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Menampilkan 1–7 dari 7 artikel
Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization
Andy Hermawan
; Aji Saputra
; Nabila Lailinajma
; Reska Julianti
; Timothy Hartanto
; Troy Kornelius Daniel
Router : Jurnal Teknik Informatika dan Terapan
Vol 3
, No 1
(2025)
Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings revea...
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Implementing XGBoost Model for Predicting Customer Churn in E-Commerce Platforms
Andy Hermawan
; Aji Saputra
; Muhammad Dhika Rafi
; Syafiq Basmallah
; Yilmaz Trigumari Syah Putra
; Wafa Nabila
Repeater : Publikasi Teknik Informatika dan Jaringan
Vol 3
, No 2
(2025)
Customer churn is a major challenge in e-commerce, directly affecting revenue and profit. This study aims to develop a machine learning model using XGBoost to predict churn probability. To handle class imbalance, SMOTE was applied as a resampling method, and hyperparameter tuning was performed to enhance performance. The model was evaluated using the F2-score, prioritizing recall while maintaining precision. The results show that the XGBoost model with SMOTE achieves strong performance, with an...
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Analisis Segmentasi Pelanggan Berbasis RFM dan Evaluasi Efektivitas Kampanye Pemasaran untuk Meningkatkan Retensi
Andy Hermawan
; Fachmi Aditama
; Lintang Rizki Ramadhani
; Nuur Muhammad Ilham
; Aji Saputra
; Nila Rusiardi Jayanti
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Vol 2
, No 4
(2024)
This research implements RFM (Recency, Frequency, Monetary) analysis to perform customer segmentation and evaluate the effectiveness of marketing campaigns in a retail company. Using a Kaggle dataset, this study identifies customers based on purchasing behaviour and assesses marketing campaign responses for each segment. The analysis reveals that Loyal, VIP, and New Customer segments showed the highest responses, especially in Campaign 6. The findings emphasize the importance of targeting resour...
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Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket
Andy Hermawan
; Bayu Wicaksono
; Tigfhar Ahmadjayadi
; Bagas Surya Prakasa
; Jasico Dacomoro Aruan
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Vol 2
, No 5
(2024)
Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various signi...
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Pengaruh Penggunaan Keywords Pada Penamaan Listing Airbnb Terhadap Tingkat Popularitas Di Kota Bangkok
Andy Hermawan
; Fatika Rahma Sanjaya
; Gregorius Aldo Primantono
; Muhammad Syahirul Alim
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Vol 2
, No 3
(2024)
This study aims to explore the impact of keyword usage in Airbnb listing names on their popularity in Bangkok. Using regular expression (re) and tokenization methods, we identified the top 100 keywords from the listing name column. These keywords were then categorized based on business knowledge. Subsequently, the relationship between keyword usage and popularity was analyzed using the chi-square test, with popularity measured by the number of reviews in the last 12 months. The data used were so...
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Optimalisasi Waktu Penjemputan Dan Lokasi Pada Data Histori Perjalanan NYC TLC Menggunakan Exploratory Data Analysis
Andy Hermawan
; Antonius Andriyanto
; Ryandri Alif Pratomoputra
; William Armand Rahardjo
; Yogga Prastya Wijaya
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Vol 2
, No 2
(2024)
This study analyzes the "NYC TLC Trip Record" dataset for the period January 1, 2023 to January 31, 2023 to understand taxi usage patterns in New York City. The objectives to be achieved in this analysis include: (1) Identify the days and times with the highest demand for taxi services, (2) Identify the boroughs with the highest demand for taxi services. We applied univariate analysis for this analysis. The results show that the day with the highest demand occurs on Tuesday for the densest time...
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Analisis Pengaruh Variabel Nilai TIU, TWK, Dan TKP Terhadap Kelulusan SKD Pada Tes CPNS Menggunakan Analisa Bivariat Sederhana
Andy Hermawan
; Aji Saputra
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Vol 2
, No 1
(2024)
This study aims to assess the impact of the General Intelligence Test (TIU), National Insight Test (TWK), and Personal Characteristics Test (TKP) on the success of the Basic Competency Selection (SKD) for Civil Servant Candidate (CPNS). Using data from participants in the Ministry of Law and Human Rights' SKD selection in 2023, we employed univariate analysis, simple bivariate analysis, and binning methods to comprehend variable relationships. Results reveal non-normal distributions for TIU, TWK...
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