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All Journal Jurnal Teknologi Perikanan dan Kelautan Jurnal Perikanan dan Kelautan Journal of Marine Research Jurnal Teknologi Informasi dan Ilmu Komputer INVOTEC Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktik dalam Bidang Pendidikan dan Ilmu Geografi Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi Jurnal Pengabdian Pada Masyarakat JURNAL REKAYASA TEKNOLOGI INFORMASI Georafflesia : Artikel Ilmiah Pendidikan Geografi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi J SIG (Jurnal Sains Informasi Geografi) Al-Kharaj: Journal of Islamic Economic and Business PENA AKUATIKA : JURNAL ILMIAH PERIKANAN DAN KELAUTAN Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Jurnal Abdi Insani Jurnal Chart Datum International Journal of Marine Engineering Innovation and Research Jurnal Teknologi Informasi dan Komunikasi Jurnal Mandala Pengabdian Masyarakat Jurnal Kemaritiman: Indonesian Journal of Maritime Zona Laut : Jurnal Inovasi Sains dan Teknologi Kelautan Simpatik: Jurnal sistem Informasi dan Informatika Indonesian Journal of Oceanography Jurnal Algoritma Journal of Scientech Research and Development JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Jurnal Perikanan dan Kelautan INOVTEK Polbeng - Seri Informatika JURNAL ILMIAH SATYA MINABAHARI JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Jurnal Pendidikan Perikanan Kelautan (Journal of Fisheries and Maritime Studies)
Willdan Aprizal Arifin
Universitas Pendidikan Indonesia
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Perbandingan Random Forest Dan Support Vector Machine Dalam Memprediksi Banjir Rob di Teluk Lampung Mar’ah, Maihuhatul; Arifin, Willdan Aprizal; Rosalia, Ayang Armelita
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2145

Abstract

Banjir rob merupakan masalah serius yang sering terjadi di daerah pesisir, termasuk wilayah Teluk Lampung, yang disebabkan oleh peningkatan permukaan air laut akibat perubahan iklim dan aktivitas manusia. Fenomena ini berdampak signifikan pada kerugian ekonomi dan sosial, serta meningkatkan kerentanan pemukiman di kawasan pesisir terhadap berbagai ancaman bencana. Penelitian ini bertujuan untuk menganalisis dan membandingkan performa algoritma random forest dan Support Vector Machine (SVM) dalam memprediksi kejadian banjir rob di Teluk Lampung. Data yang digunakan mencakup kejadian banjir rob selama lima tahun terakhir, dengan fokus pada variabel-variabel seperti tinggi muka laut, kecepatan angin, dan faktor meteorologi lainnya. Metode pembelajaran mesin, khususnya random forest dan SVM, diterapkan untuk menganalisis data dan menghasilkan prediksi. Hasil penelitian menunjukkan bahwa random forest mengungguli SVM dalam hal akurasi, dengan nilai akurasi mencapai 89% dibandingkan 69% untuk SVM. Selain itu, Random Forest juga menunjukkan nilai ROC AUC yang lebih tinggi, yaitu 0,82, yang mengindikasikan kemampuan klasifikasi yang lebih baik. Penelitian ini merekomendasikan penggunaan Random Forest sebagai metode yang lebih efektif untuk memprediksi banjir rob di wilayah Teluk Lampung, serta menyarankan penelitian lebih lanjut dengan dataset yang lebih besar dan beragam untuk meningkatkan akurasi model prediksi.
Klasifikasi Harga Ikan Koi Berdasarkan Jumlah Corak dan Ukuran Menggunakan Algoritma K-Nearest Neighbor Sihombing, Amalia Rahma Dini; Ilsa Margiana Herawati; Lubis, Naddra Haddad; Willdan Aprizal Arifin
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 10 No. 1 (2025): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v10i1.2011

