Rahma Amalia
An undergraduate degree holder in Informatics Engineering from Universitas Negeri Surabaya. Proficient in developing data products such as dashboards and experienced in data analysis using Python.
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Portfolio
DATA SCIENCE
Home Credit Risk Scoring: Predicting Loan Default with Machine Learning (Team)
Objective:
Many people want to get a financial loan to be able to meet their daily needs. Therefore, Home Credit seeks to facilitate financial loans for people who do not have a bank account. The current problem is, Home Credit wants the client data they have to be used as a prediction whether they are able to pay back the money they have lent within a predetermined time, or vice versa. The data to be used is train data. In the data train there is a "Target" variable where the values 0 and 1 are the differentiator: If the client can pay according to the due date or can pay on time, then it is marked with 0. If the client has difficulty paying the loan that has been given, it is marked with 1. The To Do is predict the characteristics of clients who have difficulty repaying loans provided by the company's cash flow.
Many people want to get a financial loan to be able to meet their daily needs. Therefore, Home Credit seeks to facilitate financial loans for people who do not have a bank account. The current problem is, Home Credit wants the client data they have to be used as a prediction whether they are able to pay back the money they have lent within a predetermined time, or vice versa. The data to be used is train data. In the data train there is a "Target" variable where the values 0 and 1 are the differentiator: If the client can pay according to the due date or can pay on time, then it is marked with 0. If the client has difficulty paying the loan that has been given, it is marked with 1. The To Do is predict the characteristics of clients who have difficulty repaying loans provided by the company's cash flow.
Prediction of House Prices
Objective:
Many people are interested in buying or selling a home and want to know the fair market price for the property. Therefore, the purpose of the house prices dataset is to predict house prices based on the features associated with the house. The "SalePrice" variable in the train dataset becomes the target variable to be predicted.
Many people are interested in buying or selling a home and want to know the fair market price for the property. Therefore, the purpose of the house prices dataset is to predict house prices based on the features associated with the house. The "SalePrice" variable in the train dataset becomes the target variable to be predicted.
Data Analysis
Data Analysis with DVDRental Dataset
Objective:
Using the DVDRental dataset, there are several questions as attached on the slide below and the results of the analysis using SQL Query on the PostgreSQL software. (Detail presentation in Canva link)
Using the DVDRental dataset, there are several questions as attached on the slide below and the results of the analysis using SQL Query on the PostgreSQL software. (Detail presentation in Canva link)
Dashboard
COVID-19 Global Dashboard
Objective:
Provide an accurate and up-to-date analysis of the Covid-19 pandemic on a global scale. The dashboard aims to forecast and track the spread of the virus, monitor key statistics, and provide valuable insights for policymakers, healthcare professionals, and the general public.
Provide an accurate and up-to-date analysis of the Covid-19 pandemic on a global scale. The dashboard aims to forecast and track the spread of the virus, monitor key statistics, and provide valuable insights for policymakers, healthcare professionals, and the general public.
E-Commers Sale Dashboard
Objective:
Provide a concise and comprehensive overview of the sales performance of an online store. The dashboard aims to present key metrics and insights that enable business owners and managers to make informed decisions and drive strategic initiatives. By aggregating and visualizing data from various sources, the dashboard will display real-time information on sales revenue, conversion rates, order volume, customer acquisition, and average order value. It will also include comparative data such as year-over-year growth, top-selling products, and customer segmentation. The ultimate goal of the E-Commerce Sale Dashboard is to empower stakeholders with actionable insights that facilitate effective sales management, optimize marketing efforts, and maximize revenue generation in the rapidly evolving digital marketplace.
Provide a concise and comprehensive overview of the sales performance of an online store. The dashboard aims to present key metrics and insights that enable business owners and managers to make informed decisions and drive strategic initiatives. By aggregating and visualizing data from various sources, the dashboard will display real-time information on sales revenue, conversion rates, order volume, customer acquisition, and average order value. It will also include comparative data such as year-over-year growth, top-selling products, and customer segmentation. The ultimate goal of the E-Commerce Sale Dashboard is to empower stakeholders with actionable insights that facilitate effective sales management, optimize marketing efforts, and maximize revenue generation in the rapidly evolving digital marketplace.
