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MODELLING DISSOLVED OXYGEN IN INTENSIVE AQUACULTURE SYSTEMS: LINEAR REGRESSION vs RANDOM FOREST APPROACHES Yani, Ahmad; Puspitasari, Asthervina W.; Poltak, Hendra; Fahrizal, Ahmad; Rusli, Rusli; Saville, Ramadhona
Jurnal Riset Akuakultur Vol 20, No 4 (2025): Desember (2025)
Publisher : Politeknik Kelautan dan Perikanan Jembrana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/jra.20.4.2025.339-354

Abstract

Dissolved oxygen (DO) is a critical water quality parameter in intensive aquaculture systems because its fluctuations directly affect farmed fish. Accurate prediction of DO is challenging due to complex, often nonlinear interactions among physicochemical and biological variables. Despite increasing interest in machine learning applications, comparative evaluations between traditional linear models and ensemble-based approaches in aquaculture contexts remain limited. This study aimed to analyse key variables associated with DO dynamics, compare the predictive performance of linear regression (LR) and random forest (RF) models, and identify dominant predictors relevant to aquaculture management. A publicly available aquaculture water quality dataset from Mendeley Data was analysed. Data were preprocessed by outlier removal and normalization, then split into training (70%) and test (30%) sets, and model robustness was assessed using 5-fold cross-validation. Dissolved oxygen concentrations ranged from 0.21 to 10.17 mg L⁻¹ (mean = 5.19 mg L⁻¹). Pearson correlation analysis showed positive associations between DO and ammonia (r = 0.60), biochemical oxygen demand (r = 0.55), and nitrite (r = 0.52), and negative associations with hydrogen sulphide (r = −0.55) and turbidity (r = −0.53). These relationships reflected indirect, management-mediated effects rather than direct causation. The RF model slightly outperformed LR (R² = 0.515 vs. 0.470), demonstrating the advantage of non-linear modelling. The feature importance analysis identified ammonia, hydrogen sulphide, nitrite, and biochemical oxygen demand as the dominant predictors. Although predictive accuracy remained moderate, the results highlight key drivers of DO variability and support the use of machine learning as a decision-support tool for smart aquaculture management. Oksigen terlarut (dissolved oxygen = DO) merupakan parameter kualitas air yang sangat penting dalam sistem akuakultur intensif karena fluktuasinya secara langsung memengaruhi komoditas yang dibudidayakan. Prediksi DO yang akurat menjadi tantangan karena adanya interaksi yang kompleks dan sering kali bersifat nonlinier antara variabel fisikokimia dan biologis. Meskipun minat terhadap penerapan machine learning terus meningkat, evaluasi komparatif antara model linier tradisional dan pendekatan berbasis ensemble dalam konteks akuakultur masih terbatas. Penelitian ini bertujuan untuk menganalisis variabel-variabel utama yang berkaitan dengan dinamika DO, membandingkan kinerja prediktif model regresi linier (LR) dan random forest (RF), serta mengidentifikasi prediktor dominan yang relevan untuk pengelolaan akuakultur. Dataset kualitas air akuakultur yang tersedia secara publik dari Mendeley Data dianalisis dalam penelitian ini. Data dipraproses melalui penghapusan pencilan dan normalisasi, kemudian dibagi menjadi data training (70%) dan pengujian (30%), dengan ketahanan model dievaluasi menggunakan validasi silang lima lipatan. Konsentrasi DO berkisar antara 0,21 hingga 10,17 mg L⁻¹ (rata-rata = 5,19 mg L⁻¹). Analisis korelasi Pearson menunjukkan hubungan positif antara DO dan amonia (r = 0,60), kebutuhan oksigen biokimiawi (BOD; r = 0,55), serta nitrit (r = 0,52), dan hubungan negatif dengan hidrogen sulfida (r = −0,55) dan kekeruhan (r = −0,53). Hubungan tersebut mencerminkan efek tidak langsung yang dimediasi oleh praktik pengelolaan, bukan hubungan kausal langsung. Model RF menunjukkan kinerja yang sedikit lebih baik dibanding LR (R² = 0,515 vs. 0,470), yang menegaskan keunggulan pemodelan nonlinier. Analisis kepentingan fitur mengidentifikasi amonia, hidrogen sulfida, nitrit, dan kebutuhan oksigen biokimiawi sebagai prediktor dominan. Meskipun akurasi prediksi masih tergolong moderat, hasil penelitian ini menyoroti faktor-faktor utama yang memengaruhi variabilitas DO dan mendukung penerapan machine learning sebagai alat pendukung keputusan dalam pengelolaan akuakultur cerdas. 
Analisis Bibliometrik: Perkembangan Metodologi dan Pemetaan Geografis Tren Penelitian Depurasi Logam Berat Pada Kekerangan (Bivalvia) Periode Tahun 2000-2025 Tantri, Akbar Falah; Desilina Arif; Hendra Poltak; Ahmad Yani; Chandika Lestariaji
ULIL ALBAB : Jurnal Ilmiah Multidisiplin Vol. 5 No. 5: April 2026
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jim.v5i5.16768

