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All Journal Jurnal Teknologi Industri Pertanian Teknotan: Jurnal Industri Teknologi Pertanian Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Aplikasi Teknologi Pangan Indonesian Journal of Chemistry IKRA-ITH Humaniora : Jurnal Sosial dan Humaniora JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal RESISTOR (Rekayasa Sistem Komputer) IKRA-ITH ABDIMAS Pontianak Nutrition Journal (PNJ) Journal of Industrial and Information Technology in Agriculture Canrea Journal: Food Technology, Nutritions, and Culinary Journal Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Abdi Insani JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Food ScienTech Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Decode: Jurnal Pendidikan Teknologi Informasi SN-Biosper Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Pontianak Nutrition Journal (PNJ) Composite Jurnal Sains dan Teknologi Pangan Jurnal Komtika (Komputasi dan Informatika) Global Science: Journal of Information Technology and Computer Science
Yana Cahyana
Fakultas Teknologi Industri Pertanian, Universitas Padjadjaran
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Benchmarking Machine Learning Models for Large-Scale Loan Default Prediction Using Real Data Devianto, Yudo; Saragih, Rusmin; Cahyana, Yana
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 1 (2026): March: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i1.181

Abstract

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.
Karakteristik Solid Lipid Nanoparticle Fe-Sulfat Berbasis Asam Stearat dan Lemak Kaya Monolaurin dengan Metode Evaporasi Pelarut Subroto, Edy; Hartini, Deta; Cahyana, Yana; Indiarto, Rossi
Teknotan: Jurnal Industri Teknologi Pertanian Vol 20, No 1 (2026): TEKNOTAN, April 2026
Publisher : Fakultas Teknologi Industri Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jt.vol20n1.17

Abstract

Zat besi sangat penting bagi tubuh manusia, terutama untuk mencegah anemia atau defisiansi zat besi. Hal ini dapat dicegah dengan mengonsumsi suplemen zat besi atau produk terfortifikasi zat besi, tetapi zat besi memiliki rasa dan bau menyengat sehingga tidak disukai. Nanoenkapsulasi menawarkan teknik enkapsulasi dimana bahan aktif terperangkap di dalam nanopartikel, diantaranya dalam sistem Solid Lipid Nanoparticle (SLN) berbasis asam stearat dan lemak kaya monolaurin menggunakan metode evaporasi pelarut. Penelitian ini bertujuan menentukan konsentrasi lemak kaya monolaurin dan konsentrasi Fe-sulfat yang menghasilkan SLN dengan efisiensi enkapsulasi yang optimal dan sifat fisikokimia yang baik. Lemak kaya monolaurin digunakan pada konsentrasi 20%, 30% dan 40% (b/b lipid), sedangkan Fe-sulfat pada konsentrasi 10%, 20% dan 30% b/b total lipid. Hasil penelitian menunjukkan bahwa konsentrasi lemak kaya monolaurin dan konsentrasi Fe-sulfat mempengaruhi karakteristik SLN Fe-sulfat. Perlakuan konsentrasi lemak kaya monolaurin 40% dengan konsentrasi Fe-sulfat 2% menghasilkan karakteristik SLN terbaik, yaitu nilai Z-Average sebesar 128,40 nm, polydispersity index (PI) sebesar 0,877, dan efisiensi enkapsulasi 97,38%. SLN Fe-sulfat memiliki morfologi partikel berbentuk bulat dan nilai loading capacity sebesar 0,17% dimana zat besi yang terkandung di dalamnya sebesar 12,97%. Dengan demikian, enkapsulasi Fe-sulfat berbasis asam stearat dan lemak kaya monolaurin dengan metode evaporasi pelarut efektif menghasilkan SLN dengan efisiensi enkapsulasi yang tinggi dan karakteristik fisikokimia yang baik.
Application of ResNet Architecture for Object Classification on Rice Grains Faisal, Sutan; Cahyana, Yana; Wicaksana, Yusuf Eka
Techno Xplore: Journal of Computer Science and Information Technology Vol. 11 No. 1 (2026): Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi
Publisher : Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/grfq1824

