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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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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.
Peningkatan Literasi Digital Untuk Remaja Masjid Yayasan Baitul Mutaqin Margamulya Sholihah, Nafiatun; Abdulloh, Ferian Fauzi; Rahardi, Majid
Abdimas Galuh Vol 6, No 2 (2024): September 2024
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v6i2.14551

Abstract

Remaja masjid di Yayasan Sabilul Mutaqin berusia 12-24 tahun, dimana rentang usia ini merupakan kelompok dengan tingkat penetrasi internet tertinggi. Usia remaja sangat penting sekali untuk belajar literasi digital sebagai bekal menjalani kehidupan sehari-hari agar dapat lebih baik mengelola kehidupan digital mereka, mengurangi risiko online, dan memanfaatkan teknologi dengan cara yang bermanfaat bagi perkembangan pribadi dan sosial mereka. Level literasi digital di Indonesia saat ini sekitar 62%, angka yang lebih rendah jika dibandingkan dengan rata-rata level literasi digital di negara-negara ASEAN lainnya. Sehingga kegiatan pengabdian kemasyarakatan ini sangatlah penting untuk menaikkan level literasi di kalangan remaja terutama pada aspek etika digital dan budaya digital. Hal ini sejalan dengan program Gerakan Nasional Literasi Digital untuk meningkatkan keterampilan digital masyarakat Indonesia yang dilakukan oleh Kementerian Kominfo dengan menyasar salah satu segmennya, yaitu masyarakat umum terutama remaja. Solusi yang dipilih adalah pemberian pembekalan dan pelatihan literasi digital, terutama pada dua aspek utama, yaitu etika digital (digital ethics) dan budaya digital (digital culture) yang islami dalam pelaksanaannya. Hasil kegiatan kami dengan adanya kegiatan ini adalah peserta menyadari pentingnya literasi digital dan dapat menerapkan penggunaan sosial media dengan bijak sesuai etika (dapat membedakan mana konten negatif dan positif), dan budaya islami (mengetahui pentingnya moderasi beragama sebagai warga negara).
Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms Kusnawi, Kusnawi; Ipmawati, Joang; Asadulloh, Bima Pramudya; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Rahardi, Majid
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2453

Abstract

This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.
Rancang Bangun E-Learning Penggolongan Jenis Napza Menggunakan Metode Waterfall Wulandari, Devi; Agun, Pasipikus Yosua; Abdulloh , Ferian Fauzi; Muin, Abdul
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
Sentiment Analysis of Online Vehicle Tax Renewal Application Users Using Support Vector Machine Algorithm Fauzy, Muhamad Ilham; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8654

Abstract

This study examines user sentiment towards online vehicle tax renewal applications by utilizing the Support Vector Machine (SVM) algorithm. The data was collected from user reviews on the Google Play Store for three major applications: New Sakpole, Sapawarga, and Timsalut. The reviews were preprocessed through steps including normalization, case folding, tokenization, and stopword removal. The SVM algorithm was then applied to classify the reviews into positive or negative sentiments. A comparative analysis was performed with K-Nearest Neighbors (KNN) and Naïve Bayes, with SVM demonstrating the best performance, achieving an accuracy of 76.5%. In addition to accuracy, metrics such as precision, recall, and F1-score were also evaluated to provide a more comprehensive assessment of the models. The results indicate that while these applications help facilitate vehicle tax payments, there remains significant user dissatisfaction, particularly related to technical issues and usability concerns. This study offers valuable insights for application developers, highlighting areas for improvement in functionality and user experience to better meet public expectations.
Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price Putra, Dhendy Mardiansyah; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8671

Abstract

This study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and availability of garages. The performance of these algorithms was evaluated using Silhouette Score and Davies-Bouldin Score to determine the quality of cluster separation. The results indicate that K-Means achieved the best performance with the highest Silhouette Score of 0.7702 for two clusters, followed by Fuzzy C-Means, which excelled in handling overlapping clusters. DBSCAN, while effective in detecting outliers, showed suboptimal performance for this housing dataset. These findings suggest that K-Means is the most suitable clustering method for housing data, while Fuzzy C-Means and DBSCAN can serve as alternatives depending on the data characteristics. This research is expected to assist in making the house searching and classification process more efficient and provide additional insights for developers in shaping housing market strategies.
Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth Abdulloh, Ferian Fauzi; Aminuddin, Afrig; Rahardi, Majid; Harianto, Fetrus Jari
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2567

Abstract

The warning of a global recession expected in 2023 is currently the world's concern. Global financial institutions have raised interest rates to lower inflation, which has led to this problem. This study aims to evaluate the effect of interest rates and inflation on Indonesia's economic growth and compare the performance of machine learning models, specifically Random Forest and XGBoost, in analyzing the impact of inflation. A qualitative methodology was used for the literature survey, while the quantitative approach involved the implementation of machine learning algorithms using the Ensemble Method. The results show that Random Forest performs better than XGBoost in predicting the impact of inflation on economic growth, with MSE values of 0.799 and 0.864 and MAE of 0.576 and 0.619, respectively. In addition, the R-squared value of Random Forest 0.908 is also higher than that of XGBoost 0.901, indicating that the model can better explain the variation in the target data. The practical implication of this study is that the Random Forest model can be more effectively used in analyzing the impact of inflation on Indonesia's economic growth. Recommendations for future research include exploring other methods and using more extended time series to deepen the understanding of the relationship between interest rates, inflation, and economic growth.
Performance Analysis of CT-Scan Covid-19 Classification Using VGG16-SVM Buana, Rifqi Genta; Abdulloh, Ferian Fauzi
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): 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.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.
Prediksi Tingkat Angkatan Kerja Terhadap Pengangguran Terbuka Di Semarang Menggunakan Regresi Linier Febrilia Hayyu Pradaningrum, Febrilia; Abdulloh, Ferian Fauzi
The Indonesian Journal of Computer Science Vol. 13 No. 1 (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.v13i1.3525

Abstract

Unemployment is a situation where a person who does not have a job is caused by many factors, not only because they are lazy to look for work but mostly in this region of Indonesia unemployment is caused by limited employment opportunities, a lot of competition in the world of work, the large number of the labor force, lack of experience in the world of work, and also too choosy in working. The unemployment that occurs in Semarang is caused by the high number of labor force that makes the unemployment rate more and more. In this research, the author predicts the level of unemployment in Semarang. This research is a quantitative research whose data is taken from BPS Semarang. In this research, the author uses linear regression algorithm. The algorithm is widely used in cases to predict a problem, this research produces an RMSE (Root Mean Square Error) value of 0.07 with an R Square value of 91%. The results obtained can be used as a reference for the government to see the high unemployment rate in Semarang.
Co-Authors Abdul Muin Afrig Aminuddin Agun, Pasipikus Yosua Ali Mustopa, Ali Anggita, Sharazita Dyah 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 Wahyu Aprilliandhika Wahyudi, Hasyim Sri Yusuf Amri Amrulloh