PROSIDING Judul Jurnal Ilmiah (Artikel) : Effectivenes

(1)

LEMBAR

HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW KARYA ILMIAH : PROSIDING

Judul Jurnal Ilmiah (Artikel) : Effectiveness of tofu waste for decreasing chlorogenic acid of robusta coffee (coffee robusta Lindl.Ex De Will)

Nama/ Jumlah Penulis : A R Sulistyaningtyas1, E Prihastanti, E D Hastuti/ 3 orang Status Pengusul : penulis ke-3

Identitas Jurnal Ilmiah : a. Judul Prosiding : 3rd Annual Applied Science and Engineering Conference (AASEC 2018)

b. Nomor ISSN : ISSN: 1757-8981, E-ISSN: 1757-899X c. Tahun terbit, tempat

pelaksanaan

: 18 April 2018, Bandung, Indonesia d.

Penerbit/ Organiser

: IOP Conference series: Materials Science and Engineering/ Universitas Pendidikan Indonesia (UPI) e. DOI artikel (jika ada) : doi:10.1088/1757-899X/434/1/012119

f. Alamat web prosiding : https://iopscience.iop.org/article/10.1088/1757- 899X/434/1/012119/pdf

g. Terindeks : SCOPUS, SJR 2018: 0.19 Kategori Publikasi Prosiding :  Prosiding Forum Ilmiah Internasional Bereputasi**

(beri pada kategori yang tepat) Prosiding Forum Ilmiah nasional

Hasil Penilaian Peer Review :

Komponen Yang Dinilai Nilai Reviewer

Nilai Rata-rata Reviewer 1 Reviewer 2

a. Kelengkapan unsur isi prosiding (10%) 2,80 3,00 2,90

b. Ruang lingkup dan kedalaman pembahasan (30%)

7,50 8,00 7,75

c. Kecukupan dan kemutakhiran data/informasi dan metodologi (30%)

8,00 9,00 8,50

d. Kelengkapan unsur dan kualitas penerbit (30%)

8,00 8,00 8,00

Total = (100%) 26,30 28,00 27,15

Nilai Pengusul = (40% x 27,15)/2= 5,43 5,43

Semarang, 20 Januari 2020 Reviewer 1

Prof. Dr. Tri Retnaningsih Soeprobowati, MAppSc NIP. 196404291989032001

Unit Kerja : Departemen Biologi - FSM UNDIP

Reviewer 2

Prof. Dr. Hermin Pancasakti Kusumaningrum, SSi, MSi NIP. 197002081994032001

Unit Kerja : Program Studi Bioteknologi, Departemen Biologi - FSM UNDIP

(2)

LEMBAR

pelaksanaan

Penerbit/ Organiser

Hasil Penilaian Peer Review :

Komponen Yang Dinilai

Nilai Maksimum Prosiding

Nilai Akhir Yang Diperoleh Internasional

Bereputasi



Nasional

a. Kelengkapan Unsur Isi prosiding (10%) 3.00 2,80

b. Ruang lingkup dan kedalaman pembahasan (30%)

9.00 7,50

c. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

9.00 8,00

d. Kelengkapan unsur dan kualitas terbitan/prosiding (30%)

9.00 8,00

Total = (100%) 30.00 26,30

Nilai Pengusul = 40% x (26,30/2) = 5,26 Catatan Penilaian artikel oleh Reviewer :

1. Kesesuaian dan kelengkapan unsur isi prosiding:

Ada abstract, introduction, material methods, result and discussion, conclusion, references. Tidak ada keywords dan aknowledgment

2. Ruang lingkup dan kedalaman pembahasan:

Penelitian menarik, ketika kopi sedang diminati banyak orang dalam bidang ekofisiologi tentang pemanfaatan limbah tahu dalam menurunkan kandungan asam klorogenat kopi robusta. Pembahasan lebih banyak di aspek fisiologi, data hanya didudkung 1 gambar

3. Kecukupan dan kemutakhiran data/informasi dan metodologi:

Hanya 3 dari 17 referensi yang up to date, 3 referensi dalam Bahasa Indonesia 4. Kelengkapan unsur dan kualitas terbitan:

