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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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Active Queue Management (AQM) Performance Analysis Based On Controled Delay (CoDel) Against Bufferbloat On Real-Time Aplication Satria Mandala; Muhammad Noer Iskandar
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 1 (2017): Maret, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.1.139

Abstract

Bufferbloat merupakan salah satu kondisi buffer dengan ukuran besar yang cenderungselalu penuh dan menyebabkan antrian panjang didalam buffer, jika hal ini terjadi secaraterus-menerus maka dapat menyebabkan jeda transmisi yang tinggi. Bufferbloat seringterjadi pada aplikasi berbasis real-time. Active Queue Management (AQM) merupakansalah satu cara untuk menangani terjadinya bufferbloat., AQM umumnya menggunakanalgoritma Drop Tail untuk menangani kondisi antrian panjang dalam buffer router dijaringan. Namun demikian, performansi AQM berbasis Drop Tail kurang dapatdiandalkan karena jeda transmisi dalam keadaan bufferbloat masih tinggi. Telah banyakstudi dilakukan untuk menangani bufferbloat, seperti Drop Tail, Random Early Detection(RED) dan Controlled Delay (CoDel). Dari riset yang telah dilakukan tersebut masih sulitditemukan performasi algoritma terbaik dalam menangani bufferbloat. Untuk hal tersebut,paper ini menyajikan studi performansi penanganan bufferbloat menggunakan ketigaalgoritma diatas. Dalam studi ini, video streaming digunakan sebagai traffic uji untukmenentukan performansi algoritma terbaik dalam mengatasi bufferbloat. Sedangkanmetriks uji yang digunakan dalam riset ini adalah latency, throughput dan packet-loss.Analisa hasil pengujian mengambil 3 hasil terbaik dalam setiap percobaan. Hasilpengujian menunjukan performansi algoritma CoDel jauh lebih baik dalam menanganilatency yang tinggi pada kondisi bufferbloat dibandingkan RED dan Drop Tail. Namununtuk packet-loss dan throughput performansi RED dan Drop Tail masih ungguldibanding algoritma CoDel
IoT on Heart Arrhythmia Real Time Monitoring Muhammad Alif Akbar; Satria Mandala
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 2 (2018): September, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.2.170

Abstract

Monitoring jantung telah populer sejak 5 tahun terakhir. Hal ini ditandai dengan munculnya berbagai produk monitoring jantung berbasis wearable sensor. Umumnya komunikasi yang digunakan pada sistem tersebut adalah menggunakan radio telemetri dengan biaya opera- sional yang mahal. Beberapa riset mencoba menggunakan konsep internet of things (IoT) untuk mengatasi hal tersebut. Namun demikian, desain komunikasi IoT yang ada belum efisien. Ini disebabkan riset yang ada hanya berfokus pada bagaimana hasil baca sensor dapat dipantau secara realtime. Untuk mengatasi hal tersebut, riset ini mengusulkan sebuah arsitektur IoT berbasis cloud untuk memonitor aritmia, salah satu jenis penyakit jantung yang umum ditemukan. Deteksi aritmia yang diusulkan adalah pengembangan algoritma deteksi aritmia berbasis Tsipuras et al, dengan menggunakan deteksi fitur R. Sistem yang diusulkan pada paper ini telah diuji menggunakan dataset MIT-BIH dan menghasilkan akurasi 93.11% terhadap 3 kelas aritmia, yaitu PAC, PVC dan VT. Menariknya, dengan penerapan IoT, efisiensi algoritma deteksi fitur R meningkat 30% dibanding yang diusulkan oleh Pan dan Tompkins. Terbukti dengan rendahnya waktu rata-rata eksekusi tiap sampel data, yaitu sekitar 0.00749 ms.
Study of Machine Learning Algorithm on Phonocardiogram Signals for Detecting of Coronary Artery Disease Satria Mandala; Miftah Pramudyo; Ardian Rizal; Maurice Fikry
Indonesia 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.
Study of Denoising Method to Detect Valvular Heart Disease Using Phonocardiogram (PCG) Muhammad Yaumil Ihza Ihza; Satria Mandala; Miftah Pramudyo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.610

