Digital Repository Universitas Jember

(1)

ISBN : 978-602-60569-5-5

(2)

i

The 1 st International Basic Science Conference 2016

(The 1 st IBSC 2016)

“Towards the extended use of basic science for enhancing health, environment, energy and biotechnology”

Editor

Fuad Bahrul Ulum M. Ziaul Arif Agung Tjahjo Nugroho

Yudi Aris Sulistiyo

(3)

Faculty of Mathematics and Natural Sciences The University of Jember

Jember Indonesia

Email : [email protected] Website : http://ibsc.fmipa.unej.ac.id/

Address : Jl. Kalimantan no 37 Kampus Tegal Boto, Jember 68121

First Published 2017

TITLE : PROCEEDINGS OF THE 1

^st

INTERNATIONAL BASIC SCIENCE CONFERENCE 2016

EDITORS : Fuad Bahrul Ulum, M. Ziaul Arif, Agung Tjahjo Nugroho, Yudi Aris Sulistiyo

ISBN: 978-602-60569-5-5

Copyright © 2017 IBSC The University of Jember and the authors of the paper

ii

(4)

iii

Preface

A Conference on the extended use of Basic Science was hosted by the Faculty of Mathematics and Natural Science (FMIPA), at CDAST building, Universitas Jember in 26-27

^th

day of September 2016. This conference is intended to promote further developments of basic science for their tangible applications, especially health, environment, energy and biotechnology.

The conference posed the question “what biological, chemical, physical, geological, mathematical, statistical, medical, agricultural and other basic science field changes must be made in order to ensure better live quality in term of health, environment, energy and biotechnology. FMIPA was fortunate to welcome researchers, educators and engineers from various backgrounds representing a variety ways to extend the application of basic science in which safety, environmental friendly and energy efficiency were being pursued.

More than two hundred contributors from fifty five different institutions presented the theory, methodology and application of the field and thus the 1

^st

IBSC 2016 was very rich as the proceeding in this volume.

The major theme that emerged from the conference which was conducted by Faculty of Mathematics and Natural Science, Universitas Jember, and The Ministry of Technology and Higher Education (KEMENRISTEKDIKTI), Republic of Indonesia, was that basic science must extend in very fundamental way if high live quality is to become a stable standard of health, environment, energy and biotechnology. Ramkrishna Ramaswamy in his very inspiring talk, present the complexity and simplicity in biological systems; while Agus Salim discussed the big data of biostatistics with stressing on the quantity does not equal quality; Manabu Abe report the design and synthesis of a new Cromophore, and Bambang Sugiharto address the regularisation of sucrose-phosphate synthesis from sugarcane, and many more expert discus the application of basic science for improving live quality.

Having introduced the 1

^st

IBSC 2016, we will introduce the 2

^nd

Conference of basic science (The 2

^nd

IBSC) in near future. This collaboration and link will be maintain. Hand in hand researcher, expert, educator and other professional in basic science is needed to improve live quality.

Jember January 26, 2017

Agung Tjahjo Nugroho

The Chairman of The 1

^st

IBSC

(5)

Reviewers

Drs. Sujito, Ph.D

Drs. Yuda Cahyoargo Hariadi, M.Sc., Ph.D Dr. Rike Oktarianti, M.Si.

Purwatiningsih, S.Si, M.Si, Ph.D Ika Oktavianawati, S.Si, M.Sc Dwi Indarti, S.Si., M.Si

Yeni Maulidah Muflihah, S.Si, M.Si Dr. Mohamat Fatekurohman, S.Si., M.Si Erlia Narulita, S.Pd., M.Si., Ph.D

iv

(6)

v

Table of Contents

PREFACE

... iii

TABLE OF CONTENTS

... v

BIOLOGY

... 1

C

OMMUNITY

S

TRATEGY FOR

M

ANAGING

T

ROPICAL

F

OREST

R

ESOURCES IN THE

A

REA OF

C

AGAR

A

LAM

P

ULAU

S

EMPU

(N

ATURE

R

ESERVE OF

S

EMPU

I

SLAND

) ...2

B

IOREDUCTION

A

DSORBENT

(B

IOSORBENT

): R

ECOVERY

T

ECHNOLOGY

O

F

H

EAVY

M

ETAL

P

OLLUTION

(C

ADMIUM

/ C

D

) I

N

P

OLLUTED

L

APINDO

W

ATER

S

OURCES

U

SING

B

ACTERIA

A

ND

D

URIAN

L

EATHER

...7

C

OMPARATIVE

S

TUDY

O

F

T

HE

M

ANAGEMENT

O

F

V

ANAME

S

HRIMP

(L

ITOPENAEUS VANNAMEI

) B

ASED ON

D

EMOGRAPHIC

F

ACTORS AT

M

OLANG

B

EACH

T

ULUNGAGUNG

... 10

A

NALYSIS OF THE

I

NFLUENCE OF

P

UBLIC

P

ARTICIPATION IN THE

M

ANAGEMENT OF

R

ESOURCES

S

USTAINABLE

W

ATER

M

ALANG

D

ISTRICT

... 13

C

ONSERVATION

C

OCCINELLA SP

.

AS

P

REDATOR OF

G

REEN

P

EACH

A

PHID

M

YZUS PERSICAE

S

ULZER ON

P

OTATO

I

NTERCROPPING

... 16

T

HE

E

FFECT

O

F

M

YCORRHIZAL

I

NOCULANT

A

ND

C

OMPOST

O

F

V

OLCANIC

A

SH

O

N

G

ROWTH

A

ND

Y

IELD

O

F

C

HILLI

(C

APSICUM

A

NNUM

L.) ... 19

T

HE

P

OTENTIAL OF

A

RTHROPODE

D

IVERSITY FOR

E

COTOURISM

D

EVELOPMENT IN

W

ONOREJO

M

ANGROVE

E

COSYSTEM

, S

URABAYA

... 23

T

HE

E

FFECTS OF

W

ATER

F

RACTION OF

B

ITTER

M

ELON

(M

OMORDICA CHARANTIA

) L

EAF

E

XTRACT IN

M

AMMARY

G

LAND

D

EVELOPMENT OF

B

ALB

/

C

M

ICE

(M

US MUSCULUS

)

WITH

H

ISTOLOGICAL AND

M

OLECULAR

B

IOLOGICAL

A

NALYSIS OF

P

ROTEIN

A

PPROACHES

... 27

C

OMPETITIVENESS

A

ND

P

OTENTIAL OF

S

HEEP

L

IVESTOCK AS

S

OURCE

I

NCREASING

I

NCOME

A

ND

P

ROVIDER OF

M

EAT

A

NIMAL IN

N

ORTH

S

UMATRA

... 30

M

ORPHOLOGICAL AND

P

HYSIOLOGICAL

C

HARACTERS OF

C

ASSAVA

(M

ANIHOT ESCULENTA

C

RANTZ

) W

HICH

W

ET

T

OLERANT

... 32

T

HE

E

FFECT OF

S

OY

T

EMPEH

F

LOUR

E

XTRACT

O

N

V

AGINA

H

ISTOLOGICAL

S

TRUCTURE OF

S

WISS

W

EBSTER

O

VARIECTOMIZED

M

ICE

(M

US MUSCULUS

) ... 36

T

HE

T

OXICITY OF

S

EEDS

E

XTRACT OF

A

NNONA SQUAMOSA

L., L

EAVES

E

XTRACT OF

T

ERMINALIA CATAPPA

L.

