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PROCEEDING

ICMSTEA 2016 : The Role of Mathematics, Sciences, Technology, and Education Towards

ASEAN Economic Community and Global Challenges

Makassar, South Sulawesi, Indonesia

3rd

– 4th

October 2016

Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar 2016

ICMSTEA 2016:

THE ROLE OF MATHEMATICS, SCIENCES, TECHNOLOGY, AND EDUCATION TOWARDS ASEAN ECONOMIC COMMUNITY AND GLOBAL CHALLENGES

Editor

:

Dr. Syafruddin Side

Dr. Ir. Wiharto Caronge, M.Si.

Uca, S.Si., M.Si., Ph.D.

Iwan Dini, S.Si., M.Si.

Ansari Saleh Ahmar, S.Si, M.Sc.

Bustang, S.Pd., M.Sc. Muh. Husnul Khuluq, S.Pd., M.Sc. Said Fachry Assagaf, S.Pd., M.Sc.

Sahlan Sidjara, S.Si., M.Si.

Ahmad Ansar, S.Pd., M.Sc.

Syamsuddin Mas’ud, S.Pd., M.Sc.

Muhammad Isbar, S.Si., M.Si.

Surahman, S.Pd., M.Pd. Editorial Assistant Layouter

: Andi Ikhsan Maulana, S.Pd

Dimas Suharto

Cover Administrator

: Nurul Kusuma Wardani, S.Pd.

Reviewer Board

: Prof. Alfred Edward Szmidt, Ph.D.

Prof. Valerio Sbordoni, Ph.D.

Prof. Birgit Loch

Dr. Richard Randriantoamanana

Prof. Fahrul Zaman bin Huyop, Ph.D.

Prof. Mohd. Ekhwan Toriman, Ph.D.

Prof. Eko Hadi Sujiono, M.Si.

Prof. Oslan Jumadi, S.Si., M.Phil., Ph.D

Muh. Abdy, S.Si, M.Si., Ph.D

Prof. Dr. Jasruddin M, M.Si.

Prof. Dr. Muris, M.Si.

Prof. Dr. Ir. Yusminah Hala, M.Si. Prof. Dr. Usman Mulbar, M.Pd.

Prof. Dr. Muh. Danial, M.Pd.

Copyright © October 2016

ISBN 978-602-99837-4-6

Diterbitkan oleh : Fakultas MIPA Universitas Negeri Makassar

P r o c e e d i n g | iii

WELCOME SPEECH

Forewords from the Chairman of Committee

Assalamu’alaykum wa Rahmatullahi wa Barakatuh

First of all, I wish to extend a warm welcome to fellow delegates from the various countries and regions. I

realize that you are fully dedicated to the sessions that will follow but I do hope you will also take time to

enjoy sparkling Makassar with its tropical setting, friendly people, and wonderful cuisine.

This 2nd

International Conference on Mathematics, Science, Technology, Education, and Their

Application 2016 is organized by the Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar to bring all experts and researches in these field sharing their important thoughts and findings.

The conference will be held in two days from 3rd

– 4th

of October 2016 with two keynote speakers, seven

invited speakers, and more than 80 parallel speakers from different backgrounds.

Let me take this opportunity, on behalf of the committee members, to express my gratitude and sincere

thanks to the keynote, invited and all parallel speakers for spending their valuable time with us in this

conference. I do hope that your time in Makassar will be valuable and memorable.

Finally, I would like to thank all steering and organizing committee for their hard work and dedication to

the success of this conference. I would like also to apologize to all of you should you find any

inconvenience during this event.

Thank you very much,

Wassalamu’alaykum Warahmatullahi Wa barakatuh

Chairman of Committee,

Dr. Drs. A. Mushawwir Tayeb, M.Kes.

P r o c e e d i n g | iv

The 2nd

ICMSTEA Speech

By The Dean of Mathematics and Natural Sciences Faculty

Universitas Negeri Makassar

Your excellency Rector of Universitas Negeri Makassar Honorable Vice Rectors and Dean of All Faculties Honorable Keynote Speakers Distinguished all invited speakers from outstanding universities Distinguished all speakers and guests All participants, Ladies & Gentlement,

Assalamu’alaykum Warahmatullahi Wabarakatuh. My greetings for all of you. May peace and

God’s blessing be upon us all. Alhamdulillah, all praises be to the Almighty God, Allah

subhanahu wata’ala.

It is my pleasure to welcome you all to the opening of The 2nd

International Conference

on Mathematics, Science, Technology, Education & their Applications (2nd

ICMSTEA). I am

delighted to see that the Mathematics and Natural Science Faculty has again organized the second conference that capitalize on our strength and built on our commitment to promoting Mathematics, Science, Technology and Education.

I do hope that this conference would bring a great opportunity for all of us to strengthen

our contribution to the advancement of our nation. I would like to take this opportunity to thank the conference organizing committee for

their diligent work. I would also like to thank participants, especially those of you coming from

abroad, for joining us and sharing your valuable experiences. Should you find any

inconveniences and shortcomings, please accept our sincere apologies. Finally, let me wish you fruitful discussion and a very pleasant stay in Makassar.

Thank you, Wassalamu’alaykum Warahmatullahi Wabarakatuh

Dean of Faculty of Mathematics and Natural

Sciences Universitas Negeri Makassar

Prof. Dr. Abdul Rahman, M.Pd.

P r o c e e d i n g | v

TABLE OF CONTENTS

Cover

Welcome Speech

Table of Contents

Invited Speakers

Genetic Structure and Evolutionary History of a Diploid Hybrid Pine Pinus densata Inferred from the Nucleotide Variation at Seven Gene Loci Alfred Edward Szmidt

Biodiversity and Big Data In A Changing World: From Museum Specimens to Citizen Sciences

Valerio Sbordoni

Engaging Students with Tablet Technology in Science, Technology, and Mathematics Education

Birgit Loch

Assesment Within Elpsa Lesson Design

Sitti Maesuri Patahuddin

Why High Performance Computing (HPC) Matters?