Abstract

Heart sound is a very weak acoustic signal, very susceptible to external acoustic signals and electrical disturbances, especially friction caused by the subject's breathing or body movements. The heart sound signal will be recorded in a phonocardiogram (PCG) and produce heart sounds, noise, and extra sounds. The purpose of this work is to denoise the signal from the heart sounds recorded on the PCG and determine valvular heart disease (VHD). Several methods have been proposed for denoising heart sound signals, both in the time domain and in the frequency domain. Most of these methods still have problems for denoising results. In this paper, the techniques used to denoise the heart sound signal are Discrete Wavelet Transform (DWT), Short Term Fourier Transform (STFT), and Low-Pass filter.
Wrapper-Based Feature Selection Analysis For Semi-Supervised Anomaly Based Intrusion Detection System Andreas Jonathan Silaban; Satria Mandala; Erwid Jadied Mustofa
International Journal on Information and Communication Technology (IJoICT) Vol. 5 No. 2 (2019): December 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2019.52.209

Abstract

Intrusion Detection System (IDS) plays as a role in detecting various types of attacks on computer networks. IDS identifies attacks based on a classification data network. The result of accuracy was weak in past research. To solve this problem, this research proposes using a wrapper feature selection method to improve accuracy detection. Wrapper-Feature selection works in the preprocessing stage to eliminate features. Then it will be clustering using a semi-supervised method. The semi-supervised method divided into two steps. There are supervised random forest and unsupervised using Kmeans. The results of each supervised and unsupervised will be ensembling using linear and logistic regression. The combination of wrapper and semi-supervised will get the maximum result.
Increasing Feature Selection Accuracy through Recursive Method in Intrusion Detection System Andreas Jonathan Silaban; Satria Mandala; Erwid Mustofa Jadied
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.216

Abstract

Artificial intelligence semi supervised-based network intrusion detection system detects and identifies various types of attacks on network data using several steps, such as: data preprocessing, feature extraction, and classification. In this detection, the feature extraction is used for identifying features of attacks from the data; meanwhile the classification is applied for determining the type of attacks. Increasing the network data directly causes slow response time and low accuracy of the IDS. This research studies the implementation of wrapped-based and several classification algorithms to shorten the time of detection and increase accuracy. The wrapper is expected to select the best features of attacks in order to shorten the detection time while increasing the accuracy of detection. In line with this goal, this research also studies the effect of parameters used in the classification algorithms of the IDS. The experiment results show that wrapper is 81.275%. The result is higher than the method without wrapping which is 46.027%.
Study the Best PenTest Algorithm for Blind SQL Injection Attacks Aldebaran Bayu Nugroho; Satria Mandala
International Journal on Information and Communication Technology (IJoICT) Vol. 5 No. 2 (2019): December 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2019.52.268

Abstract

There are several types of SQL injection attacks. One of the most popular SQL Injection Attacks is Blind SQL. This attack is performed by exploiting a gap in the database server when executing query words. If the server responds to an invalid query, the attacker will then reverse the engineering part of the SQL query, which is obtained from the error message of the server. The process of generating a blind SQL injection attack is complicated. As a result, a Pentester often requires a long time to penetrate the database server. This research provides solutions to the problems above by developing the automation of a blind SQL injection attack. The method used in this research is to generate keywords, such as the database name and table name so that the attacker can retrieve information about the user name and password. This research also compares several search algorithms, such as linear search, binary search, and interpolation search for generating the keywords of the attack. Automation of the Blind SQL Injection was successfully developed, and the performance of the keywords generation for each algorithm was also successfully measured, i.e., 1.7852 seconds for Binary Search, 1.789 seconds for interpolation and 1.902 seconds for Linear Search.
IPTEK BAGI MASYARAKAT FORUM KOMUNIKASI UKM KECAMATAN DRAMAGA KABUPATEN BOGOR Suyanto Suyanto; Satria Mandala; Wiyono Sutari; Rino Andias Anugraha; Budi Santosa; Agus Alex Yanuar
Charity : Jurnal Pengabdian Masyarakat Vol 2 No 1 (2019): Charity - Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v2i1.1827

