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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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A Study of Feature Selection Method to Detect Coronary Heart Disease (CHD) on Photoplethysmography (PPG) Signals Faizal Akbari Putra; Satria Mandala; Miftah Pramudyo
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2259

Abstract

Coronary Heart Disease (CHD) is a condition in which the heart's blood supply is blocked or disrupted by fat in the coronary arteries. This disease is the most significant cause of death in Indonesia. CHD can be detected based on the Heart Rate Variability (HRV) index of the Photophletysmograph (PPG) signal taken from a smartphone's camera. However, the use of PPG from smartphone to detect CHD is still rare in real-world applications. Moreover, studies on CHD detection based on PPG signal are also difficult to be found in the scientific literature. Currently, the Electrocardiogram (ECG) signal still dominates as a signal for detecting CHD. This research fills this research gap by proposing a study on the feature selection of PPG signal to detect CHD. There are three feature selection methods studied in this research, i.e., Analysis of Variance (Anova), Pearson Correlation, and Recursive Feature Elimination (RFE). Furthermore, a classification algorithm, called as K-Nearest Neighbors, has also been chosen to create a machine learning model based on the PPG features. The experimental results show that the Pearson Correlation feature selection method produces better CHD detection performance compared to the other two algorithms (Anova and RFE). CHD detection performance using the Pearson Correlation produces an accuracy of 90.9%, sensitivity of 75%, and specificity of 100%.
Studi Algoritma Klasifikasi Sensor Accelerometer dan Gyroscope untuk Pola Activity Daily Life (ADL) pada Dewasa Sehat Andika Nugroho Putra; Satria Mandala; Irma Ruslina Defi
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Sistem klasifikasi Activity Daily Life (ADL) ini adalah sistem untuk klasifikasi aktivitas yang dilakukan seseorang dengan menggunakan wearable sensor untuk membantu lansia sehingga aman dan nyaman dalam melakukan aktivitasnya sehari-hari. Sistem klasifikasi ADL dengan menggunakan sensor accelerometer dan gyroscope banyak menggunakan berbagai algoritma untuk klasifikasinya, seperti algoritma K-Nearest Neighbour (KNN), Support Vektor Machine (SVM) dan sebagainya. Tugas akhir ini bermaksud untuk mencari tingkat akurasi yang terbaik beserta spesitifitas dan sensitivitasnya dengan membandingkan beberapa algoritma klasifikasi dengan menggunakan dataset yang telah dibuat dengan alat yang terdiri dari mikrokontroler ESP32 berbasis sensor MPU-6050 (sensor accelerometer dan gyroscope) dan akan menguji 5 ADL yaitu berjalan, naik tangga, turun tangga, berdiri, dan duduk. Data yang didapat dari alat kemudian akan diklasifikasi untuk mengenali ADL yang dilakukan. Hasil yang didapatkan adalah ketiga algoritma sudah baik melakukan klasifikasi dengan akurasi mencapai 95%. KNN menjadi algoritma terbaik untuk klasifikasi ADL dengan menghasilkan akurasi sebesar 97,33%. Kata kunci : Klasifikasi Multiclass, ADL, Accelerometer, Gyroscope, KNN Abstract Classification system for activity daily life (ADL) is a system who classified human activity using wearable sensor to help elderly doing their activity safely and comfortably. Classifier for ADL using accelerometer and gyroscope sensor usually used classification algorithm like K-Nearest Neighbour, Support Vektor Machine, and many others. This final project aims to get high accuracy by using 1 tool that using ESP32 microcontroller and MPU-6050 sensor (accelerometer and gyroscope sensor) and will test 5 ADL like walking, walking upstair, walking downstair, stading, and sitting. The data obtained from the tool will be classified to recognition ADL. The result is the three algorithm have good accuracy up to 95%. KNN get the best algorithm for ADL classification with value of 97,33% Keywords: Activity Daily Life, Multiclass Classification , Accelerometer, Gyroscope, KNN.
AKSI CEGAH STUNTING MELALUI APLIKASI SAGITA: STATUS GIZI BALITA Muhammad Hablul Barri; Fenty Alia; Ledya Novamizanti; Rita Purnamasari; Fityanul Akhyar; Tora Fahrudin; Putu Harry Gunawan; Satria Mandala
JMM (Jurnal Masyarakat Mandiri) Vol 7, No 2 (2023): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v7i2.13231