AND

L

EAVES

E

XTRACT OF

A

CACIA NILOTICA

L.

ON

T

HE

M

ORTALITY OF

A

EDES AEGYPTI

L. L

ARVAE

... 39

E

LEPHANTOPUS SCABER AND

S

AUROPUS ANDROGYNUS

R

EGULATE

M

ACROPHAGES AND

B L

YMPHOCYTE

C

ELLS DURING

S

ALMONELLA TYPHI

I

NFECTION

... 42

T

HE

E

FFORT

T

O

I

NCREASE

P

RODUCTION OF

S

UPER

R

ED

D

RAGON

F

RUIT

(H

YLOCEREUS COSTARICENSIS

) B

Y

A

RTIFICIAL

P

OLLINATION

... 45

E

VALUATION OF

Z

ONATION OF

T

HE

M

ANGROVE

C

ONSERVATION

A

REAS IN

P

AMURBAYA

... 47

I

NPUT

O

F

N

UTRIENT

(N

ITROGEN

A

ND

P

HOSPHORUS

) F

ROM

T

HE

C

ATCHMENT

A

REA

I

NTO

R

AWAPENING

L

AKE

O

F

C

ENTRAL

J

AVA

... 50

R

ELATIONSHIP BETWEEN

W

ATER

Q

UALITY AND

A

BUNDANCE OF

C

YANOPHYTA IN

P

ENJALIN

R

ESERVOIR

... 52

H

EMATOLOGICAL

C

HARACTERISTIC OF THE

F

EMALE

A

SIAN

V

INE

S

NAKE

(A

HAETULLA PRASINA

B

OIE

, 1827) ... 57

H

IGHLY

S

PESIFIC

B

ACILLUS CEREUS

-P

HAGES

I

SOLATED FROM

H

OSPITAL

W

ASTEWATER IN

B

ANYUMAS

R

EGENCY

... 60

B

IOSYNTHESIS

S

ILVER

N

ANOPARTICLE

U

SING

F

RESH

W

ATER

A

LGAE

... 65

E

FFECT OF SAPONIN

-P

ODS

E

XTRACT

A

CACIA

(A

CACIA MANGIUM

)

TO

H

EMATOCRIT

, H

EMOGLOBIN AT

T

ILAPIA

(O

REOCHROMIS NILOTICUS

) ... 67

E

FFECT OF

D

ISSOLVED

N

UTRIENT

C

ONCENTRATION

(N

ITRATE AND

O

RTHOPHOSPHATE

)

ON

A

BUNDANCE OF

C

HLOROPHYTA IN

P

ENJALIN

R

ESERVOIR

B

REBES

R

EGENCY

... 70

T

HE

A

NATOMY OF

C

AROTENE

B

IOSYNTHESIS IN

B

ETA

V

ULGARIS

L., V

AR

. R

UBRA

U

SING

S

CAN

E

LECTRON

M

ICROSCOPE

... 74

O

PTIMIZATION OF

Y

OGURT

F

ERMENTED

M

ILK

P

RODUCTS WITH

T

HE

A

DDITION OF

N

ATURAL

S

TABILIZER

B

ASED ON

L

OCAL

P

OTENTIAL OF

T

ARO

S

TARCH

(C

OLOCASIA ESCULENTA

) ... 77

P

TERIDOPHYTES OF

A

LAS

P

URWO

N

ATIONAL

P

ARK AND

T

HEIR

M

EDICINAL

P

OTENCY

... 80

G

ENETIC

V

ARIATION OF

A

EDES

A

EGYPTI

(D

IPTERA

: C

ULICIDAE

)

BASED ON

DNA P

OLYMORPHISM

... 83

T

HE

E

FFECT OF

S

OY

T

EMPEH

F

LOUR

E

XTRACT TO

U

TERINE

H

ISTOLOGY OF

O

VARIECTOMIZED

M

ICE

... 85

M

ATING

B

EHAVIOUR OF

C

ROCIDOLOMIA PAVONANA

F. ... 88

AGRICULTURE

... 91

T

HE

D

EVELOPMENT OF

S

USTAINABLE

R

ESERVE

F

OOD

G

ARDEN

P

ROGRAM

’

S

V

IDEO IN

M

ALANG

C

ITY

... 92

E

FFECT OF

M

EDIUM

C

OMPOSITIONS ON

T

HE

G

ROWTH OF

R

ICE

(O

RYZA SATIVA

L.

CV

. C

IHERANG

) C

ALLUS

... 97

B

LOOD

F

IGURE OF

R

AMBON

C

ATTLE

F

ED

F

ORMULATED

C

ONCENTRATE

C

ONTAINING

S

OYBEAN

C

AKE

, P

OLLARD AND

C

ORN

O

IL

C

OMBINE WITH

U

REA

X

YLANASE

M

OLASSES

C

ANDY

... 101

S

TRATEGIES FOR

D

EVELOPMENT OF

B

EEF

C

ATTLE

F

ARMING

B

ASED ON

I

NNOVATION

T

ECHNOLOGY AND

F

EEDING

P

ROGRAM TO

M

EET

S

ELF

S

UFFICIENCY IN

M

EAT

... 103

(7)

vi

FOOD TECHNOLOGY

... 106

M

ODIFICATION OF

B

EAN

S

PROUT AND

U

REA

M

EDIA TO

S

PIRULINA PLATENSIS

C

ULTURE

... 107

C

OLLAGEN FROM

S

EA

C

UCUMBER

(S

TICHOPUS VARIEGATUS

)

