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<![CDATA[Model Simulasi dan Visualisasi Prediksi Potensi Hasil dan Produksi Kedelai di Jawa Timur ]]>
<![CDATA[Koentjoro, Bambang S.]]>;
<![CDATA[Sitanggang, Imas S.]]>;
<![CDATA[Makarim, Abdul Karim]]>
<![CDATA[ Jurnal Penelitian Pertanian Tanaman Pangan Vol 34, No 3 (2015): Desember 2015 ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengembangan Tanaman Pangan ]]>
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DOI: 10.21082/jpptp.v34n3.2015.p195-201
<![CDATA[The prediction of national soybean yield and production could be improved its accuracy by integrating a simulation model and Geographic Information Systems (GIS). The objective of this research was to integrate a simulation model with a GIS, to predict the potential yield and production of soybean in the soybean production centers of East Java. This study was conducted from December 2013 till May 2014. The approach used in this study was a systems approach using a simulation model as solution to the problem. The model is SUCROS.SIM (Simple Universal Crops Growth Simulator), which was written using Powersim software and Spreadsheet in order to be fully integrated with GIS. The initial phase of the integration process between SUCROS.SIM and GIS are as follows (a) model validation, using input data of soybean plant assimilate partitioning, (b) climatic data (solar radiation, maximum and minimum temperatures) collected from the climatological station (BMKG) Karangploso Malang and (c) observation data of soybean yields of two varieties (Wilis and Argomulyo) at Muneng Experiment Station. It was found that the coefficients of determination of simulation model of soybean yield potential (R2) range from 0.945-0.992 and RMSE (Root Mean Square Error) values range from 0.11 to 0.25 t/ha. The average of soybean yield potential and production in 2012 at soybean production centers of East Java were 1.94 t/ha and 293,459 ton, respectively. The conclusion is SUCROS.SIM valid to be integrated with GIS.]]>
<![CDATA[Spatial decision tree model for garlic land suitability evaluation ]]>
<![CDATA[Andi Nurkholis]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Annisa Annisa]]>;
<![CDATA[Sobir Sobir]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i3.pp666-675
<![CDATA[Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.]]>
<![CDATA[Expert System Modeling for Land Suitability based on Fuzzy Genetic for Cereal Commodities: Case Study Wetland Paddy and Corn ]]>
<![CDATA[Fitri Insani]]>;
<![CDATA[Imas S Sitanggang]]>;
<![CDATA[Marimin Marimin]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v13i3.1735
<![CDATA[ Nowadays, threats of food shortages are happen in Indonesia. Most of crops that are consumed as main food are cereals commodities. Cereals cultivation often experience some problems in determining whether land is suitable or not for the crops. Expert system can help researcher and practitioners to identify land suitability for cereal crops. In this research, an expert system model of land suitability for cereals crop was built. The model implemented soft computing methods to develop inference engine which combines fuzzy system and genetic algorithm. There are 16 parameters to define land suitability which consists of 12 numeric parameters and 4 categorical parameters. Two types of cereal crops that were used in this study namely wetland paddy and corn. Trapezoid membership function was used to represent fuzzy sets for numerical parameters. Genetic algorithm was used for tuning the membership function of fuzzy set for land suitability which consists of very suitable (S1), quite suitable (S2), marginal suitable (S3) and not suitable (N). This expert system is able to choose land suitability classes for cereals using the fuzzy genetic model with accuracy of 90% and 85% for corn and wetland paddy respectively.]]>
<![CDATA[Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau ]]>
<![CDATA[Putri Thariqa]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Lailan Syaufina]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v14i2.3540
<![CDATA[ Over one-year period (March 2013 – March 2014), 58 percent of all detected hotspots in Indonesia are found in Riau Province. According to the data, Rokan Hilir shared the greatest number of hotspots, about 75% hotspots alert occur in peatland areas. This study applied spatial decision tree algorithms to classify classes before burned, burned, and after burned from remote sensed data of peatland area in Kubu and Pasir Limau Kapas subdistrict, Rokan Hilir, Riau. The decision tree algorithm based on spatial autocorrelation is applied by involving Neigborhood Split Autocorrelation Ratio (NSAR) to the information gain of CART algorithm. This spatial decision tree classification method is compared to the conventional decision tree algorithms, namely, Classification and Regression Trees (CART), C5.0, and C4.5 algorithm. The experimental results showed that the C5.0 algorithm generate the most accurate classifier with the accuracy of 99.79%. The implementation of spatial decision tree algorithm succesfuly improve the accuracy of CART algorithm.]]>
<![CDATA[Burn Area Processing to Generate False Alarm Data for Hotspot Prediction Models ]]>
<![CDATA[Imas S Sitanggang]]>;
<![CDATA[Razali Yaakob]]>;
<![CDATA[Norwati Mustapha]]>;
<![CDATA[Ainuddin A. N]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v13i3.1543
<![CDATA[Developing hotspot prediction models using decision tree algorithms require target classes to which objects in a dataset are classified. In modeling hotspots occurrence, target classes are the true class representing hotspots occurrence and the false class indicating non hotspots occurrence. This paper presents the results of satellite image processing in order to determine the radius of a hotspot such that random points are generated outside a hotspot buffer as false alarm data. Clustering and majority filtering were performed on the Landsat TM image to extract burn scars in the study area i.e. Rokan Hilir, Riau Province Indonesia. Calculation on burn areas and FIRMS MODIS fire/hotspots in 2006 results the radius of a hotspot 0.90737 km. Therefore, non-hotspots were randomly generated in areas that are located 0.90737 km away from a hotspot. Three decision tree algorithms i.e. ID3, C4.5 and extended spatial ID3 have been applied on a dataset containing 235 objects that have the true class and 326 objects that have the false class. The results are decision trees for modeling hotspots occurrence which have the accuracy of 49.02% for the ID3 decision tree, 65.24% for the C4.5 decision tree, and 71.66% for the extended spatial ID3 decision tree.]]>