Abstract

Abstrak: Stunting merupakan salah satu masalah kesehatan masyarakat yang penting di Indonesia, terutama di Desa Lengkong, Jawa Barat. Beberapa penyebab utama yaitu kesulitan dalam pencatatan dan monitoring status gizi balita saat pelakasanaan posyandu. Pencatatan yang masih secara manual membuat beberapa data yang tersimpan sulit untuk dicari dan rentan akan adanya kesalahan pada saat penginputan. Tujuan dari pengabdian ini adalah ingin merealisasikan suatu aplikasi yang dapat memudahkan kader posyandu dalam memonitoring status gizi balita secara terpusat. Sehingga diharapkan mitra dapat dengan praktis memasukkan data, mereview akumulasi data serta membuat analisis data tersebut secara cepat dan akurat. Aplikasi ini kemudian akan disosialisasikan dalam sebuah penyluhan gizi balita. Data dari aplikasi ini nantinya dapat digunakan oleh semua pihak yang berkepentingan secara realtime. Kegiatan ini didawali dengan survei permasalahn ke lapangan kemudian dilanjutkan dengan pembuatan aplikasi lalu diakhiri dengan serah terima dan sosialisasi dari aplikasi yang telah dibuat. Dari kegiatan ini, mitra dalam hal ini adalah kader posyandu dan perangkat desa mencoba secara langsung aplikasi yang dibuat, sehingga dapat memberikan masukin secara langsung kepada tim untuk perbaikan aplikasi. Dari survei yang yang disebar ke seluruh peserta, didapati 80% peserta merasa puas dengan aplikasii yang ada dan berharap aplikasi segera dapat dilakukan perbaikan sehingga dapat langsung digunakan di desa Lengkong. Abstract: Stunting is one of the important public health problems in Indonesia, especially in Lengkong Village, West Java. Several main causes are difficulties in recording and monitoring the nutritional status of toddlers during the implementation of posyandu (integrated health post). Manual recording makes some stored data difficult to find and prone to errors during inputting. The purpose of this community service is to realize an application that can facilitate posyandu workers in monitoring the nutritional status of toddlers in a centralized manner. Thus, it is expected that partners can easily input data, review data accumulation, and quickly and accurately analyze the data. This application will then be socialized in a toddler nutrition campaign. Data from this application can be used by all stakeholders in real-time. This activity begins with a survey of problems in the field, followed by application development and ends with the handover and socialization of the application that has been made. From this activity, partners, in this case, posyandu workers and village officials, directly try out the application, so they can provide direct feedback to the team for application improvements. From the survey distributed to all participants, it was found that 80% of participants were satisfied with the existing application and hoped that the application could be improved soon, so it could be immediately used in Lengkong Village. 
Analysis of Smart Home Security System Design Based on Facial Recognition With Application of Deep Learning Rafly Athalla; Satria Mandala
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.855

Abstract

Currently, there is a rising interest in utilizing the Internet of Things (IoT) for Smart home systems. One crucial aspect of Smart home systems is their security capabilities, specifically the ability to conveniently lock and unlock doors or gates. The primary issue in smart home security systems lies in their low accuracy and image processing delays, which were observed to be approximately 65% - 70% in experiments conducted using the KNN and Decision Tree methods. This research proposes a Deep Learning approach that achieves an accuracy of over 80%. The methodology employed in this study consists of four key steps: 1. Conducting a literature review on Smart Home Security, 2. Developing an RNN model for face detection, 3. Creating a prototype for face detection in a smart home setting, and 4. Evaluating the developed prototype for smart homes. The experimental results demonstrate that the proposed prototype achieves an accuracy of 94.3%. Furthermore, the recall rate is 94.3%, the f1 score is 91.66%, and the precision is 94.8%.
Study of Feature Extraction Method to Detect Myocardial Infraction Using a Phonocardiogram Ashydiki Malik; Satria Mandala; Miftah Pramudyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6442