AS AN

A

LTERNATIVE

S

OURCE OF

H

ALAL

C

OLLAGEN

... 111

D

EVELOPMENT

O

F

N

EW

P

RODUCT

“C

OCOA

S

PIRULINA AS

F

UNCTIONAL

F

OOD

” ... 114

T

HE

P

ROTEIN

A

ND

W

ATER

C

ONTENT

O

F

T

EN

V

ARIATIONS

O

F

T

HE

F

EED

C

ASSAPRO

O

F

Y

EAST

T

APE

... 120

MEDICAL, DENDTISTRY, AND PUBLIC HEALTH

... 123

E

FFECT OF

P

OMELO

(C

ITRUS GRANDIS

) E

THANOLIC

E

XTRACT ON

A

THEROSCLEROTIC

P

LAQUE

F

ORMATION

... 124

C

LINICAL

M

ANIFESTATION OF

O

RAL

T

UBERCULOSIS

... 127

I

DENTIFICATION OF

D

ERMATOPHYTES BY

M

ULTIPLEX

-P

OLYMERASE

C

HAIN

R

EACTION

, P

OLYMERASE

C

HAIN

R

EACTION

-R

ESTRICTION

F

RAGMENT

L

ENGTH

P

OLYMORPHISM

ITS1-ITS4 P

RIMERS AND

M

VA

I,

AND

P

OLYMERASE

C

HAIN

R

EACTION

(GACA)

4

P

RIMER

... 132

I

MPACT

P

SYCHOLOGICAL

A

ND

P

SYCHO

-P

HYSICAL

W

ORK

D

ISTRESS

O

N

T

OOTH

M

OBILITY

I

N

R

AT

M

ODEL

... 136

R

OLE OF

R

EACTIVE

O

XYGEN

S

PECIES

O

N

D

EVELOPMENTS

O

F

O

STEOCLASTOGENESIS

I

N

A

GING

... 140

D

ETERMINANT

F

ACTOR

T

HAT

I

NFLUENCED

A

NXIETY

L

EVEL

A

ND

E

NERGY

I

NTAKE

A

MONG

E

LDERLY

... 144

P-C

ARE

BPJS A

CCEPTANCE

M

ODEL IN

P

RIMARY

H

EALTH

C

ENTERS

... 147

R

ISK

F

ACTOR OF

G

REEN

T

OBACCO

S

ICKNESS

(GTS)

AT

T

HE

C

HILDREN ON

T

OBACCO

P

LANTATION

... 153

PHYSICS

... 157

D

IRECT

S

CATTERING

P

ROBLEM FOR

M

ICROWAVE

T

OMOGRAPHY

... 158

M

ICROSTRUCTURE AND

M

ECHANICAL

P

ROPERTIES OF

D

ISSIMILAR

J

OINT OF

C

OLD

R

OLLED

S

TEEL

S

HEETS

1.8 SPCC-SD

AND

N

UT

W

ELD

M6

BY

S

POT

W

ELDING

... 162

F

EATURE

E

XTRACTION OF

H

EART

S

IGNALS USING

F

AST

F

OURIER

T

RANSFORM

... 165

A

NALYSIS OF

E

L

N

IÑO

E

VENT IN

2015

AND THE

I

MPACT TO THE

I

NCREASE OF

H

OTSPOTS IN

S

UMATERA AND

K

ALIMANTAN

R

EGION OF

I

NDONESIA

... 168

S

YNTHESIS OF

Z

INC

O

XIDE

(Z

N

O) N

ANOPARTICLE

B

Y

M

ECHANO

-C

HEMICAL

M

ETHOD

... 174

M

ODELLING

D

YNAMICS OF

Z

N

O P

ARTICLES IN

T

HE

S

PRAY

P

YROLISIS

R

EACTOR

T

UBE

... 177

T

HE

I

NFLUENCE OF

E

XTREMELY

L

OW

F

REQUENCY

(ELF) M

AGNETIC

F

IELD

E

XPOSURE ON

T

HE

P

ROCESS OF

M

AKING

C

REAM

C

HEESE

... 181

A

U

G

RADE OF

E

PITHERMAL

G

OLD

O

RE AT

P

ANINGKABAN

ASGM, B

ANYUMAS

D

ISTRICT

, C

ENTRAL

J

AVA

P

ROVINCE

, I

NDONESIA

... 184

R

ENEWABLE

E

NERGY

C

ONVERSION WITH HYBRID

S

OLAR

C

ELL AND

F

UEL

C

ELL

... 188

R

ADAR

A

BSORBING

M

ATERIALS

D

OUBLE

L

AYER

F

ROM

L

ATERITE

I

RON

R

OCKS

A

ND

A

CTIVED

C

ARBON

O

F

C

ASSAVA

P

EEL

I

N

X-B

AND

F

REQUENCY

R

ANGE

... 192

I

NSTANTANEOUS

A

NALYSIS

A

TTRIBUTE FOR

R

ESERVOIR

C

HARACTERIZATION AT

B

ASIN

N

OVA

-S

COTIA

, C

ANADA

... 195

D

EPLOYMENT

P

OROSITY

E

STIMATION OF

S

ANDSTONE

R

ESERVOIR IN

T

HE

F

IELD OF

H

IDROCARBON

E

XPLORATION

P

ENOBSCOT

C

ANADA

... 197

S

EISMIC

R

ESOLUTION

E

NHACEMENT WITH

S

PECTRAL

D

ECOMPOSITION

A

TTRIBUTE AT

E

XPLORATION

F

IELD IN

C

ANADA

... 199

S

IMULATION OF

I-V C

HARACTERISTICS OF

S

I

D

IODE AT

D

IFFERENCE

O

PERATING

T

EMPERATURE

:E

FFECT OF

I

ONIZED

I

MPURITY

S

CATTERING

... 204

S

IMULATIAN OF SELF DIFFUSION OF IRON

(F

E

)

AND

C

HROMIUM

(C

R

)

