits for the Sindulang Village Community and students of SMK AL Amah Sindulang.
Implementation of Ensemble Machine Learning with Voting Classifier for Reliable Tuberculosis Detection Using Chest X-ray Images with Imbalance Dataset Jauhari, Muhammad I; Wirakusuma, Muhammad P.; Sidqi, Anka; Putra, I Gusti Ngurah R. A.; Wijayanto, Inung; Rizal, Achmad; Hadiyoso, Sugondo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.472

Abstract

Tuberculosis (TB) is an infectious disease caused by bacteria. Tuberculosis is spread through the air and saliva that contain mycobacterium tuberculosis. If not treated immediately, it can spread to other vital organs, such as the heart and liver, and can even lead to death. In this study, we developed a severe tuberculosis detection system using the Tuberculosis (TB) dataset with simple computation. We used 4200 data points (3500 Normal and 700 TB). In other words, this research aimed to create lightweight computation with Machine Learning (Voting Classifier in Ensemble Learning) as the classifier using Imbalance data. Initial experiments used single machine learning with the best-performing models, Support Vector Machine (SVM), and Random Forest as classifiers. With an accuracy of 98.6% and 98%, they were combined using Ensemble Learning without feature extraction; the accuracy, AUC, Recall, Precision, and F1-score using the voting classifier were 99.1%, 99.3%, 99%, 98%, and 98%, respectively.
Co-Authors -, Suryatiningsih A. V. Senthil Kumar A.A. Ketut Agung Cahyawan W Aaron Abel Abi Hakim Amanullah Achmad Rizal Achmad Rizal ADIANGGIALI, ANYELIA Adisaputra, Rangga Adiwijaya, Agustinus Aldian Adjie Gery Ramadhan Adnan Azhary Afandi, Mas Aly Agung Muliawan Ahmad Hilmi Ahmad Muammar Agusti Akhmad Alfaruq Akhmad Alfaruq Alfaruq, Akhmad Alfaruq, Akhmad Aliffansyah, Lingga Alvinas Deva Sih Illahi Ana Durrotul Isma Anatasya Bella Andhita Nurul Khasanah Andri Juli Setiawan Andro Harjanto Anggit Syorgaffi Anggun Fitrian Isnawati ANGGUNMEKA LUHUR PRASASTI Arfianto Fahmi Arif Indra Irawan ARIS HARTAMAN Ashshiddiqqi, Muhammad Arhizal Asma Zahira Asril Ibrahim Astri Wulandari Audry Stevany Aulia Ayu Dyah Lestari Ayu Chellsya, Ananda Ayu Tuty Utami Azahra, Yasmin Azriel Gilbert Samuel Rogito Azzahra, Salwa Bagus Tri Astadi Balova , Fathrurrizqa Bambang Hidayat Bandiyah Sri Aprillia Barus, Exal Deo Jayata Bayu Erviga Yulanda Setiawan Bayuaji Kurniadhani Bimo Rian Tri Nugroho Budhi Irawan Budi Prasetya Budi Prasetya Budiyawan Naztin Burhanuddin D. Burhanuddin Dirgantoro Cucu Fitri Dadan Nur Ramadan Dadan Nur Ramadhan Dadan Nur Ramadhan Denny Darlis Dewi Budiwati, Sari Dewi Rahmaniar, Thalita Dharu Arseno Didin Bramastya Dieny Rofiatul Mardiyah Diliana, Faizza Haya Efri Suhartono Ema ERVIN MASITA DEWI Exal Deo Jayata Barus Ezi Rohmat Fadiaga Omar Michlas Fairuz Azmi FAJRI, SETIO EKA FARDAN FARDAN Farhan Alghifari Chaniago Saputro, Muhammad Farrel Fahrozi Fathrurrizqa Balova FATURRAHMAN, RAIHAN Fauzia Anis Sekar Ningrum Fony Ferliana Widianingrum Gadama, Melsan