Abstract

This research discusses the application of the ResNet architecture, a deep learning algorithm based on Convolutional Neural Networks (CNN), for classifying rice grains through digital images. The study highlights the importance of automatic classification in quality control and the rice processing industry, as manual methods are subjective and inefficient for large data volumes. A dataset of rice grain images labeled at the individual grain level was created, and image preprocessing techniques such as normalization and augmentation were applied to improve training data quality. The ResNet model with several configurations was trained to recognize visual features such as shape, color, and texture of rice grains. Evaluation results indicate that the ResNet model achieves a reasonable accuracy of around 80%, with classification errors mainly occurring between visually similar rice varieties. The research suggests expanding the dataset and optimizing hyperparameters to further enhance model performance. The findings contribute to the development of AI-based systems for automated rice grain classification and quality inspection in the food industry.
Co-Authors Abda Abda Abdullah Darussalam Addion Nizori Adi Rizky Pratama Adi Susilo Aenul Fuadah Agung Triatna Agustin, Rachmayanti Tri Ahmad Fauzi Alifa, Naila Ratu Ambarwati, Evi Karlina Amid Rakhman amril siregar Anisa Itiawanti Annisa Nurhalizah Aqib Zhaky Arum Galih Pertiwi Awal, Elsa Elvira Ayu Juwita Baihaqi, Kiki Ahmad Banafshah Shafa Bramandito Affandi Budiyanto Budiyanto Deden Wahiddin Dewi, Indah Purnama Didik Remaldhi Direja, Azhar Ferbista Duhita D Utama DWI KUSUMANINGRUM Edy Subroto Een Sukarminah Efri Mardawati Enjelia, Lola Faisal, Sutan Fauzan Azima Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Fitria, Denisa Gumilar, Rizki Bintang Hanan, Sofiah Marwah Hanny Hikmayanti Handayani Hartini, Deta Hartono Wijaya, Sony Heri Hermawan Herlina Marta Hilda Novita Humaryanto, Humaryanto Iis Sadiah Imas Siti Setiasih In-In Hanidah Indira Lanti Kayaputri Indra Lasmana Tarigan Iskandar, Muhammad Irsyad Jovan Pangestu Juwita, Ayu Ratna Kiki Baihaqi Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M. Budi Kusarpoko M. Naufal Faqih Madyawati Latief Marsetio Marsetio Melia Siti Ajijah Miptahul Ulum Mochamad Djali Mohammad Djali Mohammad Djali Mohammad Djali Mohammad Djali Mudzakir, Tohirin Al Muhamad Amirrullah Muhammad Fadillah, Farhan Muhammad Ramadhan Mursyid Djawas Narwan Nahrudin Nina Puspitaloka Nofie Prasetiyo Nova Wulandari Praditya Putri Utami Pratama, Adi Rizky Pratiwi, Sinta Amanda Putra Rizki Pangestu Putri, Septiani Nuruldharma Rachmawati, Dhea Raden Duhita Diantiparamudita Utama Rahmat Rahmat Rahmat Rahmat Rahmat Restiana, Resti Ricky Steven Chandra Ridho Pratama, Ilham Ridwan, Ridwan Rizka Ayu Permana Rizki Ananda Rizki Nur Annisa Rizky Nugraha Rizky Riyanto Robi Andoyo Rohana, Tatang Rossi Indiarto Rusmin Saragih, Rusmin Sabirin Sandra Intan Sari Santi Lestari Seow, Eng Keng Siregar, Amril Siregar, Amril Mutoi Siregar, Amril Mutoi Siti Hanifah Khairun Nisa Suci Rahma Ajiaviaty Sukmawati, Cici Emilia Sulistya, Dwi Suningwar Mujiana Surya Martha Pratiwi Sutan Faisal Syahril, Ade Tatang Rohana Tita Rialita Tjong Wan Sen Tohirin Al Mudzakir Tsani Adiyanti Tukino, Tukino Wahiddin, Deden Wahyu Setio Aji Wazzan, Huda Wenda Adi Kusnaya Wicaksana, Yusuf Eka Widiharto, Banani Yudo Devianto