IN

L

IQUID LEAD BY

M

OLECULAR

D

YNAMIC

... 207

T

HE

S

TUDY OF

E

LECTRICAL

C

ONDUCTANCE

S

PECTROSCOPY OF

T

HE

I

NNER MEMBRANE OF

S

ALAK

... 209

T

HE

A

CCURACY

C

OMPARISON OF

O

SCILLOSCOPE AND

V

OLTMETER

U

TILIZATED IN

G

ETTING

D

IELECTRIC

C

ONSTANT

V

ALUES

... 211

W

INDOW

F

ILTER

(W

IN

T

ER

) T

O

C

APTURE

P

OLLUTION OF

L

EAD

(P

B

) F

OR

H

OUSES

N

EAR

T

HE

H

IGHWAY

T

O

P

REVENT

H

EALTH

P

ROBLEMS

... 214

S

IMULATION OF

S

OLAR

C

ELL

D

IODE

I-V C

HARACTERISTICS

U

SING

F

INITE

E

LEMENT

M

ETHODE

: I

NFLUENCE OF P

- L

AYER

T

HICKNESS

... 216

GEOLOGY

... 218

GIS-

BASED OPTIMIZATION METHOD FOR UTILIZING COAL REMAINING RESOURCES AND POST

-

MINING LAND USE PLANNING

: A

CASE STUDY OF

PT A

DARO COAL MINE IN

S

OUTH

K

ALIMANTAN

... 219

Q

UANTIFICATION

M

ODEL OF

Q

UALITATIVE

G

EOLOGICAL

D

ATA

V

ARIABLES FOR

E

XPLORATION

R

ISK

A

SSESSMENT IN

P

ROSPECT

C

U

-A

U

P

ORPHYRY

D

EPOSIT

R

ANDU

K

UNING

, W

ONOGIRI

, C

ENTRAL

J

AVA

... 226

A S

ENSOR

-B

ASED OF

D

ETECTION

T

OOLS

T

O

M

ITIGATE

P

EOPLE

L

IVE IN

A

REAS

P

RONE TO

L

ANDSLIDE

... 232

R

ELOCATION OF HYPOCENTER USING

J

ACOBIAN

’

S MATRIX AND

J

EFFREYS

-B

ULLEN

’

S VELOCITY MODEL

... 237

CHEMISTRY

... 239

S

YNTHESIS AND

C

HARACTERIZATION

M

AGNETIC

F

E3

O

4

N

ANOPARTICLE BY

U

SING

O

LEIC

A

CID AS

S

TABILIZING

A

GENT

... 240

S

YNTHESIS

O

F

Z

EOLITES

F

ROM

L

OMBOK

P

UMICE

A

S

ILICA

S

OURCE

F

OR

I

ON

E

XCHANGER

... 244

P

REPARATION OF

N

ANOBIOCATALYST

M

ICROREACTOR USING

I

MMOBILIZED

E

NZYME ONTO

N

ANOPOROUS

M

ONOLITHIC

P

OLYMER FOR

H

IGH

S

PEED

P

ROTEIN

D

IGESTION

... 248

T

HE

E

FFORT OF

TB C

ADRE IN

T

HE

I

MPROVING OF

T

HE

S

UCCESS OF

TB T

HERAPY AND

R

EDUCING

S

IDE

E

FFECTS OF

A

NTI

T

UBERCULOSIS

D

RUGS

... 151

(8)

vii

A

NALYSIS OF PROTEIN PROFILE OF NEEM LEAVES JUICE

(

AZADIRACHTA INDICA L

. J

USS

) ... 253

H

YDROPHOBIC

A

EROGEL

-B

ASED

F

ILM

C

OATING

O

N

G

LASS

B

Y

U

SING

M

ICROWAVE

... 256

P

REPARATION AND

C

HARACTERIZATION OF

C

ACAO

W

ASTE

A

S

C

ACAO

V

INEGAR

A

ND

C

HARCOAL

... 259

T

HE

E

FFECT OF

P

HYSICO

-C

HEMICAL

P

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... 379

(9)

Mathematics Application of Fuzzy TOPSIS Method in Scholarship Interview

Application of Fuzzy TOPSIS Method in Scholarship Interview

Abduh Riski, Ahmad Kamsyakawuni

Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Jember, Indonesia e-mail: [email protected]

Abstract—Decision-making problem is the process of determines the best options of all feasible alternative. In some problems, decision-making process involves the attributes of linguistic as in scholarship interview, which is one of the multi- objective decision-making (MODM) problem. The aim of this paper is to apply Fuzzy TOPSIS method in scholarship interview. Scholarship Interview that defined in this paper was implemented to candidates by questions for each candidate and assessed by interviewers. Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) method is a multi-attribute decision-making (MADM) method. The basic principle of the TOPSIS method is to choose the alternative that has similarity with the ideal solution. So that the TOPSIS method can be applied in a scholarship interview, in the first step is used fuzzy method to reduce the -dimensional objective space to be one-dimensional objective space. Then applied the TOPSIS steps by fuzzy approaching to find the best alternative. In this paper will be used a scholarship interview case to illustrate more obviously steps. With this approach, the determination of the scholarship recipients can be more powerful and assured.

Keywords—Fuzzy, TOPSIS, Multi-objective decision-making, Scholarship Interview.

INTRODUCTION

Recently, along with the funds a scholarship offered by the foundation or government in Indonesia is very large and can be easily accessed by anyone. Increased number of applicants scholarship to continue his studies.

Although a quota of scholarships offered are many, but the scholarship givers must remain rigorous in doing the selection. In order for the grant of scholarships can be directly proportional to the increase in the quality of human resources that are sanctioned. Scholarship selection is done in several stages, the last stage is the interview process. Scholarship interview is included in the determination of the issue of the decision of the multi purpose. In practice, in addition to the subjective nature of the assessment in the interview, scholarship also involves a linguistic variable such as "fair", "good",

"very good", etc. So the needed methods that can properly solve the problem of determining the decisions on multi wawancaa scholarships. In this paper fuzzy topsis method will be applied to mementukan for candidates the best scholarship recipients in stages based on the assessment interview interviewer against each candidate's answer to the question that was specified earlier.

METHODS

TOPSIS Method

a. TOPSIS is a technique to sort the available alternatives based on its similarity to an ideal solution.

TOPSIS introduced by Kwangsun Yoon and Hwang Ching-Lai in 1980. Principle basis of TOPSIS method is to choose the alternative that has the shortest distance to the positive ideal solution and the farthest distance from the negative ideal solution. With the goal even though the chosen alternative is not a positive ideal solution, but the solution chosen is a solution that is as close as possible to the positive ideal solution. Because it is in real life is very difficult to obtain a positive ideal solution.

Suppose there are decision and criteria, the value of the destination decision on criteria -th is

[ ⋯ ] , 1 . So the decision

matrix X can be obtained as follows:

⋯⋯

⋮ ⋮ ⋱ ⋮

⋯

. (1) The steps in TOPSIS method is as follows.

1) Normalized decision matrix

For maximizing problem, normalization can be done using Equation 2, 1 ,

∑ . (2)

While for minimizing problem, use equation

∑ . (3)

So the retrieved results matrix the normalization of , i.e.