Paper dibuat dalam IOP Conference series: Materials Science and Engineering vol 434, 3rd Annual Applied Science and Engineering Conference (AASEC 2018) 18 April 2018, Bandung, Indonesia yang diselenggarakan oleh Program Studi Pendidikan Teknologi dan Vokasi, Sekolah Pascasarjana UPI, bekerjasama dengan UNJ, Univ Trisakti, UNSYIAH, UNESA, POLINEMA, STT Garut, UIN SGD, UNIKAMA, UMSIDA, UWKS dan Universitas Warmadewa. Scientific committee dari Indonesia semua. Keynote speaker dari Malaysia, Jepang, dan Indonesia Semarang, 20 Januari 2020

Reviewer 1

Prof. Dr. Tri Retnaningsih Soeprobowati, MAppSc NIP. 196404291989032001

Unit Kerja : Departemen Biologi - FSM UNDIP

(3)

LEMBAR

pelaksanaan

Penerbit/ Organiser

Hasil Penilaian Peer Review :

Komponen Yang Dinilai

Nilai Maksimum Prosiding

Nilai Akhir Yang Diperoleh Internasional

Bereputasi



Nasional

a. Kelengkapan Unsur Isi Prosiding (10%) 3.00 3,00

e. Ruang lingkup dan kedalaman pembahasan (30%)

9.00 8,00

f. 8Kecukupan dan kemutahiran

d9ata/informasi dan metodologi (30%)

9.00 9,00

g. Kelengkapan unsur dan kualitas terbitan/

Prosiding (30%)

9.00 8,00

Total = (100%) 30.00 28,00

Nilai Pengusul = 40% x (28,00/2) = 5,60 Catatan Penilaian artikel oleh Reviewer :

1. Kesesuaian dan kelengkapan unsur isi Prosiding: Penulisan sudah sesuai dengan “Guide for Author” (Title, Introduction, Materials and methods,Results and Discussion, Conclution, Acknowledgement, References) dengan sistem Author. Substansi artikel sesuai bidang ilmu pengusul/penulis pertama (Ilmu Tanaman). Ada benang merah dalam struktur penulisannya (skor = 3,00).

2. Ruang lingkup dan kedalaman pembahasan: Substansi artikel sesuai dengan ruang lingkup jurnal Kedalaman pembahasan cukup baik (4 dari 17 bh rujukannya dilibatkan dalam proses membahas hasil) (skor = 8,00).

3. Kecukupan dan kemutakhiran data/informasi dan metodologi: Data-data hasil penelitian belum menunjukkan ada kebaruan informasi. Dari 17 bh rujukannya, terdapat 6 bh sudah kadaluwarsa lebih dari 10 th terakhir, rujukan berupa jurnal ada 14 buah, Proses review cukup baik (skor = 9,00).

4. Kelengkapan unsur dan kualutas terbitan: Jurnal ini tergolong prosiding Internasional Bereputasi (Kontributor lebih dari 4 negara, ISSN: 1742-6588; E-ISSN:1742-6596, terindeks di scopus/SJR= SJR 2018: 0.221 H Indeks :65;

coverage 2005-on going ; proses editorial baik (skor = 8,00).

Semarang, 20 Januari 2020 Reviewer 2

Prof. Dr. Hermin Pancasakti Kusumaningrum, SSi, MSi NIP. 197002081994032001

Unit Kerja : Program Studi Bioteknologi, Departemen Biologi - FSM UNDIP

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

6/14/2020 Waist to height ratio (0.5) as a predictor for prediabetes and type 2 diabetes in Indonesia - IOPscience

https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012311 1/6

NOTICE: Ensuring subscriber access to content on IOPscience throughout the coronavirus outbreak - see our remote access guidelines.

PAPER • OPEN ACCESS

Waist to height ratio (0.5) as a predictor for prediabetes and type 2 diabetes in Indonesia

H S Djap , B Sutrisna , P Soewondo , R Djuwita , K H Timotius , Trihono , S Sharif and Y S Tjang Published 1 November 2018 • Published under licence by IOP Publishing Ltd

IOP Conference Series: Materials Science and Engineering, Volume 434, 3rd Annual Applied Science and Engineering Conference (AASEC 2018) 18 April 2018, Bandung, Indonesia

Abstract

Obesity has became a big problem for many countries. One of the anthropometric measures of risk factor for type 2 diabetes is waist to height ratio (WtHR). Unfortunately, WtHR is varies depend on the different race of nations in the world. There is no standard size that applied internationally. The aim of this study was to find the WtHR cut-off point to predict prediabetes and type 2 diabetes for Indonesia This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more,

see our Privacy and Cookies policy.



