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Menampilkan 1–10 dari 13 artikel
Sistem Rekomendasi Musik Spotify Berbasis Pendekatan Hybrid Alternating Least Square Dan Content-Based Filtering
Andy Hermawan
; Akbar Kanugraha
; Indira Faisa Afgani
; Khaerun Nisa’Tri Safaati
; Mutiara Ayu Alzahra Ramadhani
Modem : Jurnal Informatika dan Sains Teknologi
Vol 4
, No 2
(2026)
The exponential growth of digital music catalogs on streaming platforms such as Spotify has made personalized recommendation systems crucial for enhancing user experience. This study develops a hybrid music recommendation system that addresses both warm-user and cold-user scenarios by combining Alternating Least Squares (ALS) collaborative filtering with content-based filtering (CBF) augmented by a popularity component. The dataset consists of 8,549,544 user-track interactions and a master file...
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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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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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Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python
Andy Hermawan
; Nila Rusiardi Jayanti
; Aji Saputra
; Army Putera Parta
; Muhammad Abizar Algiffary Thahir
; Taufiqurrahman Taufiqurrahman
Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan
Vol 2
, No 5
(2024)
Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results...
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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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Membangun Model Prediksi Churn Pelanggan yang Akurat: Studi Kasus tentang TELCO Company
Andy Hermawan
; Nila Rusiardi Jayanti
; Zia Tabaruk
; Faizal Lutfi Yoga Triadi
; Aji Saputra
; M.Rahmat Hidayat Syachrudin
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Vol 2
, No 6
(2024)
Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compa...
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Optimalisasi Strategi Pemasaran Melalui Analisis RFM pada Dataset Transaksi Ritel Menggunakan Python
Andy Hermawan
; Nila Rusiardi Jayanti
; Aji Saputra
; Cahaya Tambunan
; Dzaky Muhammad Baihaqi
; Muhammad Alif Syahreza
; Zacharia Bachtiar
Jurnal Manajemen Riset Inovasi
Vol 2
, No 4
(2024)
This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on a retail transaction dataset obtained from Kaggle. The dataset contains 64,682 transactions from 5,242 SKUs involving 22,625 customers over one year. Data cleaning and RFM analysis were conducted to segment customers based on recency, frequency, and monetary values. The findings reveal that customers were segmented into groups such as Champions, Loyal Customers, and At Risk. These segments pro...
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Analisis Customer Retention Dalam Ritel Online Data United States E-Commerce Records 2020
Andy Hermawan
; Muhamad Fauzi Hakim
; B Hilda Nida Alistiqlal
; Bagas Dio Hanggoro
Jurnal Inovasi Ekonomi Syariah dan Akuntansi
Vol 1
, No 4
(2024)
This study investigates the level of customer loyalty in e-commerce in the United States in 2020. Using e-commerce customer data from 2020 available on Kaggle, this research analyzes cohort retention to understand the number of customers from each cohort who continue to make purchases in subsequent months. Additionally, this study evaluates the implementation of data analysis to assess the effectiveness of promotional strategies and their impact on customer loyalty and retention. The results sho...
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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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