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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Techno.Com: Jurnal Teknologi Informasi Jurnal Penelitian Pendidikan Jurnal Sistem dan Informatika Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah Data Science: Journal of Computing and Applied Informatics Jurnal Informatika Global Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika JTIK (Jurnal Teknik Informatika Kaputama) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Jurnal Pengabdian Kepada Masyarakat Simpatik: Jurnal sistem Informasi dan Informatika JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Unnes Journal of Mathematics Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal ilmiah teknologi informasi Asia Indonesian Journal of Mathematics and Natural Sciences
Florentina Yuni Arini
Universitas Negeri Semarang
Arts Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Veterinary Other

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Performance Evaluation of Gradient Boosting Techniques for Predicting Customer Purchase Decisions Arini, Florentina Yuni; Djuanda, Lyon Ambrosio; Kristianto, Ananda Hisma Putra; Tiadah, Muthia Nis; Wicaksono, Aufa Putra; Putra, Fatih Akbar Alim
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5461

Abstract

Customer purchase prediction remains a critical challenge in e-commerce and retail analytics, with significant implications for marketing strategies and business revenue. This research provides a detailed comparative evaluation of advanced gradient boosting techniques XGBoost, LightGBM, and CatBoost to predict customer purchasing behavior using review trends and demographic factors. The study employed a dataset of 100 customer records with attributes such as age, gender, review quality, and education level. Through systematic feature engineering, including age group categorization and categorical feature combinations, as well as addressing class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), all three models were trained and evaluated using default hyperparameters with optimal settings. The experimental results show that CatBoost achieved the best performance, with 78.26% accuracy, 0.8011 precision, 0.7826 recall, and a 0.7775 F1-score, outperforming LightGBM (73.91% accuracy) and XGBoost (60.87% accuracy). The evaluation includes confusion matrix analysis, precision–recall metrics, and visual comparisons across all performance dimensions. These findings provide valuable insights for practitioners selecting appropriate machine learning algorithms for customer purchase prediction tasks, particularly in scenarios involving limited datasets and categorical features. This research contributes to the growing body of literature on the use of gradient boosting techniques for predicting consumer behavior and offers important practical implications for e-commerce applications. These findings offer important contributions to machine learning applications in customer behavior prediction.
Pengembangan Aplikasi Cerita Rakyat Daerah Berbasis Mobile TriPanca Ahmad Mustofa Hadi Romadhoni; I Gede Ardhy Niratha; Muhammad Abdurrafi; Taufiqur Ramadhan; Danish Adli El Said; Florentina Yuni Arini
Simpatik: Jurnal Sistem Informasi dan Informatika Vol. 5 No. 2 (2025): Desember 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/simpatik.v5i2.11227

Abstract

Preserving local culture faces challenges in the era of globalization and digitalization, especially for the younger generation who are more familiar with foreign popular culture. Folklore, as an intangible cultural heritage rich in moral values ​​and cultural identity, is increasingly marginalized due to the lack of attractive media. This study develops TriPanca an interactive mobile application based on folklore with a User-Centered Design (UCD) approach to meet the needs of the younger generation. The development process includes user needs analysis, prototype design, and iterative evaluation. This application is equipped with story list, search, favorites, and user profile features. Testing shows that UCD is effective in increasing user interest, making this application an interactive educational tool as well as a medium for preserving local culture in the digital era.
Aspek Ergonomi Desain Antarmuka Aplikasi Signal Florentina Yuni Arini; Ariel James Maloringan; Munajid Toharo; Nico Anselmus Sihombing; Dhifansa Pradibtya Rafi; Mutia Zahra Fatiha Misbah; Asteen Retno Mukti
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 7, No 2 (2026): Jurnal SENOPATI Vol 7, No 2
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2026.v7i2.7842

Abstract

Aplikasi Signal merupakan platform komunikasi yang dikenal karena fitur keamanannya, namun aspek ergonomi antarmukanya belum banyak dievaluasi. Penelitian ini bertujuan untuk menganalisis tingkat kegunaan (usability) antarmuka aplikasi Signal berdasarkan persepsi pengguna menggunakan instrumen System Usability Scale (SUS). Penilaian difokuskan pada empat dimensi: efisiensi penggunaan, kenyamanan visual, kemudahan navigasi, dan kecepatan belajar. Penelitian ini menggunakan pendekatan kuantitatif deskriptif dengan responden sebanyak 60 mahasiswa pengguna Signal. Data diperoleh melalui kuesioner SUS dan dianalisis menggunakan SPSS. Hasil menunjukkan skor rata-rata SUS sebesar 55,92 yang dikategorikan dalam tingkat usability sedang. Temuan ini mengindikasikan bahwa antarmuka Signal masih memiliki kelemahan, terutama dalam aspek fleksibilitas dan dukungan penggunaan. Studi ini merekomendasikan penyempurnaan desain antarmuka melalui peningkatan keterbacaan visual, navigasi yang lebih intuitif, serta penyediaan bantuan dalam aplikasi. Peningkatan ini diharapkan dapat meningkatkan kenyamanan dan efisiensi penggunaan aplikasi bagi berbagai kalangan penggunaKata kunci: antarmuka pengguna, ergonomi, aplikasi Signal, System Usability Scale
ANT NESTING OPTIMIZATION UNTUK PENINGKATAN AKURASI CNN DALAM DIAGNOSTIK BRAIN TUMOR Florentina Yuni Arini; Aloysius Oktavian; Nia Nur Hidayaturrohmah; Daffa Pramata Aryaputra; Alul Hidja Syanjalih; Mohammad Farrel Aldevis; Muhammad Zidan Aisar
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3669