Richard Randriantoamanana

In Silico Study of L-specific dehalogenase from Rhizobial Species Strain RC1 Fahrul Zaman bin Huyop

Fostering Water Innovation: Securing the ASEAN Water Future Mohd. Ekhwan Toriman

Basic Research (in Physics) for Development of Nation Innovation Eko Hadi Sujiono

Parallel Speakers

The Influence Of Problem Based Learning Model Toward Motivation and Physics Learning Outcomes Of Students SMA Negeri 1 Parangloe Gowa Regency Muhajirin, Sidin Ali & Pariabti Palloan

Analysis of Student’s Conceptual Understanding of Mathematics on Set at Class VII SMP Frater Palopo

Muhammad Ilyas & Fahrul Basir

i

iii

v

1

1 – 8

9 – 15

16 – 38

39 – 48

49 – 58

59 – 68

69 – 78

79 – 86

87 – 95

96 – 102

P r o c e e d i n g | vi

Pah Characteristics In Sediment Around Makassar Coast Using GC-FID 103 – 114

Muhammad Syahrir, Nurul Hidayat Aprilita, Nuryono, & Netti Herawati

Perception of 21st Century Learners to The E-Books as A Learning Resources 115 – 122

Muhammad Takwin Machmud

The Group Of Understory Herbaceous Vegetation Stand At Tropical Lower Mountain 123 – 129

Forest Of Mount Salak, West Java

Muhammad Wiharto, Cecep Kusmana, Lilik Budi Prasetyo, Tukirin Partomiharjo,

Hamka L, & Abd. Muis

Antibacterial Compounds Characterization in Chloroform Extract Leaves of Tahiayam 130 – 135

Plant (Lantana Camara Liin.)

Muharram, St. Nurzulaiha, Nurrahmania, Iwan Dini, Pince Salempa, & Maryono

Effect of Mixture Inoculum of Lactic Acid Bacteria (LAB) and Mold Amylolytic In 136 – 143

Various Concentration And Fermentation Time of Changing Protein And HCN Content

Of Bitter Cassava Roots (Manihot aipi Phol.)

Nurhayani H. Muhiddin, Nur Arfa Yanti, & Hasanah

The Analysis of Total Cholesterol Levels in Mice (Mus musculus) MALES who were 144 – 150

Given Extracts of Methanol Leaf Cemba (Acacia pennata)

Nurul Muhlishah, Irma Suryani Idris , Andi Mu’nisa, & Ernawati

Plants in a Square: Explore Plants Description With QR Code Feature 151 – 155

Khalisha Azis, Sitti Saenab & Syamsiah

Exploring the Correlation between Metacognitive Skills and Retention of Students in 156 – 161

Different Learning Strategies in Biology Classroom

Arsad Bahri

Physical Fitness For Futsal Referee Of Football Association In Thailand 162 – 170

Acting Sub L.t.Thaweesub Koeipakvaen

Histological study of the respiratory system of Sulawesi Medaka fish (Oryzias 171 – 176

celebensis): as a candidate of animal model

Dwi Kesuma Sari, Irma Andriani, & Khusnul Yakin

Technological Innovation of Learning Mobile-Based Learning 177 – 184

Herlinah & Baso Habibi

Evaluation of Antimicrobial Activity and Phytochemical Screening of Chloroform 185 – 188

Extract of Usnea sp.

Iwan Dini, Maryono, Nurul Utami, Sitti Hajar, & Ahmad Hadani

The effectiveness of Scientific-Inquiry Learning Model to Improve Scientific Thinking 189 – 194

Skills of Grade X student of High School in Gowa Regency

Jusniar & Sumiati Side

P r o c e e d i n g | vii

Pedagogical Content Knowledge: Teacher’s Knowledge Of Students In Learning 195 – 199 Mathematics On Limit Of Function Material Ma’rufi, I Ketut Budayasa, & Dwi Juniati

Simulation Infiltration Rate of Water on Sand Media by Finite Difference Method 200 – 210

Masitah Osman, Nasrul Ihsan, & Muhammad Arsyad

The Influence Of Problem Based Learning Model Toward Motivation and Physics 211 – 218

Learning Outcomes Of Students SMA Negeri 1 Parangloe Gowa Regency

Muhajirin, Sidin Ali & Pariabti Palloan

The Influence of Experiment Methods on Science Process Skill and Cognitive Learning 219 – 226

Outcomes of the X Grade Students of MIA SMAN 1 Soppeng Riaja 2015/2016

A. Sri Sofializa, Muhammad Arsyad, & Muris

The Influence Of Problem Based Learning Model Toward Motivation and Physics 227 – 233

Learning Outcomes Of Students SMA Negeri 1 Parangloe Gowa Regency

Muhajirin, Sidin Ali & Pariabti Palloan

The Development of Critical Thinking Inventory Instrument for Biology Department 234 – 239

Students

Abd Muis, Adnan Gassing, Ismail Laumma & Nurjannah

Unleash Students’ Motivation with Blended Knowledge Transfer Instructional Model 240 – 243

Adnan, Sitti Saenab, & Andi Rahmat Saleh

The Development of Student’s Worksheet of Physics Based on Virtual Simulation and 244 – 251

Its Influence on Physics Learning Outcomes of Students

Ahmad Swandi & Bunga Dara Amin

Teaching Material Development with Challenge Based Learning (CBL) Basis to 252 – 258

Improve Critical Thinking Ability on Human Reproduction System Material of Class

XI IPA 4 Students at MAN Pinrang

Andi Asmawati Azis, Jasruddin, & Reni

Implementation of Accreditation Policy and Quality of Public Primary School 259 – 262