1 2 3 2 1 4 2 1

[email protected]

Faculty of Medicine, Krida Wacana Christian University, Jakarta, Indonesia Faculty of Public Health, University Indonesia, Depok, Indonesia

Faculty of Medicine, University Indonesia, Jakarta, Indonesia

Health Research and Development Centre, Ministry of Health Republic of Indonesia, Indonesia H S Djap et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 434 012311

https://doi.org/10.1088/1757-899X/434/1/012311 Buy this article in print

1 2 3 4

PDF

Help

(23)

[1]

[2]

[3]

[4]

population. This is a cross sectional study. Based on data from National basic health research of Indonesia, year 2013. Subjects aged 15-65 years, that performed fasting glucose and 2 hours post- prandial, height, weight and waist circumference. Total subjects of 26.213 data were analyzed using Stata version 12. The cut-off point of WtHR is 0.5 for male and females to predict the risk of

prediabetes and type 2 diabetes for Indonesian population. There is no difference between male and female. WtHR is the best anthropometric measure compared than BMI and waist circumference themselves. WtHR has an AUC = 0.5613 for prediabetes and 0.6606 for type 2 diabetes. Waist circumference >75cm (male) and >80cm (female) and BMI is 22.89kg/m as the cut-off point for predicting prediabetes and type 2 diabetes. The WtHR can be used as a screening tool for predicting risk of prediabetes and type 2 diabetes for Indonesia community. It is a simple, practical and cheap method to predict the risk of prediabetes thus more people can be tested the blood glucose as an early diagnosis and prompt treatment.

Export citation and abstract

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References

Moody A. 2012 Adult anthropometric measures, overweight and obesity 1 1-39 Google Scholar

Badan Litbangkes Depkes RI 2007 Riset Kesehatan Dasar 2010 2010 Google Scholar

Chan JM. 1992 Obesity, fat distributin and Weight Gain as Risk Factors for Clinical Diabetes in Men 1 961-969

Google Scholar

Mi S Q et all. 2013 BMI, WC, WHtR, VFI and BFI: Which Indictor is the Most Efficient Screening Index on Type 2 Diabetes in Chinese Community Population 26 485-491 Google Scholar

2

BibTeX RIS

PDF

Help

(24)

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

Nyamdorj R, Qiao Q, Söderberg S et al. 2009 BMI Compared With Central Obesity Indicators as a Predictor of Diabetes Incidence in Mauritius 17

Crossref Google Scholar

Browning LM, Hsieh SD and Ashwell M 2010 A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes : 0 o 5 could be a suitable global boundary value Nutrition Research Reviews Nutrition Research Reviews 247-269

Yildiz EA and Bersot TP 2009 Optimal waist : height ratio cut-off point for cardiometabolic risk factors in Turkish adults 13 488-495

Google Scholar

Ashwell M. 2012 Plea for simplicity : use of waist-to-height ratio as a primary screening tool to assess cardiometabolic risk 6 1-3

Google Scholar

Tatsumi Y, Ohno Y, Morimoto A, Nishigaki Y, Mizuno S and Watanabe S 2013 Lifestyle and the risk of diabetes mellitus in a Japanese population J Behav Med. 36 225-233

Lee I-T, Chiu Y-F, Hwu C-M et al. 2012 Central obesity is important but not essential component of the metabolic syndrome for predicting diabetes mellitus in a hypertensive family-based cohort.

Results from the Stanford Asia-pacific program for hypertension and insulin resistance (SAPPHIRe) Ta Cardiovasc Diabetol. 11 43

Mihardja L, Delima, Manz HS, Ghani L and Soegondo S 2009 Prevalence and determinants of diabetes mellitus and impaired glucose tolerance in Indonesia (a part of basic health

research/Riskesdas) Acta Med Indones. 41 169-174 Crossref Google Scholar

Pramono L a, Setiati S, Soewondo P et al. 2010 Prevalence and predictors of undiagnosed diabetes mellitus in Indonesia Acta Med Indones. 42 216-223

Gracey M, Faap F, Fracp VB et al. 2007 Assessment of risks of "lifestyle " diseases including cardiovascular disease and type 2 diabetes by anthropometry in remote Australian Aborigines 16 688-697 April

Google Scholar

PDF

Help

(25)

