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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Fitra Arifiansyah
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Automatic Grading System for Spreadsheet Formula Kurniandha Sukma Yunastrian; Saiful Akbar; Fitra Arifiansyah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2086

Abstract

Spreadsheet is one of the tools that can be used to learn data analysis. Data analysis in spreadsheet can be done using formula. Spreadsheet tools can also be used for exams. For the assessment, there is a problem when the number of answers that need to be checked is large, that is it takes a long time to check all the answers. For this reason, an automatic grading system (autograder) that can evaluate formula in spreadsheet is needed. The method used in developing the autograder system is matching the answer key formula with the student's answer formula. The autograder system assesses the answer by calculating the similarity of the student's answer formula with the answer key formula. This paper explains how to build an autograder system that can evaluate the formula. At the end, an autograder system has been built successfully. It has been tested with 43 testcases and all of them are passed.
DEVELOPMENT OF GRAPH GENERATION TOOLS FOR PYTHON FUNCTION CODE ANALYSIS Bayu Samodra; Vebby Amelya Nora; Fitra Arifiansyah; Gusti Ayu Putri Saptawati Soekidjo; Muhamad Koyimatu
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6177

Abstract

The increasing complexity of programs in software development requires understanding and analysis of code structure, especially in Python, which dominates machine learning and data science applications. Manual static analysis is often time-consuming and prone to errors. Meanwhile, static analysis tools for Python, like PyCG and Code2graph, are still limited to generating call graphs without including dependency and control flow analysis. This research addresses these shortcomings by proposing the development of a web-based tool that integrates the generation of function call graphs, function dependency graphs, and control flow graphs using Abstract Syntax Tree (AST), Graphviz, and Streamlit. With an iterative SDLC methodology, this tool was developed gradually to visualize Python function code as a heterogeneous graph. Evaluation of 11 Python function codes showed a success rate of 95.45% in analyzing and visualizing Python function codes with various levels of complexity. The limitations of Graphviz present an opportunity for future research to focus on improving scalability and Python code analysis.
Co-Authors Akbar, Saiful Bayu Samodra Gusti Ayu Putri Saptawati Soekidjo Kurniandha Sukma Yunastrian Muhamad Koyimatu Vebby Amelya Nora