KEYNOTE SPEAKER 1

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LEMBAR

HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW KARYA ILMIAH : PROSIDING

Judul Jurnal Ilmiah (Artikel) : Application of Robust Linear Quadratic Control for Inventory System with Unknown Demand: Single Product Case

Nama/Jumlah Penulis : Sutrisno, Widowati, R. Heru Tjahiana / 3 orang Status Pengusul : penulis ke- 2

Identitas Prosiding : a. Nama Prosiding : Proceeding of International Conference on

Informatics and Computational Sciences

(ICICoS)

b. Nomor ISSN : Electronic ISBN: 978-1-5386-7440-6

USB ISBN: 978-1-5386-7439-0

(PoD) ISBN: 978-1-5386-7441-3

c. Volume, nomor, bulan tahun : 18411159 (2019)

d. Penerbit : IEEE Explore

e. DOI artikel (jika ada) : 10.1109/ICICOS.2018.8621666 f. Alamat web Prosiding

URL PROCEEDING : https://ieeexplore.ieee.org/document/8621666

URL ARTIKEL : https://eprints2.undip.ac.id/1954/1/ARTIKELC35-WIDOWATI.pdf g. Terindeks di : IEEE

Kategori Publikasi Prosiding : Prosiding Internasional Terindeks (beri pada kategori yang tepat) Prosiding Internasional Prosiding Nasional

Hasil Penilaian Peer Review : Komponen Yang Dinilai

Nilai Reviewer

Nilai Rata-rata Reviewer I Reviewer II

a. Kelengkapan unsur isi prosiding (10%) 2,38 2.00 2.19

b. Ruang lingkup dan kedalaman pembahasan (30%) 7,13 6.00 6.57

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

7,13

5.50 6.32

d. Kelengkapan unsur dan kualitas terbitan/jurnal (30%) 7,00 6.00 6.50

Total = (100%) 23,64 19.50 21.57

Nilai Pengusul 4,73 3.90 4.31

Semarang, April 2020

Reviewer 2 Reviewer 1

Prof. Dr. St. Budi Waluya, M.Si Prof. Dr. Basuki Widodo, M.Sc

NIP. 196809071993031002 NIP. 19650506 1989031002

Unit kerja : Matematika FMIPA UNNES Unit kerja : Matematika FSAD ITS

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Judul Karya Ilmiah (Artikel) : Application of Robust Linear Quadratic Control for Inventory System with Unknown Demand: Single Product Case

Nama/Jumlah Penulis : Sutrisno, Widowati, R. Heru Tjahiana / 3 orang Status Pengusul : penulis ke- 2, corresponding author

(ICICoS)

b. Nomor ISBN : Electronic ISBN: 978-1-5386-7440-6

USB ISBN: 978-1-5386-7439-0

(PoD) ISBN: 978-1-5386-7441-3

URL ARTIKEL : https://eprints2.undip.ac.id/1954/1/ARTIKELC35-WIDOWATI.pdf g. Terindeks di : IEEE Explore

Kategori Publikasi Prosiding : Prosiding Internasional Terindeks

(beri pada kategori yang tepat) Prosiding Internasional Prosiding Nasional

Hasil Penilaian Peer Review :

Komponen Yang Dinilai

Nilai Maksimal Jurnal Ilmiah

Nilai Akhir Yang Diperoleh Prosiding

Internasional terindeks

Prosiding Internasional

Prosiding Nasional

a. Kelengkapan unsur isi prosiding (10%) 2,5 2,38

b. Ruang lingkup dan kedalaman pembahasan (30%)

7,5 7,13

c. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

7,5 7,13

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

7,5 7,00

Total = (100%) 25 23,64

Nilai Pengusul = 40% x ½ x 23,64 = 4,73 Catatan Penilaian artikel oleh Reviewer:

1. Kesesuaian dan kelengkapan unsur isi prosiding :

Penulisan artikel baik dan mengikuti standard penulisan artikel di Proceeding of International Conference on Informatics and Computational Sciences (ICICoS) 2018, yaitu abstract, Introduction, Methodology, Result and Discussion (IMRaD), Conclusion. Artikel belum memuat Acknowledgement. Artikel didukung dengan referensi yang sesuai.