Abstract

This study discusses the application of a new optimization algorithm, namely Ant Nesting Optimization (ANO), to improve the performance of Convolutional Neural Networks (CNN) in brain tumor classification based on MRI images. ANO is inspired by the behavior of Leptothorax ants in selecting optimal nest locations, which is applied in the model's exploration and exploitation processes. The optimized CNN model shows an increase in classification accuracy of up to 97%, with superior performance in detecting various types of brain tumors. The evaluation results show that the proposed model has faster and more stable loss convergence compared to the standard model. This optimization method not only improves classification precision but also accelerates model stabilization during the training process. With these results, the research proves the effectiveness of ANO as an optimization method in deep learning networks and opens up wider application opportunities in the field of artificial intelligence-based diagnostics.
Performance Evaluation of TabPFN for Student Depression Prediction Across Varying Sample Sizes Florentina Yuni Arini; Muhammad Kahvi Khakam Syah; Fernando Dinar Setiawan; Rafif Musyaffa Indarto; Muhammad Danil Aminuddin; Fairuz Trideas Hilmy; Ahmad Imam Mutaqin
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i2.1716

Abstract

Early prediction of depression in students is a critical challenge, often hindered by the scarcity of large, labelled datasets. While supervised tabular classifiers such as Random Forest, XGBoost, and CatBoost are powerful, they typically require sufficient data and careful hyperparameter optimisation (HPO) to generalise effectively. This paper evaluates the Tabular Prior-data Fitted Network (TabPFN), a foundation model for supervised tabular learning, as a zero-shot classifier for student depression prediction. We conduct a comparative study against five robustly configured baseline classifiers (Random Forest, XGBoost, CatBoost, SVM, and Naive Bayes) across three publicly available student mental health datasets of varying sizes sourced from Kaggle: a micro-sample dataset with 101 instances, a small-sample dataset with 7,022 instances, and a moderate-sample dataset with 27,901 instances. Dataset categorization by size is defined relative to TabPFN v2.5's operational capacity of 50,000 samples rather than general machine learning conventions. Using F1-Score as the primary evaluation metric, our empirical results demonstrate a performance crossover linked to data size. On the microsample, imbalanced dataset, TabPFN achieved the highest F1-Score of 0.727, outperforming the best baseline (CatBoost and Random Forest, F1 = 0.667). In the ablation study, both raw and preprocessed inputs yielded identical results for TabPFN on this dataset, highlighting its capacity to handle unprocessed data without performance loss. On the small and moderate datasets, the tuned baselines were competitive or superior, with CatBoost leading on the moderate-sample dataset (F1 = 0.869). We conclude that TabPFN is an effective and efficient baseline for depression prediction tasks in datascarce environments, providing competitive results without HPO, while traditional ensembles remain preferred for larger datasets.
Peningkatan Prediksi Kelainan Tekanan Darah dengan Logistic Regression dan Random Forest: Pendekatan Sequence Machine Learning Florentina Yuni Arini; Rahmat Hidayat; Arzaki Zunior Putra; Muhammad Nur Furqon; Muhammad Zuniar Hilmi
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 6 No 1 (2026): Mei 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v6i1.4497

