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All Journal HAYATI Journal of Biosciences Teknosains: Media Informasi Sains dan Teknologi Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JURNAL NASIONAL TEKNIK ELEKTRO JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JMM (Jurnal Masyarakat Mandiri) Jurnal Ilmiah Ecosystem Building of Informatics, Technology and Science Jurnal Aplikasi dan Inovasi Iptek (JASINTEK) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer eProceedings of Engineering Eduvest - Journal of Universal Studies AMMA : Jurnal Pengabdian Masyarakat
Satria Mandala
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Neuroscience Nursing Physics Public Health Social Sciences Other

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Klasifikasi Multikelas Infark Miokard Berdasarkan Sinyal Phonokardiogram dengan Ensemble Learning Nia Madu Marliana; Satria Mandala; Hau, Yuan Wen; Yafooz, Wael M.S.
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1121.2023

Abstract

Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.
Sebuah Identifikasi yang Ditingkatkan dari Penyakit Katup Jantung Dengan Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) Muhammad Rafli Ramadhan; Mandala, Satria; Rafi Ullah; Wael M.S. Yafooz; Muhammad Qomaruddin
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 1: March 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n1.1184.2024

Abstract

Valvular Heart Disease (VHD) is a significant cause of mortality worldwide. Although extensive research has been conducted to address this issue, practical implementation of existing VHD detection results in medicine still falls short of optimal performance. Recent investigations into machine learning for VHD detection have achieved commendable accuracy, sensitivity, and robustness. To address this limitation, our research proposes utilizing Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) to enhance VHD detection. Notably, SFD-CNN operates on phonocardiogram (PCG) signals, distinguishing itself from existing methods based on electrocardiogram (ECG) signals. We present two experimental scenarios to assess the performance of SFD-CNN: one under default parameter conditions and another with hyperparameter tuning. The experimental results demonstrate that SFD-CNN surpasses other existing models, achieving outstanding accuracy (96.80%), precision (93.25%), sensitivity (91.99%), specificity (98.00%), and F1-score (92.09%). The outstanding performance of SFD-CNN in VHD detection suggests that it holds great promise for practical use in various medical applications. Its potential lies in its ability to accurately identify and classify VHD, enabling early detection and timely intervention. SFD-CNN could significantly improve patient outcomes and reduce the burden on healthcare systems. With further development and refinement, SFD-CNN has the potential to revolutionize the field of VHD detection and become an indispensable tool for healthcare professionals.
Analisis Fitur Dinamik Elektrokardiogram Untuk Klasifikasi Aritmia Ramadhan, Yusril; Mandala, Satria
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1106

Abstract

Arrhythmia is a deviation from the normal heart rate pattern. Arrhythmias are usually harmless, but they can cause heart problems. Some types of arrhythmias include Atrial Fibrillation (AF), Premature Atrial Contractions (PAC), and Premature Ventricular Contractions (PVC). Many studies have been conducted to identify the dynamic characteristics of electrocardiogram (ECG) irregular waves in the detection of arrhythmias. However, the accuracy obtained in these studies is less than optimal. This study aims to solve the problem by evaluating three main features of arrhythmias using ECG signals: RR interval, PR interval, and QRS complex. Experiments were conducted rigorously on these three features. The accuracy achieved was 98.21%, with a specificity of 98.65% and a sensitivity of 97.37%.
Study of Machine Learning Algorithm on Phonocardiogram Signals for Detecting of Coronary Artery Disease Mandala, Satria; Pramudyo, Miftah; Rizal, Ardian; Fikry, Maurice
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.536

Abstract

Several methods of detecting coronary artery disease (CAD) have been developed, but they are expensive and generally use an invasive catheterization method. This research provides a solution to this problem by developing an inexpensive and non-invasive digital stethoscope for detecting CAD. To prove the effectiveness of this device, twenty-one subjects consisting of 11 CAD patients and 10 healthy people from Hasan Sadikin Hospital Bandung were selected as validation test participants. In addition, auscultation was carried out at four different locations around their chests, such as the aorta, pulmonary, tricuspid, and mitral. Then the phonocardiogram data taken from the stethoscope were analyzed using machine learning. To obtain optimal detection accuracy, several types of kernels such as radial basis function kernel (RBF), polynomial kernel and linear kernel of Support Vector Machine (SVM) have been analyzed. The experimental results show that the linear kernel outperforms compared to others; it provides a detection accuracy around 66%. Followed by RBF is 56% and Polynomial is 46%. In addition, the observation of phonocardiogram signals around the aorta is highly correlated with CAD, giving an average detection accuracy for the kernel of 66%; followed by 44% tricuspid and 43% pulmonary.
ANALYSIS FEATURE EXTRACTION FOR OPTIMIZING ARRHYTHMIA CLASSIFICATION FROM ELECTROCARDIOGRAM SIGNALS Satria Mandala; Ramadhan, Yusril
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1267