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

Ferreira-Hermosillo A, Ramírez-Rentería C, Mendoza-Zubieta V and Molina-Ayala M a 2014 Utility of the waist-to-height ratio, waist circumference and body mass index in the screening of metabolic syndrome in adult patients with type 1 diabetes mellitus Diabetol Metab Syndr. 6 32 Crossref Google Scholar

Kodama S, Horikawa C, Fujihara K, Heianza Y, Hirasawa R and Yachi Y 2012 Systematic Reviews and Meta - and Pooled Analyses Comparisons of the Strength of Associations With Future Type 2 Diabetes Risk Among Anthropometric Obesity Indicators, Including Waist-to- Height Ratio : A Meta-Analysis 176 959-969

Google Scholar

Tseng C-H 2008 Waist-to-height ratio and coronary artery disease in Taiwanese type 2 diabetic patients Obesity (Silver Spring) 16 2754-2759

Wei M, Gaskill SP, Haffner SM and Stern MP 1997 Waist Circumference as the Best Predictor of Noninsulin Dependent Diabetes Mellitus (NIDDM) Compared to Body Mass Index, Waist/hip Ratio and Other Anthropometric Measurements in Mexican Americans-A 7-Year Prospective Study Obes Res. 5 16-23

Lamacchia O, Pinnelli S, Camarchio D et al. 2009 Waist-to-Height Ratio Is the Best

Anthropometric Index in Association with Adverse Cardiorenal Outcomes in Type 2 Diabetes Mellitus Patients 615-619

Google Scholar

Fujita Y, Kouda K, Nakamura H and Iki M 2011 Cut-off Values of Body Mass Index, Waist Circumference, and Waist-to-Height Ratio to Identify Excess Abdominal Fat: Population-Based Screening of Japanese Schoolchildren J Epidemiol. 21 191-196

Al-zurfi BMN, Aziz AA, Abdullah MR and Mohd N. Waist Height Ratio Compared to Body Mass Index and Waist Circumference in Relation to Glycemic Control in Malay Type 2 Diabetes Mellitus Patients, Hospital Universiti 4 406-415

Google Scholar

Tabák AG, Herder C, Rathmann W, Brunner EJ and Kivimäki M 2012 Prediabetes: a high-risk state for diabetes development Lancet (London, England) 379 2279-2290

Okwechime IO, Roberson S and Odoi A 2015 Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling

PDF

Help

(26)

[23]

[24]

[25]

[26]

[27]

[28]

[29]

Approach PLoS One. 10 e0145781 Crossref Google Scholar

Baliga V and Sapsford R 2012 Diabetes and Vascular Disease Research 6 127-138 Google Scholar

Lorenzo C, Serranor M, Williams K et al. 2007 Which Obesity Index Best Explains Prevalence Differences in Type 2 Diabetes Mellitus? 15

Google Scholar

Kowall B, Rathmann W, Strassburger K et al. 2010 Association of passive and active smoking with incident type 2 diabetes mellitus in the elderly population: The KORA S4/F4 cohort study Eur J Epidemiol. 25 393-402

Haire-Joshu D, Glasgow RE and Tibbs TL 1999 Smoking and diabetes Diabetes Care. 22 1887- 1898

Sluik D, Boeing H, Li K et al. 2014 Lifestyle factors and mortality risk in individuals with diabetes mellitus: Are the associations different from those in individuals without diabetes?

Diabetologia. 57 63-72 Crossref Google Scholar

Joseph J, Svartberg J, Njølstad I and Schirmer H 2011 Risk factors for type 2 diabetes in groups stratified according to metabolic syndrome: a 10-year follow-up of the Tromsø Study Eur J Epidemiol. 26 117-124

Huxley R, Mendis S, Zheleznyakov E, Reddy S and Chan J 2010 Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk--a review of the literature Eur J Clin Nutr. 64 16-22

Export references: ^BibTeX ^RIS

PDF

Help

(27)

6/14/2020 Renewable vs. conventional energy: which wins the race to sustainable development? - IOPscience

Renewable vs. conventional energy: which wins the race to sustainable development?

