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All Journal Indonesian Journal of Physics (IJP) Coreid Journal The Indonesian Journal of Computer Science
Muttaqien, Fahdzi
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Astronomy Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Investigating the Performance of Serial and Parallel Smith-Waterman Algorithm Implementations for Genetic Sequence Alignment Using OpenMPI Fitriani, Nita Dwi; Amelia, Silmi Rahma; Muttaqien, Fahdzi; Ivansyah, Atthar Luqman
CoreID Journal Vol. 2 No. 2 (2024): July 2024
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v2i2.34

Abstract

Deoxyribonucleic acid (DNA) is composed of nucleotide chains containing nitrogenous bases, phosphate groups, and pentose sugars, with variations primarily occurring in the sequence of nitrogenous bases. The analysis of DNA sequences often employs the Smith-Waterman algorithm for sequence alignment, a fundamental technique in bioinformatics. This research evaluates the performance of the Smith-Waterman algorithm across varying sequence lengths (10, 102, 103, and 104) using both serial and parallel implementations with the OpenMPI library. The study focuses on measuring execution times and speedup on 4, 6, 10, 12, and 24 cores. Results indicate that while execution times increase with longer sequences, parallelization significantly reduces processing time for sequences longer than 102. However, smaller sequences exhibit higher overhead on shorter lengths. The findings underscore the importance of efficient parallel programming and task allocation strategies in optimizing computational performance for DNA sequence analysis.
The Utilization and Optimization of Histogram of Oriented Gradients and Machine Learning in Face Recognition System Rachmat, Muhammad Ervandy; Aditya, Irfan Dwi; Muttaqien, Fahdzi
Indonesian Journal of Physics Vol 34 No 2 (2023): vol 34 no 2 2023
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itb.ijp.2023.34.2.4

Abstract

Computer science and technology development in recent years has experienced great developments. This time, some types of technology digitise almost everything related to human life, including facial recognition. In recent years, various methods for recognising human faces have developed. One of them is using the Histogram of Oriented Gradients (HOG). On this occasion, an image processing system will be designed to recognise human faces using Histograms of Oriented Gradients (HOG) and machine learning such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). Detects the winking of the face, using computer-recognisable points in the eye area from 68 facial landmarks, so from these results, the distance between the upper and lower eyelids can be measured. If the distance (in pixels) is small enough, it can be interpreted as a wink. In addition, it is also limited by the distance of faces that can be detected to blink. In the end, if a recognised face blinks are detected, the time and date will be recorded. It will then open a solenoid lock using serial communication via Arduino Uno to become a security system. From 100 facial photos and 207 blink tests, 89.86% found that the computer could detect a "True Positive" wink. Besides, this facial recognition system's recommended tolerance parameter value is between 0.42 and 0.48.
Akurasi Metode Mesin Pembelajaran dalam Analisis Variabel Penting Faktor Risiko Sindrom Down Palit, Oscar Oleta; Dhenanta, Rafi Prayoga; Susanto, Agnes Indarwati; Syawly, Adzky Matla; Ivansyah, Atthar Luqman; Santika, Aditya Purwa; Arifyanto, Mochamad Ikbal; Muttaqien, Fahdzi
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4354

Abstract

This study aims to identify risk factors for Down syndrome using machine learning methods. Data were obtained from an epidemiological case-control study conducted at Special Needs Schools in the cities and regencies of Tangerang. Methods used include Random Forest, K-Nearest Neighbors, Support Vector Machine (SVM), Naive Bayes, K-Means, Artificial Neural Network (ANN), and Multi-Layer Perceptron (MLP). The results indicate that maternal age, paternal age, and the time interval of parents' work before the child's birth are the most influential factors in the incidence of Down syndrome. The SVM method achieved the highest accuracy of 76% with data categorized into two groups and using important variables. In addition to SVM, Naive Bayes and Random Forest methods also demonstrated good performance for analyzing epidemiological data with case-control types.
Co-Authors Aditya, Irfan Dwi Amelia, Silmi Rahma Arifyanto, Mochamad Ikbal Atthar Luqman Ivansyah Dhenanta, Rafi Prayoga Fitriani, Nita Dwi Palit, Oscar Oleta Rachmat, Muhammad Ervandy Santika, Aditya Purwa Susanto, Agnes Indarwati Syawly, Adzky Matla