Abstract

Early detection of blood pressure abnormalities plays a critical role in preventing and managing cardiovascular diseases, which remain the leading cause of death globally. This study proposes a sequence machine learning approach that combines Random Forest (RF) and Logistic Regression (LR) to enhance the accuracy of abnormal blood pressure prediction. The dataset, obtained from Kaggle, includes various clinical and lifestyle-related features. Data preprocessing involved handling missing values, label encoding, and normalization of numerical features. Evaluation of individual models showed that Random Forest achieved an accuracy of 0.83, while Logistic Regression reached 0.75. The sequence model, which incorporates Random Forest-generated prediction probabilities as an additional feature in Logistic Regression, improved the prediction performance with an accuracy of 0.84. Feature importance analysis identified hemoglobin level, chronic kidney disease, and genetic pedigree coefficient as the most influential predictors in classifying abnormal blood pressure. These findings highlight the effectiveness of the sequence approach in addressing the complexity of medical data and improving the precision of clinical decision support systems for hypertension diagnosis and management. Recommendations include developing advanced ensemble models, collecting longitudinal data, and conducting external validation to enhance model generalizability across diverse clinical populations.
Co-Authors Abas Setiawan Agus Setyawan Ahmad Imam Mutaqin Ahmad Mustofa Hadi Romadhoni Ahmad Rozaq Heryansyah Ahmad Zidhan Ilmana Aisyah Nathania Araminta Aji, Yusuf Pandu Satrio Alaida, Salma Keysha Alamsyah - Aldevis, Mohammad Farrel Aloysius Oktavian Alul Hidja Syanjalih Amin Suyitno Ananda Hisma Putra Kristianto Anggraeni, Dinda Ayu Anwar, Alfani Salsabilla Ardiansyah, Ikhsan Ardin Winata Ariel James Maloringan Aryaputra, Daffa Pramata Arzaki Zunior Putra Astagina, Paramesti Asteen Retno Mukti Aufa Putra Wicaksono Awan Saputra Romadhoni Bagaskara, Josephin Nova Brata, Prayoga Adi Brigita Winona Elvaretta Radhiti Daffa Pramata Aryaputra Danish Adli El Said Dewanti, Rahima Ratna Dhifansa Pradibtya Rafi Djuanda, Lyon Ambrosio Duankhan, Poomin Endang Sugiharti, Endang Fadhlullah, Muhammad Azzam Fairuz Trideas Hilmy Fajariansyah, Ridwan Faqih, Muhammad Najmuddin Farrel Athaillah Putra Farrel Fatih Bhimawan Fatih Akbar Alim Putra Fernando Dinar Setiawan Firdaus Zahid, Ahmad Galvin Fittra Marga Ardana Gerard Sean Dwayne Haryolukito Pambudi, Fawwaz Hernawan, Yoga Heryansyah, Ahmad Rozaq Hexa Sakti Tunjung Hidayat I Gede Ardhy Niratha Ikhsan Rakha Athaya Inoru Nian Alfita Intan Permata Sari Fauziah Irfan, Mohammad Syarif Isa Akhlis Isnaeni, Siti Itsna Sabila Hidayati Januar Pancaran Nur Fajri Julianto, Richy khairunnisa, Nadhia Adzqiya Kristianto, Ananda Hisma Putra Lyon Ambrosio Djuanda Mahdi Habibi Mardlootillah, Hanif Ilmi Mohammad Farrel Aldevis Much Aziz Muslim Muhammad Abdurrafi Muhammad Alvin Adinata Muhammad Danil Aminuddin Muhammad Kahvi Khakam Syah Muhammad Lutfi Wibowo Muhammad Nur Furqon Muhammad Rifqi Rahman Muhammad Sulthonul Izza Muhammad Zidan Aisar Muhammad Zuniar Hilmi Munajid Toharo Muthia Nis Tiadah Mutia Zahra Fatiha Misbah Muzakki, Naufal Habib Nafi', Raihan Muhammad Naryapramono, Afrilza Daffa Nathania Adristina Nia Nur Hidayaturrohmah Nico Anselmus Sihombing Oktavian, Aloysius Pambudi, Fawwaz Haryolukito Pastika, Puan Bening Pongthanoo, Patcharanikarn Prameswari, Della Egyta Pramudya Kirana Mandala Putra Putra, Fatih Akbar Alim Putriaji Hendikawati Rafif Musyaffa Indarto Raharjo, Bagus Purbo Rahima Ratna Dewanti Rahmat Hidayat Raihan, Muhammad Ramadhan, Farhan Husyen Ramdhani, Khusnun Najwa Reza Zaidan Amrullah Rifan, Slamet Rinandi, Tyto Riza Arifudin Rizky Aulia Adi Saputro Romadhoni, Awan Saputra Ryo Pambudi Salsabila, Kansa Maulina Samudra Azriel Pradana Santoso, Tony Budi Saputro, Rizky Aulia Adi Sari, Yuliana Mustika Satria, Diva Sekar Tri Handayani Septiana, Dina Wachidah Supriyono Supriyono Syanjalih, Alul Hidja Taufiqur Ramadhan Tiadah, Muthia Nis Varindya Ditta Iswari Wahyudiantoro, Rizky Tri Warianta, Dwi Tatang Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Wibowo, Muhammad Lutfi Wicaksana, Rangga Wicaksono, Aufa Putra Zaenal Abidin Zahra Zakiyah Kaltsum