Education Service in South-Sulawesi

Andi Cudai Nur & Sumarlin Mus

Plants in A Square: Explore Plants Description with QR Code Feature 263 – 272

Khalisha Azis, Wiharto, Siti Saenab, & Syamsiah

Fostering Science Learning Quality in Frame of Ecosystem Topic through Lesson 273 – 275

Study

Besse Aisyah, Nurfatima, Rina Kurnia, Andi Asmawati Aziz, & Andi Nurul Virninda

Yusuf

Fractionation Ethyl Acetate Extract of Stem Bark Soursop(a. Muricata. Linn) Potential 276 – 279

Anticancer

Pince Salempa, Muharram, & Iwan Dini

P r o c e e d i n g | viii

Entrepreneurship Education Development In Dealing Asean Economic Community 280 – 285

Mohammad Rakib

The Development of Basic Chemistry Courses Program Based Problem Solving to 286 – 291

Improve Student’s Critical Thinking Skills

Ramlawati & Ratnawaty Mamin

Increase The Production and Quality of Biogas from Waste of Cattle Rumen Content 292 – 296

Through The Addition of Molasses and Zeolite

Ramli, Satria Aly, & Hartono

Marica Goat’s Response To The Provision of Superior Feed 297 – 301

Rosdiana Ngitung

Students’ Mathematics Achievement and Its Relationship with Parents’ Education 302 – 308

Level, and Socio-Economic Status In Turkey

Rusli

The Abstraction Ability in Constructing Relation Within Triangles By The Seventh 309 – 313

Grade Students of Junior High School

Sitti Mutmainna Hasma, Suwardi Annas, & Djadir

Water Content Influence to Electrical Properties From Soil Volcanic 314 – 320

Sulistiawaty

The Influence Of Using Destination Card Through Team Games Tournament (TGT) 321 – 324

Type Of Cooperative Learning Toward Student’s XII Grade Achievement at SMA

Negeri 01 Unggulan Kamanre, Luwu Regency

Ulva Nilawaty Sudarmadi

Applying SARAC Approach and The Effect in Learning Mathematics For Students 325 – 331

Grade VIII

Usman Mulbar & Nasrullah

Analysis Von Bertalanffy Equation With Variation Coefficient 332 – 338

Usman Pagalay, Budiawan, & Anisyah

Results Increasing Student Learning Through The Use of Biology Learning Model 339 – 346

Cooperative Think Pair Share (TPS) The Student Class XI IPA SMA Negeri 5

Makassar

Wiwik Wiji Astuti, Ismail &Musyafar

Characterization of crude chitinase produced by Trichoderma virens in solid state 347 – 354

fermentation

Rachmawaty & Madihah

Authentic Assesment In Mathematics Instruction Model Based On Conceptual Conflict 355 – 362

Asdar

A Simplified Model Of Aspergillus Niger Growth Using Nephrolepis Biserrata Leaves

For Exo-Polygalacturonase Production 363 – 370

Halifah Pagarra

Design And Effectiveness Of Mathematics Learning Packages Based On Bilingual

Method 371 - 382

Hamzah Upu

The Effect Of Supercritical Co2 On The Antioxidant Activity Of The Swietenia

Mahagoni Seed Extract By Box-Behnken Design 383 - 388

Hartati, Liza Md Salleh & Mohd. Azizi Che Yunus

Implementation Of Explicit-Reflective Approach To Improve Creative Thinking Skills

Of Students In Junior High School On Environmental Pollution Matter 389 - 393

Husnul Khatimah, Parsaoran Siahaan &Agus Setiabudi

The Effect Of Mole Ratio Of Acrylamide (AM) Monomer And Methylene-Bis-

Acrylamide (MBAM) Crosslinker Toward The Hardmess Of Gelcasting Porous

Ceramics 412 - 415

Suriati Eka Putri & Diana Eka Pratiwi

The Effect Of Neem (Azadirachta Indica) Coated Urea Fertilizer Against NH4+ And

NO3- Concentration In Clay Loam Soil Texture 416 - 422

Andi Bida Purnamasari, Sirinapa Kijkuakul & Pattamon Sangin

Application Of Simulation-Based Media To Physics Learning Achievement Of Student

At XI IPA Grade SMAN 1 Bontonompo

Dewanthikumala, S. Salmiah Sari &Nurhayati 423 - 437

Test The Power Of Drag Against The Growth Of Bacteria Of Staphylococcus Aureus

And Escherichia Coli Extracts Ethanol Leaf Mangrove Rhizophora Mucronata And The

Effects Of Antidiabetik On Mice Induced Aloksan 438 - 443

Ernawati, Nurul Muhlishah & Shasmita Irawan

Implementation Of Worksheet Based On Productive Questions To Improve Inquiry

Skill Of Senior High School Students 444 - 450

Herman & A. Momang Yusuf

Critical Thinking Skill Of Student Through Top Down Approach In Physics Learning 451 - 455

Aisyah Azi,Muhammad Aqil Rusli & A. Momang Yusuf

Preliminary Study On Field Application Of Human Urine Fertilizer On Cultivation Of

Green Amaranth In Bajeng Distric, Gowa, South Sulawesi. 456 - 459

Nani Kurnia & Andi Asmawati Azis

Student’s Physics Achievement Through Gasing Physics Learning 460 - 465

Muhammad Aqil Rusli, A. Momang Yusuf, Aisyah Azis, Agus Purwanto & Jutri Taruna

Exploration Of Plant Species In Traditional Ceremonies Kajang Tribe In Bulukumba

Regency South Sulawesi 466 - 475

Sudding & Jamaluddin

The Effect Of The Methanol Extract Of Cemba's Leaves (Acacia Pennata) In Lowering