A S M Al-Obaidi and T NguyenHuynh

Published 1 November 2018 • Published under licence by IOP Publishing Ltd

Abstract

Fossil fuel is no longer a reliable source to meet an increasing energy demand and guarantee a sustainable world in the future. This is because of its known environmental effects linked to climate change, global warming, and severe pollution. The current situation of harnessing energy requires to adjust for the integration of renewable energy into transportation and electricity generation for sustainable development. Throughout the literature, two main trends in energy technology are recognized. One is continuing to use petroleum-based fuel, but with less and wise fuel consumption, This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more,



1 1

[email protected]

School of Engineering, Taylor's University, Taylor's Lakeside Campus, No.1 Jalan Taylor's, 47500, Subang Jaya, Selangor DE, Malaysia

A S M Al-Obaidi and T NguyenHuynh 2018 IOP Conf. Ser.: Mater. Sci. Eng. 434 012310 https://doi.org/10.1088/1757-899X/434/1/012310

Buy this article in print

1

PDF

Help

(28)

[1]

[2]

[3]

[4]

[5]

increased efficiency of the engines of vehicles and electricity generators, and decarbonizing carbon in the exhaust. Second is technology development in green energy from renewable sources such as sun, wind, and hydropower to reduce in construction and production costs and lessen their environmental effects. This paper firstly presents an overview on green technology in the past and present. Secondly it discusses the adoption of green technology into the current conventional energy industry, challenges and approaches and thirdly how to increase its competitiveness with fossil fuel for the future.

References

2017 U.S. Energy Information Administration, Monthly Energy Review statistical_review_full_report_workbook_2006.xls

Google Scholar

B. Petroleum, "BP Statistical Review of World Energy." [Online]. Available:

www.bp.com/liveassets/bp_internet/globalbp/globalbp_uk_english/publications/energy_reviews_2 Google Scholar

Dictionary, "sustainbability." [Online]. Available:

http://www.dictionary.com/browse/sustainability Google Scholar

James P., Magee L., Scerri A. and Steger M. 2015 Urban Sustainability in Theory And Practice Circles of Sustainability

Google Scholar

United Nations 1987 Report of the World Commission on Environment and Development, United Nations General Assembly

Google Scholar

BibTeX RIS

PDF

Help

(29)

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

14 Grand Challenges for Engineering US National Academy of Engineering Google Scholar

2017 World Energy outlook 2017 International Energy Agency Google Scholar

REN21, "Renewables 2017. Global Status Report." [Online]. Available:

http://www.ren21.net/wp-content/uploads/2017/06/17- 8399_GSR_2017_Full_Report_0621_Opt.pdf

Google Scholar

Holmberg A., Anderssona K. and Erdemirb P. 2012 Global energy consumption due to friction in passenger cars Tribol. Int. 47 221-234

Chu S. and Majumdar A. 2012 Opportunities and challenges for a sustainable energy future Nature 488 294-303 2012

Gibson L., Wilman E. N. and Laurance W. F. 2017 How Green is 'Green' Energy? Trends Ecol.

Evol. 32 922-935

Barros N., Cole J. J., Tranvik L. J., Prairie Y. T., Bastviken D., Huszar V. L. M., del Giorgio P.

and Roland F. 2011 Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude Nat. Geosci. 4 593-596 2011

Li S. and Zhang Q. 2014 Carbon emission from global hydroelectric reservoirs revisited Environ.

Sci. Pollut. Res. 21 13636-13641 Crossref Google Scholar

Dai K., Bergot A., Liang C., Xiang W.-N. and Huang Z. 2015 Environmental issues associated with wind energy – A review Renew. Energy 75 911-921 Mar. 2015

Wang S. and Wang S. 2015 Impacts of wind energy on environment: A review Renew. Sustain.

Energy Rev. 49 437-443 Sep. 2015 Crossref Google Scholar

PDF

Help

(30)

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

Hayes M. A. 2013 Bats killed in large numbers at United States wind energy facilities Bioscience 63 975-979

Shaffer J. A. and Buhl D. A. 2015 Effects of wind-energy facilities on breeding grassland bird distributions Conserv. Biol. 31 59-71 2015

Google Scholar

Smith J. A. and Dwyer J. F. 2016 Avian interactions with renewable energy infrastructure: An update Am. Ornithol. Soc. 118 411-423 2016

Google Scholar

Santos M., Bastos R., Travassos P., Bessa R., Repas M. and Cabral J. A. 2011 Predicting the trends of vertebrate species richness as a response to wind farms installation in mountain ecosystems of northwest Portugal Ecol. Indic. 10 192-205 Mar. 2010

Lee R. 2011 The outlook for population growth Science (80-. ) 333 569-573 Crossref Google Scholar

Draphcho M., Nghiem P. and Walker H. 2008 Biofuels Engineering Process Technology (USA:

McGraw-Hill) Google Scholar

"EIA's alternative fuel vehicle data. Alternatives to Traditional Transportation Fuels," Energy Information Administration EIA, 2014. [Online]. Available:

www.eia.gov/renewable/afv/users.cfm#tabs_charts-2 Google Scholar

Suresh S. K., Arisutha S. and Sharma S. 2013 Production of Renewable Natural Gas from Waste Biomass J. Inst. Eng. Ser. E 94 55-59

America's Energy Future Panel 2009 Liquid Transportation Fuels Google Scholar

Routledge W. I. 2007 Biofuels for Transport Google Scholar

"Geothermal Dry Steam Power Plants." [Online]. Available:

http://www.digtheheat.com/geothermal/dry_steam_plant.html

PDF

Help

(31)

6/14/2020 The postpartum anestrus period of lowland anoa (Bubalus depressicornis) in captivity - IOPscience

The postpartum anestrus period of lowland anoa (Bubalus depressicornis) in captivity

A Mayasari , A Suryawan , J E Halawane , J Kinho , R E Vernia , A Abinawanto , A Suryanda and A Bowolaksono

Abstract

This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more,



1 2 2 2 1 1 3

1

[email protected]

Department of Biology, Faculty of Mathematics and Natural Sciences, University of Indonesia, 16424 Depok, West Java, Indonesia

Environment and Forestry Research and Development Institute of Manado, Adipura road, Kima Atas, Mapanget, 95259 Manado, North Sulawesi. Indonesia

Department of Biology, Faculty of Mathematics and Natural Sciences, National University of Jakarta, Rawamangun Muka road No. 1, 13220 East Jakarta, Indonesia

A Mayasari et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 434 012140 https://doi.org/10.1088/1757-899X/434/1/012140

Buy this article in print

1

2

3 PDF

Help

(32)

[1]

[2]

[3]

[4]

[5]

Information about the estrus cycle in anoa is particularly important to determine the optimal mating time. The purpose of the study is to determine the duration of the estrus cycle reversion in anoa while they are in their postpartum period. Estrus cycles are determined by observing estrus behavior/signs.

Observations were conducted on 1 female postpartum anoa without any lactating activity and 1 female postpartum anoa with lactating activity at Anoa Breeding Center, Manado. The result shows that the postpartum anestrus length periode of the lactating anoa is longer than the one without it. The first anoa was giving a birth on June 2016, estrus reappears 3 months 9 days after. The second anoa was giving a birth on February 2017, estrus reappears 6 months later after the calf weaned. With all these

information, the captive center would be able to make a better mating and reproduction plan for the anoa. It is going to rise the percentage of anoa's mating success rate.

References

Lago M R M, Pudjihastuti E, Wungow R H, Ngangi L R, Kinho J and Zoo J 2016 36 Google Scholar

Najamudin, Rusdin, Sriyanto, Amrozi and Agungpriyono S 2010 J. Vet. 11 Google Scholar

Hasibuan N 2013 Tanda-Tanda Estrus Dan Tingkah Laku Kawin Kerbau Murrah (Bubalus bubalis)

Google Scholar

Malakar S, Juyena N S, Paul S, Hasan M M, Bhattacharjee J, Mollah H R and Isalam M R 2017 Asian J. Med. Biol. Res. 3

Google Scholar

rusadi R P, Hartono M and Siswanto 2015 J. Ilmiah Peternakan Terpadu 3 BibTeX RIS

PDF

Help

(33)

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

Google Scholar

Mberoto Y, Hamsun M, Saloko F and Mirajudin 2016 J. Aus. Basc. Appl. Sci. 10 Google Scholar

Ahmadzedah A, Carnahan K and Autran C 2011 Understanding puberty and postpartum anestrus Proceedings, Applied Reproductive Strategies in Beef Cattle

Google Scholar

Yendraliza, Zespin B P, Udin Z and Jaswandi 2012 J.I.T. V. 17 Google Scholar

Winugroho M 2002 J. Litbang Pertanian 21 Google Scholar

Prasetyo Y, Hartono M and Siswanto 2015 Jurnal Ilmiah Peternakan Terpadu 3 Google Scholar

Budiawan A, ihsan M N and Wahjuningsih S 2015 J. Ternak Tropika 16 Google Scholar

Tandilolo S, Wulandari R and Rukmi 2013 J. War. Rim 1 Google Scholar

Fukuda T, Lino Y, Eitsuka T, Onuma M, Katayama M, murayama M I, Hara K, Isoagai E and Koyonon T 2017 J. Cytotechnology 68

Google Scholar

Judi 2012 Kajian perilaku reproduksi, preservasi semen, dan teknik inseminasi buatan pada anoa (Bubalus Sp.)

