Financial Feasibility Study of Waste Cooking Oil Utilization for Biodiesel Production Using ANFIS

Ahmad, Imam Safawi; Hermadi, Irman; Arkeman, Yandra

doi:10.11591/telkomnika.v13i3.7122

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Some research has been performed on extracting biodiesel from waste oil, to reduce the cost of biodiesel production while also avoiding emissions. 26,74 Imam et al 75 carried out research to see if investing in waste cooking oil as a biodiesel material is feasible. They carried out this study in two steps.…”

Section: Anfis Modeling Of Biodiesel Yieldmentioning

confidence: 99%

ANFIS modeling of biodiesels' physical and engine characteristics: A review

et al. 2021

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Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R 2 value is generally obtained which is around 5% greater than the ANN modeling results reported.However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.

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Section: Anfis Modeling Of Biodiesel Yieldmentioning

confidence: 99%

ANFIS modeling of biodiesels' physical and engine characteristics: A review

et al. 2021

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“…The performance indicators based on RMSE value and the accuracy in terms of trend predictions of the JCI. The accuracy calculation method is explained through the following equation (Ahmad et al, 2015):…”

Section: Anfis Model Constructionsmentioning

confidence: 99%

Constructing a predicting model for JCI return using adaptive network-based Fuzzy Inference System

Suswono

Hakim

Bakhtiar

2019

Jurnal Keuangan Dan Perbankan

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The high price fluctuations in the stock market make an investment in this area relatively risky. However, higher risk levels are associated with the possibility of higher returns. Predicting models allows investors to avoid loss rate due to price fluctuations. This study uses ANFIS (Adaptive Network-based Fuzzy Inference System) to predict the Jakarta Composite Index (JCI) return. Forecasting JCI movement is considered to be the most influential predictor, consisting of Indonesia real interest rate, real exchange rate, US real interest rate, and WTI crude oil price. The results of this study point out that the best model to predict JCI return is the ANFIS model with pi membership function. The predicting model shows that real exchange rate is the most influential factor to the JCI movement. This model is able to predict the trend direction of the JCI movement with an accuracy of 83.33 percent. This model also has better performance than the Vector Error Correction Model (VECM) based on RMSE value. The ANFIS performance is relatively satisfactory to allow investors to forecast the market direction. Thus, investors can immediately take preventive action towards any potential for turmoil in the stock market. Abstrak Tingginya tingkat fluktuasi harga di pasar saham menjadikan investasi di bidang ini cukup berisiko. Akan tetapi, tingginya tingkat risiko tersebut sebanding dengan potensi return yang dapat diperoleh. Penggunaan model peramalan dapat menjadi alternatif bagi investor agar terhindar dari kerugian akibat fluktuasi harga. Penelitian ini mencoba mengkonstruksi model peramalan return IHSG menggunakan pendekatan ANFIS. Indikator fundamental yang dinilai paling berpengaruh terhadap pergerakan IHSG digunakan sebagai prediktor untuk model peramal ini. Prediktor yang digunakan berupa data suku bunga riil Indonesia, kurs riil antara rupiah dan dolar AS, suku bunga riil AS, dan harga minyak dunia. Hasil penelitian ini menunjukkan bahwa model terbaik untuk peramalan return IHSG adalah model ANFIS dengan bentuk fungsi keanggotaan pi (pimf). Model peramalan yang dihasilkan menunjukkan bahwa nilai tukar riil merupakan faktor yang paling berpengaruh terhadap return IHSG. Model tersebut mampu memprediksi arah tren pergerakan IHSG dengan akurasi sebesar 83,33 persen. Model ini memiliki kinerja peramalan yang lebih baik dibanding Vector Error Correction Model (VECM) berdasarkan indikator RMSE. Hasil ini cukup memuaskan dalam memberikan gambaran bagi investor kemana pasar akan bergerak. Dengan demikian, investor bisa segara mengambil langka antisipasi ketika terdapat potensi gejolak di pasar saham.

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“…The application of FA can be used either as a single method or combined with the other method such as technical analysis [4] as well as regional and sectoral economic approach [5]. FA was also employed in measuring the feasibility of the introduction of new energy sources including waste cooking oil [6], solar energy [4] and also for the feasibility of mass production [7].…”

Section: Financial Feasibility Analysismentioning

confidence: 99%

The Importance of Credit Program Scheme on Waste to Energy Program in Indonesia: Case Study on Tofu Industry

Syaifudin¹,

Nurkholis²,

Handika³

et al. 2018

MATEC Web Conf.

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Abstract. This study aimed to provide a basis for future policy making in supporting the waste to energy from tofu in Indonesia. The policy studied was the credit program scheme for tofu industry. The financial feasibility, as well as cost and benefit analysis, were conducted to meet the objectives. Tofu industries are mostly small-sized industry which supports people with middle-to-low incomes. On the other hand, tofu industry also contributes significant amounts of emissions. Thus this calls for government intervention to encourage tofu industries to convert their wastes to energy. The result of the study shows that the developments of biogas reactors are feasible for size 84 m 3 and 94 m 3 . The study also found that for both sizes, the benefits are higher than the costs.

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Financial Feasibility Study of Waste Cooking Oil Utilization for Biodiesel Production Using ANFIS

Cited by 4 publications

References 13 publications

ANFIS modeling of biodiesels' physical and engine characteristics: A review

ANFIS modeling of biodiesels' physical and engine characteristics: A review

Constructing a predicting model for JCI return using adaptive network-based Fuzzy Inference System

The Importance of Credit Program Scheme on Waste to Energy Program in Indonesia: Case Study on Tofu Industry

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