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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JURNAL PENDIDIKAN MIPA Journal of Information Systems and Informatics
Vienchenzia Oeyta Dwitama Dinatha
INTI International University
Computer Science & IT Education Mathematics Social Sciences Other

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UTAUT-Based Analysis of Factors Associated with Microsoft Teams Use in Digital Learning among Undergraduate Students Cintya Syarah Azzahra; Jap Tji Beng; Sri Tiatri; Fouad Nagm; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1697

Abstract

This study examined factors influencing Microsoft Teams adoption in digital learning using the Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative cross-sectional survey was conducted with 268 undergraduate students, and data were analyzed using reliability testing, Confirmatory Factor Analysis (CFA), classical assumption testing, and multiple linear regression. The results showed that Performance Expectancy, Effort Expectancy, and Social Influence significantly influenced Behavioral Intention, while Facilitating Conditions and Behavioral Intention significantly influenced Use Behavior. The model explained 60.2% of the variance in Behavioral Intention and 46.4% of the variance in Use Behavior. CFA demonstrated acceptable fit for most indices, although RMSEA indicated marginal fit. Performance Expectancy was the strongest predictor of Behavioral Intention, whereas Behavioral Intention was the strongest predictor of Use Behavior. Unlike previous studies focusing primarily on Behavioral Intention, this study also examined its relationship with self-reported Use Behavior among Indonesian undergraduates. The findings highlight the importance of usefulness, ease of use, social support, and facilitating conditions in promoting Microsoft Teams adoption for digital learning.
Cognitive Offloading dalam Penggunaan Generative Artificial Intelligence (GAI) dan Perannya terhadap Working Memory Mahasiswa: Scoping Review Tiara Nailah Mahmud; Sri Tiatri; Jap Tji Beng; Vienchenzia Oeyta Dwitama Dinatha; Rahmiyana Nurkholiza; Tasya Mulia Salsabila; Elga Adhi Bunarwan; Listra Chatalia Silitonga; Cintya Syarah Azzahra
JURNAL PENDIDIKAN MIPA Vol. 16 No. 2 (2026): JURNAL PENDIDIKAN MIPA
Publisher : Pusat Publikasi Ilmiah, STKIP Taman Siswa Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37630/jpm.v16i2.4252

Abstract

Penggunaan artificial intelligence dengan model Generative artificial intelligence (GAI) dalam dalam bidang akademik merupakan bentuk inovasi teknologi yang dapat digunakan oleh mahasiswa. Mahasiswa mengandalkan Generative artificial intelligence (GAI) dalam menyelesaikan tugas, dan kegiatan pembelajaran sehari-hari untuk meringankan beban kognitif atau yang disebut cognitive offloading. Mahasiswa langsung memproses informasi yang diberikan Generative artificial intelligence (GAI) dan dikhawatirkan dapat mengurangi keterlibatan mahasiswa dalam proses kognitif khususnya pada working memory. Pengumpulan data dilakukan dengan menggunakan berbagai macam publisher seperti Springer, American Psychology Association (APA), frontiers, taylor & francis, SAGE, MDPI, dan elsevier. Selain itu juga menggunakan database seperti PUBMED dengan rentang tahun maksimal 10 tahun terakhir (2016-2026). Pencarian artikel menggunakan kata kunci seperti cognitive offloading, artificial intelligence (AI), Generative artificial intelligence (GAI), working memory, dan menggunakan terjemahan kata kunci tersebut dalam bahasa Indonesia. Pencarian menghasilkan 55 artikel. Seleksi lebih lanjut berdasarkan inklusi menghasilkan 10 artikel. Hasil kajian menyatakan bahwa Cognitive offloading dalam penggunaan Generative artificial intelligence (GAI) memiliki peran membantu working memory memproses informasi dengan beban kognitif yang lebih ringan. Namun, agar kinerja working memory tetap dapat dilatih dan informasi dapat tersimpan lebih baik di long-term memory mahasiswa perlu menetapkan tujuan belajar.
Designing a Data Mart at the Indonesian Psychological Healthcare Center Using Pentaho Octarifa Angela; Jap Tji Beng; Dedi Trisnawarman; Sri Tiatri; Toong Hai Sam; Rhalf Jayson F. Guanco; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3153

Abstract

Critical data is essential for organisational success, providing essential information for decision-making. This study focuses on the Indonesian Mental Health Centre (IndoPsyCare), which utilises data to improve service performance. The research aims to design a data mart to support analytical needs by transforming the Online Transaction Processing (OLTP) database into Online Analytical Processing (OLAP). This design follows Kimball's Nine-Step Approach and integrates the Extract, Transform, Load (ETL) process. Data was collected from August 2023 to August 2024 through interviews and electronic documentation. The system was implemented using Microsoft SQL Server Management Studio 18 and Pentaho Data Integration. The resulting data mart uses a star schema with fact and dimension tables tailored to stakeholder needs, particularly in appointment analysis. Findings indicate that the data mart facilitates efficient access, targeted analysis, and improved query performance, thereby supporting IndoPsyCare's decision-making and overall service optimisation. 
Implementasi AI Agent Berbasis RAG untuk Klasifikasi Pelanggan KBLI 2025 Teddy Agustinus; Jap Tji Beng; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3739

