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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Marine and Coastal Science
Muhammad Habibi
Universitas Pendidikan Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Environmental Science Immunology & microbiology

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Spatial Modelling of Potential Fishing Zones For Narrow-Barred Spanish Mackerel (Scomberomorus commerson) in The Border Waters of Indonesia Timor Leste Based on Maximum Entropy Algorithm Muhammad Habibi; Cinta Putri Maharani; Zaskia Dwiki Utami; Muhammad Roman Rihardi
Journal of Marine and Coastal Science Vol. 15 No. 2 (2026): JUNE
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jmcs.v15i2.86112

Abstract

The narrow-barred Spanish mackerel (Scomberomorus commerson) is an important pelagic species in the waters along the Indonesia–Timor-Leste border. Fluctuations in oceanographic conditions often make it difficult for fishermen to determine efficient fishing locations. This study aims to identify Potential Fishing Zone (PFZ) for the December 2023 period by integrating Sea Surface Temperature (SST), Chlorophyll-a, and vessel track data. The methods employed include extracting Level 3 AQUA MODIS satellite data using SeaDAS, performing Inverse Distance Weighting (IDW) spatial interpolation analysis in ArcGIS Pro, and modeling habitat suitability probability using the Maximum Entropy (MaxEnt) algorithm. The analysis results indicate that in December 2023, the oceanographic conditions of the waters had an SST range of 27.36°C–32.40°C and chlorophyll-a levels of 0.073-1.503 mg/m³. Based on the calibration of 143 actual vessel presence points, the MaxEnt model produced good predictive performance with an Area Under the Curve (AUC) value of 0.7321. The Sea Surface Temperature (SST) parameter contributed dominantly at 93.7% to habitat suitability probability, while chlorophyll-a contributed 6.3%. The mapped Potential Fishing Zone (PFZ) is concentrated in the coastal transition zone. The results demonstrate spatial agreement between model predictions and actual fishing locations, which can serve as foundational marine geospatial information for fishermen to reduce search time and improve fleet operational efficiency.
KLASIFIKASI SENTIMEN PENGUNJUNG KAWASAN WISATA PANTAI TANJUNG LESUNG MENGGUNAKAN ALGORITMA DECISION TREE DAN XGBOOST MUHAMMAD HABIBI; AHMAD HAFIZH NAZMUDIN; MUHAMMAD AKBAR DEEDAT
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9743

Abstract

Penelitian ini bertujuan mengklasifikasikan sentimen ulasan wisata Pantai Tanjung Lesung pada Google Maps untuk memperoleh informasi mengenai persepsi dan tingkat kepuasan pengunjung. Dua arsitektur machine learning diimplementasikan: Decision Tree sebagai model dasar dan eXtreme Gradient Boosting (XGBoost) sebagai model optimasi. Dataset diperoleh melalui web scraping dan diproses dengan tahapan preprocessing teks, pendekatan leksikon dengan penanganan negasi, serta pembobotan fitur TF-IDF berbasis unigram dan bigram. Untuk mengatasi ketidakseimbangan kelas digunakan Synthetic Minority Over-sampling Technique (SMOTE), dan optimasi hyperparameter dilakukan menggunakan GridSearchCV. Hasil eksperimental menunjukkan XGBoost mencapai akurasi terbaik sebesar 90,76%, lebih tinggi dibanding Decision Tree sebesar 87,95%. Visualisasi WordCloud mengindikasikan ulasan positif didominasi aspek keindahan alam dan kenyamanan. Temuan ini menegaskan bahwa kombinasi preprocessing teks, SMOTE, dan penalaan hyperparameter dapat meningkatkan performa klasifikasi sentimen pada ulasan wisata berbasis Google Maps.
Co-Authors AHMAD HAFIZH NAZMUDIN Cinta Putri Maharani MUHAMMAD AKBAR DEEDAT Muhammad Roman Rihardi Zaskia Dwiki Utami