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All Journal Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Scientific Journal of Informatics
M. Faris Al Hakim
Department of Computer Science, Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Prediction of COVID-19 Using Recurrent Neural Network Model Alamsyah, Alamsyah; Prasetiyo, Budi; Hakim, M. Faris Al; Pradana, Fadli Dony
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.30070

Abstract

The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN). In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. The research results show the percentage of accuracy is 88.
Halal Food Restaurant Classification Based on Restaurant Review in Indonesian Language Using Machine Learning Hidayat, Nurul; Hakim, M. Faris Al; Jumanto, Jumanto
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.33395

Abstract

Purpose: Halal tourism or muslim friendly tourism has big potential for the tourism industry in Indonesia. According to Cresent Rating, the world’s leading authority on halal-friendly travel, one of the indicators for halal tourism is the availability of choices for halal foods. To support halal tourism, unfortunately, not all restaurants around the tourism object or in the city where the tourism object is located have labels or information that makes people know about halal food in the restaurant easily.Methods/Study design/approach: The data in this research was obtained from online media such as Google Maps, TripAdvisor, and Zoomato. The data consists of 870 data with the classification of halal food restaurants and 590 data with the reverse classification. Machine learning methods were chosen as classifiers. Some of them were Naive Bayes, Support Vector Machine, and K-Nearest Neighbor. Result/Findings: The result from this research shows that the proposed method achieved an accuracy of 95,9% for Support Vector Machine, 93,8% for Multinomial Naive Bayes, and 91% for K-Nearest Neighbor. In the future, our result will be to support the halal tourism environment in terms of technology. Novelty/Originality/Value: In this study, we utilize restaurant reviews done by visitors to get information about the classification of halal food restaurants.
Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization Yosza Dasril; Much Aziz Muslim; M. Faris Al Hakim; Jumanto Jumanto; Budi Prasetiyo
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.3060

Abstract

The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.
Sign Language Detection System Using YOLOv5 Algorithm to Promote Communication Equality People with Disabilities Maylinna Rahayu Ningsih; Yopi Julia Nurriski; Fathimah Az Zahra Sanjani; M. Faris Al Hakim; Jumanto Unjung; Much Aziz Muslim
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.6007

Abstract

Purpose: Communication is an important asset in human interaction, but not everyone has equal access to this key asset. Some of us have limitations such as hearing or speech impairments, which require a different communicative approach, namely sign language. These limitations often present accessibility gaps in various sectors, including education and employment, in line with Sustainable Development Goals (SDGs) numbers 4, 8, and 10. This research responds to these challenges by proposing a BISINDO sign language detection system using YOLOv5-NAS-S. The research aims to develop a sign language detection model that is accurate and fast, meets the communicative needs of people with disabilities, and supports the SDGs in reducing the accessibility gap. Methods: The research adopted a transfer learning approach with YOLOv5-NAS-S using BISINDO sign language data against a background of data diversity. Data pre-processing involved Super-Gradients and Roboflow augmentation, while model training was conducted with the Trainer of SuperGradients. Result: The results show that the model achieves a mAP of 97,2% and Recall of 99.6% which indicates a solid ability in separating sign language image classes. This model not only identifies sign language classes but can also predict complex conditions consistently. Novelty: The YOLOv5-NAS-S algorithm shows significant advantages compared to previous studies. The success of this performance is expected to make a positive contribution to efforts to create a more inclusive society, in accordance with the Sustainable Development Goals (SDGs). Further development related to predictive and real-time integration, as well as investigation of possible practical applications in various industries, are some suggestions for further research.
Co-Authors Budi Prasetiyo Budi Prasetiyo, Budi Fathimah Az Zahra Sanjani Jumanto Unjung Maylinna Rahayu Ningsih Much Aziz Muslim Much Aziz Muslim Nurul Hidayat Pradana, Fadli Dony S.Pd. M Kes I Ketut Sudiana . Yopi Julia Nurriski Yosza Dasril