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All Journal INTECOMS: Journal of Information Technology and Computer Science JURNAL PENDIDIKAN MIPA PAEDAGOGY : Jurnal Ilmu Pendidikan dan Psikologi
Salsabila, Tasya Mulia
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Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Perancangan Data Mart Untuk Manajemen Data Penjualan Pada Kedai Kopi X Di Jakarta Salsabila, Tasya Mulia; Caroline, Angeline; Marcydiaz, Andrew Haikal; Trisnawarman, Dedi; Beng, Jap Tji
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 6 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i6.12875

Abstract

Data yang kompleks dengan volume besar dan format bervariasi memiliki peran penting bagi suatu perusahaan. Semakin kompleksnya data transaksi penjualan, kebutuhan sistem manajemen data yang efisien untuk memberikan wawasan yang cepat dan akurat. Penelitian ini bertujuan merancang data mart untuk menganalisis data penjualan, mendukung pengambilan keputusan yang lebih baik. Data dari Online Transaction Processing diubah menjadi Online Analytical Processing untuk memudahkan pengolahan, penyimpanan, dan analisis secara efisien. Penelitian ini, menggunakan metode Nine-step Kimball untuk perancangan data mart dan mengimplementasikan dalam star schema dengan melakukan proses Extract, Transform, Load. Pengumpulan data dilakukan melalui wawancara dengan data penjualan dari tahun 2023 hingga 2024, disimpan dalam Database Management System menggunakan Microsoft SQL Server Management Studio 18. Hasil dari penelitian ini, terbentuknya data mart dengan star schema, yang terdiri dari tabel fakta dan tabel dimensi. Perancangan data mart untuk management dapat mengoptimalkan pemrosesan data penjualan pada kedai kopi x dalam pengambilan keputusan. Kata Kunci: Data Mart, Extract Transform Load (ETL), Penjualan, Nine-step Kimball
Proses Desain Pada Perancangan Dashboard Pemantauan Penjualan Produk PT. XYZ Samantha, Velline; Salsabila, Tasya Mulia; Wijaya, Angeline Carolina; Trisnawarman, Dedi; Beng, Jap Tji
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 6 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i6.12917

Abstract

Perancangan desain dashboard penjualan menjadi komponen krusial dalam mendukung efisiensi analisis data dan pengambilan keputusan di perusahaan. Proses ini melibatkan integrasi berbagai elemen visual yang mencakup penggunaan Data Analytics, Decision Support System (DSS), serta desain Human Machine Interface (HMI). Desain dashboard yang efektif tidak hanya mengutamakan penyajian data secara komprehensif, tetapi juga memfokuskan pada aspek visual, seperti pemilihan warna, tata letak, dan format tampilan yang intuitif. Penelitian ini bertujuan untuk merancang dashboard pemantauan penjualan produk di PT. XYZ yang berorientasi pada Key Performance Indicator (KPI) perusahaan, dengan penekanan pada desain user-friendly. Desain yang baik akan memudahkan pengguna dalam memahami data dan meningkatkan efektivitas pemantauan performa penjualan.
DESIGNING A DATA WAREHOUSE TO OPTIMIZE THE HOTEL BOOKING MONITORING DASHBOARD Hutagaol, Alice Shizuka; Beng, Jap Tji; Wasino, Wasino; Tiatri, Sri; Lunzaga, Ele; Salsabila, Tasya Mulia; Nurkholiza, Rahmiyana
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/rr4rhx45

Abstract

In the rapidly growing tourism industry, efficient data management is crucial for strategic decision-making. This study focuses on the design and implementation of a data warehouse to optimize the monitoring dashboard for hotel bookings on an application X, an online hotel booking platform, a product of PT. XYZ. Along with the increase in volume and complexity of booking transaction data, there is an urgent need for an efficient data management system that can provide insights quickly and accurately. To address this, the study uses Kimball's Nine-Step Methodology and implements a star schema, which simplifies complex data relationships and improves query performance. The ETL (Extract, Transform, Load) process is applied to ensure accurate extraction, transformation, and loading of data from operational systems into the data warehouse, thereby guaranteeing consistency and accuracy for analysis. Tools such as SQL Server Management Studio 2022, Visual Studio 2019, and SQL programming were used to develop the data warehouse and the star schema. The results of the study show that the star schema, with a central fact table and surrounding dimension tables, effectively optimizes data processing and query speed. The system is scalable, allowing for future expansion without significant changes, and provides a robust platform for business intelligence. Recommendations include developing dimension analysis, optimizing the ETL process, and integrating predictive analytics to enhance decision-making. Overall, this study provides a structured data warehouse design that significantly improves PT. XYZ's ability to process, analyze, and visualize hotel booking data for strategic decision-making. Keywords: Data Warehouse, Extract Transform Load (ETL), Dashboard, Nine-Step Kimball, SQL.
DESIGNING A DATA MART FOR OPTIMIZING CLOTHING SALES AT C STORE Limbor, Ellen Gabriel; Beng, Jap Tji; Arisandi, Desi; Tiatri, Sri; Nurkholiza, Rahmiyana; Salsabila, Tasya Mulia; Dinatha, Vienchenzia Oeyta Dwitama
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/7k3f3443