Abstract

Pencemaran logam berat pada ekosistem perairan menjadi isu global yang mengancam keamanan pangan laut, khususnya pada kelompok bivalvia yang memiliki kemampuan bioakumulasi tinggi (Roy, 2019). Penelitian ini bertujuan untuk memetakan perkembangan metodologi depurasi logam berat pada bivalvia selama periode 2000–2025 melalui analisis bibliometrik. Data sebanyak 3295 dokumen diekstraksi dari basis data Scopus dan dianalisis menggunakan R-package Bibliometrix. Hasil menunjukkan pertumbuhan publikasi tahunan sebesar 10,37%. Teridentifikasi evolusi metodologi yang signifikan, mulai dari penggunaan radiotracer (2000–2009), pendekatan biokimia dan fraksinasi seluler (2010–2015), hingga adopsi biomarker molekuler dan teknik omics (2016–2025). China mendominasi kontribusi global, sementara Indonesia berkontribusi 1,27% dengan tingkat sitasi yang relatif rendah. Tren penelitian saat ini bergeser menuju integrasi antara depurasi, penilaian risiko kesehatan manusia, dan perubahan lingkungan global.
Optimalisasi Rasio Pakan Alami Cacing Laut (Nereis sp.) dan Cumi-Cumi (Loligo sp.) terhadap Performa Reproduksi Induk Udang Vaname (Litopenaeus vannamei) pada Sistem Hatchery Intensif Desilina Arif; Tantri, Akbar Falah; Rusli, Rusli; Hendra Poltak; Ahmad Yani; Mustasim, Mustasim
ULIL ALBAB : Jurnal Ilmiah Multidisiplin Vol. 5 No. 5: April 2026
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jim.v5i5.16769

Abstract

Keberhasilan produksi benur pada sistem hatchery intensif sangat bergantung pada kualitas nutrisi induk untuk mendukung proses reproduksi. Penelitian ini bertujuan untuk mengevaluasi pengaruh berbagai rasio pakan alami cacing laut (Nereis sp.) dan cumi-cumi (Loligo sp.) terhadap nilai hatching rate (HR) induk Udang Vaname (Litopenaeus vannamei). Metode penelitian yang digunakan adalah eksperimental dengan Rancangan Acak Lengkap (RAL) yang terdiri dari tiga perlakuan dan tujuh ulangan: P1 (25% cacing laut : 75% cumi-cumi), P2 (50% cacing laut : 50% cumi-cumi), dan P3 (75% cacing laut : 25% cumi-cumi). Hasil analisis ANOVA menunjukkan bahwa rasio pakan berpengaruh sangat nyata (p < 0,05) terhadap daya tetas telur dengan nilai F-hitung (91,85) > F-tabel (3,55). Perlakuan P2 menghasilkan rata-rata hatching rate tertinggi sebesar 60,91%, diikuti oleh P3 (49,58%) dan P1 (38,27%). Hal ini menunjukkan bahwa keseimbangan antara asupan lipid (HUFA) dari cacing laut untuk vitellogenesis dan protein esensial dari cumi-cumi untuk embriogenesis sangat krusial dalam mengoptimalkan performa reproduksi. Disimpulkan bahwa rasio pakan 50% cacing laut dan 50% cumi-cumi merupakan komposisi optimal untuk meningkatkan produktivitas larva di hatchery.
The Effect of Suspended Solids on Phytoplankton Abundance and Water Quality Dynamics in Intensive Shrimp Ponds in Kraksaan, Probolinggo, Indonesia Chandika Lestariaji; Aang Setyawan Anjasmara; Indra Febriantoro; Hendra Poltak; Desilina Arif; Asthervina Widyastami Puspitasari; Ahmad Yani; Akbar Falah Tantri; Saidin Saidin; Fataha Ilyas Hasan
Nekton Vol 6 No 1 (2026): Nekton
Publisher : Politeknik Negeri Sambas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47767/nekton.v6i1.1149

