Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Information System for Educators and Professionals : Journal of Information System Journal Innovations Computer Science Linier: Literatur Informatika dan Komputer
Fathurrohman Fathurrohman
STMIK IKMI CIREBON
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Sentimen Ulasan Pengguna Aplikasi Kredivo Menggunakan Algoritma Support Vector Machine (SVM) dengan Metode TF–IDF Ariska Sari; Bambang Irawan; Ahmad Faqih; Arif Rinaldi Dikananda; Fathurrohman Fathurrohman
LINIER: Literatur Informatika dan Komputer Vol 2, No 4 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v2i4.3344

Abstract

Perkembangan teknologi informasi telah mendorong peningkatan substansial dalam jumlah data teks yang dihasilkan melalui berbagai interaksi pengguna pada platform digital, khususnya di bidang layanan keuangan online. Data ulasan konsumen mengandung informasi berharga terkait tingkat kepuasan dan pandangan pelanggan terhadap suatu produk atau jasa. Kajian ini mengkhususkan diri pada penerapan analisis sentimen terhadap ulasan pengguna aplikasi Kredivo, dengan memanfaatkan algoritma Support Vector Machine (SVM) serta serangkaian langkah pra-pemrosesan teks yang komprehensif. Langkah-langkah tersebut meliputi case folding, pembersihan data, tokenisasi, penghapusan kata-kata berhenti, dan stemming dengan bantuan pustaka Sastrawi yang dirancang untuk Bahasa Indonesia. Fitur teks diekstraksi menggunakan pendekatan Term Frequency–Inverse Document Frequency (TF–IDF), kemudian diklasifikasikan melalui model SVM dengan kernel Radial Basis Function (RBF). Hasil percobaan menunjukkan bahwa model SVM menunjukkan kinerja klasifikasi yang superior, dengan tingkat akurasi yang tinggi dalam membedakan sentimen positif, negatif, dan netral. Temuan ini konsisten dengan studi sebelumnya yang menekankan bahwa penggabungan stemming, penghapusan kata-kata berhenti, dan SVM dapat meningkatkan akurasi analisis sentimen secara bermakna. Secara keseluruhan, penelitian ini memberikan sumbangan bagi pengembangan teknik analisis sentimen dalam Bahasa Indonesia, terutama di sektor teknologi keuangan, dengan membuktikan bahwa integrasi antara SVM dan TF–IDF, yang didukung oleh pra-pemrosesan yang sesuai, mampu menghasilkan model klasifikasi opini pelanggan yang efektif dan mampu menyesuaikan diri dengan nuansa linguistik Bahasa Indonesia
Pengembangan Pembelajaran Interaktif Sejarah Proklamasi Berbasis Game Mobile 2D Menggunakan Metode Game Development Life Cycle Habil Jabbal Firdausyi; Ade Irma Purnamasari; Irfan Ali; Indra Wiguna Marthanu; Fathurrohman Fathurrohman
Journal Innovations Computer Science Vol. 5 No. 1 (2026): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v5i1.413

Abstract

The History subject — particularly the Indonesian Proclamation of Independence — has long been delivered through conventional, one-directional methods that leave students disengaged and, in documented cases, visibly saturated. This study designed and developed an interactive digital learning medium as a direct response to that condition. The medium produced is a narrative-driven 2D Mobile Game, built using the Game Development Life Cycle (GDLC) across four phases: Concept, Pre-Production, Production, and Testing. The resulting prototype centers on a slide-controlling mechanism and a sequentially structured historical narrative, with an integrated quiz system embedded at the end of each story chapter. Validation by a Material Expert and a Media/Technology Expert placed the product in the "Highly Feasible" category, with an overall average score of 89.85% — a result that holds across both content accuracy and technical execution. Usability testing returned a System Usability Scale score of 81.0, rated "Excellent" and "Acceptable." These findings suggest the game is a credible alternative medium for reducing learning saturation and raising student engagement with History material.  
Optimisasi Model Backpropagation untuk Meningkatkan Deteksi Kejang Epilepsi pada Sinyal Electroencephalogram Odi Nurdiawan; Fathurrohman Fathurrohman; Ahmad Faqih
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 9 No 2 (2024): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2024)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v9i2.3187

Abstract

Epilepsy is a chronic neurological disorder characterized by recurrent seizures caused by abnormal electrical activity in the brain. Fast and accurate seizure detection is crucial to support medical intervention and improve patients' quality of life. Currently, Electroencephalogram (EEG) signals are widely used to diagnose epilepsy as they record brain electrical activity in real-time. However, manual analysis of EEG signals requires time and precision, necessitating a more effective automated solution. This study aims to optimize the Backpropagation model for detecting epileptic seizures using EEG data. The research involved collaboration between Telkom University, Sumber Waras Hospital, and the University of Bonn. The EEG data collected was processed through Discrete Cosine Transform (DCT) to extract important features before being used to train the artificial neural network (ANN) model. The model was trained and tested using varying numbers of epochs to measure its accuracy. The results show that the Backpropagation model achieved optimal accuracy of 91.15% at 100 epochs and increased to 93.05% at 200 epochs. Although accuracy improved with more epochs, the longer computational time posed a risk of overfitting. This research demonstrates that the Backpropagation algorithm can be optimized to detect epileptic seizures accurately and efficiently. The implication for Sumber Waras Hospital is that this model can be implemented in EEG monitoring systems to detect seizures in real-time, supporting faster medical intervention and reducing reliance on manual analysis. Thus, this study contributes to providing a more efficient diagnostic solution and enhancing healthcare services for epilepsy patients.
Co-Authors Ade Irma Purnamasari Ahmad Faqih Arif Rinaldi Dikananda Ariska Sari Bambang Irawan Habil Jabbal Firdausyi Indra Wiguna Marthanu Irfan Ali Odi Nurdiawan