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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) Jurnal Sains dan Teknologi 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 International Journal Research on Metaverse
Widjaja, Andree E.
Universitas Pelita Harapan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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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

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (665.415 KB) | DOI: 10.29407/intensif.v3i1.12588

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1330.3 KB) | DOI: 10.29407/intensif.v5i1.14435

Abstract

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.
Pengenalan dan Pelatihan Dasar Algoritma Pemograman Menggunakan Aplikasi Thunkable Bagi Siswa SD St. Theresia Jakarta Andree E. Widjaja; Kusno Prasetya; ‪Alfa Satya Putra; Calandra Alencia Haryani; Hery; Irene Eka Sri Saraswati
GIAT Teknologi untuk Masyarakat Vol. 1 No. 1 (2022): Mei 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/giat.v1i1.5851

Abstract

Pengenalan dasar algoritma pemograman akan jauh lebih baik jika dilakukan sejak dini, misalnya dimulai semenjak sekolah dasar. Namun, bagi kebanyakan orang, algoritma dan pemrograman dianggap sebagai pelajaran yang sulit dan membosankan, sehingga diperlukan suatu metode khusus yang membuat anak-anak menjadi tertarik. Salah satu metode yang dapat digunakan adalah dengan menggunakan aplikasi Thunkable, di mana anak-anak dapat mempelajari dasar algoritma pemrograman secara visual, interaktif, dan kolaboratif. Meskipun pengenalan algoritma pemrograman penting dan sudah ada metode pembelajaran yang sesuai dengan menggunakan Thunkable, tidak banyak sekolah yang paham mengenai hal tersebut. artikel ini bertujuan untuk melaporkan kegiatan pengabdian kepada masyarakat terkait pengenalan dan pelatihan dasar algoritma pemrograman menggunakan Thunkable bagi siswa SD St. Theresia Jakarta. Pelatihan ini dilakukan secara daring melalui Zoom meeting. Setelah mengikuti pelatihan ini, para peserta diharapkan dapat meningkatkan kemampuan berpikir secara kreatif, logis, dan sistematis melalui pelatihan algoritma pemrograman
Pengembangan dan Pelatihan Sistem Informasi Persediaan Bahan Baku di PT Maju Bersama Persada Dayamu (MBPD) Calandra Alencia Haryani; Debora Kathrin Yuwono; Hery; Andree E. Widjaja; Arnold Aribowo; Aditya R. Mitra
GIAT Teknologi untuk Masyarakat Vol. 1 No. 1 (2022): Mei 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/giat.v1i1.5852

Abstract

PT Maju Bersama Persada Dayamu merupakan salah satu perusahaan pada bidang manufaktur. Proses produksi merupakan proses penting dalam bidang manufaktur. Sistem informasi persediaan bahan baku dapat membantu proses produksi berjalan dengan baik. Tujuan dari penelitian dan kegiatan Pengabdian Kepada Masyarakat ini adalah mengembangan sistem informasi persediaan bahan baku untuk membantu menyelesaikan kendala penyediaan bahan baku PT Maju Bersama Persada Dayamu saat ini yang masih bersifat manual dengan menggunakan Microsoft Excel dan kartu bahan baku. Hal ini mengakibatkan data-data tidak terintegrasi pada semua departemen, sehingga informasi ketersediaan dan pengelolaan bahan baku belum sepenuhnya akurat. Sistem ini dikembangkan menggunakan metode prototyping, bahasa pemrograman PHP dan framework CodeIgniter. Pemodelan sistem dibuat menggunakan notasi UML. Selain mengembnagkan sistem, pengujian dan pelatihan penggunaan sistem juga dilakukan untuk memastikan sistem yang dihasilkan sudah sesuai dengan kebutuhan dan membantu efektivitas proses persediaan barang PT Maju Bersama Persada Dayamu.
Pengembangan dan Penelitian Sistem Informasi Manajemen Produksi (Mitra: PT. Maju Bersama Persada Dayamu (MBPD) Tangerang) Hery; Amelia Magdalena Kaheja; Calandra Alencia Haryani; Andree E. Widjaja
GIAT Teknologi untuk Masyarakat Vol. 1 No. 1 (2022): Mei 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/giat.v1i1.5855