Abstract

Data plays a crucial role in supporting strategic decision-making, particularly in the retail sector like C Store. To enhance sales performance and understand purchasing trends, an effective system such as a data mart is needed. This study aims to design a data mart that can be used to analyze sales data at C Store to support data-driven decision-making. The methodology used is the nine-step Kimball approach in data mart design. The ETL (Extract, Transform, Load) process is used to manage sales transaction data collected from the period of 2021 to 2024. The result of this study is a data mart with a star schema, consisting of fact and dimension tables, enabling the analysis of sales performance based on product categories and time. The implementation of this data mart is expected to assist C Store in managing data efficiently, supporting strategic decision-making, and improving business operational effectiveness. Keywords: Data Mart, Kimball's Nine-Step Process, Clothing Sales.
DESIGNING A DATA WAREHOUSE FOR AIRLINE TICKET SALES AT PT ABC Qadriah, Sekar Aurannisa Ramdhani; Beng, Jap Tji; Arisandi, Desi; Nagm, Fouad; Salsabila, Tasya Mulia; Zahro, Tiara; Dinatha, Vienchenzia Oeyta Dwitama
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/hfhqv835

Abstract

The high demand for airline ticket purchases in Indonesia and ticket sales being one of the important assets for companies in the aviation industry. PT ABC requires a system that can effectively manage this data to support business analysis and strategic decision-making. This research aims to design a data warehouse that can integrate airline ticket sales data, enabling more efficient data storage, processing, and analysis. The approach used in the design of this data warehouse is the star schema, which consists of one main fact table and several dimension tables. The fact table contains ticket sales transaction data, while the dimension tables include information about time, flight routes, airlines, and customers. This research provides a primary contribution in the form of a data warehouse design that integrates airline ticket sales data, enabling faster and more accurate analysis. The resulting business impact includes increased operational efficiency, more accurate data-driven decision-making, and the ability to monitor sales trends in real-time through interactive dashboards. With this data warehouse, PT ABC can reduce data analysis time and improve the accuracy of sales trend predictions. Keywords: Data warehouse, Star Schema, PT ABC plane ticket sales, Business Intelligence.
DESIGN OF A DATA WAREHOUSE FOR CUSTOMER SEGMENTATION PREDICTION AT PT X Lawrence, Valerie; Beng, Jap Tji; Wasino, Wasino; Tiatri, Sri; Nurkholiza, Rahmiyana; Salsabila, Tasya Mulia; Sefira, Fasia Meta
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/he6dx426

Abstract

With the rapid growth of the tourism industry, particularly within the hotel and airline sectors, understanding customer behavior and segmentation has become crucial for driving strategic decisions. However, customer segmentation in these sectors often faces challenges due to the complexity of transactional data and varying service demands. This study addresses these challenges by designing a data warehouse using a Star Schema architecture, aimed at integrating hotel and airline booking data from 2023 to 2024. The methodology follows the Nine-Step Kimball approach, coupled with the Extract, Transform, Load (ETL) process, to transform transactional data from OLTP systems into a format suitable for Online Analytical Processing (OLAP). The data warehouse features fact and dimension tables that support in-depth analysis of customer segments, booking trends, and performance across service categories and time periods. Key findings show that the data warehouse significantly improves the ability to segment customers based on booking behavior, seasonality, and service preferences, leading to more effective decision-making. This integrated system provides a robust foundation for data-driven strategies, allowing businesses in the tourism industry to optimize customer targeting, improve service offerings, and enhance operational efficiency. Keywords: Data Warehouse, Star Schema, ETL, Hotel Booking, Flight
Cognitive Offloading dalam Penggunaan Generative Artificial Intelligence (GAI) dan Perannya terhadap Working Memory Mahasiswa: Scoping Review Mahmud, Tiara Nailah; Tiatri, Sri; Beng, Jap Tji; Dinatha, Vienchenzia Oeyta Dwitama; Nurkholiza, Rahmiyana; Salsabila, Tasya Mulia; Bunarwan, Elga Adhi; Silitonga, Listra Chatalia; Azzahra, Cintya Syarah
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.
PERAN PENGGUNAAN GENERATIVE ARTIFICIAL INTELLIGENCE (GAI) TERHADAP MOTIVASI BELAJAR MAHASISWA: SCOPING REVIEW Bunarwan, Elga Adhi; Tiatri, Sri; Beng, Jap Tji; Dinatha, Vienchenzia Oeyta Dwitama; Nurkholiza, Rahmiyana; Salsabila, Tasya Mulia; Silitonga, Listra Chatalia; Mahmud, Tiara Nailah; Azzahra, Cintya Syarah
PAEDAGOGY : Jurnal Ilmu Pendidikan dan Psikologi Vol. 6 No. 2 (2026)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/paedagogy.v6i2.10343

