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All Journal Jurnal Teknik Informatika TEKNO Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknik Elektro dan Komputer
Steven Ray Sentinuwo
Sam Ratulangi University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation

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Journal : Knowledge Engineering and Data Science

 1 
Human Facial Expressions Identification using Convolutional Neural Network with VGG16 Architecture Luther Alexander Latumakulita; Sandy Laurentius Lumintang; Deiby Tineke Salakia; Steven R. Sentinuwo; Alwin Melkie Sambul; Noorul Islam
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p78-86

Abstract

The human facial expression identification system is essential in developing human interaction and technology. The development of Artificial Intelligence for monitoring human emotions can be helpful in the workplace. Commonly, there are six basic human expressions, namely anger, disgust, fear, happiness, sadness, and surprise, that the system can identify. This study aims to create a facial expression identification system based on basic human expressions using the Convolutional Neural Network (CNN) with a 16-layer VGG architecture. Two thousand one hundred thirty-seven facial expression images were selected from the FER2013, JAFFE, and MUG datasets. By implementing image augmentation and setting up the network parameters to Epoch of 100, the learning rate of 0,0001, and applying in the 5Fold Cross Validation, this system shows performance with an average accuracy of 84%. Results show that the model is suitable for identifying the basic facial expressions of humans.
Debtor Eligibility Prediction Using Deep Learning with Chatbot-Based Testing Noviania, Reski; Sela, Enny Itje; Latumakulita, Luther Alexander; Sentinuwo, Steven R.
Knowledge Engineering and Data Science Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i22024p128-138

Abstract

Predicting debtor eligibility is essential for effective risk management and minimizing bad credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy and integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non-experts to interact seamlessly with the system. For financial institutions, this chatbot-based testing approach offers practical benefits by streamlining debtor evaluation processes, reducing dependency on manual assessments, and providing consistent, scalable, and efficient solutions for credit risk management. It allows institutions to handle inquiries outside business hours, ensuring a continuous service flow. Furthermore, the system’s flexibility supports better customer interaction, increasing trust and transparency. By combining advanced machine learning with accessible interfaces, this study offers a scalable solution to improve the precision and practicality of debtor eligibility assessments, making it a valuable tool for modern financial institutions.
Co-Authors Agustinus Jacobus Alan Stevenres Bentelu, Alan Stevenres Alexander, Luisan William Alicia A. E. Sinsuw Alicia Sinsuw Alwin M. Sambul, Alwin M. Alwin Melkie Sambul Ambat, Mentari Putri Ando, M Tasyrik Andre Timothy Kapugu Antameng, Gabriella S. Arie Lumenta Auliawati Buchari, Auliawati Bahar, Jasinda Bayu Sy. Kurniawan, Bayu Sy. Brave A. Sugiarso Brave A. Sugiarso Brave Sugiarso, Brave Deiby Tineke Salaki Dringhuzen J. Mamahit Enny Itje Sela Fadli Umafagur, Fadli Hans Wowor Hasan, Olivia Hera Wulanratu Wulur, Hera Wulanratu Ilhammad Maulana Ani, Ilhammad Islam, Noorul Jimmy Robot Jinifer Rori, Jinifer John, Sumual David Julio Nari Kaawoan, Yuliani Y.I Karim, Budianto Karim, Irwan Kasema, Lady O. Kasenda, Lorenzo M. Kulung, Andri Linda Jayanti, Linda Lintong, Robby Moody Lolaroh, Stefanie M.E. Lombok, Rizky Dwi Putra Sani Lontaan, Agnestasia A.S. Ludja, Febriyanti Luther Latumakulita Mananoma, Yosua mandolang, arthur Mangamba, Yunifer Martina K. E. T. Dundu, Martina K. E. T. Martoyo, Ika M.H. Mathindas, Rivaldo Rendy Monica Kumaat, Monica Muhamad Z. Buchari, Muhamad Z. Nancy Tuturoong Noorul Islam Noviania, Reski Octavian Lantang Oktavian A. Lantang Paat, Franky Paputungan, Adiwarman P. Pinrolinvic D.K. Manembu Putro, Muhamad Dwisnanto Raintung, Stephanie Marceline Roberto Rengkung, Roberto Ruindungan, Dirko G.S. Rumetor, Josua Jovan Rumondor, Aryando G. Runtuwene, Steven Runtuwene, Syalom Veninda sambul, alwin Sambul, Alwin Melkie Sandy Laurentius Lumintang Sary D. E. Paturusi Sasoeng, Arief A. Sherwin R.U.A Sompie Staal, Nofli K. Stanley D.S Karouw, Stanley D.S Stanley D.S. Karouw Stanley Karouw Sumarauw, Florensce Sumolang, Billy B. Supit, Josua Waraney Takasana, Evangelista M. Tanjung, Yudhi Pratama Tjoanapessy, Nathasya Tompoh, Jos Forman Umboh, Wisnu W. A. Virginia Tulenan Wenno, William Dave Wowor, Novita E. Xaverius B. N. Najoan, Xaverius B. N. Xaverius B.N. Najoan Xaverius Najoan Yaulie Deo Y. Rindengan Yonna Kaburuan, Yonna