Abstract

Heart disease is the primary cause of death globally, with arrhythmias, such as Premature Atrial Contraction (PAC), Atrial Fibrillation (AF), and Premature Ventricular Contraction (PVC), being critical heart rhythm abnormalities. Although numerous studies have utilized feature extraction from electrocardiogram (ECG) signals to detect these conditions, optimal accuracy has not been achieved. Therefore, this research aims to identify relevant features and achieve better results by using dynamic feature extraction methods. The extracted features used are RR Interval, PR Interval, and QRS Complex. By combining 2 feature extractions - RR Interval & PR Interval, RR Interval & QRS Complex, and PR Interval & QRS Complex - this study achieves a high level of accuracy on the RR Interval & QRS Complex feature extraction, reaching 97.60%, with a specificity of 98.30% and sensitivity of 96.58%.
Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection Adly, Muhammad Ihsan; Mandala, Satria
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.825

Abstract

Alongside the development of technology to facilitate multi-family security, security tools are also being developed. Smart home security is one of the very popular security tools in Indonesian home construction. The tool works automatically in real time and has no restrictions on environmental conditions. However, currently available tools still lack consistent accuracy and consistent performance. To solve this problem, the author proposes a smart home security system with an Arduino UNO-connected camera, two relay modules, a magnetic lock, and connecting to a home Internet of Things system. The methods used in the research for this thesis project were: 1. Literature review of ongoing Smart Home Security using facial image feature extraction algorithm research; 2. Deployment of Arduino UNO, 2 Relay Module, and Solenoid Lock; 3. The feature extraction algorithm used is Wavelet. The proposed method is expected to achieve an accuracy of 80% or more. The experimental results showed that the proposed prototype of this experiment achieved the accuracy of 85.7%. In addition to accuracy, there is also precision rate at 87.94%, recall rate at 87.56%, and f1-score rate at 87.28%
PENGEMBANGAN PARIWISATA KAMPUNG PENYU DESA BARUGAIA TERINTEGRASI DAN BERKELANJUTAN Muhajir, Humaidid; Tassakka, Asmi Citra Malina A.R.; Assir, Andi; Mandala, Satria; Alimuddin, Ilham; Marmin, Hidayat; Annas, Aswar; Indrayuni, Armi
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 6 (2024): Vol. 5 No. 6 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i6.38274

Abstract

Kampung Penyu Desa Barugaia salah satu tempat wisata yang menaik dan menjadi ikon pariwisata di Kabupaten Kepuluan Selayar. Ragam permasalahan yang terjadi di tempat wisata Kampung Penyu meliputi. Kondisi pantai yang kotor akibat sampah, pesisir pantai terdampak abrasi, prasarana dan sarana pantai wisata rusak, belum tersedianya mapping wilayah dan peta wisata yang terstruktur, belum tersedianya media promosi, kemampuan sumberdaya Kelompok Sadar Wisata (Pokdarwis) terkait sapta pesona masih minim, dan rusaknya penangkaran penyu yang mengancam kepunahan penyu diwilayah ini, dari uraian permasalahan ini sehingga pengembangan pariwisata berkelanjutan sangat perlu dilakukan. Metode pengabdian yang dilakukan menggunakan model. Pertama sosialisasi kegiatan kepada masyarakat, Kedua pelatihan dan penyuluhan terkait teknologi tepat guna yang diberikan, Ketiga penerapan teknologi inovasi yang diberikan, dan Kempat melakukan pendampingan dan evaluasi keberhasilan kegiatan. Hasil penerapan teknologi inovasi pemilah sampah dan pengeruk sampah memanimalisir volume sampah di wilayah pesisir, penataan kelembagaan Pokdarwis yang baik terkait pengelolaan wisata dan sapta pesona, tersedianya inovasi akun promosi dan peta wisata, dan terealisasinya pelestarian penyu yang dilakukan oleh Pokdarwis. Pentingnya hasil pengabdian ini untuk mengembalikan Kampung Penyu sebagai tempat wisata yang terintegrasi dan berkelanjutan di Kabupaten Kepulauan Selayar
Pengembangan Aplikasi Bimbingan Haji Versi 2.0 Untuk Jamaah Pondok Pesantren Daarut Tauhiid (DT) Mandala, Satria; Suryani, Vera; M Jadied, Erwid
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 12 : Januari (2026): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Daarut Tauhiid Islamic Boarding School (DT) has a ongoing commitment to nurturing the community, including improving the quality of Hajj and Umrah pilgrimages for its congregation. Although Hajj rituals have been carried out routinely, in practice DT pilgrims still face various challenges, such as the complexity of Hajj and Umrah procedures, limited access to contextual and direct guidance, and communication barriers between pilgrims and guides while in the Holy Land. These conditions have the potential to reduce the comfort, tranquility, and solemnity of the pilgrims in worship. As a follow-up to previous research and community service, this study proposes the further development of a mobile application for Hajj and Umrah guidance specifically for DT pilgrims. This application retains the core features that were developed previously, such as a means of communication between pilgrims and their guides, a comprehensive collection of prayers and guidelines for performing Hajj and Umrah, as well as motivational content and reminders for worship. This year, the application was further developed with the addition of strategic features to address issues still faced by pilgrims in the field. The main additional feature proposed is a location-based SOS feature that allows pilgrims to quickly and accurately share their location with Hajj guides or officials when experiencing an emergency, such as getting lost or separated from the group. In addition, the application also features customizable prayers and worship materials based on Hajj and Umrah groups, allowing guides to tailor guidance content to the needs, schedules, and characteristics of pilgrims in each group. Through the development of this application, it is hoped that DT pilgrims will receive more personalized, responsive, and contextual guidance during their Hajj and Umrah pilgrimages. This application not only serves as a technical support tool but also as a medium for continuous spiritual development. As a result, the quality of worship, sense of security, and spiritual experience of the pilgrims can significantly improve, in line with the vision of Pondok Pesantren Daarut Tauhiid in fostering a generation that excels in remembrance, reflection, and effort.   Translated with DeepL.com (free version).
Permukiman kumuh dan stunting di Kelurahan Macini Sombala Kota Makassar Mandala, Satria; Juni, Juni; Sobirin, Sobirin; Karim, Abdul; Akbar, Muh
Teknosains Vol 19 No 3 (2025): September-Desember
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v19i3.61595

