Standalone Tools and Verification

The project includes a suite of standalone Python scripts that complement the main interactive dashboard. These tools are designed for data processing, offline analysis, report generation, and calculation verification. They operate on a common CSV data source and produce various outputs, including richly formatted Excel workbooks and detailed analysis reports.

A key function of these tools, particularly the Excel generators, is to provide a transparent and verifiable model of the TOPSIS calculation. By embedding Excel formulas directly into the spreadsheets, the results from the Python-based backend can be cross-referenced and validated, ensuring the integrity of the decision support system.

Data Source and Pre-processing

All standalone scripts utilize a common input file, No-Vendor-NamaPaketPlan-CPU-RAM-DiskIOSpeed-HargaBulanUSD.csv, for the initial set of alternatives. The scripts contain functions to parse and standardize the data from this file.

The data processing flow is as follows:

This diagram shows the common data pre-processing pipeline in the standalone scripts.
Sources: topsis_spk.py:31-56, generate_excel_level.py:8-29

A notable piece of logic is the extraction and conversion of the Disk I/O Speed value, which handles different units (Gbps/Mbps) and converts them to a standard MB/s measurement.

# Source: topsis_spk.py:40-50
def extract_disk_io(val):
    # Ekstrak kapasitas disk dan kecepatan bandwidth
    disk = int(re.search(r'(\d+)\s*GB', val).group(1))
    speed_match = re.search(r'(\d+(?:\.\d+)?)\s*(Gbps|Mbps)', val)
    if speed_match:
        speed = float(speed_match.group(1))
        if 'Gbps' in speed_match.group(2):
            speed *= 1000  # Convert Gbps to Mbps
    else:
        speed = 100  # Default 100 Mbps
    # Estimasi Disk I/O Speed dalam MB/s (bandwidth / 8)
    return speed / 8

Sources: topsis_spk.py:40-50, generate_excel_formula.py:14-24, generate_excel_topsis.py:14-24

Excel Generation Scripts

The project includes three distinct scripts for generating different types of Excel workbooks, each serving a unique purpose from data entry simplification to calculation verification.

generate_excel_level.py

This script produces TOPSIS_Input_Level.xlsx, an Excel file designed to simplify data input by abstracting raw values into a 1-5 level system. The spreadsheet contains embedded Excel formulas to automatically convert the user-provided levels back into the standard numerical values required for the TOPSIS calculation.

The key feature is the use of the CHOOSE formula in the 2. Konversi ke Nilai sheet to map levels to values.

// Source: generate_excel_level.py:237
=CHOOSE('1. Input Level'!D{row},2,4,6,8,10) // CPU conversion

Generated Sheets:

generate_excel_formula.py

This script creates TOPSIS_Rumus_Otomatis.xlsx, a fully transparent and automated TOPSIS calculator within a single Excel file. Users can modify the raw criteria values in the 1. Data Input sheet, and all subsequent calculation steps in other sheets will update automatically via linked Excel formulas. This workbook serves as a primary tool for verifying the logic of the Python-based calculations.

The data flow within the generated Excel file is as follows:

This diagram illustrates how sheets in the generated Excel file are interconnected with formulas.
Sources: generate_excel_formula.py

Generated Sheets:

generate_excel_topsis.py

This script generates TOPSIS_Perhitungan_Lengkap.xlsx, which acts as a static report of a single TOPSIS analysis performed in Python. Unlike the other generated Excel files, this one contains the results of the calculations (i.e., the numerical values) rather than the formulas themselves. It provides a step-by-step snapshot of the entire calculation process for documentation and review.

Generated Sheets:

Standalone TOPSIS Analyzer (topsis_spk.py)

This script is a command-line application that performs a complete TOPSIS analysis from start to finish. It encapsulates the entire process within the TOPSISAnalyzer class, reading the input CSV, performing calculations, and generating multiple output files.

TOPSISAnalyzer Class

This class is the core of the script, managing the analysis workflow.

This diagram shows the structure of the TOPSISAnalyzer class and its main methods.
Sources: topsis_spk.py:8-20, backend/topsis_service.py:7-26

Execution Flow

The main function in the script orchestrates the analysis by calling methods on the TOPSISAnalyzer instance in a specific order.

This flowchart shows the sequence of operations when topsis_spk.py is executed.
Sources: topsis_spk.py:321-332

Outputs

Running topsis_spk.py produces the following artifacts:

1. Console Report: A formatted summary of the top 10 rankings and the best recommendation printed to the standard output.
2. hasil_visualisasi_topsis.png: A comprehensive plot including bar charts, a radar chart, a scatter plot, and a heatmap.
3. hasil_topsis_lengkap.csv: A CSV file containing the full ranked list of all alternatives with their scores.
4. rekomendasi_top5.txt: A text file with detailed information about the top 5 recommended alternatives.
   Sources: topsis_spk.py:311-319

Calculation Verification

The project ensures the correctness of its TOPSIS implementation by enabling verification between the Python scripts and the formula-driven Excel models. The TOPSIS_Rumus_Otomatis.xlsx file, generated by generate_excel_formula.py, is central to this process. Because its calculations are based entirely on standard Excel formulas, it acts as an independent ground truth.

The verification process can be visualized as follows:

This diagram shows that results from the Python execution (topsis_spk.py) can be compared against the results from the formula-based Excel sheet to ensure they match.
Sources: generate_excel_formula.py, topsis_spk.py, README.md (mention of VERIFIKASI_INTEGRASI.md)