⋯⋯

⋮ ⋮ ⋱ ⋮

⋯

. (4)

2) Determine the ideal point

Positive ideal point denoted by , while the negative ideal point denoted by . Positive and negative ideal point respectively defined by the

[ ⋯ ] , (5)

[ ⋯ ] . (6)

Where

max , min . 3) Calculate the distance

The distance from -th goal against the positive ideal point defined by

∑ (7)

While the distance to the -th goal to the negative ideal point defined

∑ (8)

4) Calculate the approach degree

The relative approach degree to the -th destination to the positive ideal point defined by

(9)

5) Gives the ranking order of preference

Every goal can be ranked by descending order of . Fuzzy System

b. Problems in real life almost always associated with uncertainty, which can be expressed in linguistic variables “older”, “too hot”, “handsome”, etc. Likewise, in MODM kesubjektifan and there are many uncertainties. To declare subjectiveness and uncertainties during the interview process, the weight of the question and the value of using variable lingistik and represented by triangular fuzzy numbers. A triangular fuzzy numbers is a convex fuzzy set, often expressed as the triple

( , , ), with membership functions defined by

(10)

Mathematics Application of Fuzzy TOPSIS Method in Scholarship Interview ( )

0 ,

, ,

0 ,

.

,

[( ) + ( ) + ( ) ].

In an interview scholarship in general, each candidate answers will be assessed by a number between 0 and 100. Also each of the questions will be weighted between 0% and 100%. In this paper, every answer will be rated using the linguistic variables “very bad”, “bad”, “bad enough”, “medium”, “good enough”, “good” or

“excellent”. While each of the questions will be weighted by linguistic variables “very not important”, “not important”, “not important enough”, “medium”,

“important enough”, “important” or “very important”. So as to obtain a triangular fuzzy numbers in Table 1 and Table 2.

Table 1. Fuzzy Numbers of Answer Score

Linguistic Variable Fuzzy Number

Very bad (0, 0, 10)

Bad (0, 10, 30)

Bad enough (10, 30, 50)

Medium (30, 50, 70)

Good enough (50, 70, 90)

Good (70, 90, 100)

Excellent (90, 100, 100)

Table 2. Fuzzy Number of Question Weight

Linguistic Variable Fuzzy Number

Very not important (0, 0, 0.1)

Not important (0, 0.1, 0.3)

Not important enough (0.1, 0.3, 0.5)

Medium (0.3, 0.5, 0.7)

Important enough (0.5, 0.7, 0.9)

Important (0.7, 0.9, 1.0)

Very Important (0.9, 1.0, 1.0)

Figures of membership degree, ( ), fuzzy number of answer score and question weight is shown in Figure 1 and Figure 2.

Fig 1. Membership Degree Of Answer Score

Fig 2. Membership Degree Of Question Weight

Fuzzy TOPSIS Method c.1. Build a decision matrix

Suppose that in an interview scholarship candidates are , questions , and interviewers.

The scores given by the -th interviewer to -th candidat’s answer for -th question is denoted by ,

where 1 , 1 , 1 . So as to

obtain a decision matrix of the -th interviewer, i.e.

⋯⋯

⋮ ⋮ ⋱ ⋮

⋯

(10)

Defined + + ⋯ + , so that it can be obtained a decision matrix .

⋯⋯

⋮ ⋮ ⋱ ⋮

⋯

(11)

1. Normalized decision matrix Normalization done using equation

̃ . (12)

So obtained normal decision matrix .

̃ ̃

̃ ̃ ⋯ ̃

⋮ ⋮ ⋯ ̃

̃ ̃ ⋱ ⋮

⋯ ̃

(13) 2. Weighted the normal decision matrix

If [ ⋯ ] is a vector weight for

question provided by -th interviewer, defined + + ⋯ + , (14)

can be obtained from the question weight vector, i.e.

[ ⋯ ] . (15)

Then defined ̃ , to obtain normal weighted decision matrix .

⋯⋯

⋮ ⋮ ⋱ ⋮

⋯

. (16)

3. Determine the weighted ideal point

Positive ideal point ̃ and negative ideal point ̃

from normal decision matrix are

̃ [ ̃ ̃ ⋯ ̃ ]

[(1, 1, 1) (1, 1, 1) ⋯ (1, 1, 1)] ,

̃ [ ̃ ̃ ⋯ ̃ ]

[(0, 0, 0) (0, 0, 0) ⋯ (0, 0, 0)] . So the weighted positive ideal point and weighted negative ideal point can be defined by

[ ⋯ ]

[ ̃ ̃ ⋯ ̃ ]

[ ⋯ ]

(17)

[ ⋯ ]

[ ̃ ̃ ⋯ ̃ ]

[(0, 0, 0) (0, 0, 0) ⋯ (0, 0, 0)] . (18) 4. Calculate distance

Using the definition of euler distance, the distance - th candidate to weighted positive ideal point and weighted negative ideal point , respectively, defined as

dan .

( , ) ∑ , , (19)

( , ) ∑ , . (20)

Where is middle value, is upper value, and is lower value from fuzzy number . So that ( ) = 0 if = or

= , and ( ) = 1 if = .

Operations of addition, multiplication, division and Euler distance to the fuzzy numbers A = ( , , ) and A = ( , , ) defined by,

1) Addition: = ( , , ),

2) Multiplication: = ( , , ),

3) Division: = , , , 4) Euler Distance:

(11)

Mathematics Application of Fuzzy TOPSIS Method in Scholarship Interview 5. Calculate the approach degree

Afterward can be calculated degree of relative approah -th candidates to weighted ideal point.

. (21)

6. Gives the ranking order of preference

Each candidate ranked by approach degrees . Candidates with largest will be given the first place, which means the most recommended to get a scholarship.

RESULTS

Scholarship interview process followed by 6 candidates, that is 1, 2, 3, 4, 5, and 6. Each candidate gets same questions, questions is like in Table 3. Assessment by 3 interviewer (1, 2, 3) that already has expertise in assessing the answer candidates.

Table 3. Scholarship Interview Questions Tell us about yourself!

What is your greatest strength and weakness?

Where do you see yourself in ten years?

With what activities are you most involved?

What makes you special to receive this scholarship?

The interviewer will first give weight to each question. In accordance with the linguistic variable weighting questions in Table 2, the interviewer gives weight questions such as in Table 4.

Table 4. Question Weight from Interviewers

Very important Important Medium Important Not important Important Very important

Not important

enough Medium Important Important enough Important Very important Not

important Medium If the linguistic variables in Table 4 is converted into fuzzy numbers accordance with Table 2, it will obtain

, , dan .