Abstract

Myocardial Infraction is one of the most dangerous and often fatal cardiovascular diseases. To detect this disease early, non-invasive methods based on Phonocardiogram (PCG) signals have become a significant focus of research. However, to present, research on feature extraction from PCG signals is still limited. In this research, we propose a study of feature extraction algorithms using Discrete Wavelet Transform (DWT), Mel Frequency Cepstral Coefficients (MFCC), and Entropy methods to detect heart attacks. In the pre-processing stage, we applied noisereduce to remove noise in the PCG signal. Further, we perform feature extraction using DWT, MFCC, and Entropy methods on the processed PCG signal. Following that, we used a detuned KNN with hyperparameters as the classification algorithm to classify the features into two categories: heart attack and non-heart attack. The test results show that DWT, MFCC, and Entropy-based feature extraction methods can make a significant contribution in detecting Myocardial Infraction. In comparison with other feature extraction algorithms, the test results show that the Entropy-based feature extraction method provides the best accuracy of 99%, with 99% sensitivity and 99% specificity. This research makes an important contribution to the development of heart attack detection methods using PCG signals. With promising results, the Entropy-based feature extraction method can be an effective and efficient approach in detecting coronary heart disease early, which in turn can improve patient prognosis and treatment.
Model Berbasis Transfer Learning untuk Deteksi Tumor Otak pada Citra MRI Faiz Rofi Hencya; Satria Mandala; Tong Boon Tang; Mohd Soperi Mohd Zahid
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1123.2023

Abstract

Brain tumors are life-threatening medical conditions characterized by abnormal cell proliferation in or near the brain. Early detection is crucial for successful treatment. However, the scarcity of labelled brain tumor datasets and the tendency of convolutional neural networks (CNNs) to overfit on small datasets have made it challenging to train accurate deep learning models for brain tumor detection. Transfer learning is a machine learning technique that allows a model trained on one task to be reused for a different task. This approach is effective in brain tumor detection as it allows CNNs to be trained on larger datasets and generalize better to new data. In this research, we propose a transfer learning approach using the Xception model to detect four types of brain tumors: meningioma, pituitary, glioma, and no tumor (healthy brain). The performance of our model was evaluated on two datasets, demonstrating a sensitivity of 98.07%, specificity of 97.83%, accuracy of 98.15%, precision of 98.07%, and f1-score of 98.07%. Additionally, we developed a user-friendly prototype application for easy access to the Xception model for brain tumor detection. The prototype was evaluated on a separate dataset, and the results showed a sensitivity of 95.30%, specificity of 96.07%, accuracy of 95.30%, precision of 95.31%, and f1-score of 95.27%. These results suggest that the Xception model is a promising approach for brain tumor detection. The prototype application provides a convenient and easy-to-use way for clinical practitioners and radiologists to access the model. We believe the model and prototype generated from this research will be valuable tools for diagnosing, quantifying, and monitoring brain tumors.
Pengaruh Peran Serta Masyarakat Dalam Pengelolaan Persampahaan di Kabupaten Polewali Mandar Sobirin, Sobirin; Mandala, Satria; Burchanuddin, Andi
Jurnal Ilmiah Ecosystem Vol. 23 No. 2 (2023): Ecosystem Vol. 23 No 2, Mei - Agustus Tahun 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35965/eco.v23i2.3248

