The limitations of energy in Indonesia, especially in Riau Province, has became polemic in the future, almost every period of the energy generated from fossil is diminishing and expensive. The government was given the mandate to develop alternative energies, one of them is the palm. The important role of goverment is to make decision regarding to the development of renewable energy. Riau Province has 8.91 million ha area potentially generating Indonesia’s largest palm oil plantation as it has 2.26 million hectares of plantation land with an average production of 6.93 million ton per year dispersing in the various districts.
This study is divided into three parts, i.e, pre-decision making, prediction, and post-decision making. Multi Attribute Decision Making (MADM) model consisting of Simple Additive Weighting (SAW) produces Bagan Sinembah as the best alternative with pre-prediction value at 0.7605 and post-prediction value at 0.7361, and followed by Mandau and Tapung. This method is reinforced by Analytic Hierarchy Process (AHP) as weighting and decision-making validation with global value at 0.2731. Weighting using AHP eigenvalue has less possibility change in every decision compared with the direct weight-given preference by decision makers.
The results of decision are independent to main criteria, that is Agricultural Sector Size (LSP) and Plantation Production (HPP) as determinant of the best decision, even though these two criteria are the most important criteria in decision-making. Related to post-production for next period production, it is yielded the best model Support Vector Regression (SVR) which is the kernel of Radial Basis Function (RBF) with 95% coefficient of determination (R2), 6% galat error (MSE) in fold 1 ranging γ = 20 to C = 23.
The potential of generation for each waste that is based on 50% waste utilization estimation and calculation of steam flow rate, dry waste composition with 6.6% water content, 50% shrinkage levels for Shells and Stems, and 65% for Empty Fruit Bunches.If this simulation is implemented and developed as alternative energy made from palm oil waste, it will be able to produce the electrification ratio at 21% to Bagan Sinembah; therefore, the total electrification ratio is 70%, or equivalent to 27 thousand out of 32 thousand residents in a district can enjoy electricity.
Keywords:
Analytic Hierarchy Process (AHP), Multi-attribute Decision Making (MADM), Renewable Energy, Oil Palm, Simple Additive Weighting (SAW), Support Vector Regression (SVR)
References:
Adhani G, Buono A, Faqih A. 2013. Support Vector Regression modelling for rainfall prediction in dry season based on Southern Oscillation Index and NINO3.4. International Conference on Advanced Computer Science and Information Systems. 2013: 315-320.
Afshari A, Mojahed M, Yusuff RM. 2010. Simple Additive Weighting approach to Personnel Selection problem. International Journal of Innovation, Management and Technology. 1(5): 511-515.
Agmalaro MA, Buono A. 2011. Pemodelan Statistical Downscaling Data GCM Menggunakan Support Vector Regression untuk Memprediksi Curah Hujan Bulanan Indramayu. [Tesis]. Bogor (ID): Institut Pertanian Bogor.
Apriani I. 2009. Pemanfaatan Limbah Cair Pabrik Minyak Kelapa Sawit Sebagai Energi Alternatif Terbaharukan (Biogas) [Tesis]. Bogor (ID): Institut Pertanian Bogor.
[BPS] Badan Pusat Statistik Provinsi Riau. 2013. Riau Dalam Angka 2006-2013 dan Kabupaten Dalam Angka 2006-2013.
Christodoulos C. 2010. Forecasting with limited data: Combining ARIMA and diffusion models. Technological Forecasting & Social Change. 77 (2010) 558–565.
Elinur. 2011. Analisis Konsumsi dan Penyediaan Energi dalam Perekonomian Indonesia. [Tesis]. Bogor (ID): Institut Pertanian Bogor.
Fishburn. 1967. “Additive Utilities with Incomlete Product Set: Application to Priorities and Assignments” dalam Yeh, Chung-Hsing. 2002. A Problem-based Selection of Multi-Attribute Decision Making Methods. International Transactions in Operational Research. 169-181.
Gunawan H. 2012. Seleksi Hyperspectral Band Menggunakan Recursive Feature Elimination untuk Prediksi Produksi Padi dengan Support Vector Regression. [Skripsi]. Bogor (ID): Institut Pertanian Bogor.
Hermantoro RWP. 2009. Prediksi Produksi Kelapa Sawit Berdasarkan Kualitas Lahan Menggunakan Model Artificial Neural Nettwork (ANN). Jurnal Agroteknose. 4(2).
Hidayat R. 2013. Sistem Prediksi Status Gizi Balita dengan Menggunakan Support Vector Regression. [Skripsi]. Bogor (ID): Institut Pertanian Bogor.
Ibrahim N dan Wibowo A. 2014. Support Vector Regression with Missing Data Treatment Based Variables Selection for Water Level Prediction of Galas River in Kelantan Malaysia. Wseas Transactions on Mathematics. 13(1), 2014 E-ISSN: 2224-2880.
Jain YK dan Bhandare SK. 2011. Min Max Normalization Based Data Perturbation Method for Privacy Protection. International Journal of Computer & communication Technology.2(8):
Jarial SK. Dan Garg RK. 2012. Ranking of Vendors Based on Criteria by MCDM-Matrix Method-A Case Study for Commercial Vehicles in an Automobile Industry. International Journal of Latest Research in Science and Technology. 1(4): 337-341.
