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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Cyberspace: Jurnal Pendidikan Teknologi Informasi JMM (Jurnal Masyarakat Mandiri) J I M P - Jurnal Informatika Merdeka Pasuruan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Indonesian Journal of Electrical Engineering and Computer Science Budimas : Jurnal Pengabdian Masyarakat Community Empowerment Jurnal Teknimedia: Teknologi Informasi dan Multimedia EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Jurnal Locus Penelitian dan Pengabdian Bakti Sekawan : Jurnal Pengabdian Masyarakat
Helmi Imaduddin
Universitas Muhammadiyah Surakarta
Religion Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Mechanical Engineering Public Health 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.
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%.
Empowerment of MIM Surodadi 1, Magelang Regency to create disaster resilient schools Ahwy Oktradiksa; Akhmad Liana Amrul Haq; Helmi Imaduddin
Community Empowerment Vol 8 No 12 (2023)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.10395

Abstract

Madrasah Ibtidaiyah Muhammadiyah (MIM) Surodadi 1 is situated in Surodadi Hamlet, Gondowangi Village, Sawangan District, and falls within the Disaster Risk Area (KRB-2) with a radius of 17 km from the surface of Mount Merapi. However, MIM Surodadi-1 has yet to implement the disaster education policy and adopt SMAB. Activities include Focus Group Discussions (FGDs), simulations, and games. The outcome of these activities involves fortifying the concept of disaster preparedness in schools, employing organizational formats within the school, and enhancing skills to mitigate disaster risks, particularly from a psychological perspective.
PENGEMBANGAN MODEL DEEP LEARNING UNTUK DETEKSI RETINOPATI DIABETIK MENGGUNAKAN METODE TRANSFER LEARNING Bintang, Yayes Kasnanda; Imaduddin, Helmi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5588

Abstract

Retinopati Diabetik (RD) merupakan komplikasi serius dari diabetes yang dapat menyebabkan kerusakan pada retina dan mengancam penglihatan. Deteksi dini RD sangat penting untuk mencegah kerusakan mata yang lebih lanjut. Dalam usaha untuk meningkatkan deteksi dini ini, teknologi deep learning, khususnya metode CNN, telah digunakan secara luas. Penelitian ini bertujuan untuk mengimplementasikan dan membandingkan kinerja empat arsitektur CNN yang berbeda, yaitu ResNet152V2, Xception, DenseNet201, dan InceptionV3, dalam klasifikasi gambar retina untuk mendeteksi RD. Pertama, dataset gambar retina dibagi menjadi kategori yang terinfeksi RD dan yang tidak. Kemudian, model CNN dikembangkan dan dilatih menggunakan data latih untuk mengklasifikasikan gambar. Penggunaan teknik augmentasi data membantu meningkatkan generalisasi model. Setelah melatih model, pengujian dilakukan menggunakan dataset uji yang terpisah untuk mengevaluasi kinerja masing-masing model. Hasil pengujian menunjukkan bahwa Xception dan DenseNet201 menghasilkan kinerja yang sangat baik dalam mendeteksi RD, dengan akurasi, presisi, recall, dan F1-Score mencapai 96%. Hasil evaluasi ini menegaskan bahwa teknologi deep learning, terutama dalam bentuk CNN, memiliki potensi besar dalam mendukung diagnosis medis, khususnya dalam deteksi penyakit mata kompleks seperti RD. Penggunaan model-model ini dapat memberikan manfaat yang signifikan bagi pasien RD, memungkinkan deteksi dini yang lebih efektif dan penanganan yang lebih tepat waktu. Dengan demikian, penelitian ini memberikan kontribusi penting dalam pengembangan solusi otomatis untuk diagnosis RD, yang dapat meningkatkan perawatan kesehatan mata secara keseluruhan.
Merancangbangun Website Puskesmas Kota Surakarta Berbasis Content Management System Wordpress Astriani, Anggit; Kirana, Vintan; Romadhon, Anita Lusi; Imaduddin, Helmi
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol 3 No 2 (2023): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v3i2.457

Abstract

UPT Pusat Kesehatan Masyarakat khususnya di Kota Surakarta saat ini masih memiliki kendala dalam penyampaian informasi kepada masyarakat sehingga proses penyampaian informasi masih menggunakan cara konvensional seperti poster atau brosur, adapun beberapa Puskesmas yang sudah memiliki akun media sosial sebagai media penyampaian informasi namun mengingat tidak semua kalangan mempunyai media sosial. Maka dari itu gagasan untuk membuat website dengan tujuan agar UPT Pusat Kesehatan Masyarakat di Kota Surakarta dapat dengan mudah menyampaikan informasi dan masyarakat dapat lebih mudah dalam mencari informasi. Website dibuat menggunakan Content management Sistem (CMS) sebagai software pilihan untuk membuat dan mendesain website. Teknik pengumpulan data dilakukan dengan cara wawancara dan dokumen serta menggunakan metode waterfall dalam perancangan website. Hasil penelitian ini adalah website puskesmas yang memuat informasi mengenai puskesmas seperti profil puskesmas, visi dan misi, struktur organisasi, dan layanan.
Fine-tuning ResNet-50 for the classification of visual impairments from retinal fundus images Imaduddin, Helmi; Utomo, Ihsan Cahyo; Anggoro, Dimas Aryo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4175-4182

Abstract

The sense of sight plays a crucial role in human perception, as it serves as our primary sensory organ for perceiving light. However, a considerable number of individuals experience a wide range of vision impairments. These impairments encompass diverse conditions such as diabetic retinopathy, glaucoma, and cataracts. Each visual impairment exhibits unique characteristics and symptoms, highlighting the need for timely and accurate detection to facilitate appropriate treatment and prevent vision loss. This research aims to develop a deep learning-based system specifically designed to detect visual impairments. The proposed solution involves creating a model using the ResNet-50 algorithm as the foundational methodology, and fine-tuning multiple parameters to enhance the model's performance. The research utilizes a dataset consisting of retinal fundus images, which are categorized into four distinct classes: diabetic retinopathy, glaucoma, cataracts, and normal. The findings demonstrate the effectiveness of the model, achieving an impressive accuracy score of 92%. This signifies a significant improvement of 6% over the accuracy achieved in the previous study, which stood at 86%. The implementation of this system is expected to make a significant contribution to the rapid and accurate detection of various eye disorders in the future, enabling timely intervention and prevention of visual impairment.
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.
Co-Authors Afiana Azizah, Synta Nur Ahwy Oktradiksa Akhmad Liana Amrul Haq Ali Zainal Abidin Anjarwati, Jesika Anton Agus Setyawan Astriani, Anggit Azizah Fatmawati Bintang, Yayes Kasnanda Brian Aditya Hermansyah Brian Aditya Hermansyah Dedi Gunawan Devi Afriyantari Puspa Putri Dewi Sasika Rani Diah Priyawati Dias Aziz Pramudita Dimas Aryo Anggoro Fatah Yasin Al Irsyadi Fiddin Yusfida Ala Frischa Aura Salsabilla B Hayati, Luthfiana Nur Ihsan Cahyo Utomo Kirana, Vintan Muhammad Arkan Setyawan Muhammad Mutawadhi’ Alfajri Muhammad Randhy Kurniawan Muslihah , Isnawati Nabil Aziz Bima Anggita Nani Sulistianingsih Novel Idris Abas Pranowo, Carissa Aprilia Romadhon, Anita Lusi Sakina, Alivia Rahma Sania Citra Palupi Siti Agrippina Alodia Yusuf