Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Coding: Jurnal Komputer dan Aplikasi Infotek : Jurnal Informatika dan Teknologi
Arum Sari, Aprilisa
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Sistem Rekomendasi Pemilihan Laptop Menggunakan Metode Content-Based Filtering Berbasis Spesifikasi Produk Pratiwi, Niken; Hartanti, Dwi; Arum Sari, Aprilisa
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30618

Abstract

The advancement of information technology has led to an increasing demand for computing devices, particularly laptops. The wide variety of options based on specifications, brands, and prices often makes it difficult for consumers to choose the device that best suits their needs. Therefore, this study aims to design a laptop recommendation system using the Content-Based Filtering (CBF) approach to provide relevant suggestions based on user preferences. The developed system applies Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity methods to measure the similarity between key laptop features such as processor type, RAM capacity, storage, and graphics card. Laptop data was obtained through a web scraping process from trusted e-commerce websites and integrated into a web-based platform. Testing results show that the system can automatically generate personalized and highly relevant recommendations. The main contribution of this study is the development of an efficient content-based laptop recommendation system utilizing a combination of TF-IDF and Cosine Similarity techniques. In addition to facilitating faster decision-making in selecting a laptop, the system also demonstrates the practical application of machine learning-based recommendation technologies in the e-commerce sector. This research further provides a foundation for developing similar recommendation systems in other contexts that require content-based personalization approaches.
Optimasi Decision Tree dengan Gradient Boosted Trees untuk Klasifikasi Kelompok Olahraga Berdasarkan Fisik Atlet Bayu Yudho Prakoso, Erlangga; Bondan Prihananto, Fauzi; Arum Sari, Aprilisa; Anugrah Islami, Tomy
Coding: Jurnal Komputer dan Aplikasi Vol. 14 No. 2 (2026): Edisi September 2026
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/coding.v14i2.111455

Abstract

Ketidakseimbangan kelas dapat membuat akurasi klasifikasi tampak memadai meskipun kelas minoritas gagal dikenali. Penelitian ini mengevaluasi Decision Tree dan Gradient Boosted Trees untuk mengklasifikasikan 1.271 atlet POPDA Kota Surakarta 2022 ke tujuh kelompok olahraga menggunakan usia, jenis kelamin, tinggi badan, berat badan, dan Indeks Massa Tubuh. Setelah audit data dan koreksi satu label yang tidak konsisten, 1.017 rekaman digunakan sebagai data latih dan 254 sebagai data uji terstratifikasi; data latih dievaluasi dengan repeated stratified 5-fold cross-validation sebanyak 10 pengulangan. Pada validasi berulang, Decision Tree memperoleh akurasi 48,52±0,82% dan macro F1 25,29±1,26%, sedangkan Gradient Boosted Trees mencapai akurasi 56,96±0,72% atau naik 8,45 poin persentase, tetapi macro F1 hanya 24,56±1,27% dan balanced accuracy 23,39±0,92%; peningkatan akurasi signifikan pada skor berpasangan (p=0,00195). Pada data uji terpisah, akurasi Gradient Boosted Trees 50,39% (95% CI 44,29–56,49%) dan perbedaannya terhadap Decision Tree tidak signifikan menurut McNemar (p=0,243), sedangkan class weighting tidak memperbaiki performa kelas minoritas. Hasil menunjukkan bahwa boosting meningkatkan akurasi agregat secara stabil, tetapi belum menyelesaikan bias kelas, sehingga model hanya layak sebagai alat penyaringan awal dengan pengawasan ahli.   Kata kunci— klasifikasi atlet, ketidakseimbangan kelas, CRISP-DM, Decision Tree, Gradient Boosted Trees
Co-Authors Anugrah Islami, Tomy Bayu Yudho Prakoso, Erlangga Bondan Prihananto, Fauzi Dwi Hartanti Niken Pratiwi, Niken