Article Per Year (5 Year)
p-Index From 2021 - 2026
2.989
P-Index
This Author published in this journals
All Journal Jurnal Transformatika JOIN (Jurnal Online Informatika) JTERA (Jurnal Teknologi Rekayasa) Jurnal Sistem Cerdas Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Harani, Nisa Hanum
Unknown Affiliation
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Model Prediksi Risiko Kanker Serviks dengan Pendekatan Support Vector Machine Hutapea, Juwita Stefany; Harani, Nisa Hanum; Prianto, Cahyo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5445

Abstract

Cervical cancer is one of the leading causes of death in women, especially in developing countries due to delays in early diagnosis. Developing a risk prediction model based on the Support Vector Machine (SVM) algorithm is one way to support a more accurate and efficient early detection process. The research object is medical records of female patients obtained from hospitals in Medan City, with a total of 164 patient data. The development process was carried out through the CRISP-DM stages, which include data cleaning, feature transformation, class balancing with SMOTE, and dimensionality reduction using PCA. The evaluation results showed that the best model was obtained with a PCA configuration with 9 principal components (90% variance) and a test size of 80:20, resulting in an accuracy of 88%, a precision of 88%, a recall of 84%, and an F1-score of 86%. Cross-validation evaluation with 5 folds provided the best average performance and the smallest standard deviation, indicating model stability. The final model was implemented in a web-based system to facilitate digital early detection. This study shows that SVM with the SMOTE and PCA approaches is effective in predicting cervical cancer risk accurately and efficiently.
Model Prediktif Indeks Kebahagiaan Berbasis Gradient Boosting Regressor dengan Optimalisasi Seleksi Fitur dan Implementasi Web Ferdinan, Dani; Harani, Nisa Hanum; Pane, Syafrial Fachri
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.59-68

Abstract

Penelitian ini menghadapi tantangan dalam memodelkan Indeks Kebahagiaan 2021 dari Badan Pusat Statistik (BPS) yang memiliki dimensi fitur sangat tinggi dan potensi redundansi, yang dapat menurunkan akurasi dan interpretabilitas model. Tujuan utama penelitian ini adalah untuk mengidentifikasi fitur-fitur paling berpengaruh dalam data tersebut untuk meningkatkan akurasi, efisiensi komputasi, dan transparansi model prediksi berbasis pohon keputusan. Metodologi mencakup pra-pemrosesan data dengan imputasi modus, transformasi Yeo-Johnson, dan Robust Scaler. Tiga algoritma regresi diuji: Decision Tree, Random Forest, dan Gradient Boosting Regressor, yang dioptimalkan menggunakan Particle Swarm Optimization (PSO). Model terbaik dievaluasi menggunakan metrik R², MSE, RMSE, dan MAE serta dianalisis lebih lanjut menggunakan SHAP untuk interpretasi. Hasil menunjukkan bahwa Gradient Boosting Regressor adalah model paling unggul dengan nilai R² sebesar 0,696 saat menggunakan 20 fitur terseleksi. Selain itu, sebagai bentuk implementasi praktis, model diimplementasikan ke dalam sebuah aplikasi web interaktif berbasis Flask yang memungkinkan pengguna memasukkan data melalui antarmuka kuisioner dan menerima prediksi indeks kebahagiaan secara real-time. Integrasi ini menjembatani hasil riset dengan pemanfaatan nyata oleh pengguna akhir.
Co-Authors Adiningrum, Nur Tri Ramadhanti Andarsyah, Roni Andriyanto, Achmad Annisah, Wulan Nur Damayanti, Fanny Shafira Fatonah, Rd. Nuraini Siti Ferdinan, Dani Habibi, Roni Hutapea, Juwita Stefany Ilyas Tri Khaqiqi, M Latifah, Isnaenti Nur Marismati, Marismati Nugraha, Fikri Aldi Pertiwi, Aryka Anisa Prianto, Cahyo Rahayu, Woro Isti Salsabila, Helmi Setyawan, Muhammad Yusril Helmi Syafrial Fachri Pane, Syafrial Fachri Tri Khaqiqi, M. Ilyas Trigreisian, Alwizain Almas Zain, Rofi Nafiis Zuhri, Burhanudin