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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Infomedia CSRID (Computer Science Research and Its Development Journal) Journal of Applied Computer Science and Technology (JACOST) SmartComp Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science
Ferian Fauzi Abdulloh
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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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.
Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset Rizki Rahman; Ferian Fauzi Abdulloh
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2023): Article Research Volume 8 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%.
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. 
Optimasi Analisis Sentimen terhadap Kinerja Direktorat Jenderal Pajak Indonesia Melalui Teknik Oversampling dan Seleksi Fitur Particle Swarm Optimization Nafiatun Sholihah; Ferian Fauzi Abdulloh; Majid Rahardi
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 4 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i4.5814

Abstract

Dalam domain kebijakan publik dan tata kelola pemerintahan, isu perpajakan senantiasa menjadi perhatian khusus di kalangan masyarakat. Dengan tujuan mendapatkan pemahaman yang lebih mendalam tentang pandangan publik terhadap performa Direktorat Jenderal Pajak Indonesia, penelitian ini mengadopsi pendekatan analisis sentimen, menggunakan dataset komentar yang terkumpul dari platform media sosial YouTube. Salah satu kendala signifikan yang dihadapi dalam analisis ini adalah ketidakseimbangan data sentimen komentar, dengan dominasi sentimen positif atau negatif. Dengan demikian, kami menerapkan teknik SMOTE oversampling dan Particle Swarm Optimization (PSO) sebagai strategi seleksi fitur, sebagai bagian dari upaya meningkatkan kualitas model analisis sentimen. SMOTE akan membuat data sintetis dari kelas minoritas sehingga data train akan berimbang dan tidak menghasilkan model yang mengandung bias yang disebabkan ketidak seimbangan data. Selanjutnya dilakukan pemilihan fitur yang dianggap memuat informasi penting untuk meningkatkan performa dari suatu model.Metode ini terbukti efektif, khususnya pada skenario dengan pembagian data latih sebanyak 70%. Di sini, nilai recall meningkat dari 0.47 menjadi 0.52, sebuah peningkatan yang signifikan dalam mendeteksi sentimen minoritas yang seringkali terabaikan dalam studi sejenis. Selain itu, teknik seleksi fitur menggunakan PSO, dengan menggunakan nilai F1 sebagai kriteria pbest, menghasilkan peningkatan substansial pada semua metrik evaluasi: akurasi mencapai 0.93, recall 0.63, presisi 0.70, dan F1 score 0.66. Ini menunjukkan keefektifan metode tersebut dalam memodelkan berbagai aspek sentimen terhadap perpajakan di Indonesia.
Rancang Bangun E-Learning Penggolongan Jenis Napza Menggunakan Metode Waterfall Devi Wulandari; Pasipikus Yosua Agun; Ferian Fauzi Abdulloh; Abdul Muin
Intechno Journal (Information Technology Journal) Vol. 6 No. 1 (2024): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i1.1661

Abstract

In the context of Sleman Regency, Indonesia, the rate of drug abuse is increasing due to insufficient dissemination of information about the types of drugs and the negative impacts resulting from drug abuse. One of the contributing factors is the lack of socialization among the community, leading to their limited knowledge about the types of drugs and the dangers of improper use or using medication without a doctor's prescription. The approach applied to combat this problem is a systematic method that involves planning the system, analysis, design, implementation, testing, and maintenance. These steps must be carried out in a sequential manner. The system itself will be developed using programming languages such as PHP, JavaScript, and MySQL server for data processing
Performance Analysis of CT-Scan Covid-19 Classification Using VGG16-SVM Rifqi Genta Buana; Ferian Fauzi Abdulloh
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): Indonesian Journal of Computer Science (IJCS) Volume 12 No. 4 (2023)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3275

Abstract

The world was shaken by the emergence of a deadly virus variant called Severe Acute Respiratory Distress Syndrome CoronaVirus 2 which causes COVID-19 disease. This phenomenon started at the end of 2019 which later became an outbreak that caused a deadly pandemic. A significant number of people lose their lives because of this outbreak. A fast and precise diagnosis is needed so that the patients can be treated immediately. This study is intended to overcome these problems by utilizing machine learning to classify lung CT-Scan images. This study propose to use the Convolutional Neural Network (CNN) based on Visual Geometry Group (VGG) 16 layers architecture and Support Vector Machine (SVM) as its classifier. The classification results of the proposed method achieve 89% and 96% accuracy on the two different datasets. This study results can help overcome problems related to the COVID-19 diagnosis and the lack of resources to classify images. The world was shaken by the emergence of a deadly virus variant called Severe Acute Respiratory Distress Syndrome CoronaVirus 2 which causes COVID-19 disease. This phenomenon started at the end of 2019 which later became an outbreak that caused a deadly pandemic. A significant number of people lose their lives because of this outbreak. A fast and precise diagnosis is needed so that the patients can be treated immediately. This study is intended to overcome these problems by utilizing machine learning to classify lung CT-Scan images. This study propose to use the Convolutional Neural Network (CNN) based on Visual Geometry Group (VGG) 16 layers architecture and Support Vector Machine (SVM) as its classifier. The classification results of the proposed method achieve 89% and 96% accuracy on the two different datasets. This study results can help overcome problems related to the COVID-19 diagnosis and the lack of resources to classify images.
Co-Authors Abdul Muin Anggita, Sharazita Dyah Arfan Yoga Aji Nugraha Devi Wulandari Fetrus Jari Harianto Jeki Kuswanto Majid Rahardi Moch Farid Fauzi Nafiatun Sholihah Nurcholis, Muhammad Tofa Pambudi, Iqbal Rilo Pasipikus Yosua Agun Rifqi Genta Buana Rismawati Nurul Ikhsani Rizki Rahman Teuku Muhammad Dista Yusuf Amri Amrulloh