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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Infotech Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH MATEMATIKA DAN TERAPAN IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jiko (Jurnal Informatika dan komputer) SEINASI-KESI METIK JURNAL Building of Informatics, Technology and Science Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Technologia: Jurnal Ilmiah Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics JIKA (Jurnal Informatika) Informatics and Digital Expert (INDEX) International Journal of Global Operations Research Jurnal Ilmiah Wahana Pendidikan Journal La Multiapp Jurnal Locus Penelitian dan Pengabdian Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Prosiding Seminar Riset Mahasiswa Enrichment: Journal of Multidisciplinary Research and Development Jurnal Informatika Polinema (JIP) Majority Science Journal Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Wina Witanti
Jurusan Teknik Informatika, Universitas Jenderal Ahmad Yani
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Prediksi Kinerja Akademik Siswa Bimbingan Belajar Menggunakan Algoritma Extreme Gradient Boosting (XGBoost) Alfarizi, Muhammad Bayu Ardi; Witanti, Wina; Komarudin, Agus
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7387

Abstract

Improving the quality of education has become a primary focus in addressing the increasingly complex challenges of the educational landscape. One promising approach to support data-driven decision-making is the prediction of students' academic performance using machine learning algorithms. This study aims to develop a classification model for predicting students' academic performance by leveraging the Extreme Gradient Boosting (XGBoost) algorithm. The dataset used was obtained from SMPN 1 Gunung Halu and includes both academic and non-academic attributes of students. Five key features were selected: initial grades, midterm grades, final grades, student behavior, and attendance. Data preprocessing involved feature selection, handling missing values, transforming categorical variables using label encoding, and balancing the classes using the SMOTE method. The XGBoost model was then trained using an 80:20 data split and hyperparameter tuning was performed using Grid Search. Evaluation results showed that the model achieved an accuracy of 84% with balanced F1-scores across all classes. The model outperformed other algorithms such as Bagging and Random Forest. With its strong accuracy and stability, the XGBoost model has the potential to serve as a tool for identifying students who require academic intervention. This study makes a significant contribution to the development of AI-based education systems and provides a foundation for the application of machine learning in improving the quality of secondary-level learning.
Prediksi Penyakit Kanker Payudara Menggunakan Algoritma Synthetic Minority Oversampling Technique dan Categorical Boosting Classifier Mandala, Muhamad Bintang; Witanti, Wina; Komarudin, Agus
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7403

Abstract

Breast cancer remains one of the leading causes of mortality worldwide, with high prevalence rates among women in Indonesia. Accurate and efficient diagnostic models are essential to support early detection and reduce mortality. This study aims to develop a predictive model for breast cancer classification using the CatBoost algorithm, a gradient boosting method known for its ability to natively handle categorical features and reduce overfitting through ordered boosting. The dataset used consists of diagnostic features of breast tumors, which were preprocessed by checking completeness and transforming numerical attributes into categorical bins to capture value distribution more effectively. To address class imbalance between benign and malignant cases, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied, resulting in a balanced training set. Optimal hyperparameters for the CatBoost model were obtained using Bayesian optimization, with key parameters including depth, learning rate, and L2 regularization. The model was then trained and evaluated using recall, accuracy, and F1-score metrics, with a confusion matrix used to assess prediction quality. The results demonstrate that CatBoost achieved high performance with a recall of 1,0, accuracy of 98,6%, and F1-score of 0,99, outperforming or matching other benchmark models such as SVM, Neural Network, and XGBoost. These findings highlight the reliability and effectiveness of CatBoost in supporting medical decision-making for breast cancer diagnosis.
Co-Authors Abiyoga, Arji Adzani, Nadhif Nurul Fajri Afifah, Hira Nur Alfarizi, Muhammad Bayu Ardi Andhika, Firza Rafiandi Aranda, Sepri Arianto, Ilham Gumeraruloh Asendra, Irfan Ashaury, Herdi Asrul Atamimi, Fadel Muhamad Hafid Budiman, Zillan Taufiq Dewi, Wulan Dian Nursantika Edvin Ramadhan Eka Purnama Rijaludin, Muhamad Fahrezi, Rizal Febrian Faiza Renaldi, Faiza Fauzan, Ariq Firdaus, Syahrul Ghozali, Mohamad Aditya Muttaqin Gunawan Abdillah, Gunawan Hadiana, Asep Id Hardianto, Mohamad Aris Hasbia, Muhamad Kania Ningsih, Ade Kasyidi, Fatan Kiki Hidayat, Kiki Komarudin, Agus Komarudin, Agus Liesdiannisa, Mutiara Virgi Lusy Sundary, Lusy Mandala, Muhamad Bintang Maulina, Ninda Melina Melina, M Nabilah, Mitamufida Kusuma Nabilah, Mitamufida Kusuma Nugraha, Faisal Okta Nugraha, Faisal Okta Nurhakim, Riri Qorib Nurhasanah, Wulandari Nurputra, Windi Raihan Nurul Sabrina, Puspita Pinasty, Syarifudin Yoga Pradana, Muhammad Rifqi Prasetyo, Nur Faid Pratiwi, Siska Hutami Putra, Eddie Krishna Putri, Ika Rahmah Rakhmat Umbara, Fajri Ridwan Ilyas Rusmana, Hendri Diana Saputri, Intan Vidia Syarafina, Fildzah Tacbir Hendro Pudjiantoro Trianggara, Dhani Umbara, Fajri Rakhmat Wahidin, Farhan Raihan Wahyudiputra, Helmy Fauzan