Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

6.187

P-Index

This Author published in this journals

All Journal International Journal of Advances in Applied Sciences Prosiding SNATIF Jurnal Pekommas INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Ilmiah Matrik Journal Information System Development International Journal of New Media Technology Technomedia Journal Jurnal ULTIMA InfoSys Jurnal ULTIMATICS IKRA-ITH ABDIMAS SENSITEK Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Proceeding National Conference Business, Management, and Accounting (NCBMA) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi GIAT: Teknologi untuk Masyarakat International Transactions on Education Technology (ITEE) International Transactions on Artificial Intelligence (ITALIC) Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain

Widjaja, Andree E.

Universitas Pelita Harapan

Author-ID : 2634356

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 54 Documents Claim Missing Document

Claim Missing Document

Articles

2 3 4 5 6

Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales Hery; Widjaja, Andree E.
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.2

Market basket analysis is a crucial technique in retail for uncovering associations between items frequently purchased together. This study aims to compare the effectiveness of the Apriori and FP-Growth algorithms using sales data from "The Bread Basket" bakery, comprising 20,507 transactions. Key variables include TransactionNo, Items, DateTime, Daypart, and DayType. The data underwent preprocessing steps, including cleaning, tokenization, and feature extraction using TF-IDF. The Apriori and FP-Growth algorithms were implemented with hyperparameter tuning and an 80/20 training/testing split. Performance metrics were evaluated, revealing that Apriori had an execution time of 4.08 seconds and memory usage of 45.36 MiB, whereas FP-Growth exhibited an execution time of 4.15 seconds and significantly lower memory usage at 0.08 MiB. The quality of the association rules was assessed by metrics such as support, confidence, and lift. For example, the Apriori algorithm generated the rule {Alfajores} -> {Coffee} with support 0.018885, confidence 0.520000, and lift 1.087090, while FP-Growth produced the rule {Scone} -> {Coffee} with support 0.017829, confidence 0.519231, and lift 1.085482. FP-Growth generally outperformed Apriori, particularly in memory efficiency, due to its use of the FP-tree data structure, which reduces the need for multiple database scans. The practical implications for "The Bread Basket" bakery include optimizing product placement and inventory management based on the identified associations, such as placing Coffee near Cake or Medialuna to encourage complementary purchases. The study concludes that while both algorithms effectively generate meaningful association rules, FP-Growth's superior memory efficiency makes it more suitable for large datasets. Limitations include data quality and the study's scope, confined to a single bakery. Future research should explore hybrid approaches, real-time data analysis, and applications across different retail sectors to enhance market basket analysis techniques further.

Predictive Modeling of Blockchain Stability Using Machine Learning to Enhance Network Resilience Hery; Widjaja, Andree E.
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.15

Blockchain technology is widely recognized for its security, transparency, and decentralization, yet ensuring the stability of blockchain networks as they scale remains a significant challenge. This study introduces a novel approach by integrating machine learning models to evaluate and predict blockchain stability, offering a proactive solution to maintain network reliability. The primary objective was to identify the key factors influencing stability and assess the effectiveness of different machine learning models in predicting instability events. Using a dataset derived from blockchain transaction data and network metrics, we applied Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) neural networks, and K-Means Clustering algorithms. The LSTM model demonstrated the highest accuracy (94.3%) and an AUC-ROC of 0.952, significantly outperforming other models in predicting stability events. The Random Forest model revealed that transaction throughput and network latency are the most critical factors, contributing 35.2% and 28.1% to network stability, respectively. Additionally, K-Means Clustering identified three distinct stability patterns, each representing different risk levels, providing actionable insights for network management. The key contribution of this research lies in the integration of machine learning into blockchain management, presenting a novel approach that enhances the predictability and resilience of blockchain systems. The findings suggest that machine learning can be effectively employed to develop early warning systems, enabling timely interventions to prevent network instability. This study not only advances the understanding of blockchain stability but also offers practical solutions for its enhancement, marking a significant step forward in the field. Future work should focus on the real-time implementation of these models and the exploration of more advanced techniques to further improve predictive capabilities.

Pengembangan Aplikasi Manajemen Rekrutmen Karyawan Menggunakan Metode Profile Matching Hery, Hery; Christopher, Raphael; Widjaja, Andree E.; Suryasari, Suryasari
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 3 No 1 (2019): Vol. 3 No. 1 Februari 2019
Publisher : Universitas Nusantara PGRI Kediri

Human Resources (HR) is one of the important aspects in the company, because it will manage many aspects such as technology, resources, and capital, therefore the process of hiring and allocating training is important in the company. Human Resource Departement (HRD) is responsible for recruiting new employees and developing training programs to equip employees or prospective employees. The Recruitment process consist of three steps: CV gathering, psychotest work, and interview). In PT. XYZ recruitment process is done manually,Therefore an application is required that can support the decision-making process in the employee recruitment process that can analyze the appropriate training for prospective employees. Employee recruitment applications development and allocation of employee training using the System Development Life Cycle (SDLC) system development methodology. For employee training allocation system with profile matching method. The Final result from this research is an application that could support HRD in recruitment process and training allocation.

The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN E. Widjaja, Andree; Hery, Hery; Habsara Hareva, David
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 5 No 1 (2021): February 2021
Publisher : Universitas Nusantara PGRI Kediri

The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.

Co-Authors Alencia Haryani, Calandra Alvira Putri Yudini Alya M. Amalia Amalia, Alya M. Amelia Magdalena Kaheja Amelinda Chendra Arnold Aribowo Arnold Aribowo Arnon M Sugiarto Azim Ashar Calandra A. Haryani Calandra Alencia Haryani Calandra Alencia Haryani Carolyn Feiby Supit Christian Marsel Wijaya2 Christopher, Raphael Debora Kathrin Yuwono Debora Margareta Efendi Tarigan, Riswan Eric Jobiliong Feliks Victor Parningotan Samosir Ferdinand, Ferry Vincenttius Filbert Chan Fransisko, Andy Gabrielle Florencia Gennady, Erick Goestjahjanti, Francisca Sestri Habsara Hareva, David Harjono, Nathanael Joshua Haryani, Calandra A. Haryani, Calandra Alencia Hery Hery Hery Hery Hery Hery Hery Hery Hery Hery Juan Situmorang Hikam, Ihsan Nuril Husni Teja Sukmana Irene Eka Sri Saraswati Jamesdry Jefrin Laia Joshua Nathanael Justin A. Haratua Karnawi Kamar, Karnawi Kristina G. Simanjuntak Kusno Prasetya Kusno Prasetya Laurentia Anggun P Lisia, Vanella Maya Avinda Mayumi Utama Michelle Angelica Mitra, Aditya R. Mouw, Christ Wibowo Mulyati Mulyati Nathalie, Julia Nathanael, Joshua Prasetya, Kusno Renaldi, Ary Renaldo Luih, Joshua Ririn Ikana Desanti Riswan E Tarigan Riswan E. Tarigan Riswan E. Tarigan Riswan Efendi Tarigan Rosanna, Nadya Sugiarto, Arnon M Supriyanti, Dedeh Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Tania Jovita Wibowo Tarigan, Riswan E. Vanella Lisia Veronica, Winnie Vincent Cahyadi Vivi Melinda Wijaya, Yoana Sonia Willy Darmawan Yumna, Saidah ‪Alfa Satya Putra

Title

Found 54 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 54 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 54 Documents
Search