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All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Andy Hermawan
Universitas Indraprasta PGRI
Computer Science & IT

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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): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v2i6.398

Abstract

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 compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..
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): Februari : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i1.64

Abstract

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, and TKP scores, highlighting the intricate nature of distribution in CPNS selection. While a positive correlation exists between variable values and total SKD score, binning analysis emphasizes TKP competitiveness over TIU and TWK scores. These findings offer practical insights for SKD participants to prepare effectively, with a focus on TIU, TWK, and TKP. Additionally, they contribute to transparency and effectiveness in the CPNS selection process. Further studies are recommended to explore additional factors, like age and gender, for the development of more holistic and accurate selection methods. This research supports enhancements in the adaptive and efficient CPNS SKD selection system.
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): Juni: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v1i2.175

Abstract

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 occurs in the vulnerable time of 3 pm to 6 pm. The boroughs with the highest taxi demand are Manhattan, Queens, and Brooklyn. This analysis provides the results for NYC TLC to develop a data-driven optimization strategy. This analysis not only helps in identifying demand hotspots but also provides insights for more efficient taxi scheduling and placement. With this analysis, it is expected that more effective pick-up time and location optimization strategies can be developed, thereby improving operational efficiency and customer satisfaction in taxi services in New York City.
Co-Authors Aji Saputra Antonius Andriyanto Faizal Lutfi Yoga Triadi M.Rahmat Hidayat Syachrudin Nila Rusiardi Jayanti Ryandri Alif Pratomoputra Solly Aryza William Armand Rahardjo Yogga Prastya Wijaya Zia Tabaruk