Google Scholar

al-Amin A F, Hartono M and Suharyati S 2017 J Penelitian Peternakan Indonesia 1 Google Scholar

Christoffor W T H M and Baliarti E 2008 J. Sain Peternakan 6 Google Scholar

Suharyati S and Hartono M 2016 J. Penelitian Pertanian Terapan 16 Google Scholar

PDF

Help

(34)

6/14/2020 Profession recommended system for higher education students using Bayesian method - IOPscience

Profession recommended system for higher education students using Bayesian method

V R Palilingan and J R Batmetan

Abstract

The profession becomes an important part for the graduate after graduating from higher education.

High school graduates do not know how to choose the right profession after graduation and less ready before graduating from higher education. The purpose of this study is to build a system that can provide appropriate profession recommendations in accordance with the personalities of higher education student. The method used in this research is Bayesian method. The results of this study indicate that This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more,



1 2

[email protected]

Information Technology and Communication Education Department, Universitas Negeri Manado, 95618, Tondano, Indonesia

Information Engineering Department, Universitas Negeri Manado, 95618, Tondano, Indonesia V R Palilingan and J R Batmetan 2018 IOP Conf. Ser.: Mater. Sci. Eng. 434 012040

1

2

PDF

Help

(35)

[1]

[2]

[3]

[4]

[5]

systems using Bayesian methods can provide recommendations with a 98% accuracy rate. The majority of respondents as much as 99% stated that the profession recommendations system that has been given is true. Why? because this system provides the right solution in accordance with each personality. This system is expected to help graduates of higher education have the right profession in accordance with the personality he has. This system can be used to provide appropriate profession recommendations for higher Education students. Bayesian methods are very well used to build an accurate and efficient recommendation system.

References

Johan Reimon Batmetan VRP 2018 Higher Education Students' Behaviour to Adopt Mobile Learning Higher Education Students' Behaviour to Adopt Mobile Learning In Series I O P Conference Science, Materials

Google Scholar

Jacob W J and Gokbel V 2017 Global higher education learning outcomes and financial trends : Comparative and innovative approaches Int. J. Educ. Dev.

Google Scholar

Emery J M, Grigoriu M D and Field R V Jr 2016 Bayesian methods for characterizing unknown parameters of material models Applied Mathematical Modelling 40 6395-6411

Batmetan J R and Santoso A J 2017 A Multiple-Objective Ant Colony for Optimizing Disaster Relief Logistics Adv Sci Lett. 23 1-7

Averill A F, Ingram J M, Holborn P G, Battersby P and Benson C M 2018 Application of Bayesian methods and networks to ignition hazard event prediction in nuclear waste

BibTeX RIS

PDF

Help

(36)

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

decommissioning operations Process Safety and Environmental Protection 116 396-404 Crossref Google Scholar

Li Q and Lu Q 2018 A hierarchical Bayesian method for vibration-based time domain force reconstruction problems Journal of Sound and Vibration 421 190-204

Botta L, Capocaccia R, Trama A, Herrmann C, Salmerón D, De Angelis R and Gatta G 2018 Bayesian estimates of the incidence of rare cancers in Europe Cancer epidemiology 54 95-100 Crossref Google Scholar

Fang T, Mackillop W, Jiang W, Hildesheim A, Wacholder S and Chen B E 2017 A Bayesian method for risk window estimation with application to HPV vaccine trial Computational Statistics & Data Analysis 112 53-62

Makowski D 2017 A simple bayesian method for adjusting ensemble of crop model outputs to yield observations European journal of agronomy 88 76-83

Betz W, Papaioannou I, Beck J L and Straub D 2018 Bayesian inference with subset simulation:

strategies and improvements Computer Methods in Applied Mechanics and Engineering 331 72- 93

Dalpatadu P, Ahmed S and Khan F 2015 Bayesian method for event-based alarm annunciation IFAC-PapersOnLine 48 832-837

Batmetan Johan Reimon and Jaime Da Costa Lobo Suares S An Empirical Investigation on Customer Behavior to Adopt Mobile Commerce among the Y Generation in Indonesia In Sriwijaya International Conference on Engineering, Science and Technology 2016 (SICEST 2016) 333-7