Blood Glucose Levels Of Male Mice (Mus Musculus) 476 - 482

Aji Maulana, Irma Suryani & A.Mu'nisa

Comparison Of Learning Outcomes On Chemical Bonding Of Student Class X Senior

High School State 1 Ma’rang That Taught By Cooperative Learning Model Type TPS

And NHT 483 - 489

Sugiarti & Sugiarti

The Processing Of Coconut Shell Based On Pyrolysis Technology To Produce

Reneweable Energy Sources 498 - 508

Sudding & Jamaluddin

Oldeman Climate Zoning For The Agricultural Area 511 - 521

Rosmini Maru, M. Nur Zakariah Leo, Syamsiah Rahim & Nur Fatimah Basram

Application Of Sutte Indicator In Predicting Movement Of Indonesian Stock (A Case

Study Of XL Axiata Tbk And Smartfren Telecom Tbk) 522-530

Ansari Saleh Ahmar & Andi Nurani Mangkawani Arifin

511

Proceedings of ICMSTEA 2016 : International Conference on Mathematics, Science, Technology, Education, and their Applications, Makassar, Indonesia, 3rd – 4th October 2016

Oldeman Climate Zoning for the Agricultural Area

Rosmini Maru*, M. Nur Zakariah Leo, Syamsiah Rahim,

Nur Fatimah Basram

Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Negeri

Makassar, Parangtambung, Makassar, Indonesia

Email: [email protected]

Abstract : Knowledge of the climate of a region is needed. One way to get to know the further climate is classified by calculating rainfall. One method to do that is Oldeman method by determining the average number of wet months and dry months later on interpolation and processed automatically in ArcGIS. This classification aims to determine the type of climate conditions based on Oldeman classification and regions

spreading on each type of climate types in Soppeng. The results of this study are to map climate types using Oldeman classification system in Soppeng. In order to know the type of climate and Agro-climate zones in the study sites. The attribute value of the determination of the type of climate in the interpolation using kriging method. Data collection techniques are secondary data collection, observation, and documentation. The data obtained were processed and counted then displayed in the form of maps and described systematically. The results showed there are three types of climate B2, C3 and E. Distribution B2 climate includes DonriDonri District and Marioriawa District with an area of distribution of 13%. C3 Climate contained in Citta, Donri-Donri, Lalabata, Lilirilau, Marioriawa and Marioriwawo District with an area of

distribution of 43%. E Climate contained in the District Citta, Donri-donri, Ganra, Lalabata, Liliriaja, Lilirilau, and Marioriwawo with an area of distribution of 44% and there is 24,6 % areas wich conformity between agroclimatic zone and planting frequency.

Keywords: Climate Type, Oldeman, Soppeng Regency, Interpolation

INTRODUCTION

The climate is an air temperature average condition, precipitation, air pressure, wind direction,

humidity, and the other climate parameters in the long term (Tjasyono, 2004). In general,

Indonesia has become the high-risk area for the climate change included around South Sulawesi.

The climate change is an important issue and always keep going on in these past few years. Climate change is already and is going to happen as long as the rising of human activity.

Climate information of an area is needing. In the tropical area, air temperature rarely becomes

the limit factor in the agriculture production, but the water supply is the most deciding factor in the

agriculture cultivation. The long and extreme dry season brings wide consequences for the

environment and human living.

Climate also affects the plant type in Soppeng Regency for cultivating in an area, scheduling

and cultivation techniques that will be carried out by farmers. However, Utilization of climate

information in this area is very little for the agricultural sector, whereas the climate will affect the

distribution of plants. This happens because of the lack of resources that can be utilized by people

to know the type of climate somewhere and types of plants suitable for the location of the right

mailto:[email protected]

512


climate. Climate classification used for agriculture is climate classification according to Oldeman

climatic. Oldeman climatic classification uses the element of rainfall as a basis for determining the

classification its climate. The main type Oldeman classification is based on the number of

consecutive wet months, namely: Zone A, Zone B, Zone C, Zone D, and E. The zone subtypes

based on the number of consecutive dry months, namely: Zone 1, Zone 2, Zone 3 and zone 4

(Lakitan, 1994).

Based on the description of the background, it is necessary to do a research on the

climatic conditions currently affecting various aspects of human life and other living organisms

and is presented in the form of a research report entitled "Oldeman Climatic Zoning in Soppeng”.

The purpose of this study was to determine the distribution of climate types with Oldeman method.

METHODS

The research stage

This research stage includes data collection phase in the form of Soppeng rainfall data for the last 30 years. Then check the field, observing and plotting of each rainfall station. Data processing

by calculating the amount of rainfall based formula Oldeman, and GIS analysis in making

Oldeman climate distribution map in Soppeng.

Determination of climate types according to Oldeman

According to Oldeman climate to determine the climate types is the month of consecutive wet and dry consecutive month as well. To determining the Oldeman’ wet months and dry months,

there are:

Wet month is a month of rainfall over 200 mm

Dry month is the month of rainfall less than 100 mm (Kartasapoetra, 2008)

Based on the classification that focuses on the wet months, Oldeman offers five main zones of

wet months in a row as follows:

1) Zone A, wet months are more than 9 times in a row

2) Zone B, wet months are 7 to 9 times in a row

3) Zone C, wet months are 5 to 6 times in a row

4) Zone D, wet months are up 3 to 4 times

5) Zone E, wet months are less than 3 times (Kartasapoetra, 2008)

Table 1 Climate Criteria according to Oldeman

Month Rainfall

Wet >200 mm

Dry <100 mm

Source: Kadarsah, 2007

Table 2 Climate Zone by wet Month According to Oldeman

Climate The number of wet month in a row

A >9

B 7-9

C 5-6

D 3-4

E <3

Source: Kadarsah, 2007

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Table 3 Oldeman Climate Classification based on the wet month and dry month

Climate Classification The number of wet month in a row The number of dry month in a row

