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All Journal 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 Simpatik: Jurnal sistem Informasi dan Informatika JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Unnes Journal of Mathematics Konstelasi: Konvergensi Teknologi dan Sistem Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal ilmiah teknologi informasi Asia Indonesian Journal of Mathematics and Natural Sciences
Florentina Yuni Arini
Universitas Negeri Semarang
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Public Health Other

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ANT NESTING OPTIMIZATION UNTUK PENINGKATAN AKURASI CNN DALAM DIAGNOSTIK BRAIN TUMOR Arini, Florentina Yuni; Oktavian, Aloysius; Hidayaturrohmah, Nia Nur; Aryaputra, Daffa Pramata; Syanjalih, Alul Hidja; Aldevis, Mohammad Farrel; Aisar, Muhammad Zidan
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.
Optimizing K-Nearest Neighbor Using Ant Colony Optimization for Heart Disease Classification Arini, Florentina Yuni; Pongthanoo, Patcharanikarn; Salsabila, Kansa Maulina; Raihan, Muhammad; Muzakki, Naufal Habib
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-23647

Abstract

Heart disease is one of leading causes of death globally, making early detection essential for improving clinical outcomes. This study presents a heart disease prediction approach using the K-Nearest Neighbor (KNN) algorithm, addressing class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) and enhancing feature selection through Ant Colony Optimization (ACO). Exploratory data analysis identified age, gender, cholesterol, blood pressure, e xercise-Induced Angina (EIA), ST-segment depression, number of affected vessels, and thalassemia status as key indicators of disease severity. KNN model achieved 0.90 accuracy with balanced precision and recall. The employment of SMOTE improved sensitivity for the minority class, slightly reducing overall accuracy to 0.88. However, ACO as hyperparameter tuning KNN able to produce promising accuracy 0.91. This result indicate that combining KNN with metaheuristic optimization provides a reliable, interpretable method for heart disease prediction, offering valuable support for clinical decision-making and risk assessment.
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.
Analisis Aksesibilitas Tokopedia Berbasis Mobile Menggunakan User Experience Questionnaire Arini, Florentina Yuni; Habibi, Mahdi; Kaltsum, Zahra Zakiyah; Rahman, Muhammad Rifqi; Putra, Pramudya Kirana Mandala; Pradana, Samudra Azriel
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 4 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.4.519

Abstract

This study aims to comprehensively examine the accessibility level and user experience of Tokopedia on mobile based on the Human-Computer Interaction (HCI) approach by employing the User Experience Questionnaire (UEQ) as the primary instrument. Data collection involved 50 active Tokopedia users, with the UEQ instrument comprising 26 statements and a 7-point response scale. Six core scales—attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty—were evaluated and analyzed by calculating the average score for each aspect and comparing it to the standard UEQ benchmark. The results demonstrate that Tokopedia excels in attractiveness, perspicuity, and efficiency, while innovation in the novelty and stimulation scales remains a challenge that requires further optimization. This study underlines the importance of UX evaluation based on HCI as a foundation for developing inclusive features to support an optimal and sustainable user experience
Co-Authors Abas Setiawan Abdurrafi, Muhammad Agus Setyawan Ahmad Rozaq Heryansyah Ahmad Zidhan Ilmana Aisar, Muhammad Zidan Aisyah Nathania Araminta Aji, Yusuf Pandu Satrio Alaida, Salma Keysha Alamsyah - Aldevis, Mohammad Farrel Amin Suyitno Amrullah, Reza Zaidan Ananda Hisma Putra Kristianto Anggraeni, Dinda Ayu Anwar, Alfani Salsabilla Ardiansyah, Ikhsan Aryaputra, Daffa Pramata Asfino, Fadli Nugraha Astagina, Paramesti Athaya, Ikhsan Rakha Aufa Putra Wicaksono Awan Saputra Romadhoni Bagaskara, Josephin Nova Bhimawan, Farrel Fatih Brata, Prayoga Adi Dewanti, Rahima Ratna Djuanda, Lyon Ambrosio Duankhan, Poomin Endang Sugiharti, Endang Fadhlullah, Muhammad Azzam Fajariansyah, Ridwan Faqih, Muhammad Najmuddin Farrel Athaillah Putra Fatih Akbar Alim Putra Fatiha Misbah, Mutia Zahra Firdaus Zahid, Ahmad Galvin Fittra Marga Ardana Gerard Sean Dwayne Habibi, Mahdi Haryolukito Pambudi, Fawwaz Hernawan, Yoga Heryansyah, Ahmad Rozaq Hidayaturrohmah, Nia Nur Inoru Nian Alfita Intan Permata Sari Fauziah Irfan, Mohammad Syarif Isa Akhlis Isnaeni, Siti Itsna Sabila Hidayati Januar Pancaran Nur Fajri Julianto, Richy Kaltsum, Zahra Zakiyah khairunnisa, Nadhia Adzqiya Kristianto, Ananda Hisma Putra Lyon Ambrosio Djuanda Maloringan, Ariel James Mardlootillah, Hanif Ilmi Milannisya, Anya Kawakibi Much Aziz Muslim Muhammad Alvin Adinata Muhammad Lutfi Wibowo Muhammad Sulthonul Izza Mukti, Asteen Retno Muthia Nis Tiadah Muzakki, Naufal Habib Nafi', Raihan Muhammad Naryapramono, Afrilza Daffa Nathania Adristina Niratha, I Gede Ardhy Oktavian, Aloysius Pambudi, Fawwaz Haryolukito Pastika, Puan Bening Pongthanoo, Patcharanikarn Pradana, Samudra Azriel Prameswari, Della Egyta Pratama, Eric Vibriano Julia Putra, Fatih Akbar Alim Putra, Pramudya Kirana Mandala Putri, Farah Wahida Rizkia Putriaji Hendikawati Radhiti, Brigita Winona Elvaretta Rafi, Dhifansa Pradibtya Raharjo, Bagus Purbo Rahima Ratna Dewanti Rahman, Muhammad Rifqi Raihan, Muhammad Ramadhan, Farhan Husyen Ramadhan, Taufiqur Ramdhani, Khusnun Najwa Rifan, Slamet Rinandi, Tyto Riza Arifudin Rizky Aulia Adi Saputro Romadhoni, Ahmad Mustofa Hadi Romadhoni, Awan Saputra Ryo Pambudi Said, Danish Adli El Salsabila, Kansa Maulina Santoso, Tony Budi Saputra, Gagah Suryanatha Athallah Saputro, Rizky Aulia Adi Sari, Yuliana Mustika Satria, Diva Sekar Tri Handayani Septiana, Dina Wachidah Sihombing, Nico Anselmus Supriyono Supriyono Syanjalih, Alul Hidja Tiadah, Muthia Nis Toharo, Munajid Varindya Ditta Iswari Wahyudiantoro, Rizky Tri Warianta, Dwi Tatang Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Wibowo, Muhammad Lutfi Wicaksana, Rangga Wicaksono, Aufa Putra Winata, Ardin Zaenal Abidin