Google Scholar

Zhang L, Li D, Tang X, Cao Z and Phoon K 2017 Computers and Geotechnics Bayesian model comparison and characterization of bivariate distribution for shear strength parameters of soil Comput Geotech

Google Scholar

Kaedi M and Ahn C W 2017 Robust optimization using Bayesian optimization algorithm: Early detection of non-robust solutions Applied Soft Computing 61 1125-1138

PDF

Help

(37)

[15]

[16]

[17]

[18]

[19]

Bergmann H, Geissler M, Hundt C and Grave B 2018 The climate for entrepreneurship at higher education institutions Research Policy 47 700-716

Cater-Steel A and Toleman M 2007 The role of universities in IT service management education PACIS 2007 Proceedings 49

Google Scholar

Viberg O and Grönlund Å 2013 Cross-cultural analysis of users' attitudes toward the use of mobile devices in second and foreign language learning in higher education: A case from Sweden and China Computers & Education 69 169-180

Sun J, Ellerbroek J and Hoekstra J M 2018 Aircraft initial mass estimation using Bayesian inference method Transportation Research Part C: Emerging Technologies 90 59-73 Crossref Google Scholar

Godsill S 2014 In Academic Press Library in Signal Processing (Elsevier) Bayesian computational methods in signal processing 3 143-185

PDF

Help

(38)

6/14/2020 Single population modelling on predicting global carbon dioxide concentration - IOPscience

Single population modelling on predicting global carbon dioxide concentration

A Rukmananda and C Barenz

Abstract

Every year, the concentration of carbon dioxide is continuously increasing. In regard to this, it becomes a concern if this phenomena occurs repeatedly over time. When the concentration reaches at a certain point, it may have some bad effects on earth and will threaten life. If this event is ignored, then the earth and life on it will gradually be more effected and turned its condition to be worse. Regarding to this, it might be helpful to have a model that can predict the concentration of carbon dioxide. Since a preventive action can be taken before the concentration gets higher in the future based on the

prediction. The prediction model can be formulated by a population modelling, where the concentration This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more,



1 1

[email protected]

Faculty of Engineering and Technology, Sampoerna University, 12780 Jakarta, Indonesia A Rukmananda and C Barenz 2018 IOP Conf. Ser.: Mater. Sci. Eng. 434 012005

1

PDF

Help

(39)

[1]

[2]

[3]

[4]

[5]

[6]

amount is perceived as a single population. In this case, carbon dioxide emission and its absorption by photosynthesis will be two considered factors. Where emission will represent the birth, and the

absorption represents the death of the population. By this model, it is predicted that about 58 years later the concentration will reach 500ppm. It has been evaluated that the model has small error to predict the current data.

References

Robinson A B, Robinson N E and Soon W 2007 Environmental effects of increased atmospheric carbon dioxide J. Am. Phys. Surg. 12 83-85

Google Scholar

Tindall G B and Shi D E 2013 America: A Narrative History (New York: W. W. Norton &

Company) 384-392 Google Scholar

Marland G and Boden T AIDS And Infectious Diseases (Tennessee: Oak Ridge National Labolatory) The increasing concentration of atmospheric CO2: how much, when, and why?

Google Scholar

Robertson D S 2006 Health effects of increase in concentration of carbon dioxide in the atmosphere Curr. Sci. 90 1-2

Google Scholar

Barnes B and Fulford G R 2015 Mathematical Modelling with Case Studies (Boca Raton: CRC Press) 52-53

Google Scholar

Mader S S 2009 Concepts of Biology (New York: McGraw-Hill) 758 BibTeX RIS

PDF

Help

(40)

[7]

[8]

[9]

Google Scholar

Sabine C L, Feely R A, Gruber N, Key R M, Lee K, Bullister J L, Wanninkhof R, Wong C S, Wallace D W R, Tilbrook B, Millero F J, Peng T-H, Kozyr A, Ono T and Rios A F 2004 The oceanic sink for anthropogenic CO2 Sci. 305 370-371

Google Scholar

Trevisan L A and Luz F M D M 2008 Prey-predator modeling of CO2 atmospheric concentration arXiv preprint arXiv 0805.0819

Preprint Google Scholar

Morrow W R, Gallagher K S, Collantes G and Lee H 2010 Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector Energy Policy 38 1305-1320

PDF

Help

(41)