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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia CSRID (Computer Science Research and Its Development Journal) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika (JUTIF) Journal of Applied Computer Science and Technology (JACOST) SmartComp Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Abdulloh, Ferian Fauzi
Universitas AMIKOM Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Nursing

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Analisis Sentimen Pengguna Youtube Terhadap Program Vaksin Covid-19 Abdulloh, Ferian fauzi; Pambudi, Iqbal Rilo
CSRID (Computer Science Research and Its Development Journal) Vol 13, No 3 (2021): CSRID OKTOBER 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.13.3.2021.141-148

Abstract

Vaksinasi telah mulai dilakukan pemerintah Indonesia per tanggal 13 Januari 2021 menjadi momen yang penting. Vaksinasi menjadi pilihan pemerintah Indonesia untuk menekan grafik penularan virus Covid-19. Indonesia telah menyiapkan dosis vaksin sebesar 371 juta vaksin corona. Namun, isu tentang vaksin juga beredar luas di masyarakat. Rumor tentang vaksin yang belum aman dan kurangnya sosialisasi menimbulkan pro dan kontra terhadap vaksinasi ini. Maka dari itu mencari tahu opini masyarakat tentang vaksinasi ini menjadi opsi untuk menentukan sentimen masyarakat terhadap vaksin.Media sosial menjadi salah satu sumber opini masyarakat dalam menuangkan pendapatnya. Pengolahan opini tersebut dapat menjadi sebuah alternatif untuk menentukan respon publik terhadap suatu peristiwa tertentu. Menurut HootSuit Indonesia tahun 2021 Youtube menjadi social media dengan pengguna terbanyak. Dengan pertimbangan tersebut, Youtube menjadi sebuah pilihan untuk menjadi sumber dataset.Selanjutnya dataset itu diolah dan diproses dengan metode Support Vector Machine data yang diolah tersebut akan dijadikan model klasifikasi untuk melakukan pengklasifikasian teks dari komentar Youtube dengan topik bahasan “vaksin covid-19”. Hasil dari pengklasifikasian tersebut diharapkan dapat menjadi informasi maupun masukan kepada pihak tertentu untuk dijadikan pertimbangan.
Clustering Pengunjung Mall Menggunakan Metode K-Means dan Particle Swarm Optimization Teuku Muhammad Dista; Ferian Fauzi Abdulloh
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4172

Abstract

This research aims to cluster mall visitors. This is motivated by the mall's income which has decreased since the pandemic. Later from these several clusters we can find out the characteristics of the mall's visitors. Those characteristics will be used later to increase the income from the mall. In this research, we use a dataset from Kaggle named Pengunjung_mall in CSV format which will later be processed using Python language on Jupiter Notebooks using the K-Means method. To ensure how accurate the K-Means method is, optimization is carried out using the PSO (Particle Swarm Optimization) method. After performing clustering and optimization using Jupyter Notebook, the results will then be evaluated with DBI (Davies Bouldin Index) in Microsoft Excel to find out how well the Clustering is generated. The Clustering results obtained are used as a reference to determine the characteristics of mall visitors which is one strategy to increase Mall profits. As a result, we have succeeded in dividing mall customers into 5 clusters based on their annual earned income and expense scores. The cluster has been optimized with PSO and has succeeded in increasing the cluster resulting from the K-Means method which is proven by the Davies Bouldin Index method. This research has concluded that customers who have high income levels and have high spending scores are the targets with the highest priority level for malls.
Optimasi Naive Bayes dan Cosine Similarity Menggunakan Particle Swarm Optimization Pada Klasifikasi Hoax Berbahasa Indonesia Arfan Yoga Aji Nugraha; Ferian Fauzi Abdulloh
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4170

Abstract

The widespread circulation of hoax news in the information technology era is increasingly troubling, therefore in this era an algorithm to classify hoax news is necessary, in this study researchers focused on optimizing the accuracy of hoax news classification in text documents. The algorithm that will be used is Naive Bayes and cosine Similarity which previously has been applied with particle swarm optimization algorithm. In this study, it was concluded that after feature selection using PSO in the Naive Bayes algorithm the accuracy obtained increased from 0.91 to 0.93 while in the cosine similarity algorithm the accuracy increased from 0.62 to 0.73 after feature selection using PSO
Inovasi Sistem Pembayaran E-Parkir Cashless Dengan Teknologi Hybrid Payment System Berbasis QRIS Moch Farid Fauzi; Tofa Nurcholis; Jeki Kuswanto; Ferian Fauzi Abdulloh; Yusuf Amri Amrulloh
Jurnal Infomedia:Teknik Informatika, Multimedia & Jaringan Vol 7, No 2 (2022): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v7i2.3392

