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Menampilkan 1–10 dari 11 artikel
Analisis Klasifikasi Pengaruh Kegagalan dan Keterbatasan Metode Pembayaran Digital terhadap Churn Pelanggan Menggunakan Decision Tree
Dewa Ayu Putu Angelina Dewi
; I Wayan Sudiarsa
; Ni Made Dwi Junita Sariyani
; Yuvensia Armelia Sumu
; Gusti Ngurah Abhimanyu
Jurnal Bisnis Inovatif dan Digital
Vol 3
, No 1
(2026)
The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limita...
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Analisis Klasifikasi Keputusan Belanja Konsumen Pada Toko Online XX Menggunakan Algoritma Decision Tree
Putri Maria Theresia Kehi
; I Wayan Sudiarsa
; Maria Oktaviani Suryati
; Yosefina Dehadi
; Maria Karlinda
Saturnus: Jurnal Teknologi dan Sistem Informasi
Vol 4
, No 1
(2026)
This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the...
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Data Pipeline Engineering untuk LSTM Forecasting Seismisitas Melalui Integrasi Proses ETL Katalog Gempa Indonesia
Dewa Gde Agung Wisnu Anantha
; I Wayan Sudiarsa
; I Kadek Adi Erawan
; I Ketut Okta Suastika
; Gde Wardika Nugraha
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Vol 4
, No 1
(2026)
Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, d...
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Implementasi Pipeline ETL dan Pemodelan Prediktif ARIMA dalam Memetakan Pola Pembelian Konsumen pada Dataset Marketplace
I Wayan Manik Mas Sri Dantya
; I Wayan Sudiarsa
; I Putu Kabinawa Raesa Putra
; Brian Adi Sapurta
; I Komang Hari Sastrawan
Repeater : Publikasi Teknik Informatika dan Jaringan
Vol 4
, No 1
(2026)
In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the ent...
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Optimasi Prediksi Harga Saham BBNI melalui Integrasi Proses ETL dan Algoritma Long Short-Term Memory
I Gusti Ngurah Rangga Mahesa
; I Wayan Sudiarsa
; I Putu Dicky Dharma Suryasa
; Putu Agus Aditya Putra
; Yulianus Kevin Dharmawa Sagur
Repeater : Publikasi Teknik Informatika dan Jaringan
Vol 4
, No 1
(2026)
Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including...
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Analisis Prediksi Penjualan Bisnis Retail Menggunakan Metode Decision Tree dan Random Forest
Agung Narayana Adhi Putra
; I Wayan Sudiarsa
; I Kadek Adi Gunawan
; Kadek Bagus Karunia Dwi Dharmayasa
; I Wayan Eka Saputra
Saturnus: Jurnal Teknologi dan Sistem Informasi
Vol 4
, No 1
(2026)
The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of...
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Analisis Tren Gaji Profesi AI di Pasar Kerja Global Tahun 2025 Berdasarkan Data Lowongan Pekerjaan
Saturnus: Jurnal Teknologi dan Sistem Informasi
Vol 4
, No 1
(2026)
Rapid developments in the Artificial Intelligence (AI) industry have triggered an increased need for workers with specialized competencies, which has implications for significant variations in salary levels. This research aims to analyze the factors that influence salaries in the AI sector using the multiple linear regression method. The dataset used includes 15,000 AI job vacancies with variables including job and company characteristics. The data was engineered via the one-hot encoding method...
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Analisis Prediksi Customer Churn pada Sektor E-Commerce Berdasarkan Perilaku Transaksi Menggunakan Pendekatan Machine Learning
Nadeerah Hani’ Fauziyyah
; I Wayan Sudiarsa
; Ida Ayu Eka Sastradewi
; Kadek Agustine Yueyin Parisya
; Sartika Sartika
Jurnal Manajemen Bisnis Digital Terkini
Vol 3
, No 1
(2026)
Because it directly impacts revenue, customer loyalty, and long-term business sustainability, customer churn is a critical issue for the e-commerce industry. High churn rates indicate that a business is unable to retain existing customers, which means it is more expensive to acquire new customers. Therefore, a precise analytical approach is needed to identify customer behavior patterns that are likely to churn. Using machine learning methods, this study analyzes and predicts customer churn. For...
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Implementasi Algoritma Random Forest untuk Klasifikasi Rentang Harga Ponsel Berdasarkan Spesifikasi Teknis
Yustinus Liguori
; I Wayan Sudiarsa
; I Made Jagat Dita
; I Gusti Ngurah Galih Jimbar Baskara
; Pande Wisnu Wijaya Putra
Router : Jurnal Teknik Informatika dan Terapan
Vol 3
, No 4
(2025)
The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA)...
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Analisis Machine Learning pada Data Netflix Shows untuk Mengklasifikasikan Tren Genre dan Karakteristik Film
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Vol 3
, No 6
(2025)
The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netf...
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