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All Journal Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro
mufida, ely
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Computer Science & IT Electrical & Electronics Engineering

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Perancangan Alat Pendeteksi KWH Meter Berbasis Arduino Uno R3 dan ESP8266 mufida, ely; Adriansyah , Mochammad Iqbal; Ihsan , Nur Muhammad; Anwar , Rian Septian
INSANtek Vol 2 No 1 (2021): Mei 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.251 KB) | DOI: 10.31294/instk.v2i1.442

Abstract

The amount of electrical energy consumption by each consumer can be measured using an electrical energy measuring device, namely the KWH Meter, as used by the State Electricity Company (PLN). Each PLN electricity customer will pay the electricity fee according to the value on the KWH Meter. Electricity customers only know the amount of fees that must be paid and cannot detect or control the electricity consumption for each day. This study aims to create a detection device for electrical energy consumption or KWH Meter that can display electrical power at a certain time in the form of digits. This KWH Meter detection tool is built using the PZEM-004T sensor and Split-CT which is used as a voltage and current sensor, the Arduino Uno R3 microcontroller system which functions to process and process data that has been obtained by the sensor, as well as an LCD and ESP8266 module that will send the results. measurement to the user, and will be displayed on a web-based application. The buzzer will light up when the power used exceeds a predetermined limit to give a warning to the user. This tool can be used by the user to provide information on the use of electricity in a place. The user can set the maximum electricity usage value. If the use of electrical energy exceeds the maximum limit, the buzzer will give a warning, so that the user can control the use of electricity as desired.
Customer Churn Prediction Pada Sektor Perbankan Dengan Model Logistic Regression dan Random Forest Mufida, Ely; Andriansyah, Doni; Hertyana, Hylenarti
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7576

Abstract

– Customer churn is a detrimental phenomenon in the banking sector because it can reduce revenue and increase the cost of acquiring new customers. This research aims to compare the performance of two models, Logistic Regression and Random Forest, to predict customer churn using datasets from Kaggle. The research process involves data preprocessing such as z-score normalization and dividing the dataset into training data (70%) and testing data (30%). The model was evaluated using a confusion matrix with Accuracy, precision, recall and F1-Score values. Logistic Regression achieved 76.85% Accuracy, 79% precision, 94% recall, and 86% F1-Score, showing quite good performance but susceptible to false positives. In contrast, Random Forest shows superior performance with 83.12% Accuracy, 84% precision, 96% recall, and 90% F1-Score. Random Forest is suitable for problems with high recall requirements because it is more reliable in detecting potential customer churn. To further improve model performance, it is recommended to perform hyperparameter optimization and feature importance analysis. This churn prediction model is expected to help banks reduce churn and increase customer retention.
Customer Churn Prediction Pada Sektor Perbankan Dengan Model Logistic Regression dan Random Forest Mufida, Ely; Andriansyah, Doni; Hertyana, Hylenarti; mufida, elly
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7576

Abstract

– Customer churn is a detrimental phenomenon in the banking sector because it can reduce revenue and increase the cost of acquiring new customers. This research aims to compare the performance of two models, Logistic Regression and Random Forest, to predict customer churn using datasets from Kaggle. The research process involves data preprocessing such as z-score normalization and dividing the dataset into training data (70%) and testing data (30%). The model was evaluated using a confusion matrix with Accuracy, precision, recall and F1-Score values. Logistic Regression achieved 76.85% Accuracy, 79% precision, 94% recall, and 86% F1-Score, showing quite good performance but susceptible to false positives. In contrast, Random Forest shows superior performance with 83.12% Accuracy, 84% precision, 96% recall, and 90% F1-Score. Random Forest is suitable for problems with high recall requirements because it is more reliable in detecting potential customer churn. To further improve model performance, it is recommended to perform hyperparameter optimization and feature importance analysis. This churn prediction model is expected to help banks reduce churn and increase customer retention.
Co-Authors Adriansyah , Mochammad Iqbal Anwar , Rian Septian Doni Andriansyah Elly Mufida, Elly Hylenarti Hertyana Ihsan , Nur Muhammad