A A1

A2

10-12 Months

10-12 Months

0-1 Month

2 Months

B

B1

B2

B3

7-9 Months

7-9 Months

7-8 Months

0-1 Month

2-3 Months

4-5 Months

C

C1

C2

C3

C4

5-6 Months

5-6 Months

5-6 Months

5-6 Months

0-1 Month

2-3 Months

4-6 Months

7 Months

D

D1

D2 D3

D4

3-4 Months

3-4 Months 3-4 Months

3-4 Months

0-1 Month

2-3 Months 4-6 Months

7-9 Months

E

E1

E2

E3

E4

E5

0-2 Months

0-2 Months

0-2 Months

0-2 Months

0-2 Months

0-1 Month

2-3 Months

4-6 Months

7-9 Months

10-12 Months

GIS Analysis with Kriging Interpolation

Spatial data processing is performed to determine the classification of the type of climate based

on Oldeman classification in the form of the area. The data in this spatial processing is rainfall for

over the past 30 years has been known in the wet and the dry months, climate type, and data

latitude and longitude coordinates post a graduated rainfall. The method is the Interpolation. The

interpolation method is a method of filling the data gaps with certain methods of a dataset to

generate a continuous distribution in the form of the area.

The methods in Geographic Information System that is used in the processing of these is by

interpolation of Kriging. Kriging interpolation is a method of estimating a value of a point on each

grid focus to the value of a point that has real value.

Steps for Kriging interpolation: 1. Input Oldeman climate types into excel.

2. In Excel contained Oldeman climate classification mode, then input the point coordinates in

the decimal degree of each rainfall measuring station.

3. In ArcGIS, export the excel data in a shapefile format.

4. Analyze the type of climate in order to interpolate by selecting ArcToolbox.

5. Because kriging interpolation is used in Arc Toolbox, select raster and select kriging

interpolation.

6. Click reclassify to reclassify raster re-class and select.

7. Due to the resulting interpolated still has no specific boundaries so the next step is clipping

Area. Where the move was made to cut the interpolation of the administrative map of the

region, in this case, is Soppeng area. 8. Once the results of the clip area appearing on the layer, then the next step is to export the

polygon as a result clip is still in the shape of raster area, by choosing conversion tools.

9. Showing interpolated in the form of a map layout, the distribution map of Oldeman climate

classification in Soppeng Regency area.

RESULTS AND DISCUSSION

This research used rainfall data in rainfall measuring posts in Soppeng for 30 years from 1985

to 2014. Calculation of rainfall as the basis for determining the classification of Oldeman climate

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done by calculating the average rainfall from 1985 to 2014 in each rainfall measuring posts in

Soppeng.

Table 4 Rainfall Data Monthly Average of 30 Years (1985-2014)

(Source: The results of the processing data, 2016)

Information:

BB= Month Wet,

BK = Month Dry.

In determining the wet months and dry months, according to Oldeman, is when an area has a

rainfall average monthly greater than 200 mm, the monthly rainfall in the area belonging to the wet month. If an area has an average monthly rainfall is less than 100 mm then the monthly rainfall in

the area is classified as dry months.

According to the table 7 Latappareng has 0 wet months and 5 months of dry, Leworeng have 2

months of wet and two dry months, Lapajung have one wet month and 3-month dry, Lalange have

one wet month and 3-month dry, Umpungeng has 5 wet months and 4 dry months, Sero has 0 wet

months and 3 months of dry, wet Salobunne have 8 months and 2 months of dry, wet and Congko

0 months 3 months to dry.

After wet months and dry months are known, the rainfall station data are classified based on

Table of the main type of Oldeman Classification Climate and Table of The sub-type of Oldeman

Classification Climate. The following data is:

Table 5 The Climate type Based on the main Oldeman climate type and Oldeman climate sub-type

of Soppeng

Number Name of Statiun Wet Month Dry Month Climate Type

1 Latappareng 0 5 E3

2 Leworeng 2 2 E2

3 Lapajung 1 3 E2

4 Lalange 1 3 E2

5 Umpungeng 5 3 C3

6 Sero 0 3 E2

7 Salobunne 8 2 B2

8 Congko 0 3 E2

Source: The results of the analysis of climate type, 2016

Based on Table 4.5 the regions with an E climate type (E2, E3) are the regions of Latappareng

station, Leworeng station, Lapajung station, Lalange station, Sero station, and Congko station and the region of C3 climate type is the region of Umpungeng station, and the climate of B2 type is

the region of Salobunne station. After knowing the type of Oldeman climate classification, next is

No Nama Stasiun JAN FEB MAR APR MEI JUN JUL AGS SEP OKT NOP DES BB BK

1 Latappareng 107.8 88.83 89.8 185.5 163.9 113 115.7 71.7 53.8 89 109.8 142.7 0 5

2 Leworeng 163.5 166 156.8 206.2 240.4 168.8 163 60 55.9 125 167.4 100.8 2 2

3 Lapajung 181.8 140.8 148.1 167 206.3 144.2 134.2 43.9 34.3 95 133.2 184.7 1 3

4 Lalange 130.2 108.6 132.6 150.8 202.6 167.9 129.8 56.4 46.1 86 121 118.1 1 3

5 Umpungeng 323.8 313.9 213.5 209.4 183 131.9 88.97 52 39.1 86 137.4 241.2 5 3

6 Sero 173.3 140.3 124.3 143.3 182.6 126.1 119.8 36.6 37.8 92 138.6 186.8 0 3

7 Salobunne 276.8 224.1 273.1 279.6 396.5 282.6 191.9 98.6 92.6 178 224.5 321.7 8 2

8 Congko 169.9 118.3 134.7 163.3 193.4 148.1 118 52.5 28.6 74 122.8 154.2 0 3

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an interpolation of Oldeman climate type classification with the shaped element must be first

converted into a form of numbers.

