Analysis of Fishermen's Perception on Sustainable Fishing Equipment in Banda Sakti, Lhokseumawe

2.1 Study Area & Population

The research was conducted in Banda Sakti Subdistrict, Lhokseumawe City. The target population consisted of local fishermen engaged in capture fisheries within the Malacca Strait. The sample was drawn from this population to ensure representation of the community's socio-economic diversity.

2.2 Data Collection & Analysis

Data on fishermen's perceptions and socio-economic variables (income, number of dependents, exposure to socialization programs) were collected via surveys. Analysis involved two key statistical tools:

• Class Interval Formula: Used to categorize and quantify the level of fishermen's perception (e.g., low, medium, high).
• Spearman's Rank Correlation: A non-parametric test used to analyze the strength and direction of the relationship between ordinal socio-economic variables and perception scores. The correlation coefficient ($\rho$) ranges from -1 to +1.

3.1 Perception Level Analysis

The overall perception level of fishermen regarding sustainable fishing equipment was found to be high. Using the class interval formula, perceptual scores fell predominantly within the range of >224-288, indicating a generally positive and receptive attitude towards environmentally friendly gear among the community.

3.2 Socio-Economic Correlation Analysis

Spearman's Rank Correlation revealed specific relationships:

• Income & Number of Dependents: Showed a low positive correlation ($\rho$ in range 0.20-0.399) with the perception of gear selectivity. Higher income/more dependents slightly correlated with greater appreciation for selective gear.
• Socialization: Exhibited a moderate positive correlation ($\rho = 0.571$) with the perception of gear security. Fishermen who participated in awareness programs had a better understanding of equipment safety.
• Other Variables: Most other socio-economic factors showed very low or insignificant correlation ($\rho$ near 0, significance > 0.05) with overall perception.

4.1 Core Insight

The study's pivotal finding isn't that fishermen are resistant to sustainability—they're not. The high perception scores debunk that myth. The real insight is that adoption is gated by pragmatic socio-economic calculus, not environmental apathy. Fishermen view gear through a lens of risk (security) and efficiency (selectivity), which are directly tied to livelihood stability. This aligns with broader behavioral economics models, like those discussed in Thaler & Sunstein's "Nudge," where decision-making is context-dependent and often prioritizes immediate, tangible benefits over abstract long-term gains.

4.2 Logical Flow

The research logic is sound but linear: measure perception → correlate with demographics → identify drivers. It correctly identifies socialization as the strongest correlate, which is a robust and actionable finding. However, the flow stops short of exploring the causal mechanisms. Why does socialization work? Is it building trust, demonstrating economic benefit, or reducing perceived risk? The study hints at but doesn't dissect this black box, a common limitation in perception-based correlational studies.

4.3 Strengths & Flaws

Strengths: The application of Spearman's Rank is appropriate for ordinal data from Likert-scale surveys. Isolating "selectivity" and "security" as key perceptual dimensions is analytically sharp. Focusing on a specific locale (Banda Sakti) provides valuable granularity often missing in national-level reports.

Critical Flaws: The elephant in the room is the gap between perception and actual behavior. High perception scores don't guarantee gear adoption. The study lacks a behavioral outcome measure, a point emphasized in the Fishbein & Ajzen Theory of Planned Behavior. Furthermore, the "low" correlation of income is potentially misleading; a threshold effect might exist where adoption only becomes viable above a specific income level, which linear correlation might miss.

4.4 Actionable Insights

For policymakers and NGOs, this study offers a clear playbook:

1. Reframe Socialization: Move from generic "sustainability is good" messaging to demonstrations focused on gear security and catch selectivity. Use peer-to-peer learning from respected fishermen.
2. Design Targeted Subsidies: Since income and dependents matter, create conditional subsidy or microfinance programs that reduce the upfront cost barrier for larger, more vulnerable families.
3. Pilot Behavioral Nudges: Instead of just measuring perception, run pilot programs combining gear access with simple commitments or social recognition (e.g., "Sustainable Fisherman of the Month") to bridge the intention-action gap.
4. Iterate with Data: Treat this as a baseline. The next study must measure actual adoption rates post-intervention, creating a feedback loop for program improvement.

5.1 Statistical Methodology

The core of the quantitative analysis relies on Spearman's Rank Correlation Coefficient, calculated as: $$\rho = 1 - \frac{6 \sum d_i^2}{n(n^2 - 1)}$$ where $d_i$ is the difference between the ranks of corresponding variables for the $i$-th observation, and $n$ is the sample size. This method is ideal for ordinal data (like perception scores) and is non-parametric, not assuming a normal distribution. The class interval formula for perception levels likely followed a simple structure: $\text{Range} = \frac{\text{Max Score} - \text{Min Score}}{\text{Number of Categories}}$, with categories (e.g., Low, Medium, High) defined accordingly.

5.2 Analytical Framework Example

While the PDF does not involve programming, the analytical logic can be framed as a decision-tree model for predicting perception drivers:

# Conceptual Framework for Intervention Design (Pseudo-Code)
# Input: Fisherman's Socio-Economic Profile
profile = {
    'income_tier': 'medium',  # e.g., low, medium, high
    'dependents': 4,
    'socialization_exposure': True
}

# Decision Logic based on Study Findings
def recommend_intervention(profile):
    intervention = []
    
    # Priority 1: Leverage Socialization Correlation
    if profile['socialization_exposure'] == False:
        intervention.append('ENROLL_IN_PEER_DEMO_PROGRAM')
    
    # Priority 2: Address Economic Barriers for Selectivity
    if profile['income_tier'] == 'low' and profile['dependents'] >= 3:
        intervention.append('SUBSIDIZED_GEAR_ACCESS')
        intervention.append('FOCUS_ON_SELECTIVITY_BENEFITS')
    
    # Priority 3: Universal Security Messaging
    intervention.append('HIGHLIGHT_GEAR_SAFETY_FEATURES')
    
    return intervention

# Example Output
# For the profile above, output might be:
# ['HIGHLIGHT_GEAR_SAFETY_FEATURES']
# (Since they have socialization exposure and medium income)

This framework translates statistical correlations into actionable program logic, moving from analysis to implementation.