Article Per Year (5 Year)
p-Index From 2019 - 2024
1.091
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Wildan Budiawan Zulfikar
UIN Sunan Gunung Djati
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Latent semantic analysis and cosine similarity for hadith search engine Wahyudin Darmalaksana; Cepy Slamet; Wildan Budiawan Zulfikar; Imam Fahmi Fadillah; Dian Sa’adillah Maylawati; Hapid Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.14874

Abstract

Search engine technology was used to find information as needed easily, quickly and efficiently, including in searching the information about the hadith which was a second guideline of life for muslim besides the Holy Qur'an. This study was aim to build a specialized search engine to find information about a complete and eleven hadith in Indonesian language. In this research, search engines worked by using latent semantic analysis (LSA) and cosine similarity based on the keywords entered. The LSA and cosine similarity methods were used in forming structured representations of text data as well as calculating the similarity of the keyword text entered with hadith text data, so the hadith information was issued in accordance with what was searched. Based on the results of the test conducted 50 times, it indicated that the LSA and cosine similarity had a success rate in finding high hadith information with an average recall value was 87.83%, although from all information obtained level of precision hadith was found semantically not many, it was indicated by the average precision value was 36.25%.
Marketplace affiliates potential analysis using cosine similarity and vision-based page segmentation Wildan Budiawan Zulfikar; Mohamad Irfan; Muhammad Ghufron; Jumadi Jumadi; Esa Firmansyah
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2018

Abstract

One success factor of an online affiliate is determined by the quality of the content source. Therefore, affiliate marketplaces need to do an objective assessment to retrieve content data that will be used to choose the right product in the appropriate product filter. Usually, the selection is not made using a good and measured system so that the selection of product content is only based on parts that are not in accordance with what is seen or subjective. However, if analyzed using a good and measurable system will produce an objective product content and can have a positive impact on users because the selection is based on factual data. The purpose of this research is to analyze the potential of the affiliate marketplace by combining cosine similarity with vision-based page segmentation. This is a new breakthrough made for optimization to get the best content in accordance with the required criteria. This work will produce a number of product recommendations that are appropriate for publication and then made use of for comparison that matches the required criteria. At the limited evaluation stage, the performance of the proposed model obtained satisfactory results, in which 5 queries tested were all as expected. 
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Yana Aditia Gerhana; Aaz Muhammad Hafidz Azis; Diena Rauda Ramdania; Wildan Budiawan Dzulfikar; Aldy Rialdy Atmadja; Deden Suparman; Ayu Puji Rahayu
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor Mohamad Irfan; Pebri Alkautsar; Aldy Rialdy Atmadja; Wildan Budiawan Zulfikar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4123

Abstract

Asthma disease is a major global health issue that affects at least 300 million people worldwide. Even for clinicians working in emergency rooms, predicting the severity of asthma is difficult. Predicting the intensity of an asthma attack is much more challenging because it is dependent on several factors, including the person's illness's features and severity. Forward Chaining and Certainty Factor algorithms can be implemented to diagnose the degree of asthma control, so the consultation process through the system becomes more detailed. The expert system can be used as an initial reference for the diagnosis process. The forward Chaining algorithm is useful for reasoning, starting from a fact to a solution. On the other hand, the Certainty Factor algorithm is used to provide a level of confidence in the conclusions by generating from the Forward Chaining algorithm. The research implemented several phases as follows analysis, data preparation, modeling, and evaluation. On evaluation, this research conduct three stages and tested using 80 medical record data. The result of the study has produced an expert system and generated an accuracy level of 65%, a precision value of 58.3%, and a recall also produced 57.13%. Therefore, the Chaining and Certainty Factor performs reasonably well in the diagnosis of asthma disease.
Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes Zulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Pratama, Satrya Fajri
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.39952

Abstract

Purpose:  The purpose of this study is to analyze text documents from Twitter about public policies in handling COVID-19 that are currently or have been determined. The text documents are classified into positive and negative sentiments by using Multinomial Naive Bayes.Methods:  In this research, CRISP-DM is used as a method for conducting sentiment analysis, starting from the business understanding process, data understanding, data preparation, modeling, and evaluation. Multinomial Naive Bayes has been applied in building classification based on text documents. The results of this study made a model that can be used in classifying texts with maximum accuracy.Result:  The results of this research are focused on the model or pattern generated by the Multinomial Naive Bayes Algorithm. The classification results of social media users' tweets against the new normal policy obtained good results with an accuracy value of 90.25%. After classifying the tweets of social media users regarding the new normal policy, the results show that more than 70% agreed and supported the new normal policy.Novelty:  This study resulted in how classification can be done with Multinomial Naive Bayes and this algorithm can work well in recognizing text sentiments that generate positive or negative opinions regarding public policies handling COVID-19. So, the research provided conclusions about the views of people around the world on new normal public policy.
Co-Authors Aaz Muhammad Hafidz Azis Aldy Rialdy Atmadja Ayu Puji Rahayu Deden Suparman Dian Sa’adillah Maylawati Diena Rauda Ramdania Esa Firmansyah Hapid Ali Imam Fahmi Fadillah Jumadi Jumadi Mohamad Irfan Muhammad Ghufron Pebri Alkautsar Pratama, Satrya Fajri Slamet, Cepy Wahyudin Darmalaksana Yana Aditia Gerhana