Abstract

Permukiman kumuh berkontribusi besar terhadap meningkatnya kejadian stunting pada anak. Kondisi hunian yang tidak layak mendorong munculnya penyakit infeksi, paparan lingkungan yang tidak aman, serta defisit asupan gizi rangkaian faktor yang mempertinggi risiko gangguan pertumbuhan. Riset ini menerapkan metode kuantitatif melalui rancangan deskriptif observasional. Subjek penelitian meliputi keseluruhan penduduk di wilayah Kelurahan Macini Sombala, Kota Makassar (N = 22.584 jiwa). Sampel dipilih secara acak sederhana dari rumah tangga dengan kepemilikan tempat tinggal pribadi, dengan ukuran 5–15% dari total populasi. Data diolah melalui statistik deskriptif dan diperkaya telaah kualitatif atas temuan lapangan. Validitas instrumen ditinjau pada aspek isi dan konstruk menggunakan uji korelasi Pearson serta analisis faktor. Hasil menunjukkan bahwa percepatan penurunan stunting di kawasan kumuh menuntut perluasan akses sanitasi dan air layak, penguatan layanan kesehatan, serta edukasi gizi dan pola asuh bagi orang tua/pengasuh. Temuan ini memberikan masukan bagi perumusan kebijakan penataan permukiman perkotaan di Kota Makassar dengan mempertimbangkan determinan lingkungan, sanitasi, sosial ekonomi, dan pendidikan.
Co-Authors Abd. Rasyid Syamsuri Abdul Karim Adiwijaya Adly, Muhammad Ihsan Agus Alex Yanuar Akbar, Muh Aldebaran Bayu Nugroho Alwan, Maryam Hameed Andika Nugroho Putra Andreas Jonathan Silaban Annas, Aswar Ardian Rizal Arifin, Rezki Fauzan Ashydiki Malik Asmi Citra Malina, Asmi Citra Assir, Andi Azha Alvin Rahmansyah Bilal Ibrahim Bakri Budi Santosa Burchanuddin, Andi Eko Darwiyanto Endro Ariyanto Erwid Jadied Mustofa Erwid Mustofa Jadied Erwied M. Jadied Faiz Rofi Hencya Faizal Akbari Putra Fenty Alia Fikry, Maurice Fityanul Akhyar Hameed, Zainab Hasanuddin Hasanuddin Ilham Alimuddin, Ilham Indrayuni, Armi Irma Ruslina Defi Jumadil, Jumadil Juni, Juni Ledya Novamizanti M Jadied, Erwid Marmin, Hidayat Maurice Fikry Miftah Pramudyo Miftah Pramudyo Miftah Pramudyo Ming, Eileen Su Lee Mochamad Reza, Dandi Mohd Fadzil Hasssan Mohd Shahrizal Sunar Mohd Soperi Mohd Zahid Muhajir, Humaidid Muhammad Alif Akbar Muhammad Aniq Wafa Muhammad Hablul Barri Muhammad Ihsan Adly Muhammad Qomaruddin Muhammad Rafli Ramadhan Muhammad Yaumil Ihza Ihza Musfirah, Andi Nadia Ariana Nia Madu Marliana Niken Dwi Wahyu Cahyani Nugroho, Kahargyan Ario Pramudyo, Miftah Putu Harry Gunawan Raey Faldo Rafi Ullah Rafly Athalla Ramadhan, Yusril Rino Andias Anugraha Rita Purnamasari Rizal, Ardian Salim M. Zaki Sobirin Sobirin Suyanto Suyanto Tassakka, Asmi Citra Malina A.R. Tong Boon Tang Tora Fahrudin Vera Suryani Wael M.S. Yafooz Wiyono Sutari Yafooz, Wael M.S. Yuan Wen Hau Yusril Ramadhan Zaki, Salim M.