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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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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.
Optimization of Random Forest Algorithm Using Random Search for Alzheimer's Disease Detection Wahyudi, Hasyim Sri; Ferian Fauzi Abdulloh
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16554889

Abstract

Alzheimer's disease is a type of neurodegenerative disorder that causes a decline in cognitive function. Early detection is crucial to enable more effective interventions and slow the progression of the disease. However, the diagnosis of Alzheimer's disease often faces challenges, particularly in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the Random Forest algorithm using the Random Search method for detecting Alzheimer's disease. The Random Forest algorithm was applied as the primary model in this research, while hyperparameter optimization was performed using the Random Search method to improve model performance. The results showed that the Random Forest model without optimization achieved an accuracy of 96%. After performing hyperparameter optimization, the model's accuracy increased to 97%. In conclusion, the application of hyperparameter optimization using the Random Search method successfully enhanced the performance of the Random Forest model. The resulting model provides more accurate predictions, making it a reliable tool for the early detection of Alzheimer's disease.
Analisis Algoritma K-Means dalam Pengelompokkan Persebaran Covid-19 di Indonesia Fitriyani, Nurul Khasanah; Abdulloh, Ferian Fauzi
MEANS (Media Informasi Analisa dan Sistem) Volume 6 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (880.061 KB) | DOI: 10.54367/means.v6i2.1372

Abstract

Covid-19 or Coronavirus is a virus that is found in humans and animals. This virus can infect humans to cause various diseases such as flu, to serious diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, the spread of Covid-19 cases continues to increase and is evenly distributed in all provinces in Indonesia because of the fairly rapid spread due to the vast area in Indonesia, making it possible for grouping based on regions in Indonesia to be needed which will result in the center points of the spread of this Covid-19 case. This study aims to group Covid-19 data into a cluster using the K-Means Clustering Data Mining Algorithm. The Covid-19 data used in this study is Covid-19 data on July 6, 2021 which was taken from the official website of Kawal Covid-19 (KawalCovid-19.id). The attributes used are positive cases, recovered, and died. The clusters formed from the results of research using K-Means Clustering are 3 clusters with the first cluster consisting of 2 provinces, the second cluster 3 provinces, and for the third cluster 29 provinces. The cluster with the largest Covid-19 spread rate is cluster one. From this study, the accuracy was 91.176% and evaluated using the Davies-Bouldin Index yielded a fairly good cluster result with a value of 0.493371469.
Comparison Of Efficientnet And Yolov8 Algorithms In Motor Vehicle Classification Ferian Fauzi Abdulloh; Favian Afrheza Fattah; Devi Wulandari; Ali Mustopa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16561038

Abstract

The YOLOv8 accuracy curve highlights clear overfitting. As shown in the graph, the model reaches 100% training accuracy from the first epoch and remains flat, indicating it memorized the training data. However, validation accuracy lags behind, fluctuating between 90% and 92% without significant improvement. This discrepancy between training and validation performance suggests that YOLOv8 struggles to generalize to unseen data. The issue likely stems from its architecture, which is optimized for object detection tasks that prioritize object localization over feature extraction for classification. When repurposed for classification, YOLOv8 may not extract the nuanced visual patterns needed to differentiate similar classes, such as trucks and buses. Consequently, although YOLOv8 performs well on the training set, its classification accuracy in real-world scenarios is limited. Addressing this may require architectural adjustments, stronger regularization, or more diverse training data to enhance the model’s generalization for pure classification tasks.
COMPARISON OF K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE ALGORITHM OPTIMIZATION WITH GRID SEARCH CV ON STROKE PREDICTION Aprilliandhika, Wahyu; Abdulloh, Ferian Fauzi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1951

Abstract

Stroke ranks second as the leading cause of death globally, with disability being the primary accompanying factor. The cause of death in stroke patients is due to the lack of an optimal stroke prediction system; therefore, identifying whether a patient is experiencing a stroke or not becomes the focus of this research. Thus, the objective of this study is to compare the performance of stroke prediction using two classification models, namely K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), with and without using the GridSearchCV optimization technique. In this experiment, the dataset is processed and divided into training and testing data using the SMOTE oversampling technique. Initial testing is conducted without GridSearchCV. The results of the initial testing show that the KNN model performs better than SVM, with accuracies of 91% and 83%, respectively. After optimizing parameters using GridSearchCV, both models experience a significant performance improvement. The KNN model increases accuracy to 95% with precision of 91% and recall of 98%, while the SVM model increases accuracy to 94% with precision of 90% and recall of 99%. These results indicate that using GridSearchCV to optimize parameters of KNN and SVM models can significantly enhance stroke prediction performance. There are differences in precision and recall between KNN and SVM. The KNN model tends to have higher recall, while the SVM model has higher precision, and for accuracy, the KNN algorithm outperforms SVM in stroke prediction.
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