Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Advanced Vocational Information and Communication Technology (JAVICT)
Nirwana Haidar Hari
Politeknik Elektronika Negeri Surabaya (PENS)

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Web-Based Monitoring and Control System for Aquaponics Using IoT and Fuzzy Logic Ranu Arga Noor Yuusufa; Nirwana Haidar Hari; Yudha Dwi Putra Negara; Khotibul Umam
Journal of Advanced Vocational Information and Communication Technology Vol. 1 No. 2 (2026)
Publisher : ISAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/javict.v1i2.1494

Abstract

Aquaponics integrates aquaculture and hydroponic cultivation to improve resource efficiency and support food security under constraints of limited land and water. Water quality is a critical determinant of aquaponics productivity; however, many small-scale systems still rely on manual and periodic monitoring, which limits timely intervention. This study proposes an Internet of Things (IoT)–enabled, web-based aquaponics monitoring and control system for catfish (Clarias gariepinus) cultivation integrated with water spinach (Ipomoea aquatica). The system acquires water quality parameters including temperature, pH, dissolved oxygen, and ammonia using multiple sensors connected to an Arduino Mega. Sensor data are transmitted in real time via an ESP communication module using the MQTT protocol through the HiveMQ broker and stored in a MongoDB database for real-time visualization and historical analysis. In addition, pump actuator control is implemented using fuzzy logic based on water temperature and ammonia concentration to regulate water circulation and provide an interpretable qualitative assessment of water quality. Experimental validation confirms reliable sensing, transmission, storage, and web-based monitoring in an aquaponics pond scenario. The end-to-end transmission delay from the microcontroller to the web application achieves an average of 0.369 s (369 ms), indicating near real-time performance suitable for continuous monitoring and control.
Comparative Evaluation of String-Similarity Algorithms for Keyword Classification in Mobile Library OCR Systems Damar Galih AJi Pradana; Sritrusta Sukaridhoto; Nirwana Haidar Hari
Journal of Advanced Vocational Information and Communication Technology Vol. 1 No. 3 (2026)
Publisher : ISAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/javict.v1i3.1653

Abstract

Automated keyword classification is a critical step in OCR-based library metadata systems, where OCR-extracted synopsis terms must ultimately be mapped to standardized subject keywords despite character-level recognition noise. While prior mobile OCR pipelines for library metadata extraction have adopted Levenshtein-distance-based fuzzy matching for this task, no systematic comparison exists to justify this choice against alternative string-similarity algorithms. This study isolates the keyword-classification sub-task and empirically compares three algorithms — Levenshtein-based similarity ratio, Jaro-Winkler similarity, and TF-IDF with cosine similarity — using noisy keyword-level queries as a controlled proxy for OCR-corrupted terms, under simulated OCR-noise conditions (clean, light, and heavy character-level corruption). Evaluation uses a curated 20-keyword library subject database and 900 test queries per algorithm across five random seeds. Jaro-Winkler achieves the highest overall classification accuracy (97.8%) and the fastest average matching time (22.68 microseconds per query), compared with Levenshtein-based matching (96.4% accuracy, 34.45 microseconds) and TF-IDF with cosine similarity (74.2% accuracy, 581.01 microseconds). A paired t-test across seeds confirmed that Jaro-Winkler's accuracy advantage over TF-IDF was statistically significant, while its advantage over Levenshtein was not and instead rests on its markedly lower and more stable matching latency. The gap against TF-IDF widened sharply under heavy noise (95.3% vs. 34.0% accuracy), showing that TF-IDF-based matching, despite its popularity for document-level text similarity, is poorly suited to short-keyword matching under character-level noise. These findings, based on a 20-keyword benchmark, offer preliminary algorithm-selection guidance for developers of OCR-based metadata classification pipelines, pending validation on larger, real-world OCR-noise data.
Co-Authors Damar Galih AJi Pradana Khotibul Umam Ranu Arga Noor Yuusufa Sritrusta Sukaridhoto Yudha Dwi Putra Negara