(0.9, 1.0, 1.0) (0.7, 0.9, 1.0) (0.3, 0.5, 0.7) (0.7, 0.9, 1.0) (0.0, 0.1, 0.3) , (0.7, 0.9, 1.0) (0.9, 1.0, 1.0) (0.1, 0.3, 0.5) (0.3, 0.5, 0.7) (0.7, 0.9, 1.0) , (0.5, 0.7, 0.9) (0.7, 0.9, 1.0) (0.9, 1.0, 1.0) (0.0, 0.1, 0.3) (0.3, 0.5, 0.7) .

Therefore, by using Equation 14 is obtained weight vector

(0.70, 0.87, 0.97) (0.77, 0.93, 1.00) (0.43, 0.60, 0.73) (0.33, 0.50, 0.67) (0.33, 0.50, 0.67) .

The third assessment by the interviewer to answer the candidates are presented in Table 5, Table 6 and Table 7.

Table 5. Score from First Interviewer

Very bad Bad Excellent Excellent Excellent

Bad Good

enough Good

enough Good Very bad

Bad enough Excellent Bad Good

enough Excellent Medium Excellent Very bad Medium Very bad Good

enough Good Medium Bad

enough Medium

Good Medium Good Bad Good

Table 6. Score from Second Interviewer

Bad Bad Excellent Excellent Medium Bad enough Medium Good

enough Excellent Bad Medium Good Bad

enough Good Medium

Medium Good Bad Good Medium

Good

enough Good

enough Medium Good Good

Good Medium Good Medium Good

enough Table 7. Score from Third Interviewer

Bad enough Bad

enough Excellent Excellent Medium Medium Good

enough Good

enough Good Good Good

enough Excellent Medium Good Good enough Good

enough Excellent Bad enough Good

enough Medium

Good Good Medium Good

enough Bad Excellent Good enough good Bad

enough Good enough From the results of these assessments can be obtained by the decision matrix , , and .

(0, 0, 10) (0, 10, 30) (90, 100, 100) (0, 10, 30) (50, 70, 90) (50, 70, 90) (10, 30, 50) (90, 100, 100) (0, 10, 30)

(30, 50, 70) (90, 100, 100) (0, 0, 10) (50, 70, 90) (70, 90, 100) (30, 50, 70) (70, 90,100) (30, 50, 70) (70, 90, 100) (90, 100, 100) (90, 100, 100)

(70, 90, 100) (10, 30, 50) (50, 70, 90) (90, 100, 100)

(30, 50, 70) (0, 0, 10) (10, 30, 50) (30, 50, 70)

(0, 10, 30) (70, 90, 100) ,

(0, 10, 30) (0, 10, 30) (10, 30, 50) (30, 50, 70) (30, 50, 70) (70, 90, 100)

(90, 100, 100) (50, 70, 90) (10, 30, 50) (30, 50, 70) (70, 90, 100)

(50, 70, 90) (50, 70, 90) (70, 90, 100) (30, 50, 70)

(0, 10, 30) (30, 50, 70) (70, 90, 100) (90, 100,100) (30, 50, 70)

(90, 100,100) (0, 10, 30) (70, 90, 100) (30, 50, 70) (70, 90, 100) (30, 50, 70) (70, 90, 100) (70, 90, 100)

(30, 50, 70) (50, 70, 90) ,

(12)

Mathematics Application of Fuzzy TOPSIS Method in Scholarship Interview (10, 30, 50) (10, 30, 50)

(30, 50, 70) (50, 70, 90) (50, 70, 90) (90, 100, 100)

(90, 100, 100) (50, 70, 90) (30, 50, 70) (50, 70, 90) (90, 100,100)

(70, 90, 100) (70, 90, 100) (90, 100, 100) (50,70, 90)

(10, 30, 50) (30, 50, 70) (70, 90, 100) (90, 100, 100) (30,50, 70)

(70, 90, 100) (70, 90, 100) (70, 90, 100) (50,70, 90)

(50, 70, 90) (30, 50, 70) (50, 70, 90) (0, 10, 30) (10, 30, 50) (50, 70, 90)

.

So as to obtain a decision matrix , (3.3,13.3,30) (3.3,16.7,36.7) (13.3,30, 50) (43.3,63.3,83.3)

(30, 50, 70) (83.3,96.7,100)

(90, 100, 100) (50 , 70, 90) (13.3,30, 50) (36.7,56.7,76.7) (83.3,96.7,100)

(56.7,76.7,93.3) (63.3,83.3,96.7) (76.7,93.3,100) (36.7,56.7,76.7)

(3.3,13.3, 30) (30, 50, 70) (70, 90,100) (90, 100, 100) (50, 66.7,80)

(76.7,93.3,100) (26.7,43.3,60) (63.3,83.3,96.7) (56.7,73.3,86.7)

(50, 70,86.7) (20, 33.3, 50) (43.3,63.3,80) (33.3,50, 66.7)

(13.3,30, 50) (56.7,76.7, 93.3) .

The next step is to normalize the decision matrix , thus obtained normal decision matrix ,

(0, 0, 0) (0, 0.04,0.1) (0, 0, 0) (0.47,0.53, 0.67) (0.24,0.3, 0.4) (1, 1, 1)

(1, 1, 1) (0.58,0.63,0.8)

(0, 0, 0) (0.42,0.52,0.67) (1, 1, 1)

(0.8,0.8, 0.89) (1, 1, 1) (1, 1, 1) (0.37,0.42, 0.53)

(0, 0, 0) (0, 0, 0.11) (0.89,0.95, 1) (1, 1, 1) (0.54,0.62,0.71)

(1, 1, 1) (0.21,0.21,0.2) (0.71,0.8, 0.93) (0.62,0.65,0.73) (0.58,0.68, 0.81) (0.21,0.24,0.29)

(0.4,0.4, 0.44) (0.1,0, 0) (0, 0, 0) (0.68,0.74,0.87)

.

By using the weighting matrix and the decision matrix can normally be obtained normal weighted decision matrix .

(0, 0, 0) (0, 0.04,0.1) (0.43,0.60, 0.73) (0, 0, 0) (0.36,0.49, 0.67) (0.25,0.38,0.59) (0.17,0.26,0.39) (0.77,0.93,1) (0, 0, 0) (0.29,0.45,0.64) (0.77,0.93,1) (0, 0, 0) (0.56,0.69,0.86) (0.77,0.93,1) (0, 0, 0.08)

(0.7,0.87,0.97) (0.28,0.39, 0.53) (0.39,0.57, 0.73) (0.33,0.5, 0.67) (0.18,0.31,0.48)

(0.33,0.50,0.67) (0.07,0.11,0.13) (0.24,0.4, 0.62) (0.21,0.32,0.49) (0.19,0.34,0.54) (0.07,0.12,0.19)

(0.13,0.2, 0.3) (0.03,0, 0) (0, 0, 0) (0.23,0.37,0.58)

.

