Article Per Year (5 Year)
p-Index From 2021 - 2026
6.651
P-Index
This Author published in this journals
All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Jurnal Ilmu Teknik dan Komputer Jurnal Penelitian Sistem Informasi JGGAG (Journal of Games, Game Art, and Gamification) Edu Komputika Journal Jurnal Serina Abdimas Jurnal Teknik Informatika dan Teknologi Informasi
Darius Andana Haris
Program Studi Teknik Informatika Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 113 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 113 Documents
Search

 8   9   10   11   12 
Penerapan Business Intelligence pada Visualisasi Data Produksi Film Horor Geovani Haryo Saputra; Darius Andana Haris; Meirista Wulandari
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.4095

Abstract

This study aims to analyze horror film production data using a Business Intelligence approach to generate information that can support the evaluation and planning of film production processes. The data used in this study were obtained from horror treatment documents and shot lists of a feature-length horror film, containing various information related to the filming process, such as scene numbers, shot numbers, scene descriptions, involved characters, shooting locations, and cinematographic techniques employed during production. The collected data were processed and visualized using Microsoft Power BI to develop an interactive dashboard capable of presenting important information in a visual format. The dashboard was designed to provide insights into scene distribution, shot type composition, character involvement in each scene, and shooting patterns throughout the film production process. Through data visualization in the form of an interactive dashboard, directors and production teams are expected to gain a clearer understanding of the film’s visual structure and production complexity. The resulting insights can support production evaluation and facilitate more informed decision-making for future film projects.
Desain Dashboard Business Intelligence untuk Monitoring Kinerja Operasional Freight Forwarding Menggunakan Power BI Vanness Matthew Tjoeng; Darius Andana Haris; Meirista Wulandari
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.4112

Abstract

This study aims to design a Business Intelligence dashboard to support operational performance monitoring in Freight Forwarding Project Cargo services at PT XYZ. The company currently relies on manual reporting processes by exporting operational data from SAP and Microsoft SharePoint into spreadsheet files, resulting in inefficiencies and difficulties in monitoring shipment activities. This research adopts the Data Warehouse Lifecycle methodology using The Nine Steps of Kimball approach. Operational data obtained from SAP and Microsoft SharePoint were processed through the Extract, Transform, Load (ETL) stages using Power Query and visualized using Microsoft Power BI Desktop. The resulting dashboard provides key performance indicators (KPIs), including Total Shipment, Delayed Shipment, Average Lead Time, Average Delay Time, and On-Time Shipment Percentage, along with supporting visualizations for operational analysis. The implementation results indicate that the dashboard is capable of integrating operational data into a centralized platform and presenting information interactively to support monitoring activities and operational decision-making. The developed dashboard is expected to improve reporting efficiency, enhance visibility of shipment performance, and support data-driven decision-making within the company.
A Comparison of Machine Learning and Deep Learning Methods for Temperatures Predictions on Java Island Teny Handhayani; Janson Hendryli; Jeanny Pragantha; Wasino; Darius Andana Haris; Andrew Castello Purba
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.23812

Abstract

Climate change is a global long-term change in temperatures and weather. Climate change is a worldwide issue that requires proper handling to reduce the negative impact on humans and the environment. Analyzing historical data is beneficial for studying climate change. Machine learning and deep learning methods are useful tools for data analysis. The goal of this paper is to find the best model for forecasting temperatures, a case study in Java Island. Java Island is the most densely island and the central economy and business in Indonesia. Climate change research in Java Island is important for sustainability. It runs several algorithms i.e., Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM, Random Forest, Support Vector Regression, Extreme Learning Machine, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The experiment uses a historical daily time series of temperatures from 1 January 1990 to 31 December 2024. In general, the experimental results show that Gradient Boosting produces the highest average coefficient of determination R2 scores of 0.34 and the lowest Mean Absolute Error scores of 0.69. Long Short-Term Memory and Gated Recurrent Units are the deep learning models that also work well for forecasting. According to the experimental results, in some cases, machine learning models outperform deep learning models and vice versa.
Co-Authors Aditya Dwi Septian Adrian Hartanto, Adrian Agus Danarto Agus Hendrah Roni Albert Ensen Albert Sany Alex Fenturi Andi Wijaya Andrean Danawan Andreas Hernawan Andres Andrew Castello Purba Andrew Marcelino Andy Wijaya Anthony Anthony Arthian Terry Sammatha Sudarthio Arya Sena Aurellia Clearesta Sumarlie bagus Mulyawan Bill Kevin Caesario Refint Sia Carlene Lim Chazio Jonathan Christine Christine Christine Christine Chung, Cecillia Cliffen Allen, Cliffen Dannny Kristianto Darryl Kresnadi Nugroho Darwin Darwin Davin Pratama Derrick Ivan Dominic Oscar Donny Kurniawan Dwiky Anderson Eric Ang, Eric Ewaldo Filbert Felicia Natashia Ferdy Tanumihardjo Fernando Fernando Fernando Fernando, Fernando Ferry Ruben Yudistira Ferry Ruben Yudistira Yudistira, Ferry Ruben Yudistira Garry Wiratama Garry Wiratama, Garry Geovani Haryo Saputra Geovanny Valerian Soemitha Grandy Grandy Harley Leo Liman Helmy Yusuf Sutanto Hendaryie Hendaryie Hendy Sofjan Herdiman Herdiman Isammudin Isammudin James Feriady Sulistiyo Janson Hendryli Januar Pangestu Jason Jason Jason Wirawan Jastin Ng Jeanny Pragantha Jeanny Pragantha Jeanny Pragantha Johny Andersen Jonathan Andreas Rawung Jonathan Sunarjo Jonathan Suryadi Julio Alexander Justin Hensel Kenaz Reisha Kennedy Kennedy Kenneth Hakim Kevin Alexander Kevin kevin Kevin The Kevin, Bill Kirey Larasati Kristianto, Joseph Lely Hiryanto Leonardo Leonardo Lim, Carlene Liman, Harley Leo Lina Lina Linda Sari Livia Margarita Lukie Lukie Lukie Lukie, Lukie Luthfi Kamal Margatan, Natalicia Marsel Dwiputra Marsel Dwiputra, Marsel Martindo Martindo Marveen Million Medisha Araz Meirista Wulandari Michael Michael Muliadi Muliadi Natalicia Margatan Novaldo Rustandi Pramudita, Melvin Puendra, Valentino Rasna Rei Malchiel Reinardus Tirto Tryharyanto Ricky Yulianto Rizki Rian Anugrahani Robby Setiawan Robert Matthew Ronny Setiawan Ruby Chrissandy, Ruby Ruby Chrissandy, S.Sn., M.Ds Sacchio Orlando Salim, Hansen Samuel Samuel Sanchez Haryon Rohani, Sanchez Haryon Sandy Tanudjaja Sanjaya, Steven Serliana Serliana Silvana Aprilia Stanley, Tarsisius Steven Sanjaya Sumarlie, Aurellia Clearesta Sylvanus, Jericho Tarsisius Stanley Teny Handhayani Timothy Reynaldi Tommy Wicaksono Tony Tony Tony Tony Vanness Matthew Tjoeng Vicko Fernando Victor Christian Tania Vincent Vincent Vincentius Viny Christanti M Viny Christiani Mawardi Viona Eka Mustika Virginia Virginia Wasino William Hartanto Wirawan, Jason Xaverius, Vincentius Yobel Octavinus Yohanes Yohanes Yoseph Victor Kusuma Yosia Alvin Lie Fandy Yovan Yolanta Yulianto Yulianto Zevanya Richen, Zevanya Zinpo Subetha Zyad Rusdi