Binary Logistic Regression with BFGS using package maxLik

I tried binary logistic regression with BFGS using maxlik, but i have included the feature as per the syntax i attached below, but the result is, but i get output like this

Maximum Likelihood estimation

BFGS maximization, 0 iterations

*Return code 100: Initial value out of range.

https://docs.google.com/spreadsheets/d/1fVLeJznB9k29FQ_BdvdCF8ztkOwbdFpx/edit?usp=sharing&ouid=109040212946671424093&rtpof=true&sd=true (this is my data)*

library(maxLik)
library(optimx)
data=read_excel("Book2.xlsx")
data$JKLaki = ifelse(data$JK==1,1,0) 
data$Daerah_Samarinda<- ifelse(data$Daerah==1,1,0)
data$Prodi2 = ifelse(data$Prodi==2,1,0)
data$Prodi3 = ifelse(data$Prodi==3,1,0)
data$Prodi4 = ifelse(data$Prodi==4,1,0)
str(data)
attach(data)

ll<- function(param){
  mu <- param[1]
  beta <- param[-1]
  y<- as.vector(data$Y)
  x<- cbind(1, data$JKLaki, data$IPK, data$Daerah_Samarinda, data$Prodi2, data$Prodi3, data$Prodi4)
  xb<- x%*%beta
  pi<- exp(xb)
  val <- -sum(y * log(pi) + (1 - y) * log(1 - pi),log=TRUE)
  return(val)
}  
gl<- funtion(param){
  mu <- param[1]
  beta <- param[-1]
  y <- as.vector(data$Y)
  x <- cbind(0, data$JKLaki,data$IPK,data$Daerah_Samarinda,data$Prodi2,data$Prodi3,data$Prodi4)
  sigma <- x*beta
  pi<- exp(sigma)/(1+exp(sigma))
  v= y-pi
  vx=as.matrix(x)%*%as.vector(v)
  gg= colSums(vx)
  return(-gg)}

mle<-maxLik(logLik=ll, grad=gl,hess=NULL,
            start=c(mu=1, beta1=0, beta2=0, beta3=0, beta4=0, beta5=0, beta6=0,beta7=0), method="BFGS")  
summary(mle)

can i get some help, i tired get this solution, please.