Abstract

Retribusi parkir adalah salah satu jalur pendapatan daerah atas usaha pemerintah daerah dalam menyediakan fasilitas parkir yang ditujukan untuk memenuhi kepentingan masyarakat. Penarikanya retribusi parkir selama ini berjalan kurang efektif dan rentan terjadi kebocoran, ada sebagian oknum petugas parkir yang tidak menyetorkan uang parkir sesuai dengan kenyataanya, sehingga merugikan potensi pendapatan daerah. Research and Development merupakan metode penelitian yang dipergunakan dalam penelitian ini, sedangkan metode pengembangannya menggunakan prototyping. User Acceptance Test juga dilaksanakan untuk mengukur seberapa tinggi penerimaan terhadap sistem oleh para pengguna aplikasi tersebut. Pengujian dilaksanakan secara komprehensif dan mendapatkan prosentase sebesar 87%, yang menandakan bahwa sistem E-Parking terbukti bisa diterima dan dipergunakan dengan baik oleh pengguna. Dengan begitu, diharapkan sistem e-parking hybrid payment ini mampu meningkatkan pendapatan retribusi daerah
Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen Sharazita Dyah Anggita; Ferian Fauzi Abdulloh
Journal of Applied Computer Science and Technology Vol 4 No 1 (2023): Juni 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i1.524

Abstract

Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis.
Optimasi SVM dan Decision Tree Menggunakan SMOTE Untuk Mengklasifikasi Sentimen Masyarakat Mengenai Pinjaman Online Rismawati Nurul Ikhsani; Ferian Fauzi Abdulloh
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6809

Abstract

With the development of technology, many applications and social media make it easier for users to do various desires, one of which is borrowing money online on the Online Loan Application with easy terms. The convenience provided causes many violations committed by irresponsible people, such as the breach of important information and data of online loan application users. This causes many people to express their comments and opinions on social media, especially on twitter. Sentiment analysis is conducted to see the tendency of public opinion to fall into negative, neutral, or positive sentiment. Furthermore, public opinion will be classified using two algorithms, namely the Support Vector Machine and Decision Tree algorithms. The aim of this research is to compare the performance of SVM and Decision Tree classification algorithms on the tendency of public opinion on twitter regarding online loans. Furthermore, optimization is carried out using SMOTE to optimize the accuracy of the two algorithms. The results obtained neutral sentiment as much as 78.96%, positive sentiment as much as 14.98%, and negative sentiment as much as 6.06%, people are more inclined to neutral sentiment. Then classification using SVM gets an accuracy of 87% and on Decision Tree gets an accuracy of 89%.. Then to optimize the performance results of the two algorithms, optimization using SMOTE is carried out. After SMOTE optimization, the accuracy produced by SVM is 99% and Decision Tree is 97%. Optimization using SMOTE proves that the SVM algorithm is better than Decision Tree. 
Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset Rahman, Rizki; Fauzi Abdulloh, Ferian
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12958

Abstract

The background is the increasing cybersecurity threats in the form of phishing attacks that can be detrimental to individuals and organizations. The purpose of this research is to compare the performance of four Naive Bayes variants in classifying phishing emails with a method that involves a data pre-processing stage, phishing emails are collected, cleaned, and converted into appropriate numerical features. Next, the GridSearch approach was used to find the best parameters. This research objective is to understand how each Naive Bayes variant works on phishing email datasets. This phishing detection task is based on the following performance evaluation criteria such as accuracy, precision, recall, and F1-score. In this study, Bernoulli got the best accuracy of 97.34% but when the results obtained a hyperparameter, the results showed an increase with the most optimal results and the best performance is Bernoulli 97.38%. The research results are to provide an in-depth insight into the effectiveness of each variant of Naive Bayes in dealing with phishing email datasets and researchers in selecting the most suitable Naive Bayes variant for phishing detection tasks. In addition, the applied GridSearch method can guide how to find the best parameters for Naive Bayes models in other contexts. In summary, this study focuses on analyzing the performance of four variants of Naive Bayes Gaussian, Multinomial, Complement, and Bernoulli with the best algorithms Bernoulli 97.38%.
Comparison PSO And IWPSO Performance In Optimizing Decision Tree Algorithm On Heart Disease Dataset Oktaviani, Inggit Dwi; Ferian Fauzi Abdulloh
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13208