<![CDATA[Detection and Prediction of Peatland Cover Changes Using Support Vector Machine and Markov Chain Model ]]>
<![CDATA[Ulfa Khaira]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Lailan Syaufina]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v14i1.2400
<![CDATA[Detection and prediction of peatland cover changes needs to be done in the rapid rate of deforestation in Indonesia. This work applied Support Vector Machine (SVM) and Markov Chain Model on multitemporal satellite data. The study area is located in the Rokan Hilir district, Riau Province. SVM classification technique used to extract information from satellite data for the years 2000, 2004, 2006, 2009 and 2013. The Markov Chain Model was used to predict future peatland cover. The SVM classification result showed that the Kappa accuracy of peatland cover classification is more than 0.92. The non vegetation areas increased to 307% and the sparse vegetation areas increased to 22% between 2000 and 2013, while dense vegetation areas decreased to 61%. Prediction of future land cover by the Markov Chain Model showed that the use of multitemporal satellite data with 3 years interval provides accurate result for predicting peatland cover changes.]]>
<![CDATA[A Decision Tree Based on Spatial Relationships for Predicting Hotspots in Peatlands ]]>
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Razali Yaakob]]>;
<![CDATA[Norwati Mustapha]]>;
<![CDATA[Ainuddin A. N.]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 2: June 2014 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v12i2.68
<![CDATA[Predicting hotspot occurrence as an indicator of forest and land fires is essential in developing an early warning system for fire prevention. This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. Spatial data consist of a target layer and ten explanatory layers representing physical, weather, socio-economic and peatland characteristics in the study area Rokan Hilir District, Indonesia. Target objects are hotspots of 2008 and non-hotspot points. The result is a pruned spatial decision tree with 122 leaves and the accuracy of 71.66%. The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4.5 algorithm. The ID3 decision tree has accuracy of 49.02% while the accuracy of C4.5 decision tree reaches 65.24%.]]>
<![CDATA[Agent Based Modeling on Dynamic Spreading Dengue Fever Epidemic ]]>
<![CDATA[Heti Mulyani]]>;
<![CDATA[Taufik Djatna]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v15i3.4511
<![CDATA[Agent based model (ABM) is a computational model for simulation, behavioral representation and interaction of autonomous agents. The main problem definition related to how to make a dynamic model of dengue fever with consideration of their behavioral and interaction agent. This paper aims to develop interactive behavioral agents and related simulation models for such dynamic spreading dengue fever epidemic. This model construction consists of two agents, namely a human agent as a host and mosquito as a vector, where temperature and humidity are the environmental parameters. These environmental parameters deployed data and information from National Meteorology Climatology and Geophysics agency and supported by recent community health data of Bogor region. The verification stage evaluated model performance of two periods between January to June and between July to December 2015 showed the fitness of the model. During simulation stage where 100 humans agent and 10 mosquitoes agent were observed, indicating the decreasing of mosquito by 26.3% and the number of infected human decrease to 16% from the period of January until June to July until December 2015 respectively. These evaluation results showed that the agent based model results succeeded to follow a similar trend of decreasing pattern as actual data.]]>
<![CDATA[Decision Support System for Bat Identification using Random Forest and C5.0 ]]>
<![CDATA[Deden Sumirat Hidayat]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Gono Semiadi]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v15i3.3638
<![CDATA[Morphometric and morphological bat identification are a conventional method of identification and requires precision, significant experience, and encyclopedic knowledge. Morphological features of a species may sometimes similar to that of another species and this causes several problems for the beginners working with bat taxonomy. The purpose of the study was to implement and conduct the random forest and C5.0 algorithm analysis in order to decide characteristics and carry out identification of bat species. It also aims at developing supporting decision-making system based on the model to find out the characteristics and identification of the bat species. The study showed that C5.0 algorithm prevailed and was selected with the mean score of accuracy of 98.98%, while the mean score of accuracy for the random forest was 97.26%. As many 50 rules were implemented in the DSS to identify common and rare bat species with morphometric and morphological attributes.]]>
<![CDATA[Potential Usage Estimation of Ground Water using Spatial Association Rule Mining ]]>
<![CDATA[Suci Sri Utami Sutjipto]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Baba Barus]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v15i1.4750
<![CDATA[The utilization of ground water in the long term will lead to a number of negative impacts on groundwater resources and the environment, such as the decrease of groundwater level, seawater intrusion, land subsidence as well as scarcity of ground water. Furthermore, the use of ground water has directly affected the consumption pattern of Regional Water Company Bogor City (PDAM) customers. This study aims to determine the patterns and characteristics of PDAM customers in the utilization of ground water by using spatial association rule mining, so it can help PDAM to approximate the increase of customers that utilize ABT and the losses incurred. This research shows that as many as 53.362 (41.27%) PDAM customers that have the potential to use groundwater. The said customers are featured by several characteristics, such as being active customers, with monthly water bill of less than Rp. 53.358 and are not close to river.]]>
