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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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The Comparative Analysis of K-Nearest Neighbors Algorithm and Random Forest Regressor for House Price Prediction in Bandung City Ananda, Dimas Yudhistira; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.10718

Abstract

The rapid population growth and continuous urban expansion in Bandung have contributed to volatile and escalating housing prices, creating significant challenges for market transparency and affordability. This study aims to develop and evaluate machine-learning models to predict house prices in the Bandung region using a publicly available dataset consisting of 7,609 property records. Following the CRISP-DM methodology, the research includes data exploration, preprocessing (outlier handling using IQR, one-hot encoding, and feature standardization), model training, and performance evaluation. Two regression models K-Nearest Neighbors (KNN) Regressor and Random Forest (RF) Regressor—were compared through systematic hyperparameter tuning using Grid Search and Random Search techniques. The experimental results show that the Random Forest Regressor achieves the best performance with an R² score of 0.7838 and a mean absolute error (MAE) of approximately Rp 399.7 million, outperforming the optimized KNN model. Feature importance analysis also indicates that land area, building area, and location are the most influential predictors of property prices. The findings highlight the effectiveness of ensemble methods in handling complex real-estate data and demonstrate the potential of machine-learning-based predictive tools to support buyers, sellers, and policymakers in making informed and data-driven decisions in the Bandung housing market.
Linear Regression Algorithm Analysis to Predict the Effect of Inflation on the Indonesian Economy.: Analysis of the accuracy level of RMSE using Linear Regression Algorithm to Predict the Effect of Inflation on the Indonesian Economy. Harianto, Fetrus Jari; Abdulloh, Ferian Fauzi
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3224

Abstract

Tujuan penelitian ini adalah untuk melihat perkembangan penelitian mengenai dampak inflasi global terhadap perkembangan perekonomian Indonesia. Penelitian ini melihat hubungan antara pengaruh inflasi dan perkembangan ekonomi di Indonesia. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian kuantitatif yang dimulai dari pengumpulan data, preprocessing, Proses Implementasi Algoritma Regresi Linier, dan pengujian model. Root mean square error (RMSE) adalah model regresi prediktif yang melihat seberapa akurat PDB tahunan berdasarkan tingkat inflasi tahunan. Dalam hal ini, nilai RMSE sekitar 0,60. Artinya, secara rata-rata model peramalan memiliki kesalahan sebesar 0,66 dalam memperkirakan nilai PDB tahunan. Semakin rendah nilai RMSE, semakin baik kinerja model karena menunjukkan kesalahan yang lebih kecil.
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
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
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.
Target-Characteristic-Aware Forecasting of Fuel–Equity Rolling Correlations: Evidence That PCA and Persistence Outperform Hybrid Deep Learning Models Chrisnawan Prastya Atmaja; Ferian Fauzi Abdulloh
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16410

Abstract

Background: Forecasting dynamic fuel–equity market correlations is important for financial forecasting because fuel price movements can affect market risk, investor sentiment, and cross-market stability. However, most previous studies focus on direct price or volatility prediction, while the forecasting of rolling correlations between multiple fuel types and global equity indices remains less explored. Objective: This study analyzes and predicts time-varying correlations between global fuel prices and major stock market indices by comparing baseline, PCA-based, standalone deep learning, and hybrid deep learning models. Methods: The proposed framework applies data preprocessing, return transformation, 60-day rolling correlation construction across three fuel variables (petrol, diesel, LPG) and five stock indices (S&P 500, NASDAQ, FTSE 100, Nikkei 225, IHSG/JKSE), normalization, Principal Component Analysis (PCA), sequence generation, and comparative evaluation of 15 forecasting models. Model performance was measured using RMSE, MAE, R-squared (R²), and Directional Accuracy. Results: The PCA Model achieved the lowest RMSE of 0.016909 and the highest R² of 0.967045, while the Persistence Model produced the lowest MAE of 0.002293 and the highest Directional Accuracy of 97.767280%. Hybrid deep learning models showed higher errors and negative R² values, indicating weaker performance on smooth and persistent rolling correlation targets. Conclusion: The findings show that architectural complexity does not necessarily improve forecasting performance when the target series is smooth and persistent. This study contributes empirical evidence and a replicable comparative framework showing that model selection in financial time-series forecasting should consider target characteristics, particularly smoothness and temporal persistence, rather than relying solely on complex deep learning architectures.
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
Co-Authors Abdul Muin Afrig Aminuddin Ali Mustopa Ananda, Dimas Yudhistira Anggita, Sharazita Dyah Aprilliandhika, Wahyu Arfan Yoga Aji Nugraha Asadulloh, Bima Pramudya Buana, Rifqi Genta Chrisnawan Prastya Atmaja 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 Pasipikus Yosua Agun Putra, Dhendy Mardiansyah Rahman, Rizki Rifqi Genta Buana Rismawati Nurul Ikhsani Teuku Muhammad Dista Wahyudi, Hasyim Sri Yusuf Amri Amrulloh