The changes of attribute data are based on agro-climatic zones of each type of climate. Climate

types that have the most growing season will rate of at least a small number, whereas the type of

climate that has a growing season of at least will become the greatest score, and if the climate type

there is the same agro-climate zones, the greatest scoring will be given to the climate type that has

a number behind capital letter, and the following results are:

Table 6 Classification Notation Climate Data Attribute PosOldeman by a graduated Rainfall in

Soppeng

No Name of

station

Climate

type Agro-climate zone

Attribute

data

1 Latappareng E This area is too dry may only be one time

depending on their crops and even then rain 80

2 Leworeng E area is too dry may only be 1 time palawiaja

even then depending on their rain 80

3 Lapajung E E area is too dry may only be 1 time palawiaja


4 Lalange E area is too dry may only be 1 time palawiaja


5 Umpungeng C3 crops Rice once and 2 times but crops that both

must be carefully 50

6 Sero E area is too dry may only be 1 time palawiaja


7 Salobunne B2 Rice Twice a year with crops 30

8 Congko E area is too dry may only be one time depending

on their crops and even then rain 80

Source: data processing, 2016

E-climate Type with agro-climate zones with the more dry areas can only be used one time to

plant the crops planted as the 2d crop in the dry season are given 80 attribute data that is located in

the region of Latappareng station, Leworeng station, Lapajung station, Lalange station, Sero

station and Congko station. C3 climate type with Agro-climate zone that have 1 times planting of

rice and 2 times planting the crops given the 50 attribute data in Umpungeng station area, and B2

climate type with agro-climatic zones that have Rice planting for 2 times a year and crops in 1

time are given the 30 attribute data in Salobunne station area.

The distribution of Oldeman Climate types can be viewed on an Oldeman climate classification map. The Interpolation used was Kriging, which were interpolated is the attribute values have been

determined based on the type of climate and the point coordinates at each observation station in

the area of research. The climate dissemination is basically aimed shows the limits of the coverage

area of climate types have been counted on each observation station. On the map of the

distribution of Oldeman climate types that have been generated can show zoning criteria and type

of climate as well as the breadth of each region at each observation station. Based on the results in

the reclassification of distribution map of Oldeman climate types can be seen that the climate type

spread in Soppeng is climate B2, C3, and E. Each type of climate is spreading in various sub-

districts in Soppeng. This climate types in its range of each region. For more details can be seen in

the following table:

Table 7 Distribution Type Size Region Climate Research Areas

No Climate Type Area (Ha) Percentage (%)

1 B2 17590.05 13%

2 C3 58923.29 43%

3 E 59901.33 44%

Total 136414.67 100%

Source: data processing, 2016

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From the rainfall monthly data, the average during 30 years (1985-2014) used and by using GIS, it

was found that Soppeng has climate type mode based on the Oldeman’ classifications are type B2,

C3, and E.

a. B2 Type

Oldeman Climate classification type of B2 spread in Soppeng total of 13% of the entire region

of Soppeng or an area of 17590.05 hectares which include: partial region of Marioriawa

District, the Bulue village, Laringgi village, ManorangSalo village, Tellulimpoe village, and

Patampanua village as well as a relatively small area of Donri-donriSubdistrict in the northern

part of the LalabataRiaja village. This climate types according to agroclimatic zones can be

planting two crops in one year with a variety of short lifespan and the dry season is short

enough for planting crops. b. C3 Type

Oldeman Climate classification type of C3 spread in Soppeng as much as 43% of the total

area in Soppeng or an area of 58923.29 hectares which include: partial region District of

Marioriawa, the AttangSalo village, Desa stones village, Bulue village on the west side,

eastern Laringgi village, ManorangSalo eastern in the Eastern side, Limpomajang village,

Kaca village, TelluLimpoe village on the eastern side, Panincong Village, Patampanua village

on the eastern side, most of Donri-donri District, there are Tottong village, the small part of

Labokong village on the north side, Sering village, Kessing Village on the eastern side, Donri-

Donri village, Pesse village in thw Western side, RiajaLalabata village, Pising village on the

Eastern side, Leworeng village on the northern side, a half part of Lalabata village, there are

Bila village on the northern side, the Mattabulu village on the eastern side, the Botto village, and most of Lebbae village. Lilirilau district there are Tetewatu village and Palangisang

village on the eastern side. And the most of Mariorawa district, there are Barae village, goarie

village, Marioriaja village, Gattareng Toa village, and Gattareng village. Agro-climatic zone

of this type is just one time of planting rice and the second crops planting have to be careful

not to fall in the dry months.

c. E Type

E-climate type classification based on Oldeman in Soppeng spread over as much as 44% of

the entire area in Soppeng or broad of 59901.33 hectares that include: Most of Citta district,

the Kampir village, Citta village, and Tinro village. Most of Donri-Donri district there are

Labokong village, Pisisng Village, Pesse village, kissing village danLeworeng village. Ganra

district, there are Ganra village, Belo village, enrekeng village, danLompulle village.

Lalabata district, there are LalabataRilau village, Ompo village, Bila village, SaloKaraja village, Lemba village, Maccile village, Mattabulu village, Lapajung village, Umpungeng

village, Liliriaja district there are Galung village, jennae village, Rompegading village,

Appanang village, Pattojo village, Jampu village, Lilirilau district there are Paroto village,

Ujung village, Tetewatu village, Macanre village, Pajalesang village, Masing village,

Parenring village, Abbanuangnge village, Palangiseng village, Baringeng village, danKebo

village, MarioriwawoLabessi district, Congko village, Mario Rilau village, Tettikenrarae

village, Watutoa village, Soga village, Goarie village, Watu village, Marioritengnga village,

danMarioriaja village This type of regional agro-climatic zone is too dry may only be 1 times

the crops and it depends on the rain.

