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All Journal Bulletin of Electrical Engineering and Informatics Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Cyberspace: Jurnal Pendidikan Teknologi Informasi J I M P - Jurnal Informatika Merdeka Pasuruan Budimas : Jurnal Pengabdian Masyarakat Jurnal Teknimedia: Teknologi Informasi dan Multimedia
Helmi Imaduddin
Universitas Muhammadiyah Surakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Comparison of transfer learning method for COVID-19 detection using convolution neural network Helmi Imaduddin; Fiddin Yusfida Ala; Azizah Fatmawati; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3525

Abstract

Currently, one of the most dangerous diseases is Coronavirus disease 2019 (COVID-19). COVID-19 is a threat to the whole world, and almost all countries are experiencing the COVID-19 pandemic, including Indonesia. Various ways to detect COVID-19 sufferers have been carried out, such as swab tests, rapid tests, and antigens. One way that can be done to detect COVID-19 infection is to look at X-ray images of the patient's lungs because someone infected with COVID-19 has a different lung shape from normal people. Many studies have been carried out to detect COVID-19, using either machine learning (ML) or deep learning (DL). In this study, we propose to use transfer learning as an extraction feature in the classification of the covid dataset. The study was conducted four times using four different methods, namely ResNet 50, MobileNet V2, Inception V3, and DensNet-201. After experimenting, we compared the results to find out which method has the best results in detecting COVID-19. From this research, it was found that the ResNet 50 model has the best results with 92.3% accuracy, 93% precision, 93% F1-Score, 99% sensitivity, and 90.7% specificity.
COMPARISON OF SUPPORT VECTOR MACHINE AND DECISION TREE METHODS IN THE CLASSIFICATION OF BREAST CANCER Helmi Imaduddin; Brian Aditya Hermansyah; Frischa Aura Salsabilla B
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 5, No 1 (2021)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.898 KB) | DOI: 10.22373/cj.v5i1.8805

Abstract

One of the most dangerous cancers in the world is breast cancer. This cancer occurs in many women, in some cases this cancer can also affect men, but it is very rare. The effects of this cancer are very dangerous for humans, in the worst case it can lead to death. So that serious prevention is needed against this cancer. One prevention can be done by early detection. This study aims to implement machine learning methods to detect breast cancer in women. The algorithms used are Support Vector Machine (SVM) and Decision Tree (DT). After classifying the data provided, a comparison is made to find out which machine learning method has the best performance. The data used comes from the Gynecology Department of the University Hospital Center of Coimbra (CHUC), and can be downloaded for free on the UCI repository website. The results of this study indicate that the SVM algorithm with feature selection obtains the best classification results by obtaining an accuracy of 87.5%, a sensitivity of 90%, and a specificity of 85%. Thus this research obtains good results to be able to help provide solutions to detect breast cancer.
Transfer learning for detecting COVID-19 on x-ray using deep residual network Helmi Imaduddin; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4334

Abstract

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.
EKSTRASI FITUR SINYAL EKG MYOCARDIAL INFARCTIN MENGGUNAKAN DISCRETE WAVELET TRANSFORMATION Siti Agrippina Alodia Yusuf; Nani Sulistianingsih; Helmi Imaduddin
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.96

Abstract

One important step in the process of identifying EKG signals is feature extraction, where the obtained features characterize the condition of the heart. The condition of the heart can be observed based on the waves produced in the EKG signal, which are generated by the electrical activity of the heart. In this study, two types of mother wavelets will be compared to determine which type is most suitable for extracting features from EKG signals. The types of mother wavelets to be compared are Daubechies and Symlet with orders of 5, 6, and 7 for Daubechies, and 6, 7, and 8 for Symlet. EKG signals with MI and normal heart conditions that have been improved in quality and have undergone signal segmentation are extracted using Discrete Wavelet Transformation (DWT) with Daubechies and Symlet mother wavelets at the two-level decomposition, and statistical features such as mean, median, standard deviation, kurtosis, and skewness are taken. Features are extracted from the D2 and D1 sub-bands, resulting in a total of 10 features obtained. The EKG signals are then classified using the KNN method, and to obtain generalized results, K-fold cross-validation is also applied. Based on the experiments conducted, the highest accuracy obtained was 94% with sensitivity and specificity of 82% and 91% by applying the Daubechies mother wavelet of order 7.
Implementasi MERN Stack pada Pengembangan Sistem Penerimaan Peserta Didik Baru Dedi Gunawan; Ihsan Cahyo Utomo; Fatah Yasin Al Irsyadi; Devi Afriyantari Puspa Putri; Helmi Imaduddin; Ali Zainal Abidin; Nabil Aziz Bima Anggita; Dewi Sasika Rani; Sania Citra Palupi
SWABUMI (Suara Wawasan Sukabumi): Ilmu Komputer, Manajemen, dan Sosial Vol 11, No 2 (2023): Volume 11 Nomor 2 Tahun 2023
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v11i2.15965