WEBSITE DESIGN
A Sneaker Recommendation Website: GetSneakers
Objective:
GET SNEAKERS WEBSITE is a website design that contains sneakers recommendations for men, women, and children. This website has several shoe brand recommendations, namely Nike, Adidas, Converse, and Fila. Apart from that, this website also has a "Contact Us" page so that users can give their opinion about sneakers.
GET SNEAKERS WEBSITE is a website design that contains sneakers recommendations for men, women, and children. This website has several shoe brand recommendations, namely Nike, Adidas, Converse, and Fila. Apart from that, this website also has a "Contact Us" page so that users can give their opinion about sneakers.
Research
Mobile-Based Learning Application for Junior High Scool Level “MATH EDU” Using Agile Method
Abstract:
Towards society 5.0 all activities become more dominant in the eraof digitalization. The amount of material that is carried for students but is constrained by time constraints at school makes it difficult to repeat the material individually.Due to the anxiety of students who find it difficult to get complete material in their review, we made this mobile application. The system concept in this mobile application is that students can access materials online and offline, download subject matter in the application, take tests that have been provided by the administrator, and conduct discussions in the forums available in the application. The modeling used in the preparation of this application is the agile method. In accordance with the goal, namely to provide convenience to junior high school students, the output and results of the design and development of this mobile application are to become a new container and means of learning.
Original Title (in Indonesian): Pengembangan Aplikasi Belajar Jenjang SMP MATH EDU Berbasis Mobile dengan Metode Agile
Towards society 5.0 all activities become more dominant in the eraof digitalization. The amount of material that is carried for students but is constrained by time constraints at school makes it difficult to repeat the material individually.Due to the anxiety of students who find it difficult to get complete material in their review, we made this mobile application. The system concept in this mobile application is that students can access materials online and offline, download subject matter in the application, take tests that have been provided by the administrator, and conduct discussions in the forums available in the application. The modeling used in the preparation of this application is the agile method. In accordance with the goal, namely to provide convenience to junior high school students, the output and results of the design and development of this mobile application are to become a new container and means of learning.
Original Title (in Indonesian): Pengembangan Aplikasi Belajar Jenjang SMP MATH EDU Berbasis Mobile dengan Metode Agile
Classification of High School Teachers’ Satisfaction in Banyuwangi Regency Towards the Merdeka Mengajar Application Using the Naïve Bayes Algorithm
Abstract:
This research aims to identify indicators that influence the satisfaction of high school teachers in Banyuwangi Regency as users of the Merdeka Mengajar application, and to classify and evaluate using the Naïve Bayes algorithm. The subjects of the study were high school teachers at SMAN 1 Pesanggaran, SMAN 1 Bangorejo, and SMAN 1 Glenmore. Data were collected through a questionnaire based on the DeLone and McLean user satisfaction model. The research method was a quantitative survey. Data analysis used Information Gain and Gain Ratio for feature selection, followed by the application of the Naïve Bayes algorithm. The analysis results showed that indicators such as Empathy (SEQ2), Availability (SQ2), and Responsiveness (SEQ3) had the highest Information Gain and Gain Ratio values. Model classification evaluation with confusion matrix and evaluation metrics (accuracy, precision, and recall) indicated that Information Gain and Gain Ratio feature selection improved model performance. Information Gain feature selection showed the highest accuracy of 92.63%, precision of 98.18%, and recall of 90.31%, while Gain Ratio feature selection showed an accuracy of 92.89%, precision of 98.75%, and recall of 90.31%. The conclusion of this study is that the use of Information Gain and Gain Ratio feature selection can improve the classification performance of the Naïve Bayes algorithm in measuring teacher satisfaction with the Merdeka Mengajar application. Indicators such as Empathy, Availability, and Responsiveness are crucial in determining user satisfaction. Recommendations for future research include testing this model on a larger sample and applying other machine learning methods to compare model performance.