Abstract

Pada tahun 2013, beberapa pemilik Usaha Kecil dan Menengah (UKM) Kabupaten Bogor, didukung oleh Dekranasda, Dinas Koperasi dan UMKM serta Dinas Perindustrian dan Perdagangan Kabupaten Bogor, mendirikan sebuah Forum Komunikasi UKM (FKUKM). Forum ini diharapkan dapat membantu para pemilik UKM di Kabupaten Bogor dalam menyelesaikan masalah usaha mereka. Sebagai salah satu pelaku usaha di Indonesia, UKM tak bisa menghindari dampak persaingan global. Ketika jarak menyempit dan pemasaran kian luas cakupannya, beberapa UKM di daerah masih belum dapat menikmati hal ini karena kurangnya pengetahuan. Produksi usaha yang beragam dan berkualitas, belum dapat dipasarkan secara online dan hanya bisa dirasakan oleh masyarakat di lingkungan sekitar. Akibatnya, omset pun tak bisa optimal. Untuk membantu para anggota FKUKM, pada kegiatan abdimas ini dibangun sebuah e-commerce platform untuk pemasaran online produk anggota FKUKM. Selain itu juga dilakukan pelatihan aplikasi e-commerce dan mobile photography bagi anggota FKUKM.
Performance Analysis of PPG Signal Denoising Method Using DWT and EMD for Detection of PVC and AF Arrhytmias: Analisis Performansi Metode Denoising Sinyal PPG Menggunakan DWT dan EMD untuk deteksi Aritmia PVC dan AF Muhammad Aniq Wafa; Satria Mandala; Miftah Pramudyo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.648

Abstract

In the cardiac arrhythmia detection system using a Photoplethysmography (PPG) sensor, noise is often found in the PPG signal due to internal and external factors in the signal retrieval process. So it is necessary to do a denoising process to remove noise before the signal is used. This study aims to test the Discrete wavelet transform (DWT) and Empirical Mode Decomposition (EMD) methods in removing noise from the PPG signal and to test the denoising signal on the Premature Arrhythmia Verticular Contractions (PVC) and Atrial Fibrillation (AF) detection systems. The parameters used to compare the performance of the denoising method are Mean Square Error (MSE), Signal to Noise Ratio (SNR), Accuracy, F1, Precision, and Recall. The method with the highest SNR, Accuracy, F1, Precision, and Recall values ​​and the lowest MSE values ​​is the best denoising method.
Study of Classification Method to Detect Coronary Heart Disease Based On Signal Photoplethysmography (PPG) Azha Alvin Rahmansyah; Satria Mandala; Miftah Pramudyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4823

Abstract

Coronary heart disease (CHD) is one of the deadliest diseases in the world, especially in Indonesia. This disease is caused by the accumulation of fat in blood vessels and can cause heart attacks that can endanger a person's health and safety. There are several methods for detecting CAD, such as using Electrocardiogram (ECG) signals and Photophlethysmograph (PPG) signals. However, studies that have tested machine learning classification methods to detect CAD using PPG signals are rarely found compared to detection using ECG. This study uses PPG signals taken from smartphone cameras to detect CHD, so that CHD detection is easier and affordable. To be able to diagnose CHD, machine learning assistance is needed to determine whether CHD is positive or negative. This study proposes a classification algorithm study to detect CAD. There are 3 classification methods used in this study. The three methods are KNN, SVM, and decision tree. The final results obtained in this study resulted in the best classification for KNN 81%, SVM 90%, and Decision Tree 90%. Each classification used has been carried out before and after tuning
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.