517


Frequency of Planting in Soppeng

Based on the data of BPS in Soppeng Regency, the Frequency results of Planting Rice and

Crops in Soppeng, there are:

Table 8 Frequency of Investment Soppeng

No Name of Sub-district Frequency Annual Investment

Paddy Corps

1 Marioriwawo 2 Times -

2 Lalabata 2 Times -

3 Liliriaja 2 Times -

4 Ganra 1 Time -

5 Citta 2 Times -

6 Lilirilau 2 Times -

7 Donri-donri 2 Times -

8 Marioriawa 2 Times 1

Source: BPS, 2014

518


Based on the results of questioners and surveys of the frequency of rice and corps planting,

there are:

a. 2 times rice planting period

2 times rice planting in some district region in Soppeng regency, there are Mariorawa district,

Lalabata district, Liliriaja district, citta district, Lilirilau district, and Donri-donri district.

b. 1 time Rice Planting Period

Rice planting season is only found in one of the districts in the region Soppeng, it is Ganra district.

c. 2 times for rice planting and 1-time Crops-planting period

Rice planting Periods are 2 times and the crops planting period is 1 time in Mariorawa, one of

the districts in the northern Soppeng Regency.

Based on the results of interpolation using GIS with Kriging method is found that the agro-

climate zone and climate types in Soppeng divided into three zones of Agro-climatic based on

Oldeman climate classification, there are rice planting is twice within one year with a variety of

short lifespan and the dry season short enough for planting crops (type B2), only one rice planting

and the second planting crops must be careful not to fall in the dry (type C3), this area is generally

too dry, it may only be one of the crops, and even then subject to the rain (type E). While BPS on

2014 and the results of field survey Frequency annual planting in Soppeng can be divided into three, that is 2 times for rice planting per year, 1-time rice planting per year and 2 times for rice

planting and 1 times for crops. Based on the data generated map compatibility between agro-

climatic zones according to the frequency Oldeman rice cultivation in Soppeng.

519


The frequency of the most dominanOldeman Climate classification type of B2 spread in

Soppeng total of 13% of the entire region of Soppeng or an area of 17590.05 hectares which

include: partial region of Marioriawa District, the Bulue village, Laringgi village, ManorangSalo

village, Tellulimpoe village, and Patampanua village as well as a relatively small area of Donri-

donriSubdistrict in the northern part of the LalabataRiaja village. This climate types according to

agro-climate zones can be planting two crops in one year with a variety of short lifespan and the

dry season is short enough for planting crops.

The type of climate and agro-climatic zones in Citta Sub-district divided by 2 is E-Type with too dry areas that only allows 1-time crops and C3 type with agro-climatic zones with 1 times for

rice planting and 2 times for corps planting. However, based on the frequency of planting Soppeng

in District Citta occur 2 times the cultivation of rice in a year, it indicates a mismatch between the

frequency Agroclimatic Zone planting in Citta sub-district. Citta sub-district which has Size

3493.14 Ha. Sub-district Citta with agro-climatic zone 1 time for crops are not in accordance with

the frequency of 2 times rice cultivation in a year has an area of 2210.68 hectares or 63% of the

total area in Citta sub-districts. Then, the Citta sub-district with agro-climatic zones for the rice

planting in 1 times and for the crops planting in 2 times are inappropriate for the frequency of rice

cultivation in a year has an area of 1282.46 hectares or 37%. Overall, the Citta sub-district between

agro-climatic zones and planting occurs inexpediency. These frequency caused by the irrigation

technology that provides the water supply so Crop Water Requirement (CWR) or so-called water

the plant needs are met and to do rice cultivation as much as 2 times a year. Donri-donri sub-district which has an area of 21902.81 hectares based on the type of climate

and agro-climatic zone is divided into three, there are E-Type with areas too dry that only allows 1

time for corps planting, C3 type with agro-climatic zones with 1 time for rice planting and 2 times

for crops planting, and B2 type with agro-climatic zones are 1 times for rice planting and 2 times

for crops planting. Based on Table 4.9 Donri-Donri sub-district has corresponding regions and is

not suitable for agro-climatic zones with a frequency between plantings. Donri-donri Sub-district

with the agro-climatic zone is 1 time for crops planting are not in accordance with the frequency of

2 times rice cultivation in a year has an area of 4337.36 hectares or 20% of the total area of Donri-

donri sub-district. And the Donri-donri sub-district with agro-climatic zones have 1 time for corps

planting and 2 times for crops planting, also do not correspond to the twice paddy planting

frequency in a year that has broad 16831.25 or 77% of the total area of Donri-donri districts. So, the Donri-donri sub-district amounted to 97% of its territory between agro-climatic zones and

planting occurs inexpediency. It happened because of irrigation technology that provides water

supply and helps the farmers to apply the CWR way for paddy cultivation and the paddy planting

time can become twice in a year. And there are 3% of Donri-Donri sub-district with agro-climate

zone have 2 times for corps planting in a year and the crops are the same as paddy planting in

Donri-Donri, twice in a year.

520


The GanraSubdistrict have E climate type with the agro-climate zone. This area is too dry that

only allows 1 time for crops planting. However, based on the frequency of the planting of the

Ganra area planted with 1-time paddy planting in a year. So, the Ganra district have an

inexpediency between agroclimatic zone and planting frequency. It is because the irrigation

technology that provides the water supply so that CWR and fulfill the once a year of paddy

planting, and in this district, the irrigation water supply is only able to help for once a year of

paddy planting because it is the last area for the flow of water irrigation so that the water supply is

less.

SubdistrictLalabata, based on the type of climate and agro-climatic zones divided by 2 is Type

E with areas too dry that only allows 1-time crops and C3 climate type with agro-climatic zones

have 1 time for rice planting and 2 times for crops planting. However, based on the frequency of

planting in Soppeng exactly in Lalabata, have rice cultivation occurs two times in a year, it indicates a mismatch between the frequency Agro-climate Zone planting in Lalabata Sub-district.

