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About Me

Hello, I am a passionate data enthusiast with a fervor for exploring the intricacies of information through the lens of analytics. Although I am yet to step into the professional realm, my journey has been marked by engaging in diverse projects. My ventures include delving into the realms of time-series analysis, numeric data exploration, and text data evaluation. Despite my limited work experience, I have honed my skills in Python and various specialized libraries to proficiently conduct data visualization and analysis. These projects have not only provided me with a hands-on understanding of data but have also nurtured my ability to unravel insights from complex datasets.

Currently, my focus lies in furthering my expertise in quantitative analysis and time-series exploration. I am eager to immerse myself in the dynamic world of data, applying my skills to unravel patterns, trends, and meaningful insights. With a positive mindset and a genuine passion for the field, I am excited about the prospect of contributing to the realm of data analytics, driven by an insatiable curiosity to continuously learn and grow. As I embark on this journey, I am confident that my dedication and enthusiasm will propel me toward becoming a proficient and impactful data analyst.

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Portfolio

Collection of projects	Description	Skills i've achieved
Feynman Einsteinia GPT-prompt	Description The prompt that I made is to carry out the understanding process from simple to complex level. Even though this is not related to data, it trains my ability to understand complex LLMs and do prompting. Apart from that, I use chatGPT as a mentor, teacher, and discussion friend when I learn about data.	Skills LLM prompting Understanding of LLM Utilization of chatGPT as a free learning medium
Implementing XGBoost on "Spaceship Titanic" Dataset to Predict Passenger Transport Condition	Description Final assignment in statistics course. I did it with my friend. Students were assigned to choose an assignment and we chose to create machine learning by participating in a competition for ML beginners on Kaggle. The competition is spaceship-titanic. This is the first machine learning I created.	Skills Statistics Analytics Data Analysis Regression Machine Learning Python (Programming Language)
Pembangunan Model Analisis Sentimen Penerimaan AI pada Masyarakat melalui Komentar di Youtube	Description This project involves conducting sentiment analysis on public opinions regarding AI adoption in Indonesia, using YouTube comments. Ensemble learning techniques were employed to train the model. Data was translated into English using GoogleTrans library, and automatic labeling was done with the Twitter-RoBERTa-Base-Sentiment-Latest model. TF-IDF and word embeddings were used for data extraction, and models were built with algorithms like Naive-Bayes, SVM, KNN, and Gradient Boosting. Results revealed that TF-IDF extraction outperformed word embeddings, with the Gradient Boosting algorithm being the most effective. Despite these findings, the project did not succeed, as the focus on creating an optimal model deviated from the essence of data mining, which involves extracting insights from data. This realization came after a deeper understanding of the nature of data mining.	Skills Sentiment analysis Ensemble learning techniques Data translation using GoogleTrans library Automatic labeling with Twitter-RoBERTa-Base-Sentiment-Latest model Data extraction using TF-IDF and word embeddings Model construction with Naive-Bayes, SVM, KNN, and Gradient Boosting algorithms Comparison of extraction methods and algorithms Interpretation of model results Reflection on project outcomes and identifying flaws Understanding the essence of data mining
Forecasting Foods Prices in Indonesia for the next 3 Months	Description Secured 1st place in Data Science Indonesia Kaggle competition, triumphing in a solo endeavor to forecast essential commodity prices across Indonesian provinces over a three-month period. Achievements include mastering time series analysis, implementing advanced forecasting models (ARIMA and SARIMA), and utilizing the Panel library for interactive data visualization. Key takeaways involve rapid knowledge acquisition in just two months, embracing challenges as learning opportunities, and successful self-learning through free resources. Gratitude extended to Data Science Indonesia for organizing the competition, with excitement to continue the data science journey and tackle more challenges.	Skills Time series analysis Forecasting essential commodity prices Implementation of forecasting models (ARIMA and SARIMA) Utilization of the Panel library for interactive data visualization Data preprocessing for Kaggle competition Statistical analysis of commodity prices Application of machine learning techniques to real-world data Independent problem-solving in a solo endeavor Rapid knowledge acquisition in a short timeframe Adaptability to challenges and turning them into learning opportunities
Visualization for Foods Prices Data in Indonesia using Streamlit App with Linear Graph Forecasting	Description This project, titled "Visualization for Foods Prices Data in Indonesia using Streamlit App with Linear Graph Forecasting," centers around the development of an interactive platform for visualizing food prices data in Indonesia. The Streamlit App is employed to create a user-friendly interface featuring linear graphs, enabling users to explore and interpret trends in food prices. The emphasis on linear graph forecasting enhances the application's capabilities by providing users with predictive insights into potential future price trajectories. Through this streamlined and focused visualization approach, the project aims to deliver a straightforward yet powerful tool for users to analyze and comprehend food price dynamics in Indonesia.	Skills Data visualization using Streamlit App Graphical representation of food prices data Implementation of linear graphs for trend analysis Forecasting using linear graph models Data analysis and interpretation User interface design for interactive exploration Integration of forecasting capabilities into the Streamlit App Understanding and processing food prices data Effective communication of insights through visualizations Application of statistical and forecasting concepts
LLM-generated essay using PaLM from Google Gen-AI	Description In this project, I curated a dataset focusing on essays generated by Large Language Models (LLM) using PaLM from Google Gen-AI. The primary objective was to contribute to the external dataset for the competition on detecting text generated by artificial intelligence (AI) in order to address data imbalance issues. Additionally, I created a detailed tutorial outlining the process, which can be accessed here. This project makes a positive contribution to mitigating data imbalance concerns by supplementing the external dataset, and the tutorial provides valuable insights for those interested in understanding the dataset development process.	Skills Dataset curation for LLM-generated essays using PaLM Data imbalance mitigation in Kaggle competition Contributed to external dataset for AI-generated text detection Comprehensive tutorial creation for dataset development Effective communication of project objectives and insights

Publications

Bunga Rampai Pemanfaatan Sistem Smart Campus untuk Optimalisasi Pendidikan Tinggi Mandiri

Description
I actively contributed to a publication focused on the intersection of modern technology in educational institutions. Specifically, I served as the second author for the article titled "Implementation and Management of Data Centers in Higher Education." This publication, crafted in Bahasa Indonesia, delves into pertinent insights and practices related to the deployment and administration of data centers within the higher education sector. The collaborative effort aims to provide a comprehensive resource for readers seeking valuable perspectives on the strategic integration of technology in academic environments.

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Blog

How I Generate LLM-Generated Text Dataset Using PaLM Google Generative-AI in Google Colab (Medium)

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Skills

Still under development

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Work Experiences, Education, and Certification

Work Experiences

Still don't have it yet

Education

Education Level	Institution	Year Started	Year Graduated	Major/Field of Study
Elementary School	SDIT AL AKHYAR	2010	2016	-
Junior high school	MTsN 1 Kudus	2016	2019	-
Senior high school	SMAN 1 Kudus	2019	2022	Mathematics and Science
University	Universitas Negeri Semarang	2022	Still ongoing	Computer Science - Information System