Abstract

Penelitian ini menggunakan algoritma K-Nearest Neighbors (KNN) untuk mengklasifikasikan harga ikan Koi berdasarkan jumlah corak dan ukuran ikan. Pengelompokan diperlukan karena kurang umumnya pengetahuan terhadap spesies ikan koi yang diminati. Data diambil dari dataset Kaggle yang mencakup 801 data harga, ukuran, dan jumlah corak ikan Koi. Studi literatur dilakukan untuk memahami algoritma KNN dan faktor-faktor yang mempengaruhi harga ikan Koi. Model KNN diterapkan untuk mengklasifikasikan harga menjadi tiga kategori: murah, sedang, dan mahal. Evaluasi model menunjukkan akurasi sebesar 75%, dengan precision bernilai 0,71 dan recall sebesar 0,80, menunjukkan efektivitas KNN dalam memprediksi harga ikan Koi. maka dapat disimpulkan bahwa metode K-Nearest Neighbors (KNN) dapat memprediksi kelas ikan berdasarkan jumlah corak dan ukuran ikan yang diuji dengan performa yang baik.
PENERAPAN METODE REGRESI DALAM ANALISIS TINGKAT KONSUMSI IKAN DI JAWA TIMUR AKBAR fitransyah, Hikmal; Muhamad Ashari, Mahathir; Rahaditya Aryadi, Naufal; Aprizal Arifin, Willdan
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.2982

Abstract

The Indonesian marine ecosystem, rich in water resources, makes the fishing sector important for the economy, food, and jobs. East Java, with the highest catch production in Indonesia, has huge potential in the fishing sector. Despite its high potential, fish consumption in Indonesia is still relatively low due to lack of awareness, unoptimal distribution, and other factors. The study aims to analyze the level of fish consumption in Eastern Java and determine the best regression method to predict the rate of fish intake based on the region and the types of available commodities. This study uses three regression methods, namely Linear Regression, Support Vector Regression (SVR), and Gradient Boosting Machine. (GBM). Data visualization is done using bar diagrams, and results are validated using the determination coefficient R2 which is then analyzed descriptively. The Sumenep region has the highest level of fish consumption in East Java during the period 2018-2020. Whereas the commodities with the highest consumption are Tuna, Tongkol, Cakalang (TTC) Diawetkan. The GBM method showed its best performance and proved to be the most effective and accurate in predicting the level of fish consumption in East Java with a perfect determination coefficient (0,9999), compared to Linear Regression (0,8755) and SVR (0,9825).
PEMETAAN KAPAL TERBENGKALAI BERBASIS WEB DI WILAYAH OPERASIONAL PERAIRAN PPN KARANGANTU Virgianisa, Tania; Rosalia, Ayang Armelita; Arifin , Willdan Aprizal
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 15 No. 1 (2024): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v15i1.796

Abstract

The current condition of the Karangantu Archipelago Fisheries Port (PPN) is still considered unfavorable because the operational area of ​​the port waters looks dirty and shabby. There are many ships that are no longer used in the Karangantu PPN operational area. Based on the results of interviews with PPN Karangantu officers, there are several things that cause ships to be abandoned in the operational area of ​​PPN Karangantu waters, including the age of the ship, costs or capital, and human resources. Abandoned ships that are not immediately handled will result in obstacles to the flow of water traffic at the port and create a risk of collision for other ships passing by. Therefore, web-based mapping of abandoned ships (WebGIS) is needed in the operational area of ​​PPN Karangantu waters. It is hoped that the results of this abandoned ship mapping can be used by the port to report abandoned ships to the center so that they can immediately take action to transport the abandoned ships. With the existence of WebGIS mapping abandoned ships, the port does not need to go directly to the field to find the location of abandoned ships because the information can be accessed via the web that has been created. Data collection on coordinate points was carried out by field survey and tagging using the GPS Map Camera application. The research results show that there are 74 abandoned ships in the Karangantu PPN operational area.
Analysis of Carbon Stock Estimation in Mangroves with Climate Variability in West Java 2019-2023 Nurghea, Shelena Yasmin; Darmawan, Arief; Arifin, Wildan Aprizal
International Journal of Marine Engineering Innovation and Research Vol 10, No 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i1.22694