Abstract

The migration of the ERP system from SAP ECC to SAP S/4HANA requires customer master data to have a complete industry classification that complies with the 2025 Indonesian Standard Industrial Classification (KBLI) to support the implementation of the Business Partner model. This research aims to develop an AI Agent based on Retrieval-Augmented Generation (RAG) to automate the customer classification process within the Master Data Management unit at PT X. The research employs an implementational approach by building a RAG system that integrates the KBLI 2025 knowledge base, hybrid search, and a large language model to classify 235 active customers into five-digit KBLI codes. The system’s performance was evaluated based on the accuracy of the classification results. The research findings indicate that the system achieved an accuracy of 98 per cent and successfully mapped 50 customer line-of-business categories into 21 KBLI Level 1 sectors. These findings demonstrate that the AI Agent is capable of generating standardised customer master data and is ready to support the migration to SAP S/4HANA.Keywords: AI Agent; Retrieval-Augmented Generation; Hybrid Search; Customer Classification; KBLI 2025AbstrakMigrasi sistem ERP dari SAP ECC ke SAP S/4HANA menuntut data master pelanggan memiliki klasifikasi industri yang lengkap dan sesuai dengan standar Klasifikasi Baku Lapangan Usaha Indonesia (KBLI) 2025 agar mendukung implementasi model Business Partner. Penelitian ini bertujuan mengembangkan AI Agent berbasis Retrieval-Augmented Generation (RAG) untuk mengotomasi proses klasifikasi pelanggan di unit Master Data Management PT X. Penelitian menggunakan pendekatan implementatif dengan membangun sistem RAG yang mengintegrasikan basis pengetahuan KBLI 2025, pencarian hibrida, dan large language model untuk mengklasifikasikan 235 pelanggan aktif ke kode KBLI level lima digit. Kinerja sistem dievaluasi berdasarkan tingkat akurasi hasil klasifikasi. Hasil penelitian menunjukkan bahwa sistem mencapai akurasi sebesar 98% serta berhasil memetakan 50 kategori line of business pelanggan ke dalam 21 sektor KBLI Level 1. Temuan ini menunjukkan bahwa AI Agent mampu menghasilkan data master pelanggan yang terstandarisasi dan siap mendukung migrasi ke SAP S/4HANA. 
Pengembangan Sistem Reporting Purchasing Berbasis Python dan SQL pada PT. X Leny Marshalina; Jap Tji Beng; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3738

Abstract

The Purchasing Division of a pharmaceutical company plays a strategic role in inventory control and distribution fulfillment across retail outlets. Previously, the Key Performance Indicator (KPI) reporting system at PT. X was conducted manually using spreadsheets, requiring up to two working days and being prone to calculation errors that affected employee incentive payments. This study aims to design and implement a web-based KPI reporting system using Python, the Django framework, SQL Server with stored procedures, and the OpenPyXL library. The system was developed using the Waterfall methodology with a modified prototyping approach. Eighteen KPI indicators were classified into four categories: procurement efficiency, inventory and expiration management, distribution planning, and data compliance and negotiation. Data processing employed an SQL Server stored-procedure-based ETL pipeline integrated with Pandas and NumPy. The system reduced report generation time from two working days to less than one hour while eliminating incentive calculation errors.Keywords: Key performance indicator; Reporting automation; ETL pipeline; Purchasing; information systems.AbstrakDivisi purchasing pada perusahaan farmasi menjalankan peran strategis dalam pengendalian stok dan pemenuhan distribusi ke jaringan outlet. Sistem pelaporan Key Performance Indicator (KPI) PT. X sebelumnya dilakukan secara manual menggunakan spreadsheet dengan dua hari kerja dan rentan terhadap kesalahan kalkulasi yang berimplikasi pada pembayaran insentif karyawan. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pelaporan KPI berbasis web menggunakan Python dan framework Django, SQL server dengan stored procedure, dan library openyxl. Sistem dikembangkan menggunakan metode waterfall dan dimodifikasi dengan pendekatan prototipe. 18 indikator KPI dikelompokkan kedalam empat kategori: efisiensi pengadaan (KPI 1), manajemen stok, dan kedaluwarsa (KPI 2), perencanaan distribusi (KPI 3), serta kepatuhan data dan negosiasi (KPI 4). Pengolahan data dilakukan melalui pipeline ETL berbasis SQL Server Stored Procedure dengan mengintegrasikan data heterogen, kemudian ditransformasi menggunakan pandas dan NumPy. Sistem berhasil mereduksi waktu penyusunan laporan dari rata-rata dua hari kerja menjadi kurang dari satu jam dan mengeliminasi kesalahan kalkulasi insentif. 
Designing a Dashboard for Sales Trends and Analysis at Coventown Glodok Jakarta Tasya Mulia Salsabila; Jap Tji Beng; Dedi Trisnawarman; Sri Tiatri; Toong Hai Sam; Ele Lunzaga; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3156