Abstract

Generative Artificial Intelligence (GAI) is increasingly used in higher education to help students understand complex materials, receive feedback, explore ideas, and complete academic tasks. Nevertheless, evidence on the role of GAI in students’ learning motivation remains fragmented and has not sufficiently explained the conditions under which GAI may strengthen or weaken motivation. This gap is important because learning motivation shapes students’ engagement, self-regulation, autonomous learning, and academic outcomes in technology-enhanced learning environments. This scoping review aimed to map empirical evidence on how GAI use relates to university students’ learning motivation. The review followed Arksey and O’Malley’s methodological framework and was reported according to PRISMA-ScR. Eligible studies were analyzed using narrative-thematic synthesis to identify patterns of findings, supporting factors, inhibiting factors, and research gaps. The thematic synthesis indicates that GAI may support learning motivation when it is used for conceptual exploration, feedback, personalized learning, and competence development. However, GAI does not automatically increase students’ learning motivation. Its effects depend on students’ self-efficacy, lecturer support, self-regulated learning, the quality of interaction and output generated by GAI, and the risk of technological dependence. These findings imply that higher education institutions should design GAI-supported learning activities that are guided, reflective, ethical, and oriented toward strengthening, rather than replacing, students’ thinking processes. ABSTRAK Generative Artificial Intelligence (GAI) semakin banyak digunakan dalam pendidikan tinggi sebagai alat bantu untuk memahami materi, memperoleh umpan balik, mengeksplorasi ide, dan menyelesaikan tugas akademik. Namun, bukti mengenai peran GAI terhadap motivasi belajar mahasiswa masih terfragmentasi dan belum menjelaskan secara memadai kondisi yang membuat GAI dapat memperkuat atau justru melemahkan motivasi belajar. Kesenjangan ini penting dikaji karena motivasi belajar berperan dalam menentukan keterlibatan, regulasi diri, kemandirian belajar, dan kualitas capaian akademik mahasiswa dalam lingkungan pembelajaran berbasis teknologi. Penelitian ini bertujuan memetakan bukti ilmiah mengenai peran penggunaan GAI terhadap motivasi belajar mahasiswa melalui scoping review. Review ini menggunakan kerangka Arksey dan O’Malley serta dilaporkan dengan mengacu pada PRISMA-ScR. Artikel yang memenuhi kriteria inklusi dianalisis melalui sintesis naratif-tematik untuk mengidentifikasi pola temuan, faktor pendukung, faktor penghambat, dan kesenjangan penelitian. Sintesis tematik menunjukkan bahwa GAI dapat mendukung motivasi belajar ketika digunakan untuk eksplorasi konsep, umpan balik, personalisasi pembelajaran, dan penguatan kompetensi. Namun, GAI tidak otomatis meningkatkan motivasi belajar mahasiswa. Dampaknya bergantung pada efikasi diri, dukungan dosen, regulasi diri, kualitas interaksi dan output GAI, serta risiko ketergantungan teknologi. Implikasinya, perguruan tinggi perlu merancang penggunaan GAI yang terarah, reflektif, dan etis agar teknologi ini memperkuat proses berpikir mahasiswa, bukan menggantikannya.
Co-Authors Azzahra, Cintya Syarah Bunarwan, Elga Adhi Caroline, Angeline Dedi Trisnawarman Desi Arisandi Dinatha, Vienchenzia Oeyta Dwitama Hutagaol, Alice Shizuka Jap Tji Beng Lawrence, Valerie Limbor, Ellen Gabriel Lunzaga, Ele Mahmud, Tiara Nailah Marcydiaz, Andrew Haikal Nagm, Fouad Nurkholiza, Rahmiyana Qadriah, Sekar Aurannisa Ramdhani Samantha, Velline Sefira, Fasia Meta Silitonga, Listra Chatalia Sri Tiatri Wasino Wasino Wijaya, Angeline Carolina ZAHRO, TIARA