Lalabata, that has 29899.28 hectares area with agro-climatic zone have 1-time crops planting is not

in accordance with the frequency of 2 times paddy cultivation in a year with 11624.66 hectares or

39% of the total area in Lalabata sub-districts. Then, the Lalabata District with agro-climatic zones

have 1 time for rice planting and 2 times for crops planting is a mismatch to the frequency of rice

cultivation in a year have an area of 18274.62 hectares or 61% of the total area of Lalabata district.

So, the Lalabata District occurs an inexpediency between agro-climatic zones and planting

frequency. It is because the irrigation technology that provides the water supply so that CWR and

fulfill twice a year of paddy planting.

District of Liliriaja has one type of climate and agro-climatic zone E-type with areas too dry

that only allows 1 time for corps planting. The whole District of Liliriaja occurs an inexpediency between agro-climatic zones and planting frequency. It is caused by the irrigation technology that

provides the water supply so that CWR rice plant are met and to do rice cultivation as much as 2

times in a year, so the lack of rainfall affecting agro-climatic zones did not significantly affect rice

cultivation in of this area.

LilirilauSubdistrict based on the type of climate and agro-climatic zones divided by 2, there is

E-Type with areas too dry that only allows 1-time crops and C3 type with agro-climatic zone have

1 time for rice planting and 2 times for crops planting. However, based on the frequency of

planting in Soppeng, exactly in Lilirilau District rice cultivation occurs two times in a year, it

indicates a mismatch between the planting frequency and Agro-climatic Zone. SubdistrictLilirilau

that has 15874.12 hectares area. LilirilauSubdistrict with agro-climatic zone has 1-time crops are

not in accordance with the frequency of 2 times paddy cultivation in a year that has an area of

899.08 hectares or 6% of the total area of Lilirilau districts. Then the District Lilirilau with agro-climatic zones and crops Rice 1 times 2 times do not correspond to the frequency of rice

cultivation in a year has an area of 14975.04 hectares or 94% of the total area of the District

Lilirilau. So overall the District Lilirilau between agro-climatic zones and planting occurs

inexpediency. it is caused by the irrigation technology that provides the water supply that causes

CWR can fulfill rice cultivation and can be done 2 times a year so the lack of rainfall affecting

agro-climatic zone is not too affecting rice cultivation in this area.

SubdistrictMarioriawa which has an area of 27054.32 hectares based on the type of climate and

agro-climatic zones divided by 2 is the C3 mode with agro-climatic zones with crops of rice 1

times 2 times and Type B2 with two rice agro-climatic zones with crops. According to Table 4.9,

Sub-District Marioriawa has corresponding regions and is not suitable agro-climatic zones with a

frequency between plantings. SubdistrictMarioriawa zone agroclimatic rice 1 time and pulses 2 times do not correspond to the frequency of planting two times the rice and 1 times crops a year

has broad 10295.63 or 38% of the total area districts Donri-donri, between zones agroclimatic and

frequency planting, occurs nonconformity. It is caused by the irrigation technology that provides

the water supply so that CWR f dan rice plant rice cultivation can be done 2 times a year. As 62%

of the District of Marioriawa with agro-climatic zones Rice 2 times a year to crops in accordance

with the frequency of planting in the District Marioriawa this is caused by the high rainfall in the

region.

SubdistrictMarioriwawo based on the type of climate and agro-climatic zones divided by 2 is

Type E with areas too dry that only allows 1-time crops and C3 mode with agro-climatic zones

521


have 1 time for rice planting and 2 times for crops planting. However, based on the frequency of

planting in Soppeng exactly Marioriwawo District rice cultivation occurs two times in a year, it

indicates a mismatch between the frequencyAgroclimatic Zone Investment in Sub Marioriwawo.

SubdistrictMarioriwawo who has 23458.12 hectares area. SubdistrictMarioriwawo with agro-

climatic zone has 1-time crops planting are not in accordance with the frequency of 2 times paddy

cultivation in a year that has an area of 12499.40 hectares or 53% of the total area of Marioriwawo

districts. Then the District of Marioriwawo with agro-climatic zones have 1 time for rice planting

and 2 times for crops planting do not correspond to the frequency of rice cultivation in a year has

an area of 10958.72 hectares or 47% of the total area of the District Marioriwawo. So overall the

District Lilirilau between agro-climatic zones and planting occurs inexpediency. It is caused by the

irrigation technology that provides the water supply so that CWR rice plant are met and to do rice

cultivation as much as 2 times a year so the lack of rainfall affecting agro-climatic zone is not too affecting rice cultivation in this area.

CONCLUSION

Based on Geographic Information System analysis, shows that Soppeng has three types of climate

and the Classification of Oldeman Agro-climate zone are B2 climate type with agro-climate zone

have twice for paddy planting with the short life span varieties and once for the crops planting,

C2 climate type with agro-climate zone have one time for paddy planting and one time for crops planting and the E climate type with Agro-climate zones have once for crops planting and there

are 24.6% of the area in Soppeng have compatibility between agro-climatic zones and planting

frequency.

REFERENCES

1. Kartasapoetra, A.G. 2008. Klimatologi :PengaruhIklimterhadap Tanah danTanaman (EdisiRevisi). BumiAksara. Jakarta

2. Lakitan, B. 1994.Dasar-Dasar Klimatologi.Jakarta : PT. Raja GrafindoPersada.

3. Lakitan, B. 2002.Dasar-Dasar Klimatologi.Cetakanke-dua.RajaGrafindoPersada. Jakarta

4. Tjasyono, B. 2006.Klimatologi.Cetakan KE-2.IPB Press. Bandung.

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