Gartina Husein, Inne Gelar Budiman Ghilman Hafizhan Gifari, Rizqi Al Goldfried Manuel Lbn Tobing Habib, Arrijal Hadjwan, Razel Hannissa Sanggarini Hariyani , Yuli Sun Hasanah Putri Hengky Yudha Bintara Heru Nugroho Hilman Fauzi, Hilman HUMAIRANI, ANNISA Hurianti Vidyaningtyas HW, EVA AISAH Ilham Edwian Berliandhy Ilmi, M. Bahrul Indrarini Dyah Irawati Inung Wijayanto Irsyad Abdul Basit Istikmal Ivany Sesa Rehadi Ivosierra Andrea Larasaty Jannah, Firna Noor Jannah, Sabila Hayyinun Jasmine, Diva Dhila Jauhari, Muhammad I Javani Sekar Larasati Jehan Pratama Herdaning Jondri Jondri Koredianto Usman Kridanto Surendro Kris Sujatmoko Kurnia Ismanto, Rima Ananda Larasaty, Ivosierra Andrea Lata Tripathi, Suman LATIP, ROHAYA Ledya Novamizanti Lurina, Manda Luthfi Muhammad Pahlevi Lutvi Murdiansyah Murdiansyah M. Nur Imam DJ Mahmud Dwi Sulistiyo Manda Lurina Meidatomo , Muhammad Haykal Milan Adila Amalia Mohamad Ramdhani Muh. Kurniawan, A. Muhamad Roihan Muhammad Adnan Muhammad Afif Ridwansyah Muhammad Alfachri Akbar Muhammad Arhizal Ashshiddiqqi Muhammad Farhan Alghifari Chaniago Saputro Muhammad Iqbal MUHAMMAD JULIAN, MUHAMMAD Nadya Silva Arline Nasution, Muhammad Ilham Kurniawan Nasution, Seri Wahyuni Naufal Juhaidi Jafal Naufal Rizky Pratama Nur Arviah Sofyan Nur Pratama, Yohanes Juan Nur Ramadhani Nursanto Nursanto NURSANTO NURSANTO, NURSANTO Nurwan Reza Fachrurrozi Okki Rahmalisty, Fiona Pahira, Ela Diranda Patricia Lovenia Garcia Periyadi Permana, Andri Satia Prahara, Dzakwan Bahar Prajna Deshanta Ibnugraha Putra, I Gusti Ngurah R. A. Putri Fatoni, Salwa Berliana Putri, Athaliqa Ananda Putri, Silvi Dahlia R. Dhenake Aghni Bunga R. Yunendah Nur Fu’adah Radial Anwar, Radial Radian Sigit Raditiana Patmasari Rahmaniar, Thalita Dewi Rahmat Widadi Ramdani, Ahmad Zaky Ratna Mayasari Reivind P. Persada RENALDI, LUKY RENALDI, LUKY RENDIKA, ANANDA Rendy Munadi Reni Dyah Wahyuningrum Reny Yuliani Arnis Ridha Muldina Negara Rina Pudji Astuti Riska Aprilina Rita Magdalena Rita Purnamasari Rizal Fachrudin Maulana Rizky Aulia Rahman Robinzon Pakpahan Rogito, Azriel Gilbert Samuel ROHMAT TULLOH Rosmiati, Mia Ruli Pandapotan, Bagas Ryan Bagus Wicaksono Safitri, Ayu Sekar Said, Ziani Sania Marcellina Bryan Sasmi Hidayatul Yulianing Tyas Sa’idah, Sofia Sekar Safitri, Ayu Septiansyah, Rizky SETIAWAN, AWAN WAHYU Sianturi, Kristian Fery Sidqi, Anka Sigit, Radian Siti Sarah Maidin Siti Zahrotul Fajriyah Sofia Naning Hertiana Suci Aulia Sugeng Santoso Sulistyo, Tobias Mikha Surya Putra Agung Saragih Suyatno Suyatno Syifa Nurgaida Yutia Tasya Chairunnisa Tati Latifah Erawati Rajab Teguh Musaharpa Gunawan Thomhert Suprapto Siadari Tita Haryanti Tobing, Goldfried Manuel Lbn Tri Nopiani Damayanti Triadi Triadi Unang Sunarya Untari Novia Wisesty Vany Octaviany Vera Suryani Wahyu Hauzan Rafi Wibowo, Raiyan Adi Wirakusuma, Muhammad P. Yasmin Azahra Yoza Radyaputra Yudha Purwanto Yudiansyah Yudiansyah YULI SUN HARIYANI YUYUN SITI ROHMAH Zahrah, Nasywa Nur Zhillan Al Rashif, Mohammad Zulfikar F.M. Ramli