Abstract

Heart disease, one of the most common and potentially fatal chronic diseases, has become a major focus in global health efforts. In this study, researchers used the decision tree algorithm on the heart disease dataset with the stages of the decision algorithm including the EDA, Split Data, and Decision tree modeling stages. Furthermore, hyperparameters use PSO and IWPSO to optimize the algorithm. The purpose of this research is to analyze the performance of Particle Swarm Optimization (PSO) and Inertia Weight Particle Swarm Optimization (IWPSO) in heart disease prediction based on relevant datasets. PSO and IWPSO were applied to the heart disease dataset, with the results showing an accuracy rate of 78% for PSO and 84% for IWPSO. These results indicate that IWPSO provides significant performance improvement compared to PSO in the context of heart disease prediction. The implications of these findings can support the development of more efficient prediction systems for early detection of heart disease, making a positive contribution to prevention efforts and further treatment of this critical health condition. In addition, the purpose of this research is to continue research in the form of C4.5 on heart disease with a result of 80.43%. In this study, IWPSO got the best accuracy of 84.23% greater than previous research. The results of this study are to provide insight that PSO and IWPSO hyperparameters can optimize decision trees in handling heart disease datasets and continue research.
Sentiment Analysis on KPU Performance Post-2024 Election via YouTube Comments Using BERT Sholihah, Nafiatun; Abdulloh, Ferian Fauzi; Rahardi, Majid
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14040

Abstract

This research aims to analyze public sentiment regarding the performance of the General Election Commission after the 2024 presidential election using the BERT (Bidirectional Encoder Representations from Transformers) model. Given the General Election Commission's crucial role in maintaining election integrity and the importance of transparency in Indonesian democracy, understanding public opinion through sentiment analysis is essential. Data was collected from YouTube comments, a platform increasingly popular for public expression. The analysis process began with data preprocessing, including case folding, text cleaning, tokenization, and stop word removal. The BERT model was then applied to classify the sentiment of the comments, with the model's performance evaluated using 10-fold cross-validation. The evaluation results showed that the first fold (k=1) achieved the best performance with an accuracy of 96%, precision of 96%, recall of 96%, and an F1-score of 96%, indicating the model's effectiveness in accurately classifying sentiment. In contrast, the ninth fold (k=9) exhibited the lowest accuracy at 86% with other metrics also lower, suggesting performance instability potentially caused by data variability. Accuracy and loss graphs confirmed that the first fold experienced consistent accuracy improvements and significant loss reduction, while the ninth fold showed performance fluctuations. This study provides valuable insights into public sentiment regarding the General Election Commission performance, with BERT demonstrating significant potential for sentiment analysis on social media platforms like YouTube.
COMPARISON OF MACHINE LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS OF DIGITAL IDENTITY APPLICATION USERS Maulana Abrari, Muhammad Naufal; Abdulloh, Ferian Fauzi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5736

Abstract

In the rapidly evolving digital era, the Population Identity Application (IKD) plays a crucial role in streamlining civil administration processes in Indonesia, allowing easier and faster access to population services. This study aims to explore the application of machine learning algorithms in analyzing user responses to the IKD application. Three popular algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes were chosen to classify sentiment from 1301 user reviews on the Google Play Store into positive and negative categories. After performing data preprocessing such as tokenization and stemming, hyperparameter optimization was conducted using GridSearchCV to enhance classification accuracy. The research results indicate that the SVM algorithm, optimized with hyperparameters, including the use of the rbf kernel and a parameter value of C = 1, achieved the highest accuracy of 85.60%, making it the most effective method for sentiment classification of the IKD application. These findings provide valuable insights for the government and developers in refining the features and performance of IKD, contributing to the efficiency and security of digital administration in Indonesia. Furthermore, this study opens opportunities for further development that is more responsive to user needs and expectations in the future.
Co-Authors Abdul Muin Afrig Aminuddin Agun, Pasipikus Yosua Ali Mustopa Ananda, Dimas Yudhistira Anggita, Sharazita Dyah Aprilliandhika, Wahyu Arfan Yoga Aji Nugraha Asadulloh, Bima Pramudya Buana, Rifqi Genta Devi Wulandari Devi Wulandari Fauzy, Muhamad Ilham Favian Afrheza Fattah Febrilia Hayyu Pradaningrum, Febrilia Fetrus Jari Harianto Fitriyani, Nurul Khasanah Harianto, Fetrus Jari Jeki Kuswanto Joang Ipmawati Kusnawi Kusnawi Majid Rahardi Maulana Abrari, Muhammad Naufal Moch Farid Fauzi Nafiatun Sholihah Nurcholis, Muhammad Tofa Oktaviani, Inggit Dwi Pambudi, Iqbal Rilo Putra, Dhendy Mardiansyah Rahman, Rizki Rifqi Genta Buana Rismawati Nurul Ikhsani Teuku Muhammad Dista Wahyudi, Hasyim Sri Yusuf Amri Amrulloh