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Journal : JIKA (Jurnal Informatika)

OPTIMASI PREDIKSI RISIKO KREDIT DENGAN PREPROCESSING DAN HYPERPARAMETER TUNING Rais, Amin Nur; Warjiyono, Warjiyono; Putra, Jordy Lasmana
Jurnal Informatika Vol 9, No 1 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i1.12782

Abstract

Risiko kredit menjadi tantangan dalam industri keuangan, yang dapat berdampak pada stabilitas lembaga keuangan. Penelitian ini mengevaluasi kinerja model machine learning dalam memprediksi risiko kredit menggunakan dataset dari Kaggle. Empat model yang diuji adalah Logistic Regression, Random Forest, Gradient Boosting, dan K-Nearest Neighbors (KNN), yang masing-masing diuji dalam tiga versi: baseline, preprocessing, dan tuned. Proses preprocessing mencakup penanganan nilai hilang, encoding fitur kategori, dan standarisasi fitur numerik. Model dievaluasi berdasarkan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model Gradient Boosting (Tuned) memberikan performa terbaik dengan akurasi 93.79%, presisi 94.91%, recall 76.05%, dan F1-score 84.44%. Penelitian ini memberikan manfaat bagi lembaga keuangan dalam memilih model yang optimal untuk memprediksi risiko kredit dan mendukung pengambilan keputusan berbasis data.
SENTIMENT ANALYSIS OF VIDEO EDITING APPLICATIONS USING SUPPORT VECTOR MACHINE ON GOOGLE COLAB Raharjo, Mugi; Putra, Jordy Lasmana; Heristian, Sujiliani; Napiah, Musriatun
Jurnal Informatika Vol 9, No 2 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i2.13699

Abstract

Sentiment analysis is an important approach in understanding user opinions about an application. This study aims to analyze user reviews of the CapCut application using the Support Vector Machine (SVM) algorithm on the Google Colab platform. The preprocessing stages include data cleaning, word normalization using a dictionary from Kaggle, case folding, tokenization, stopword removal, and stemming. Furthermore, the data is converted into a numerical representation using the TF-IDF vectorization method. The labeling process is carried out using a sentiment lexicon obtained from GitHub. After performing data splitting, the SVM model is applied to classify sentiment into three categories: positive, negative, and neutral. The evaluation results show that the SVM model achieves the best accuracy of 90.12%. Based on the classification report, the model has high precision of 0.94 for positive and negative classes and 0.83 for the neutral class. Additionally, the confusion matrix indicates that the model can classify sentiment quite well, although there are still minor errors in predicting neutral sentiment. The findings of this study demonstrate that the SVM method can be effectively used to analyze user sentiment toward the CapCut application, providing valuable insights for improving user experience in the future.
Co-Authors Agus Subekti Alda Zevana Putri Widodo Alengka, Son Gohan Alfian Armawan Sandi, Tommi Amin Nur Rais Atiyah Aldawiyah Bayhaqy, Achmad Chandra Kesuma Denis Chandra Prabowo Destriana, Rachmat Dirgahayu Erri Dwi Krisnandi Dwiza Riana Eni Heni Hermaliani Eni Heni Hermaliani, Eni Heni Esron Rikardo Nainggolan Esron Rikardo Nainggolan Evita Fitri Fachrurozi, Ahmad Fajar Shidiq Fariq Mulia, Akbar Fatiha, Zulfati Dinul Febri Ainun Jariyah Fikri Ismaya Findi Ayu Sariasih Firmansyah Firmansyah Fitra Septia Nugraha Fitri, Evita Frieyadie Gata, Windu Hadianto, Nur Hafifah Bella Novitasari Hafifah Bella Novitasari Hafifah Bella Novitasari Hani Harafani Hengki Rusdianto I Gusti Bagus Arya Pradnja Paramitha Inggit Dessy Susanti Khamdun Khamdun Khuluq, Anjahul Krisnandi, Dwi Laela Kurniawati lazuardi, sandy ibrahim Leksono, Ilham Nur Luthfi Indriyani M. Iqbal Alifudin Mahdi Abdullah Mahmud Mahmud Marwan Marwan Miharja, Jaja Mufid Junaedi Mugi Raharjo Mugi Raharjo Mugi Raharjo Muhamad Hasan Musriatun Napiah Mustofa Mustofa Mustofa Mustofa Negoro, Alexander Rio Adi Nila Hardi Novitasari, Hafifah Bella Nurul Arifin Oky Kurniawan Prasetyo, Rizal Rachmawati Darma Astuti Rachmawati Darma Astuti Raharjo, Mugi Ridwan ridwan Ridwan, Ridwan Rino Indra Muhammad Rino Indra Muhammad1 Sidik Sidik Sidik Sri Rahayu Sujiliani Heristian Susafa'ati Susafa'ati Susafa’ati, Susafa’ati Suwanda Aditya Saputra Syarah Seimahuira Tika Adilah M Tommi Alfian Armawan Sandi Tyas Setiyorini Tyas Setyorini Ummu Radhiyah Ummu Radiyah Virda Mega Ayu Waeisul Bismi Wahyutama Fitri Hidayat Warjiyono Warjiyono, Warjiyono Watmah, Sri Windu Gata Windu Gata Windu Gata Windu Gata WITRIANA ENDAH PANGESTI Yeni Angraini Zulfati Dinul Fatiha