Abstract

Sistem informasi manajemen produksi dapat digunakan untuk membantu serta meningkatkan kualitas manajemen produksi pada sebuah perusahaan manufaktur agar semua proses produksi yang dilakukan dapat menjadi lebih optimal. Tujuan dari penelitian dan PkM ini yaitu mengembangkan sistem informasi manajemen produksi untuk PT. Maju Bersama Persada Dayamu (MBPD) Tangerang serta memberikan pelatihan kepada pihak PT. Maju Bersama Persada Dayamu (MBPD). Sebelumnya, manajemen produksi yang dilakukan oleh PT. MBPD masih bersifat manual, misalnya proses produksi dicatat dengan menggunakan kertas, serta tidak adanya sistem yang terintegrasi secara baik pada bagian produksi. Hal ini menyebabkan beberapa masalah, seperti ketidakakuratan pencatatan dan analisis data yang tentunya dapat mengganggu proses kelancaran kegiatan produksi perusahaan. Sistem yang diusulkan ini diharapkan dapat mendukung PT. MBPD dalam meningkatkan kualitas manajemen dan proses produksinya. Selain mengembangkan dan menyerahkan sistem usulan kepada PT. MBPD, kegiatan pelatihan kepada user terkait cara penggunaan sistem yang dibuat. Pelatihan ini ditargetkan kepada karyawan produksi, manajer produksi, maupun direktur PT. MBPD.
Clustering Digital Governance Adoption Patterns in the Metaverse Using K-Means and DBSCAN Algorithms Widjaja, Andree Emmanuel; Hery; Toer, Guevara Ananta
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.42

Abstract

The rapid advancement of immersive digital environments has accelerated global interest in leveraging metaverse technologies as extensions of public governance systems. This study analyses citizen readiness and perception toward metaverse-based digital governance in The Gambia using two unsupervised machine learning algorithms: K-Means and DBSCAN, applied to a dataset of 115 survey responses. After preprocessing and feature standardization, the K-Means algorithm identified two distinct adoption clusters, consisting of Cluster 0 with 76 respondents and Cluster 1 with 39 respondents. The centroid projections in PCA space revealed a clear behavioural separation, with Cluster 1 exhibiting a substantially higher mean PC1 score (2.5270) compared to Cluster 0 (−1.2968), indicating stronger readiness, optimism, and trust among respondents in the former group. In contrast, DBSCAN produced a single dominant cluster of 107 respondents and identified 8 outliers, suggesting a generally cohesive perception landscape with a small number of respondents expressing atypical attitudes toward metaverse-enabled governance. Collectively, these findings demonstrate that while public sentiment toward metaverse governance is broadly aligned, significant intra-group differences exist, making behavioural segmentation crucial for informing policy strategies. The results underscore the need for tailored approaches that address both enthusiastic adopters and more cautious individuals to support equitable and inclusive metaverse governance adoption.
Perancangan Sistem Informasi Persediaan, Penjualan & Pembelian Barang pada PT.XYZ Berbasis Web Ellyse Obe Johanis; Hery, Hery; Eric Jobiliong; Calandra A. Haryani; Widjaja, Andree E.
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6638

Abstract

In today's digital era, internet technology plays a vital role in the business sector, enabling companies to grow more rapidly and adapt to market demands. The integration of information technology has transformed business operations, as evidenced by the increasing number of large enterprises adopting such technologies. One of the main advantages is the ability to conduct online transactions, allowing customer to make purchases without queuing and enabling employees to work more efficiently. Consequently, transaction processes become faster and more convenient, while also expanding the company’s customer base and increasing profitability. This study presents the development of an information system using the prototyping methodology. The proposed system is built using PHP programming language and the CodeIgniter framework. The system design includes use case diagrams, activity diagrams, class diagrams, and entity relationship diagrams. The implemented information system aims to optimize the purchasing and sales processes at PT. XYZ, enhancing overall business efficiency and supporting strategic decision-making. The final outcome of this research is a web-based application that facilitates and streamlines product sales and purchases within the company.
Co-Authors Alencia Haryani, Calandra Alvira Putri Yudini Alya M. Amalia Amalia, Alya M. Amelia Magdalena Kaheja 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 Kathrin Yuwono Debora Margareta Efendi Tarigan, Riswan Ellyse Obe Johanis 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 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. Toer, Guevara Ananta Vanella Lisia Veronica, Winnie Vincent Cahyadi Vivi Melinda Wijaya, Yoana Sonia Willy Darmawan Yumna, Saidah ‪Alfa Satya Putra ‪Alfa Satya Putra