Decision Support System for Determining Critical Land in Klaten Regency

Critical land has become a problem in the world. Critical land is very detrimental to the health of the land. Several factors cause the land to become critical. One of them is the use of land that is not by the capabilities of the land. If no repairs made, the land will be physically, chemically, and biologically damaged. Klaten Regency is one of the regencies in Central Java Province, which has quite extensive critical land. It is necessary to monitor and improve land quality regularly to avoid critical land problems. Data and information on critical land obtained from Klaten Regency processed into a decision support system. Decision Support System uses a combination of Analytical Hierarchy Process (AHP) and Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) methods. In this research, a Web-based Decision Support System created to determine the critical land area in Klaten Regency. The information system created has an alternative menu and criteria that determine the potential of critical land in Klaten Regency, making it easier for users to obtain information.


Introduction
Critical land becomes a problem in data processing in the environmental field [1,2]. Regional Development Planning Agency Klaten Regency has difficulty in determining critical land. This is due to various parameter values and criteria. So we need a system that can provide information about critical land in Klaten Regency accurately, effectively and

Literature Review
Utilizing AHP method was previously used to rank options according to the relevance of the criteria weights for delineation of groundwater potential zones [3]. AHP method is a decision making method to calculate priority criteria that meet the requirements of several alternatives based on the judgment of the decision maker [4,5]. Although the AHP method has several advantages, it cannot be separated from the shortcomings, the method is less effective if it is used in cases that have a large number of criteria and alternatives. Other methods are needed to be combined in order to obtain more effective results [6][7][8]. AHP when used to rank requires a relatively longer time and only compares between criteria, there is no normalization process and calculation of its costs and benefits [9,10,12]. The AHP method is also used to choosing the optimal technology to rehabilitate the pipes in water distribution systems [13]. TOPSIS is a multi-criteria method used to identify solutions from alternative sets based on simultaneous minimization of ideal point distances and maximizing distances from low points [14][15][16][17].

Research Methodology
In this study using the Analytic Hierarchy Process (AHP) method, will be calculated from the criteria weight value to obtain a total priority value (tpv), which will then be used in calculations with the Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method.

Criteria Weights
First we need criteria data which contains the weights which in this case are 6 influential criteria obtained from the Klaten Regency field. These data are shown in the table 1. From the data above will be made one by one comparison for each criterion so that the data obtained as shown in table 2.

Normalization of Criteria Weights
The next stage is the weighted criteria that have been compared normalized by dividing by the number of values in one row. Following is the acquisition of normalization values shown in table 3.

Determination of Priority Weight Value Criteria
At this stage the processing has been carried out using the formula weight criteria or total priority value, namely: (1) Explanation: -TPV : Priority weights criteria value -: Total normalization sum of weights criteria n : Number of criteria Samples of processing the criteria weights from the slope parameters can be obtained with the formula for priority weights criteria, namely: Likewise with other criteria will be done the same way with the formula of the priority value of criteria weights. The results of data processing the priority value of criteria weights can be shown in table 4. In the Topsis method, a calculation that takes the tpv value from the results of the previous method is AHP to obtain the final value from each of its districts.

Determination The Value Divisor
The next step is to determine the value of the divisor by using the following formula: (2) Explanation : -y : Divider Value -: i-alternative performance rating for the j-criterion Samples determining the value of the divider from the slope parameter can be obtained by the formula of the divider value : Likewise with other criteria will be done in the same way with the divisor value formula. The results of processing the divider value data can be shown in table 6.

Determination The Normalization Value
At this stage the processing has been carried out using the normalization formula Likewise with other parameters and sub-districts carried out with the same normalization formula. Normalization data processing results can be shown in table 7.

Determination of Weighted Normalization
At this stage a multiplication is made between the normalized value and the priority value of the criteria weights. Samples of normalization values & priority values for criteria weights are shown as follows: Pedan Subdistrict with the slope criteria has a normalization value of 0 based on table 7. With the priority value of the slope criteria weight is 0.1721 based on table 4. Then, the weighted normalization value is obtained in the ampelgading area with the slope parameter is 0. The results of weighted normalization data processing are shown in table 8.   table 8 and table 1 obtained by the evaluation factor formula,  namely: = 0,118 = 0 Likewise with other parameters and sub-districts carried out with the same normalization formula. Normalization data processing results can be shown in table 9.

Determination of The Value of the Ideal Solution
At this stage there are two types of alternative value distances, namely the distance of the positive ideal solution value (d + ) and the distance of the negative ideal solution value (d -), performed data processing using the alternative value distance formula : Likewise with other sub-districts carried out with the same alternative value distance formula. The results of distance data processing of alternative values can be shown in table 10.  A Web-based Decision Support System created to determine the critical land area in Klaten Regency. The information system created has an alternative menu and criteria that determine the potential of critical land in Klaten Regency, making it easier for users to obtain information. The results of ranking the decision support system using the web-based AHP-TOSIS method are shown in Figure 1.

Result
The implementation of Analytic Hierarchy Process and Topsis methods for this Critical Land Decision Support Support System was built using web-based. Alternative data and criteria data can be accessed on the sub-district and data pages. After the data is ready to be processed, the system will dynamically perform the processing using the AHP and TOPSIS methods and the results will be displayed on the system at each step. The use of the combination of AHP and TOPSIS is because the AHP method is less effective when used in cases that have a large number of criteria and alternatives, in this case there are quite a number of alternatives namely 26 alternatives, so combined with the TOPSIS method used to determine the priority level of alternatives to be more effective and efficient in the calculation process. The system that has been made can recommend the Regional Development Planning Agency of Klaten (Bappeda) in determining the priority of critical land in the Klaten Regency. The Supporting System for Critical Land Determination Decisions in Klaten District using the Analytic Hierarchy Process and TOPSIS methods built on a web-based result shows that the Karangdowo District area has the highest final yield value of 0.6489 and Jogonalan District with the lowest value of 0.1426.