Abstract

Mangrove ecosystems are important in carbon sequestration and climate regulation and contribute to climate change mitigation. However, carbon stock estimation is still mostly done manually, which is less efficient. This study utilizes remote sensing to investigate the correlation between mangrove carbon stocks and climate variability in West Java from 2019 to 2023. Mangrove land cover classification was performed using the Random Forest algorithm with NDVI and NDWI indices, while the relationship between carbon stock and climate factors was analyzed using linear regression. The results showed that increased precipitation was associated with higher carbon stocks (R2=0.5514), while carbon stocks had a negative correlation with 2-meter temperature (R2=0.8242) and sea surface temperature (SST) (R2=0.7111). This study enhances our understanding of mangrove-climate interactions and provides valuable insights for developing remote sensing-based climate resilience and coastal ecosystem management policies.
Deep Learning for Tidal Flood Prediction in West Pandeglang Waters, Banten Ramaputra, Nevin Adel; Budiman, Asep Sandra; Arifin, Willdan Aprizal
International Journal of Marine Engineering Innovation and Research Vol 10, No 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i1.22615

Abstract

Tidal flooding poses a significant threat to coastal areas, exacerbated by rising sea levels. In West Pandeglang Waters, Banten, frequent tidal floods impact communities, necessitating accurate prediction models for effective disaster mitigation. This study aims to develop a deep learning-based tidal flood prediction model using Keras and TensorFlow. The model incorporates oceanic and atmospheric variables, including sea surface height, wave characteristics, wind components, and precipitation data from 2003 to 2023. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and MinMax scaling were applied, ensuring balanced class distribution. The model was trained and evaluated using a dataset comprising 11,808 samples, achieving an accuracy of 86% and an area under the curve (AUC) of 0.93. These results indicate a strong capability to differentiate between flood and non-flood conditions. The study demonstrates the effectiveness of deep learning in predicting tidal floods, providing valuable insights for early warning systems and coastal management in flood-prone regions.
Klasifikasi Kualitas Air Budidaya Ikan Nila Menggunakan Support Vector Machine Aulia, Tazkiah; Arifin, Willdan Aprizal; Rudi, Mad
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2356

Abstract

Kualitas air merupakan faktor kunci yang menentukan keberhasilan dan keberlanjutan budidaya ikan nila. Ketidaksesuaian parameter air, seperti suhu atau kandungan zat kimia, dapat menimbulkan stres fisiologis, menurunkan laju pertumbuhan, hingga menyebabkan kematian pada ikan. Kondisi ini menjadikan pemantauan kualitas air sebagai aspek yang sangat krusial dan mendesak dalam praktik budidaya. Penelitian ini bertujuan melakukan pemodelan klasifikasi kelayakan kualitas air menggunakan algoritma Support Vector Machine (SVM) yang dikombinasikan dengan Radial Basis Function (RBF) kernel. Kernel RBF merupakan fungsi matematis yang memungkinkan SVM memetakan data yang tidak terpisah secara linear ke dalam ruang berdimensi lebih tinggi, sehingga pola klasifikasi menjadi lebih terlihat. Data numerik diperoleh dari delapan parameter kualitas air: suhu, pH, total dissolved solids (TDS), oksigen terlarut, nitrit, nitrat, fosfat, dan amonia. Selanjutnya, data diklasifikasikan ke dalam dua kategori: layak dan tidak layak, berdasarkan ambang batas biologis yang telah ditentukan. Model dibangun menggunakan pendekatan supervised learning dan dievaluasi melalui metrik akurasi dan confusion matrix. Hasil pengujian menunjukkan bahwa model SVM dengan kernel RBF menghasilkan akurasi sebesar 82%, dengan nilai presisi dan recall mencapai 100% pada kategori “tidak layak”. Ini menunjukkan bahwa model mampu mengidentifikasi kondisi air yang berisiko dengan sangat baik, menjadikannya solusi potensial untuk pemantauan kualitas air budidaya. Model ini berkontribusi terhadap pengelolaan budidaya ikan yang efisien melalui otomatisasi pemantauan kualitas air dan pengambilan keputusan yang lebih akurat, sekaligus mendukung keberlanjutan dengan meminimalkan risiko lingkungan dan penggunaan sumber daya secara berlebihan.
Deep Learning for Tidal Flood Prediction in West Pandeglang Waters, Banten Nevin Adel Ramaputra; Asep Sandra Budiman; Willdan Aprizal Arifin
International Journal of Marine Engineering Innovation and Research Vol. 10 No. 1 (2025)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i1.4759

