Article Per Year (5 Year)
p-Index From 2021 - 2026
4.05
P-Index
This Author published in this journals
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) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) 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 Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Nursing

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Optimasi Analisis Sentimen terhadap Kinerja Direktorat Jenderal Pajak Indonesia Melalui Teknik Oversampling dan Seleksi Fitur Particle Swarm Optimization Sholihah, Nafiatun; Abdulloh, Ferian Fauzi; Rahardi, Majid
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.
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.
Leakage-Aware Benchmarking of Lightweight Models for Robust Handwritten Hanacaraka Character Recognition Abdulloh, Ferian Fauzi; Sharazita Dyah Anggita; Ikmah; Ali Mustopa; Majid Rahardi; Devi Wulandari
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1039

Abstract

Handwritten Hanacaraka character recognition is important for preserving Javanese script, but reported results can be difficult to compare because datasets, preprocessing procedures, and train-test separation protocols vary across studies. This study presents a leakage-aware benchmark of lightweight models on a public handwritten Hanacaraka dataset containing 20 basic character classes. A data audit removed 17 unreadable image files and retained 1,562 valid images. Two experimental settings were evaluated: a perceptual-hash grouped split for leakage-aware testing and a random-stratified split as an optimistic upper-bound scenario. The leakage-aware benchmark compared HOG with SVM, HOG with Random Forest, MobileNetV2 head-only training, fine-tuned MobileNetV2, and a confusion-aware MobileNetV2 variant. Fine-tuned MobileNetV2 achieved the best leakage-aware result with 53.82% accuracy and 49.59% macro-F1, while robustness testing under image distortions produced 47.85% accuracy and 44.53% macro-F1. In the optimistic random-stratified experiment, an ensemble of EfficientNetB0 and MobileNetV2 with test-time augmentation reached 74.11% accuracy and 74.24% macro-F1. The results indicate that stricter evaluation substantially lowers performance and that visually similar classes remain difficult. Therefore, future Hanacaraka recognition work should report leakage control, robustness, and confusion analysis, not only clean-set accuracy.
Co-Authors Abdul Muin Afrig Aminuddin Agun, Pasipikus Yosua Ali Mustopa Ali Mustopa, Ali 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 Ikmah 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