Abstract

Pengembangan aplikasi web membutuhkan arsitektur yang sederhana namun kuat dari sisi back-end sampai front-end. Berkaitan dengan hal tersebut framework MERN Stack menjadi populer digunakan. Teknologi ini merupakan kombinasi dari beberapa layer seperi MongoDB, ExpresJS, ReactJS dan NodeJS yang berfokus pada satu bahasa pemrograman yaitu JavaScript. Implementasi MERN Stack pada studi kasus ini adalah untuk pengembangan dan implementasi sitem Penerimaan Peserta Didik Baru (PPDB) berbasis web pada SMA Muhammadiyah 1 Program Khusus Kartasura. Evaluasi kualitas sistem dilakukan dengan tiga metode testing yaitu black-box testing, system usability scale (SUS), dan page speed test. Hasil pengujian black-box menunjukan sistem memiliki fungsionalitas yang sesuai dengan prosentase kesalahan 0%. Sedangkan pengujian SUS menghasilkan nilai rata-rata 78,98 yang berarti aplikasi berada pada level acceptable dan bisa digunakan untuk kasus riil. Pengujian performa kecepatan akses web menggunakan Google page speed test dan GTmetrix menunjukan performa yang baik dengan nilai mencapai 73 dan waktu load rata-rata 7 detik.Web application development requires a simple yet robust architecture. Thus, MERN Stack framework has gaining popularity. MERN Stack combines several layers like MongoDB, ExpressJS, ReactJS and NodeJS. The framework focuses on JavaScript programming language. The MERN Stack implementation in this case is for the development of a web-based Student Admissions (PPDB) system at SMA Muhammadiyah 1 Kartasura. System evaluation is carried out using three testing methods, namely black-box testing, system usability scale (SUS), and page speed test. The results of the black-box show that the system has perfect functionality with error percentage of 0%. Meanwhile, the SUS test shows an average value of 78.98 which means the application is acceptable and can be implemented. The performance of web access speed is evaluated using Google page speed test and GTmetrix. It shows good performance with a value reaching 73 and an average load time of 7 seconds.
Klasifikasi Kematian Akibat Gagal Jantung Menggunakan Algoritma Logistic Regression Berbasis Forward Selection Helmi Imaduddin; Brian Aditya Hermansyah; Muhammad Mutawadhi’ Alfajri
J I M P - Jurnal Informatika Merdeka Pasuruan Vol 7, No 3 (2022): Desember
Publisher : Fakultas Teknologi Informasi Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51213/jimp.v7i3.565

Abstract

Gagal jantung adalah masalah kesehatan masyarakat utama yang beban penyakitnya meningkat seiring bertambahnya usia. Kondisi jantung dalam kasus ini menandakan bahwa jantung tidak mampu lagi untuk memompa darah secara optimal dan ketidakmampuan jantung dalam memenuhi kuota darah normal yang dibutuhkan oleh tubuh. Berdasarkan timbulnya gejala, gagal jantung dapat terjadi secara tiba-tiba atau lebih dikenal dengan gagal jantung akut, dan gagal jantung yang berkembang secara perlahan karena kondisi jantung yang melemah atau lebih dikenal dengan istilah gagal jantung kronis. Tujuan dari penelitian ini adalah mendapatkan model klasifikasi penyakit gagal jantung untuk membuat sistem penunjang keputusan sebagai deteksi dini penyakit gagal jantung. Setelah itu model yang sudah diperoleh akan dievaluasi untuk mengetahui performanya dengan akurasi, spesifisitas dan sensitivitas. Metode yang digunakan untuk melakukan klasifikasi menggunakan metode Support Vector Machine, Decision Tree, Logistic Regression dan Random Forest. Pengukuran performa klasifikasi menggunakan matrik akurasi, sensitivitas dan spesivisitas, hasil klasifikasi menunjukan bahwa algoritma logistic regression memiliki performa paling baik dengan memperoleh akurasi sebesar 90% dan spesivisitas 80%.
DIGITALISASI SOSIALISASI KEGIATAN PADA RANTING MUHAMMADIYAH DESA NGEMPLAK MELALUI PENGEMBANGAN SISTEM INFORMASI BERBASIS WEBSITE Ihsan Cahyo Utomo; Diah Priyawati; Helmi Imaduddin
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 2 (2024): BUDIMAS : Jurnal Pengabdian Masyarakat
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v6i2.13292

Abstract

Pimpinan Ranting Muhammadiyah (PRM) Ngemplak yang berada di kecamatan Kartosuro Kabupaten Sukoharjo merupakan salah satu ranting Muhammadiyah yang berada pada daerah dengan populasi masyarakat muslim yang minoritas, sehingga dibutuhkan upaya dan strategi yang baik dalam melakukan dakwah dan memperluas syair islam dan kemuhammadiyahan pada lingkungan tersebut. Namun, dalam pelaksanaan dan mengenalkan kegiatan tersebut terkendala dengan sosilalisasi ke warga sekitar. Saat ini terdapat beberapa masalah yang timbul akibat tidak diterapkan sistem informasi, seperti kesulitan dalam melakukan sosialisasi kegiatan yang akan dijadwalkan dan dokumentasi kegiatan sebelumnya. Kegiatan yang sudah terlaksana di PRM Ngemplak masih di informasikan secara manual melalui grup Whatsapp dan di tempel di papan pengumuman, sehingga terkadang informasi mengenai kegiatan belum tersampaikan secara maksimal. Program kegiatan pengabdian masyarakat ini bertujuan untuk mengembangkan aplikasi berbasis Website pada Pimpinan Muhammadiyah Ranting Ngemplak sehingga dapat digunakan sebagai media sosialisasi seluruh kegiatan di organisasi serta dokumentasi kegiatan yang telah berkangsung. Kegiatan ini dilanjutkan pelatihan pengelolaan aplikasi website tersebut kepada Pimpinan Muhammadiyah Ranting Ngemplak.
Co-Authors Ali Zainal Abidin Azizah Fatmawati Brian Aditya Hermansyah Brian Aditya Hermansyah Dedi Gunawan Devi Afriyantari Puspa Putri Dewi Sasika Rani Diah Priyawati Fatah Yasin Al Irsyadi Fiddin Yusfida Ala Frischa Aura Salsabilla B Ihsan Cahyo Utomo Muhammad Mutawadhi’ Alfajri Nabil Aziz Bima Anggita Nani Sulistianingsih Sania Citra Palupi Siti Agrippina Alodia Yusuf