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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) 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 Jurnal Pemanfaatan Teknologi untuk 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 Social Sciences Other

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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%
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.
Meningkatkan Pemahaman dan Pemanfaatan AI dalam Pembelajaran bagi Siswa SMA Rio Guntur Utomo; Satria Mandala; Hidayatus Sholikhin; Rifqi Syafiq Hibatul Aziz; Zelmi Muhammad Adjel; Martin Halomoan Tamba; Rafael Sebastian
JAPATUM: Jurnal Pemanfaatan Teknologi untuk Masyarakat Vol 2 No 4 (2023): Desember 2023
Publisher : MATRADIPTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59328/JAPATUM.2023.2.4.68

Abstract

Kegiatan penyuluhan ini bertujuan untuk meningkatkan pemahaman dan pemanfaatan kecerdasan buatan (AI) dalam proses pembelajaran bagi siswa SMA. Dengan pendekatan interaktif dan materi yang mudah dipahami, kegiatan ini diharapkan dapat memperkenalkan konsep dasar AI serta mendorong minat pelajar terhadap teknologi. Pelaksanaan dilakukan melalui presentasi, diskusi, dan evaluasi partisipatif. Hasil umpan balik menunjukkan mayoritas peserta merasa puas dan tertarik untuk mempelajari AI lebih lanjut. Penyuluhan ini tidak hanya menambah wawasan siswa tentang teknologi, tetapi juga meningkatkan kesiapan mereka menghadapi era digital. Rekomendasi perbaikan mencakup perpanjangan waktu pelatihan dan peningkatan interaktivitas media. Kegiatan ini menjadi langkah awal dalam membangun literasi AI di kalangan pelajar dan mendukung pengembangan sumber daya manusia yang siap menghadapi masa depan.
Penerapan Sistem Monitoring Kelembapan Tanah Berbasis Internet of Things (IoT) untuk Pemberdayaan Petani di Desa Wareng Sutiyo; Kusuma Adi Achmad; Satria Mandala
JAPATUM: Jurnal Pemanfaatan Teknologi untuk Masyarakat Vol 5 No 1 (2026): Maret 2026
Publisher : MATRADIPTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59328/JAPATUM.2026.5.1.113

Abstract

Desa Wareng di Kabupaten Gunungkidul memiliki potensi pertanian yang besar, namun masih menghadapi kendala dalam pemantauan kondisi lahan dan efisiensi penggunaan air irigasi. Praktik pemantauan kelembapan tanah yang selama ini dilakukan secara manual cenderung tidak akurat dan sulit dilakukan secara berkelanjutan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk menerapkan sistem monitoring kelembapan tanah berbasis Internet of Things (IoT) yang terintegrasi dengan dashboard berbasis web, serta meningkatkan literasi teknologi petani melalui pelatihan dan pendampingan. Metode pengabdian dilakukan dengan pendekatan partisipatif dan berbasis kebutuhan mitra, melalui tahapan perencanaan dan analisis kebutuhan, pengadaan dan instalasi perangkat IoT, konfigurasi sistem dan integrasi dashboard, pelatihan serta pendampingan masyarakat, hingga implementasi, monitoring, dan evaluasi. Sistem yang dikembangkan memanfaatkan sensor kelembapan tanah dan mikrokontroler untuk mengirimkan data secara real‑time ke server dan divisualisasikan dalam bentuk grafik time‑series serta indikator nilai rata‑rata pada dashboard. Hasil implementasi menunjukkan bahwa sistem mampu merekam data secara kontinu dengan jumlah lebih dari 1.000 sampel, dengan nilai rata‑rata kelembapan tanah sebesar 91,29%, suhu tanah 27,38 °C, dan kelembapan udara 80,74%. Pemanfaatan data sensor membantu petani dalam menentukan waktu penyiraman yang lebih tepat dan mendorong efisiensi penggunaan air. Selain itu, kegiatan pelatihan dan pendampingan menunjukkan peningkatan pemahaman dan kemandirian petani dalam memanfaatkan teknologi monitoring berbasis IoT. Kegiatan ini membuktikan bahwa penerapan teknologi IoT yang disertai pemberdayaan masyarakat dapat menjadi solusi teknologi tepat guna yang aplikatif, berkelanjutan, dan berpotensi untuk direplikasi pada wilayah pedesaan lainnya.
KMS Digital: Sistem Monitoring Tumbuh Kembang Balita Di Desa Lebakwangi Satria Mandala; Adiwijaya; Endro Ariyanto; Eko Darwiyanto
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 9 : Oktober (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

This community service program aims to develop a monitoring system for toddler growth and development through Digital KMS in Lebakwangi Village, Arjasari District, Bandung Regency, by integrating digital technology, improving digital literacy, encouraging active community participation, and utilizing local potential. This program is designed as a response to the limitations of the manual recording system that has been used so far, which often causes data input errors, delays in monitoring, and difficulties in early detection of growth disorders and nutritional problems in toddlers. Through an integrated approach, the activity began with the development of a Digital KMS system that included in-depth needs analysis with all stakeholders—from posyandu cadres and health workers to parents—to identify the constraints of traditional recording and determine the main features that must be included, such as automatic data input, notifications, and an interactive forum dashboard that displays data in real time. The stages of system development include needs analysis, user-friendly interface design, development of reporting and analytics modules, pilot testing in several strategic posyandu, and full implementation integrated with the local health system. This digital KMS application was built using Laravel tools for the backend and React for the frontend. Furthermore, the program improves digital literacy through workshops and technical training held at health centers and facilities, the creation of educational modules in the form of video tutorials, written guides, and interactive materials, as well as ongoing assistance with the formation of a technical support team that is ready to provide assistance in the field. A participatory approach is implemented through community discussion forums and the involvement of community leaders as agents of change to optimize system usage, enhance a sense of ownership, and empower the community to actively participate in system evaluation and improvement. In addition, data-based monitoring and evaluation are carried out by activating interactive dashboards for periodic monitoring, data collection and analysis for early risk identification, and periodic evaluation through surveys and questionnaires to compile evaluation reports as a basis for system improvement. Synergy among partners, involving local governments, educational institutions such as Telkom University, and local communities, strengthens the ecosystem supporting this program through strategic collaboration that ensures policy support, material development, and ongoing assistance. By utilizing existing geographical and infrastructure potential as well as the cultural value of mutual cooperation, this program has successfully implemented and socialized a digital KMS application that is expected to not only improve the accuracy and effectiveness of health monitoring for toddlers, but also empower the community through increased digital literacy and sustainable digital transformation, thereby generating a long-term positive impact on the quality of health services and the quality of life of children in Lebakwangi Village.
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 Hidayatus Sholikhin Ilham Alimuddin, Ilham Indrayuni, Armi Irma Ruslina Defi Jumadil, Jumadil Juni, Juni Kusuma Adi Achmad Ledya Novamizanti Marmin, Hidayat Martin Halomoan Tamba Maurice Fikry 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 Putu Harry Gunawan Raey Faldo Rafael Sebastian Rafi Ullah Rafly Athalla Ramadhan, Yusril Rifqi Syafiq Hibatul Aziz Rino Andias Anugraha Rio Guntur Utomo Rita Purnamasari Rizal, Ardian Salim M. Zaki Sobirin Sobirin Sutiyo Suyanto Suyanto Tong Boon Tang Tora Fahrudin Wael M.S. Yafooz Wiyono Sutari Yafooz, Wael M.S. Yuan Wen Hau Yusril Ramadhan Zaki, Salim M. Zelmi Muhammad Adjel