Original Title (in Indonesian): Klasifikasi Kepuasan Guru SMA di Kabupaten Banyuwangi Terhadap Aplikasi Merdeka Mengajar Menggunakan Algoritma Naive Bayes
This research aims to identify indicators that influence the satisfaction of high school teachers in Banyuwangi Regency as users of the Merdeka Mengajar application, and to classify and evaluate using the Naïve Bayes algorithm. The subjects of the study were high school teachers at SMAN 1 Pesanggaran, SMAN 1 Bangorejo, and SMAN 1 Glenmore. Data were collected through a questionnaire based on the DeLone and McLean user satisfaction model. The research method was a quantitative survey. Data analysis used Information Gain and Gain Ratio for feature selection, followed by the application of the Naïve Bayes algorithm. The analysis results showed that indicators such as Empathy (SEQ2), Availability (SQ2), and Responsiveness (SEQ3) had the highest Information Gain and Gain Ratio values. Model classification evaluation with confusion matrix and evaluation metrics (accuracy, precision, and recall) indicated that Information Gain and Gain Ratio feature selection improved model performance. Information Gain feature selection showed the highest accuracy of 92.63%, precision of 98.18%, and recall of 90.31%, while Gain Ratio feature selection showed an accuracy of 92.89%, precision of 98.75%, and recall of 90.31%. The conclusion of this study is that the use of Information Gain and Gain Ratio feature selection can improve the classification performance of the Naïve Bayes algorithm in measuring teacher satisfaction with the Merdeka Mengajar application. Indicators such as Empathy, Availability, and Responsiveness are crucial in determining user satisfaction. Recommendations for future research include testing this model on a larger sample and applying other machine learning methods to compare model performance.
Original Title (in Indonesian): Klasifikasi Kepuasan Guru SMA di Kabupaten Banyuwangi Terhadap Aplikasi Merdeka Mengajar Menggunakan Algoritma Naive Bayes
Certification
RevoU Mini Course – Intro to Data Analytics
Published: January 2023
Learning about Introduction to Data Anlytics, Data Analytics in Business, Basic SQL, and Data Visualization (Google Data Studio and Looker Studio). There are also Career Session and Alumni Sharing Session (How to Prepare Your Data Analytics Career + How Data Analytics Changes My Life), CV Review, and Interview Tips.
Learning about Introduction to Data Anlytics, Data Analytics in Business, Basic SQL, and Data Visualization (Google Data Studio and Looker Studio). There are also Career Session and Alumni Sharing Session (How to Prepare Your Data Analytics Career + How Data Analytics Changes My Life), CV Review, and Interview Tips.
MySkill Short Class – Introduction to SQL
Published: February 2023
Learning about Introduction to Database and SQL, SQL Function, SQL Data Type (Using PostgreSQL), SQL Role in the Industrial World, Demonstration.
Learning about Introduction to Database and SQL, SQL Function, SQL Data Type (Using PostgreSQL), SQL Role in the Industrial World, Demonstration.
Zenius Program Certified Independent Studies (Kampus Merdeka’s Program) – Data Analytics
Published: Juni 2023
Learning about Logic, argumentation, and scientific reasoning; Basics of data science, python, data analysis, data visualization; Exploratory data analysis and statistics for data science; database, sql, and dashboard data; also there is a Final Project. Final project regarding the creation of data science solutions to credit scoring problems (Home Credit Risk Scoring Dataset) using the CRISP-DM framework and creation of a dashboard related to NLP monitoring.
Learning about Logic, argumentation, and scientific reasoning; Basics of data science, python, data analysis, data visualization; Exploratory data analysis and statistics for data science; database, sql, and dashboard data; also there is a Final Project. Final project regarding the creation of data science solutions to credit scoring problems (Home Credit Risk Scoring Dataset) using the CRISP-DM framework and creation of a dashboard related to NLP monitoring.