Abstract

Coventown is a coffee shop with a Chinese theme located in the Petak Enam Glodok area, Jakarta. The sales dashboard design allows Coventown Glodok Jakarta to identify sales patterns, and predict sales performance through historical data analysis. This study aims to present data in easy-to-understand visualizations to demonstrate the use of dashboards in improving the efficiency of sales monitoring, providing strategic insights into trends, and achieving sales targets to support more precise and effective decision-making. The prototype development iterations were planned quickly, and the data was processed using Microsoft Power BI and the Python programming language. The results of this study show that the sales dashboard at Coventown Glodok Jakarta effectively monitors sales in line with Key Performance Indicators (KPIs) such as time, category, product, price, and quantity. The resulting dashboard improves the efficiency of monitoring sales performance and helps the company display sales trends for best-selling products.  
Pengembangan Digital Acquisition Product Sebagai Layanan Pelanggan di Industri Perbankan Intan Ardiani Putri; Jap Tji Beng; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3737

Abstract

This study evaluates the success of Digital Acquisition Product development through the implementation of the Quick Response Code Indonesian Standard payment system at Bank X using a computational data analytics approach. Most previous studies have focused on consumer adoption through perception-based surveys, leaving a research gap in evaluating the operational capacity of internal banking systems. This study processes real transactional Big Data from 2023 to April 2026 using the Python programming language within the Google Colab environment. The computational stages include data wrangling using the Pandas library, data structure transformation (melting), missing value handling, and the development of custom algorithms to standardize time-log formats. The novelty of this study lies in the application of computational scripts to validate system performance longitudinally. The computational analysis of the 2026 projected data indicates high system scalability, as evidenced by the dominance of transaction loads through digital channels, accounting for 96.1 percent of total transactions compared to physical infrastructure. The study concludes that the Digital Acquisition Product architecture has proven robust in handling increasing data traffic and recommends that banking institutions begin integrating predictive algorithms to ensure the sustainability of the digital server ecosystem.Keywords: Data Analytics; System Evaluation; Descriptive Computing; Python; Digital Acquisition Product.AbstrakPenelitian ini mengevaluasi keberhasilan pengembangan Digital Acquisition Product melalui implementasi sistem pembayaran Quick Response Code Indonesian Standard pada Bank X menggunakan pendekatan data analytics komputasional. Sebagian besar penelitian terdahulu berfokus pada adopsi konsumen melalui survei persepsi, sehingga menyisakan kesenjangan analisis pada evaluasi kapasitas operasional sistem internal perbankan. Penelitian ini memproses data transaksional riil (Big Data) periode 2023 hingga April 2026 menggunakan bahasa pemrograman Python di lingkungan Google Colab. Tahapan komputasi mencakup data wrangling menggunakan pustaka Pandas, transformasi struktur data (melting), penanganan missing values, serta pembuatan algoritma kustom untuk standardisasi format log waktu. Kebaruan studi ini terletak pada penerapan script komputasi untuk memvalidasi performa sistem secara longitudinal. Hasil analisis komputasi pada proyeksi data 2026 menunjukkan skalabilitas sistem yang tinggi, ditandai dengan dominasi beban transaksi pada kanal digital sebesar 96,1 persen dibandingkan infrastruktur fisik. Penelitian ini menyimpulkan bahwa pengembangan arsitektur Digital Acquisition Product terbukti tangguh dalam menangani lonjakan traffic data, dan perbankan disarankan mulai mengintegrasikan algoritma prediktif untuk menjaga keberlanjutan ekosistem server digital. 
Co-Authors Cintya Syarah Azzahra Cintya Syarah Azzahra Dedi Trisnawarman Ele Lunzaga Elga Adhi Bunarwan Fouad Nagm Intan Ardiani Putri Jap Tji Beng Jap Tji Beng Leny Marshalina Listra Chatalia Silitonga Octarifa Angela Rahmiyana Nurkholiza Rahmiyana Nurkholiza Rahmiyana Nurkholiza Rhalf Jayson F. Guanco Sri Tiatri Sri Tiatri Tasya Mulia Salsabila Tasya Mulia Salsabila Teddy Agustinus Tiara Nailah Mahmud Tiatri, Sri Toong Hai Sam