Abstract

Tidal flooding poses a significant threat to coastal areas, exacerbated by rising sea levels. In West Pandeglang Waters, Banten, frequent tidal floods impact communities, necessitating accurate prediction models for effective disaster mitigation. This study aims to develop a deep learning-based tidal flood prediction model using Keras and TensorFlow. The model incorporates oceanic and atmospheric variables, including sea surface height, wave characteristics, wind components, and precipitation data from 2003 to 2023. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and MinMax scaling were applied, ensuring balanced class distribution. The model was trained and evaluated using a dataset comprising 11,808 samples, achieving an accuracy of 86% and an area under the curve (AUC) of 0.93. These results indicate a strong capability to differentiate between flood and non-flood conditions. The study demonstrates the effectiveness of deep learning in predicting tidal floods, providing valuable insights for early warning systems and coastal management in flood-prone regions.
Analysis of Carbon Stock Estimation in Mangroves with Climate Variability in West Java 2019-2023 Shelena Yasmin Nurghea; Arief Darmawan; Willdan Aprizal Arifin
International Journal of Marine Engineering Innovation and Research Vol. 10 No. 1 (2025)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i1.4769

Abstract

Mangrove ecosystems are important in carbon sequestration and climate regulation and contribute to climate change mitigation. However, carbon stock estimation is still mostly done manually, which is less efficient. This study utilizes remote sensing to investigate the correlation between mangrove carbon stocks and climate variability in West Java from 2019 to 2023. Mangrove land cover classification was performed using the Random Forest algorithm with NDVI and NDWI indices, while the relationship between carbon stock and climate factors was analyzed using linear regression. The results showed that increased precipitation was associated with higher carbon stocks (R2=0.5514), while carbon stocks had a negative correlation with 2-meter temperature (R2=0.8242) and sea surface temperature (SST) (R2=0.7111). This study enhances our understanding of mangrove-climate interactions and provides valuable insights for developing remote sensing-based climate resilience and coastal ecosystem management policies.
Perbandingan Algoritma Machine Learning untuk Prediksi Multi-Output Variabel Osean-Atmosfer Maulidia, Raisa; Arifin, Willdan Aprizal; Syafri, Herman
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 4 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i4.25976