Abstract

Tujuan penelitian ini adalah untuk menginvestigasi dampak partisipasi masyarakat dalam pengolahan limbah di wilayah Kabupaten Polewali Mandar. Metode penelitian ini adalah kuantitatif dan mengadopsi desain observasional deskriptif. Populasi yang menjadi fokus dalam penelitian ini adalah keseluruhan warga di Pemerintah Kabupaten Polman, yang berjumlah sekitar 478.534 orang. Pengambilan sampel dilakukan melalui pendekatan metode sampel acak sederhana (Simple Random Sampling). Penentuan sampel difokuskan pada rumah tangga dengan status kepemilikan pribadi. Jumlah sampel yang diambil berkisar antara 5% hingga 15% dari total populasi. Data yang terkumpul dianalisis untuk menggambarkan aspirasi dan harapan masyarakat terkait pengelolaan limbah di wilayah penelitian. Validitas data diperiksa melalui dua tahap, yaitu validitas konstruksi dan validitas isi. Validitas konstruksi dinilai dengan melibatkan pandangan dari para ahli (ahli penilaian). Setelah data terkumpul, uji validitas konstruksi dilakukan melalui analisis faktor dengan mengkorelasikan skor masing-masing item instrumen, menggunakan metode korelasi product moment. Metode analisis data yang digunakan adalah Statistik Deskriptif untuk menguji hipotesis dan mendeskripsikan karakteristik sampel dari variabel yang diteliti dalam penelitian ini. Hasil analisis menunjukkan bahwa pertumbuhan pembangunan telah berkontribusi pada peningkatan jumlah tumpukan limbah, mendorong perlunya perencanaan pengelolaan limbah di Kabupaten Polewali Mandar. Beberapa faktor yang signifikan dalam pengelolaan limbah di Kabupaten Polewali Mandar adalah partisipasi aktif masyarakat dan dukungan dari pemerintah The purpose of this study is to investigate the impact of community participation in waste management in the Polewali Mandar Regency. This research method is quantitative and adopts a descriptive observational design. The population that is the focus of this research is all residents in the Polman Regency Government, which totals around 478,534 people. Sampling was carried out using a simple random sampling approach (Simple Random Sampling). Determination of the sample is focused on households with private ownership status. The number of samples taken ranged from 5% to 15% of the total population. The collected data is analyzed to describe the aspirations and expectations of the community regarding waste management in the research area. Data validity was examined through two stages, namely construction validity and content validity. Construction validity is assessed by involving the views of experts (assessment experts). After the data is collected, the construction validity test is carried out through factor analysis by correlating the scores of each instrument item, using the product moment correlation method. The data analysis method used is descriptive statistics to test hypotheses and describe the sample characteristics of the variables studied in this study. The results of the analysis show that development growth has contributed to an increase in the amount of waste piles, driving the need for waste management planning in Polewali Mandar Regency. Several significant factors in waste management in Polewali Mandar Regency are the active participation of the community and support from the government
Analisis Fitur Dinamik Elektrokardiogram Untuk Klasifikasi Aritmia Yusril Ramadhan; Satria Mandala
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%.
Digitalisasi Proses Simpan Pinjam Pada Koperasi Kebal Al Muttaqien Kota Bandung Satria Mandala; Niken Dwi Wahyu Cahyani; Erwied M. Jadied
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 11 : Desember (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Koperasi Simpan Pinjam adalah lembaga non bank yang memberikan layanan simpan dan pinjam kepada anggota. Dasar hukum tentang koperasi simpan pinjam terdapat pada Peraturan Otoritas Jasa Keuangan (POJK) Nomor 5 tahun 2014 tentang Penyelenggaraan Usaha Lembaga Keuangan Mikro. Koperasi Kebal Al Muttaqien merupakan Koperasi Simpan Pinjam yang berdiri sejak bulan Desember 2018 di Kota Bandung; dan pada tahun 2020 memiliki anggota sebanyak 600 orang. Permasalahan utama yang dihadapi oleh koperasi ini adalah proses bisnis simpan pinjam yang masih dilakukan secara manual. Dampak hal tersebut adalah layanan yang diberikan kepada anggota kurang maksimal karena memerlukan waktu yang lama. Untuk mengatasi masalah diatas, kami mengembangkan digitalisasi proses simpan pinjam online pada koperasi tersebut sehingga pengajuan maupun persetujuan kredit kepada anggotanya dapat dilakukan lebih cepat, kapan saja dan dimana saja. Digitalisasi yang kami usulkan adalah pengembangan aplikasi berbasis website yang memuat seluruh proses simpan dan pinjam di koperasi tersebut. Kuisioner tentang Feedback kegiatan telah kami sebar ke pengurus dan anggota koperasi tersebut. Hasil yang didapat menunjukkan bahwa kegiatan pengmas ini sangat baik dengan range score feedback 4. Persentase jumlah setuju dan sangat setuju terhadap hasil kegiatan adalah sebesar 100%.
Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection Muhammad Ihsan Adly; Satria Mandala
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%
Co-Authors Abd. Rasyid Syamsuri Adiwijaya Adly, Muhammad Ihsan Agus Alex Yanuar 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 Ledya Novamizanti 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 Wael M.S. Yafooz Wiyono Sutari Yafooz, Wael M.S. Yuan Wen Hau Yusril Ramadhan Zaki, Salim M.