By using Equation 19 and Equation 20 can be calculated distances and .

[1.223 1.042 0.851 0.823 0.834 0.728] , [0.867 0.858 1.110 1.105 1.175 1.187] . So as to obtain the approach degree by using Equation 21,

[0.415 0.452 0.566 0.573 0.585 0.620] ,

and seen that . In order

to obtain such a rating in Table 8.

Table 8. Ranking from Scholarship Interview Result

Candidat Ranking

6 1

5 2

4 3

3 4

2 5

1 6

So the most recommended candidates for scholarship was the candidate of the 6, and after that the recommended candidate is 5, and then follow the rankings that have been obtained.

CONCLUSION

Fuzzy TOPSIS method can be properly and effectively used to solve the problem MODM containing linguistic variables. This method is also quite simple, so easy to use.

On the issue of this scholarship interviews, with fuzzy TOPSIS method in the judging process of the candidates by the interviewer, the result that the candidate C6 has ranked first and most recommended to get a scholarship.

REFERENCES

[1] Ariapour, A., Veisanloo, F. and Asgari, M., “An Application of Fuzzy TOPSIS Method for Plant Selection in Rangeland Improvement (Case Study:

Boroujerd Rangeland, Lorestan Province, Iran),”

Journal of Rangeland Science, vol 4, pp. 183-194, 2014.

[2] Wang, Y. J., “The evaluation of financial performance for Taiwan container shipping companies by fuzzy TOPSIS,” Applied Soft Computing, vol. 22, pp. 28-35, 2014.

[3] Mahdevari, S., Shahriar, K. and Esfahanipour, A.,

“Human health and safety risks management in underground coal mines using fuzzy TOPSIS,”

Science of The Total Environment, vol. 488–489, pp. 85-99, 2014.

[4] Kim, Y., Chung, E.S., Jun, S.M. and Kim, S.U.,

"Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS," Resources, Conservation and Recycling, vol. 73, pp. 23-32, 2013.

[5] Ashrafzadeh, M., Rafiei, F.M., Isfahani, N.M. and Z. Zare, “Application of fuzzy TOPSIS method for the selection of warehouse location: A case study,”

Interdisciplinary Journal of Contemporary Research in Business, vol. 3, issue 9, pp.655-671, 2012.

[6] Huang, J. W., Chen, R., Wang, X. X. and Zhou, Y.

H., "Study on the Application of Fuzzy TOPSIS to the Multi-objective Decision Making," 2010 International Conference on Intelligent Computation Technology and Automation, Changsha, pp. 560-563, 2010.

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Author Index

Aan Yulianingsih ...132

Abduh Riski ...295

Abdul Azis ...23

Achib Irmawati ...7

Achmad Subagio ...114

Ade Efiyanti ...165

Ade L.N.F ...244

Afifah Nur Aini ...27

Agatha Sih Piranti ... 50, 52 Agoes Soegianto ...67

Agung Harijoko ... 226, 366 Agung Hermawan Susanto ...107

Agung Tjahjo Nugroho ...158

Agus Subianto ...47

Ahmad Dodi Setiadi ...262

Ahmad Kamal Sudrajat ...13

Ahmad Kamsyakawuni ... 295, 311 Aju Tjatur Nugroho Krisnaningsih ...77

Akhmad Sabarudin ... 240, 248 Alfian Futuhul Hadi ... 307, 316, 349 Aminah Abdullah ...114

Andika Kristinawati ...181

Angga Saputra ...107

Anita Yuliati ...174

Anny Suryani ...299

Antonius C. Prihandoko ...330

Anwar Rovik ...60

Ardila Yananto ...168

Arifudin Idrus ... 184, 219, 226, 366 Arika I. Kristiana ...330

Arika Indah Kristiana ...336

Aris Ansori ...188

Arkundato Artoto ... 195, 199 Artoto Arkundato ...207

Asbar...47

Asmak Afriliana ...114

Asnawati ...276

Astri Hasbiah ...279

Atik Kurniawati ...127

Awaluddin ...132

Badrun Mahera Agung...52

Badrut Tamam Ibnu Ali ...282

Baiq Octaviana ...248

Barlah Rumhayati ... 262, 270 Bayu Aslama ...192

Benny Satria Wahyudi ...92

Bowo Eko Cahyono ...211

Budi Lestari ...307

Burhanuddin Bahar ...132

Carmudi ... 52, 70 Catur Retnaningdyah ...262

Chairunisa Fadhilah ...361

Christia Meidiana ...256

Christiani ...70

D. Anggraeny ...248

Daﬁk ...344

Dafik ... 319, 325, 330, 333, 336 Dahlia ... 65, 74 Daning Nindya Fitri Arianti ...13