Abstract

Prediksi variabel osean-atmosfer merupakan komponen penting dalam mendukung keselamatan dan efisiensi aktivitas maritim. Kompleksitas data osean-atmosfer yang bersifat multivariat dan dinamis memerlukan pendekatan komputasional yang mampu menangkap hubungan non-linear dan temporal secara simultan. Penelitian ini membandingkan performa tiga algoritma machine learning, yaitu Long Short-Term Memory (LSTM), Random Forest, dan XGBoost dalam memprediksi multi-output variabel osean-atmosfer menggunakan data Automatic Weather Station (AWS) periode 2022–2025. Tahapan penelitian meliputi pra-pemrosesan, normalisasi menggunakan StandardScaler, pembagian data (90% latih dan 10% uji), pelatihan model teroptimasi, serta evaluasi menggunakan RMSE dan R². Hasil penelitian menunjukkan bahwa XGBoost memiliki performa terbaik pada sebagian besar variabel dengan nilai RMSE terendah pada windspeed (0,77), waterlevel (0,12), RH (2,40), dan winddir (28,79), serta nilai R² tertinggi masing-masing sebesar 0,840; 0,940; 0,870; dan 0,730. LSTM menunjukkan performa terbaik pada variabel watertemp dengan RMSE sebesar 0,31 dan R² sebesar 0,814. Sementara itu, Random Forest memiliki performa yang relatif lebih rendah dengan nilai R² berkisar antara 0,680 hingga 0,982 tergantung variabel. Secara keseluruhan, XGBoost terbukti paling konsisten dan efektif dalam menangani prediksi multi-output variabel osean-atmosfer yang kompleks dan non-linear.
Co-Authors Acep Saepul Zamil Adhitiyah Rosy Wijayanti Agung Setyo Sasongko Ahmad Beryliumsyah Ikmaludin Ahmad Satibi Ahmad Satibi AKBAR fitransyah, Hikmal Alya Dina Wilujeung Amien Rais Andre Aprinaldo Angga Fadzar Ani Anisyah Anton Daud Anton Daud Anzani, Luthfi Apriansyah, Muhamad Renaldi Apriansyah, Muhammad Renaldi Arief Darmawan Arsanti, Yulia Asep Sandra Budiman Aulia, Tazkiah Aulia, Tazkiah Kamilah Ayang Armelita Rosalia Azhari, Dhea Rahma Budiman, Asep Sandra Cakra Rahardjo cakra rahardjo Darmawan, Arief Daud, Anton Della Ayu Lestari Della Ayu Lestari Dhea Rahma Azhari Dhea Rahma Azhari Fadillah, Annisa Nur Fadzar, Angga Fahriza, Salsabila Putri Fawaz Fawaz Fernaldy Akbar Faudzan Futriansyah Lipalda Haekal Ghossan Firdaus Hajijah, Karimatul Aulia Handyanto, Lukman Hari Din Nugraha Herli Salim Hikmattulloh, M. Bintang Ilsa Margiana Herawati Ishak Ariawan Ita Arianti Jelita, Dinda Kiffah Kayyisah Ahmad Kiran Aulia Putri Kiran Aulia Putri Kukuh Widiyanto La Ode Alam Minsaris Larasati, Wenny Ananda Lubis, Naddra Haddad Lukman Handyanto Lukman Handyanto Lukman Lukman Lukman Lukman Luthfi Anzani M Ma'ruf Ma'ruf, Ma'ruf Mad Rudi Makhtar, Muhammad Ottmar Mar’ah, Maihuhatul Maulana, Pardip Maulidia, Raisa Maulsyid, Ramzan Pradana Minsaris, La Ode Alam Muhamad Ashari, Mahathir Muhamad Renaldi Apriansyah Muhammad Renaldi Apriansyah Murtianingsih, Dzakiya Fikri Nabila Tufailah Nevin Adel Ramaputra Novi Sofia Fitriasari Nurghea, Shelena Yasmin Nurokhim, Arif Pardip Maulana Puti Hanifa Kamal Putri Adi Khotimah Raden Mohamad Herdian Bhakti Rahaditya Aryadi, Naufal Rahardjo, Cakra Raisa Maulidia Raisa Maulidia Ramaputra, Nevin Adel Ramdhani, Muhammad Akbar Ramzan Pradana Maulsyid Ramzan Pradana Maulsyid Rifki Andreana Sutrisno Rudi, Mad Sabilla, Annisa Maulana Sariwardoyo, Awanda Muthia Septiantina, Shinta Shafa Salsabilla Buchori Salsabilla Buchori Shelena Yasmin Nurghea Shinta Septiantina Sihombing, Amalia Rahma Dini Sinurat, Anting B.N Sriyanto, Sesar Prabu Dwi Syafri, Herman Syamsul Arifin Tarigan, Daniel Julianto Taufiq Ejaz Ahmad Tirtana, Denta Titania Ferodova Shonda Umi Holifah Mashur Virgianisa, Tania Wenny Ananda Larasati Wilujeung, Alya Dina Yoga Estu Nugraha Nugraha Zamil, Acep Saepul