Risk Cause Analysis on E-Procurement Bidding

Purpose: The current procurement of goods and services refers to the Electronic Procurement Service (E-Procurement) system in accordance with Presidential Regulation No.16 of 2018, however in its implementation the contractors still need adjustments. One of them is the contractor involved in the case study of this research which has failed in several tenders. This can create risks associated with value and profitability of the company. This study aims to cover this gap by knowing the risk factors for failure, level and the form of handling carried out by distributing questionnaires to the related contractors. Design/methodology/approach : Data analysis was performed using statistical software and the value was mapped using the Risk Relative Importance Index (RRI). Findings : The result show that the capacity of the bid files to upload is too large and there is no supplier support, are in moderate risk levels with scale respectively of 8,836 and 7,407. Problematic internet signal factors and incomplete design maturity levels are in high risk levels with a scale of 11.868 and 12.586. Meanwhile, the extreme risk level, namely the price cannot compete with other participants and the very limited time for calculating the tender, on a scale of 12,586 and 15,339 are factors that need attention. Originality/value: This paper is original.


I. INTRODUCTION
The Auction of Goods and Services Procurement has used the Electronic Procurement Service (E-Procurement) system especially on projects carried out by the Government, as quoted in Presidential Regulation No. 16 of 2018 Chapter X Article 69 section (1) "The Procurement of Goods/Services is carried out electronically using an information system consisting of an Electronic Procurement System (LPSE) and a supporting system". Dyah, Komara, and Djuniati (2015) stated that the implementation of goods / services electronically will increase transparency, improve the level of efficiency of the procurement process, support the monitoring process, audit and meet the need for real-time access to information in order to realize clean and good government in the procurement of government goods / services so that it will be effective to encourage the creation of healthy competition between businesses.
In the implementation there are risks that will occur. The risks that will be faced by the bidders will certainly affect the performance of the company that will have an impact on the implementation of the project in the future. Matters related to the promptness to face the risk certainly refer to the auction documents provided. Where it must be studied in full and carefully so that bidders can succeed in participating in E-Procurement

A. Non-Parametric Analysis
Non-Parametric Analysts in this study used homogeneity tests. Homogeneity test is used to determine the difference in level of understanding based on existing respondent data. The differences are divided into respondents' backgrounds which include work experience, position, and recent education. The use of Homogeneity test using Kruskal-Wallis H (free sample K test) calculation used in more than 2 categories.
The guidelines used to accept or reject if the proposed zero hypothesis (Ho) are as follows: 1. Ho is accepted if the p-value in the column asymp. Sig > level of significant (α) of 0.05 and chi square value < of X20.05(df). 2. Ho is rejected if the p-value in the column Asymp. Sig < level of significant (α) of 0.05 and chi square value > of X20.05 (df).

B. Validity Test
Test validity to determine the feasibility of question items in the list of questions on predefined variables. The formula for calculating product moment correlation is as follows: Where: R = product moment correlation coefficient X = score per question/item Y = total score N = number of respondent The basis of decision making in the validity test are: If the rhitung value > rtabel, then the question item or statement in the questionnaire has a significant 1) correlation to the total score (meaning the item of the questionnaire is declared valid). If the rhitung value < rtabel, then the question item or statement in the questionnaire does not have a 2) significant correlation to the total score (meaning the item of the questionnaire is declared invalid).

C. Reliability Test
Reliability test aims to determine the level of data reliability produced by an instrument to ensure the consistency of research instruments in the same concept. An instrument is said to be realibel if the answer to the map is consistent over time (Basrie, Homsiah, Abdillah Willy 2015). In this study using a tool in the form of SPSS (Statistical Product for Service Solition), a technique used to measure the reliability of dangan using cronbach alpha that is a construct or variable will be said to be realibel if the cronbach alpha has a value greater than 0.60 and vice versa is said to be not reliable if the cronbach alpha is less than 0.60 (Basrie, Homsiah, Abdillah Willy 2015).

D. Descriptive Statistical Analysis
Descriptive statistics is a method of researching a group of people, an object, a set of conditions, a thought system or a class of events at a current time (Rizkiyanto 2018). The purpose of descriptive analysis is to create a systematic, factual and accurate description of the facts examined in the field. Descriptive analysis using SPSS will be obtained an overview of the factors of electronic tender failure (e-procurement) with output in the form of min, max, mean, standard deviation, and others.

E. Risk Level Analysis
Risk level analysis uses a risk matrix with probability and impact parimeters to determine the level of risk that will occur on an indicator where it can be structured on a priority scale. This matrix is in the form of a table that determines which of the combination of probability of impact and the result of such impact in the classification of high risk (red), medium risk (yellow) and low risk (green). To get a risk classification rating, use the RRI index where:

V. RESULT
Based on the results of questionnaires and data analysis obtained variables with risk ratings from each aspect of the risk category namely: technical aspects, administrative aspects, human resource aspects, external aspects, design or image aspects, and price aspects. So that the highest rank of each aspect will be the dominant failure risk factor that can be analyzed as follows. Next the ranking results of the overall variables described in the risk mapping.