Kumar DS, Radhika S dan Suman KNS. 2013. MADM Methods for Finding The Right Personnel in Academic Institutions. International Journal of u- and e- Service, Science and Technology. 6(5): 133-144.
Kusdiana D. 2008. Kondisi Rill Kebutuhan Energi di Indonesia dan Sumber-sumber Energi Alternatif Terbarukan. Diektorat Jendral Listrik dan Pemanfaatan Energi Departemen Energi dan Sumber Daya Mineral.
Kusmanto H. 2004. Perkebunan Kelapa Sawit dalam Menyongsong Otonomi Riau. Pekanbaru: Pusaka Riau.
Kusuma IP._____. Studi Pemanfaatan Biomassa Limbah Kelapa Sawit Sebagai Bahan Bakar Pembangkit Listrik Tenaga Uap di Kalimantan Selatan (Studi Kasus Kabupaten Tanah Laut). Prociding Seminar Nasional Teknologi Industri.
Kusumadewi S dan Hartati S. 2011. Sensitivity Analysis of Multi-Attribute Decision Making Methods in Clinical Group Decision Support System. Interational Conference Informatics Department, Indonesia Islamic University Yogyakarta, Indonesia.
Kusumadewi S. 2005. Pencarian Bobot Atribut Pada Multiple Attribute Decision Making (MADM) Dengan Pendekatan Obyektif Menggunakan Algoritma Genetika. Gematika Jurnal Manajemen Informatika. 7(1): 48-56.
Kusumadewi S, Hartati S, Harjoko A, Wardoyo R. 2006. Fuzzy Multi-Attribute Decision Making (Fuzzy MADM). Jogjakarta: Graha Ilmu.
Lacrosse L. 2004. Clean and Eicient Biomass Cogeneration Technology in ASEAN. COGEN 3 Seminar on “Business Prospects In Southeast Asia For European Cogeneration Equipment”. 23 November 2004. Krakow. Poland.
Mahajoeno E. 2008. Pengembangan Energi Terbarukan Dari Limbah Cair Pabrik Minyak Kelapa Sawit. [Tesis]. Bogor (ID): Institut Pertanian Bogor.
MemarianiA, Amini A dan Alinezhad A. 2009. Sensitivity Analysis of Simple Additive Weighting Method (SAW): The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives. Journal of Industrial Engineering 4 (2009): 13- 18.
Nur MS. 2014. Karakteristik Kelapa Sawit Sebagai Bahan Baku Bioenergi. Bogor: SanDesign.
Partogi D, Amin MN, dan Kasim ST. 2013. Analisis Biaya Produksi Listrik Per KWh Menggunakan Bahan Bakar Biogas Limbah Cair Kelapa Sawit (Aplikasi pada PLTBGS PKS Tandun). Singuda Ensikom. 3 (1): 17-22.
Patel VR dan Mehta RG. 2011. Impact of Outlier Removal and Normalization Approach in Modified K-Means Clustering Algorithm. IJCSI International Journal of Computer Science Issues. 8(5):
Paz JF. 2010. A Support Vector Regression Approach to Predict Carbon Dioxide Exchange. A.P. de Leon F. de Carvalho et al. (Eds.): Distrib. Computing & Artif. Intell., AISC 79, pp. 157–164
Piantari E. 2011. Feature Selection Data Hiperspektral Untuk Prediksi Produktivitas Padi dengan Algoritme Genetika Support Vector Regression. [Skripsi]. Bogor (ID): Institut Pertanian Bogor.
Pintoasari AP. 2011. Penentuan Alokasi Check-In Counter Terminal Bandara untuk 10 Tahun kedepan dengan Metode Support Vector Regession. [Tesis]. Jakarta (ID): Universitas Indonesia
Pudyantoro AR. 2012. Dampak Kebijakan Fiskal dan Sektor Hulu Migas Terhadap Perekonomian Provinsi Riau. [Tesis]. Bogor (ID): Institut Pertanian Bogor.
Rao SS. 2009. Engineering Optimization: Theory and Practice. John Wiley and Sons, Newyork.
Saaty TL. 1990. How to Make a Decision: The Analytic Hierarchy Process. Euroupean Journal of Operational Research. 48. North-Holland:9 – 26
Saaty TL. 2008. Decision making with the Analytic Hierarchy Process. International Journal Services Sciences. 1(1):
Saepudin A. 2010. Energi Terbarukan (Biogas) dari Limbah Kelapa Sawit. Pusat Penelitian Tenaga Listrik dan Mekatronik. LIPI
Santosa B. 2007. Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Graha Ilmu. Yogyakarta
Sembiring K. 2007. Penerapan Teknik Support Vector Mechine untuk Pendeteksian Intrusi pada Jaringan. [Skripsi]. Bandung (ID): Institut Teknologi Bandung.
Smola A, Schölkopf B. 2003. “A Tutorial on Support Vector Regression”, NeuroCOLT, Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK.
Sunarwan B dan Juhana R. 2013. Pemanfaatan Limbah Sawit untuk Bahan Bakar Energi Baru dan Terbarukan. Jurnal Tekno Insentif Kopwil 4. 7(2): 1-14.
Tesis Ilmu Komputer IPB
Mustakim Telematika 