Abstract

Intensive cultivation of whiteleg shrimp (Litopenaeus vannamei) has the potential to increase suspended solids concentrations, which can affect water quality and phytoplankton community structure. This study aims to analyze the effect of reducing Total Suspended Solids (TSS) concentrations through the application of a filtration system on phytoplankton abundance and water quality dynamics in intensive ponds. The study was conducted in intensive shrimp ponds in Kraksaan District, Probolinggo Regency, East Java, using an observational-experimental approach comparing pond conditions without filtration, mechanical filtration, and chemical filtration. The parameters observed included TSS, phytoplankton abundance, and several water quality parameters such as temperature, clarity, pH, dissolved oxygen (DO), salinity, nitrate, and orthophosphate. The results showed that mechanical filtration was able to reduce TSS most effectively with a range of 38–47 mg/L compared to chemical filtration (57–63 mg/L) and without filtration (81–91 mg/L). The decrease in TSS was followed by an increase in water clarity and DO concentration. The highest phytoplankton abundance was found in the mechanical filtration treatment (2,870–3,084 ind/L), followed by chemical filtration (2,121–2,291 ind/L), while without filtration showed the lowest abundance (1,062–1,401 ind/L). These results indicate that reducing TSS through the filtration system increases light penetration, thus supporting phytoplankton photosynthetic activity. Overall, the application of filtration systems, especially mechanical filtration, plays an important role in improving water quality and increasing phytoplankton productivity, thus potentially supporting ecosystem stability and the sustainability of intensive vannamei shrimp farming systems.
Co-Authors AA Sudharmawan, AA Aang Setyawan Anjasmara Abadi, Agung Setia Abu Darda Razak Abudarda Razak Adi Purnomo Agung Setia Abadi AHMAD FAHRIZAL Ahmad Yani Ahmad Yani ahmad yani Akbar Falah Tantri Ali ulat, Muhamad Amir M Suruwaky Amir M. Suruwaky Amir Machmud Suruwaky Anak Agung Gede Sugianthara Anjas AS Komboe Arhandy Arfah Asmira Rumalolas Astervina W. Puspitasari Astuti Astuti Bagas Prakoso Bakhtiar Bakhtiar Bambang Winarno Boby W Ziliwu Chandika Lestariaji Chandika Lestariaji Charles Nalle Desilina Arif Desilina Arif Dheni Rossarie Endang - Gunaisah Ernawati Ernawati Ernawati Ernawati Ernawati Ernawati Fataha Ilyas Hasan Ferdinand Hukama Taqwa Gatot Hendra Prakoso Hafita, Yuniar Ayu Handayan Handayani HANDAYANI Handayani - Handayani Handayani Handayani, Handayan Heri Sutanto Hismayasari, Intanurfemi B I Komang H Permana Indra Febriantoro Intanurfemi Bacandra Hismayasari Intanurfemi Hismayasari Iskandar Iskandar Ismail Ismail Ismail Ismail Ismail Ismail Ismail Jaulim Sirait Kadarusman Karubaba, Octavianus Kristian Katili, Vicky Katili, Vicky Rizky Lay Tjarles Leoni Hayu Sabrina Putri Lila Gardenia Maqdits Hikmatian Matahari, Matahari Misbah Sururi Moejiono Moejiono Muh Guntur Ketuut Muh Kasim Muh. Kasim Muhamad Ali Ulat Muhammad Ali Ulat Muhammad Idris Muhammad Zaky Latif Muhfizar Muhfizar Muhfizar, Muhfizar Mulya, Anjar Mustasim, Mustasim Ndahawali, Daniel Heintje Ndahwali, Daniel Heintje Nur Anjelika Saputri Nurfitri Rahim Nurul Huda Oktovianus Cristian Karubaba Pujianto, Andreas Puspitasari, Asthervina W. Puspitasari, Asthervina Widyastami R. Rusli Ratna Ratna Renaldo Fredly Rumaherang Rezza Ruzuqi Ristiana Dwi Cahyani Ristiani Ruzuki, Rezza Saidin Saidin Samsul Muhammad Saville, Ramadhona Sayuti, Mohammad Serisanthi Irsani Oklan Bothmir Sigit Purnomo Sidhi Simon Manutilaa Siti Hadijah Sri Wahyuni Firman Sudirman Sudirman Suharyanto Suharyanto Suhermanto, Achmad Sulistyowati, Beta Indi Suruwaky, Amir Machmud Syafril Sanusi Tamaulina Br Sembiring Tantri, Akbar Falah Tinggal Hermawan Tio Arriela Doloksaribu Tirsa Ninia Lina Tjarles, Lay Ulat, M Ali Ulat, Muhammad Ali Valentine, Riris Yuli Vicky Rizky Affandy Katili Wamnebo, Muhammad Ikhsan Yani Nurita Purnawanti Zakeus Wuarbanaran