Darminto ...244

Darnah ...303

Derlini ...373

Desi Febriani Putri ...336

Dewi Nur Arasy ...13

Dewi Rokhmah ... 151, 153 Diah Agustina...256

Dian Anggraeni ... 307, 316, 349 Diana RUS Rahayu ...50

Didik Puji Restanto ...32

Diky Anggoro ...177

Dinia Rizqi Dwijayanti ...42

Dita Ayu Faradila ...36

Djoko H. Santjojo ...240

Dwi Agustin Retno ...330

Dwi Indarti ... 282, 379 Dwi Setyati ...80

Dwi Wahyuni ... 39

Dyah Indartin Setyowati ... 140

Dyah Lestari Yulianti ... 77

E Chasanah ... 111

Edy Supriyanto ... 204, 216 Ega Novialent ... 192

Eka Imbia Agus Diartika ... 7

Elly Nurus Shakinah ... 151

Elva Nurul F ... 207

Elvina D. Iftitah ... 248

Elyana Asnar ... 136

Ema Rahmawati ... 151

Emy, K. ... 101

Endah Sri Palupi ... 57

Endang Budiasih ... 92

Endhah Purwandari ... 204, 216 Ernik Dwi ... 207

Ernitha Panjaitan ... 19

Euis Sartika ... 299

Eva Tyas Utami ... 36, 85 Eza Rahmanita ... 289

Ezra Matondang ... 19

Faid Muhlis ... 237

Faridha Ilyas ... 132

Fatmawati... 286

Feri Frandika ... 162

Ferry Chrismiadi Nalle ... 240

Firda Ama Zulfia... 10

Firdaus Hamid ... 132

Fitri Azizah ... 214

Fuad Bahrul Ulum ... 80

Gembong A.W ... 319

Gentur Waluyo ... 50

Greta Andika Fatma ... 216

Haiyina H.A ... 244

Hario P.S ... 103

Hendra Amijaya ... 219

Hendro Pramono ... 60, 361 Herlina¹ ... 373

Herry Suprajitno ... 286

Heru Baskoro ... 207

Hilda Khairinnisa ... 124

Hindarto ... 165

Hiskiawan Puguh ... 195, 197, 199 Hosizah ... 147

I Dewa Ayu Ratna Dewanti ... 253

I G.A. Ayu Ratna Puspita Sari ... 57

I.H Agustin ... 325

Iim Fatimah ... 177

Ika Airin Nur Rohmadhani ... 10

Ika Hesti Agustin ... 319

Ika Rosemiyani... 270

Ilmawati ... 273, 275 Imam Solekhudin ... 353

Imam Thohari ... 77

Indra Herlamba Siregar ... 188

Indrawaty Sitepu ... 120

Indriati Retno Palupi ... 237

Ira Yudistira ... 307

Irwan Endrayanto ... 226, 366 Is Yuniar ... 67

Izza Anshory ... 165

Jaka Purnama ... 289

Januar Fery Irawan ... 232

Joko Pitoyo ... 162

Khasanah Himmah... 197

Khasanah Sripalupi ... 7

Khoiron ... 151, 153, 156 Kiky Martha Ariesaka ... 124

Kiswara A Santoso ... 286

Kuni Mawaddah ... 358

Kurnia Ahadiyah ... 349

Kusbudiono ... 333, 336 Lailatun Naria Latifah ... 45

Lamria Sidauruk ... 16

Lely Mardiyanti ... 2

Lili Mulyatna ... 279

Linda Silvia ... 192

Lulut Dwi Nurmamulyosari ... 42

LY Susanti ... 111

M. Jahiding ... 273

M. Zainuri ... 192

M. Ziaul Arif ... 311

Mahriani ... 36, 85 Mangestuti Agil ... 127

Mayasari Hariyanto ... 174

Mega Putri K ... 244

Melania Muntini ... 177

Memi Norhayati ... 303

MH Khirzin ... 111

Miftahul Ulum ... 316

Mimien H.I Al-Muhdhar ... 92

Mirza Devara ... 88

Misto ... 211

Mochamad Nuruddin ... 266

Mohamad Anis ... 219

Mohammad Wijaya ... 259

Mudzakkir Taufiqurrahman ... 124

Muhammad Basyarudin ... 42

Muhammad Ramadhan ... 39

Muhammad Razali ... 373

Muhammad Sasmito Djati ... 42

Muhammad Wiharto ... 259

Musnina ... 273

Mustakim ... 162

Nadya Adharani ... 107

Nafisatuzzamrudah ... 27

ND Yuliana ... 111

Neny Andayani ... 45

Netty Ermawati ... 97

Ni Made Mertaniasih... 127

Nidaul Hikmah ... 85

Ninna Rohmawati ... 144

Noor Nailis Sa’adah ... 23

Nova Bagus Hidayat ... 107

Nova Maulidina Ashuri ... 23

Nova Yesika Gultom ... 10

Novi Anitra ... 262

Novi Ariyanti ... 70

Novita Prastiwi ... 132

Novita Sana Susanti ... 344

Nur Hayati ... 27

Nur Jannah ... 42

Nur Syntha Napitupulu ... 19

Nura H ... 244

Nurkhamim ... 226, 366 Petrus ... 184

Poerwadi Bambang ... 256

Praseti Pebrian Illavi ... 199

Puguh Surjowardojo ... 77

Pujiana Endah Lestari ... 253

Purwatiningsih... 88

Qonitah Fardiyah ... 270

Qurrota A’yuni Ar Ruhimat ... 353

Qurrota Ayun ... 276

Rafiantika ... 325

Rahmawati ... 32, 46 Ratna Kartikasari ... 162

Regina G.L.D ... 244

Rifalatul Isnaini¹... 2

Rifang Pri Asmara ... 214

Rika Ernawati... 184

Rike Oktarianti ... 83

Risca Listyaningrum ... 237

Rizalinda Sjahril ... 132

Rizky Arief Shobirin ... 240

Rizqon... 379

Robiatul Hadawiyah ... 65

Roedy Budirahardjo... 253

Rofiatun ... 211

Rohmatul Wahid ... 240 Romziah ... 101, 103, 105

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Author Index

Ruliana Umar ...97

Ruliyanti ...195

S.M. Yunika ...333

Saefuddin ‘Aziz ...60

Saefuddin Aziz ...361

Sandy Pradipta ...39

Sapto Putranto ... 226, 366 Saraswati Dewi ...168

Sarim Sembiring...30

Satryo Budi Utomo ...232

Selly Candra Citra...107

Septi F. Raeni ...248

Sherry Aristyani ...65

Sholeh Avivi ...32

Sigit Soeparjono ...32

Siska Nuryanti ...132

Siswanto ... 15, 174 Siswoyo ...276

Siti Lailatul Arofah ... 204

Siti S. Purwaningsih ... 299

Siti Umi Afifah ... 214

Slamin ... 319, 333 Sri Hartatik ... 32

Sri Mumpuni ... 83

Subagyo ... 219

Subuh Isnur Haryuda ... 188

Sudarsono ... 177

Sudarti ... 181

Sueb ... 7, 10, 12, 92, 358 Suhartono Taat Putra ... 136

Sukarno ... 111

Supriyadi ... 96, 197, 207 Susilowati ... 92

Tri Agus Siswoyo ... 32

Tri Bhawono ... 101, 103 Tri Mulyono ... 282, 379 Umie Lestari ... 27

Viv Djanat Prasita ... 47

Wenny Maulina ... 209

Widya Yopita ... 279

Win Darmanto ... 67

Yayu Fuadah ... 42

Yeni Kustiyahningsih... 289

Yeni Maulidah Muflihah ... 276

YN Fawziya ... 111

Yohana Theresia Maria Astuti ... 45

Yonik M Yustiani ... 279

Yossi Wibisono ... 97

Yuana Susmiati ... 266

Zahara Meilawaty ... 140

Zahreni Hamzah ... 136, 140 Zainul Anwar ... 311

Zulfikar ... 276

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ISBN : 978-602-60569-5-5