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Comparison of Electrical Properties and Endurance of Papaya Peel and Cassava Peel-Based Bio-Batteries as Potential Alternative Energy Sources Heriansyah, Heriansyah; Rahmadani, Novia Gena; Rahman, Refpo; Ramandani, Adityas Agung
Journal of Innovation in Applied Natural Science Vol. 1 No. 1 (2025): Journal of Innovation in Applied Natural Science
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jinas.v1i1.48

Abstract

Background of study: Bio-batteries represent an attractive energy solution utilizing organic substances to generate electrical energy. Organic waste materials, such as fruit peels, contain electrolytic compounds harnessed for bioelectricity generation. Cassava and papaya peels, rich in natural acids and carbohydrates, offer potential as low-cost, eco-friendly materials for bio-battery development. Aims and scope of paper: This study investigates the electrical performance and endurance of biobatteries made from cassava peel and papaya peel subjected to varying fermentation durations (0, 2, and 4 days).Methods: The study employed an experimental comparative approach using 1.5 V battery casings filled with fermented cassava and papaya peel pastes. Electrical parameters (voltage, current, and power) were measured using a digital multimeter. Additionally, endurance was tested by using the biobatteries to power a 1.2 W LED until discharge.Result: Cassava peel-based biobatteries showed higher electrical output than those based on papaya peel, especially after 4 days of fermentation. The cassava battery reached a peak voltage of 1.6 V and power of 0.107 mW, while papaya reached 1.57 V and 0.105 mW. Cassava peel biobatteries also demonstrated longer endurance, operating up to 27 hours compared to 21 hours for papaya.Conclusion: Fermentation enhances the electrical properties of fruit peel biobatteries, with 4 days as the optimal duration. Cassava peel is more effective than papaya peel due to its higher content of fermentable substrates and organic acids. This study supports the feasibility of using fermented fruit waste as sustainable bio-battery material and suggests further optimization for practical applications.
Comparison of Electrical Properties and Endurance of Papaya Peel and Cassava Peel-Based Bio-Batteries as Potential Alternative Energy Sources Heriansyah Heriansyah; Novia Gena Rahmadani; Refpo Rahman; Adityas Agung Ramandani
Journal of Innovation in Applied Natural Science Vol. 1 No. 1 (2025): Journal of Innovation in Applied Natural Science
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jinas.v1i1.48

Abstract

Background of study: Bio-batteries represent an attractive energy solution utilizing organic substances to generate electrical energy. Organic waste materials, such as fruit peels, contain electrolytic compounds harnessed for bioelectricity generation. Cassava and papaya peels, rich in natural acids and carbohydrates, offer potential as low-cost, eco-friendly materials for bio-battery development. Aims and scope of paper: This study investigates the electrical performance and endurance of biobatteries made from cassava peel and papaya peel subjected to varying fermentation durations (0, 2, and 4 days).Methods: The study employed an experimental comparative approach using 1.5 V battery casings filled with fermented cassava and papaya peel pastes. Electrical parameters (voltage, current, and power) were measured using a digital multimeter. Additionally, endurance was tested by using the biobatteries to power a 1.2 W LED until discharge.Result: Cassava peel-based biobatteries showed higher electrical output than those based on papaya peel, especially after 4 days of fermentation. The cassava battery reached a peak voltage of 1.6 V and power of 0.107 mW, while papaya reached 1.57 V and 0.105 mW. Cassava peel biobatteries also demonstrated longer endurance, operating up to 27 hours compared to 21 hours for papaya.Conclusion: Fermentation enhances the electrical properties of fruit peel biobatteries, with 4 days as the optimal duration. Cassava peel is more effective than papaya peel due to its higher content of fermentable substrates and organic acids. This study supports the feasibility of using fermented fruit waste as sustainable bio-battery material and suggests further optimization for practical applications.
ANN-ENHANCED ARIMA MODELS FOR SST-BASED SEASONAL FISH-CATCH FORECASTING AND DECISION-SUPPORT IN BENGKULU WATERS, INDONESIA Rizal, Jose; Afandi, Nur; Rahman, Refpo; Astuti, Mulia; Mayasari, Zulfia Memi; Faisal, Fachri; Yosmar, Siska
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 4 (2026): Volume 10, Nomor 4, August 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i4.55311

Abstract

This study presents a comparative evaluation of hybrid ARIMA-family forecasting models combined with Artificial Neural Networks (ANNs) for seasonal fish-catch prediction in Bengkulu waters, Indonesia, while assessing the role of sea surface temperature (SST) as an environmental predictor. Monthly SST data from NASA’s Giovanni portal and pelagic fish-catch records collected between January 2017 and June 2025 were used to develop ARIMA, ARIMAX, SARIMA, and SARIMAX models, whose residuals were subsequently modeled using Feedforward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) networks to capture nonlinear temporal dependencies. Among the evaluated models, the hybrid ARIMA–LSTM achieved the highest forecasting accuracy on the available dataset, with an RMSE of 76.779 and a MAPE of 19.223%, whereas hybrid models that explicitly incorporate SST as a linear exogenous predictor showed lower predictive performance. These findings suggest that although SST remains an ecologically important environmental driver of pelagic fisheries, its predictive contribution may be better captured by nonlinear, lag-dependent relationships embedded in historical fish-catch observations rather than by contemporaneous linear exogenous modeling. Overall, this study provides empirical evidence for selecting appropriate hybrid forecasting models under practical fisheries data conditions and highlights their potential application as analytical components within fisheries Decision Support Systems (DSS) for adaptive fisheries management.