2. Ruang lingkup dan kedalaman pembahasan:

Lingkup bahasan dari artikel ini adalah bidang matematika terapan, khususnya pada bidang sistem kontrol. Dalam artikel ini dibahas dengan baik tentang persamaan dinamis untuk mengontrol produk tunggal dari permasalahan sistem persediaan atau gudang dengan permintaan yang tidak diketahui. Relevansi hasil terkait volume produk pembelian yang optimal untuk semua periode waktu dan tingkat persediaan produk sesuai dengan yang diinginkan oleh pembuat keputusan.

3. Kecukupan dan kemutakhiran data/informasi dan metodologi :

Informasi yang disajikan cukup baru dan hasil yang diperoleh memuat substansi aplikasi yang penting. Sumber gagasan penulis untuk artikel ini banyak, komprehensif dan update, yang lebih sepuluh tahun terakhir hanya 2 paper dari 19 sumber yang dirujuk. Methodologynya cukup baik..

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4. Kelengkapan unsur dan kualitas terbitan:

Artikel memenuhi standard penulisan dan isi untuk artikel di Proceeding of International Conference on Informatics and Computational Sciences (ICICoS) 2018 terindeks di SCOPUS, IEEE Explore.

Surabaya, 20 April 2020 Reviewer 1

Prof. Dr. Basuki Widodo, M.Sc NIP. 19650506 1989031002 Unit kerja : Matematika FSAD ITS

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LEMBAR

Judul karya Ilmiah (Artikel) : Application of Robust Linear Quadratic Control for Inventory System with Unknown Demand: Single Product Case

Nama/Jumlah Penulis : Sutrisno, Widowati, R. Heru Tjahiana / 3 orang Status Pengusul : penulis ke- 2

(ICICoS)

b. Nomor ISBN : Electronic ISBN: 978-1-5386-7440-6

USB ISBN: 978-1-5386-7439-0

(PoD) ISBN: 978-1-5386-7441-3

URL ARTIKEL : https://eprints2.undip.ac.id/1954/1/ARTIKELC35-WIDOWATI.pdf

g. Terindeks di : IEEE

Kategori Publikasi Prosiding : Procedia/Prosiding Internasional Terindeks (beri pada kategori yang tepat) Prosiding Internasional Prosiding Nasional

Hasil Penilaian Peer Review :

Komponen Yang Dinilai

Nilai Maksimal Jurnal Ilmiah

Nilai Akhir Yang Diperoleh Prosiding

Internasional terindeks

Prosiding Internasional

Prosiding Nasional

e. Kelengkapan unsur isi prosiding (10%) 2,5 2.00

f. Ruang lingkup dan kedalaman pembahasan (30%)

7,5

6.00 g. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

7,5

5.50 h. Kelengkapan unsur dan kualitas

terbitan/jurnal (30%)

7,5

6.00

Total = (100%) 25 19.50

Nilai Pengusul = 40% x ½ x19.50 =3.90

Catatan Penilaian artikel oleh Reviewer :

1. Kesesuaian dan kelengkapan unsur isi prosiding:

Kesesuaian dan kelengkapan unsur isi cukup baik. Artikel tersusun dalam kaidah penuliasan karta ilmiah. Terdiri atas 4 bagian : Introduction, Materials and methods, Computational simulation, Conclusion. Didukung 19 referensi yang sebagian besar berupa jurnal.

2. Ruang lingkup dan kedalaman pembahasan:

Ruang lingkup dan kedalaman pembahasan cukup baik. Pendahuluan dan pembahasan kurang mendalam dalam menunjukkan kebaharuan penelitian. Pembahasan mengenai application of the robust linear quadratic regulator for inventory controlling of.single product case of warehouse system considering unknown demand Termasuk dalam lingkup Matematika Terapan yang sesuai dengan bidang keilmuan pengusul.

3. Kecukupan dan kemutakhiran data/informasi dan metodologi :

Kecukupan dan kemutakhiran data/informasi dan metodologi cukup baik. Terdapat 19 referensi yang sebagian besar berupa jurnal (1 diantara referensi lebih dari 10 tahun).

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4. Kelengkapan unsur dan kualitas terbitan:

Kelengkapan unsur dan kualitas terbitan cukup baik. Artikel diterbitakan dalam Proceeding of International Conference on Informatics and Computational Sciences (ICICoS). Penerbit IEEE. Terindeks di Scopus, IEEE. Hasil Turnitin similarity index=11%. Masih terdapat editorial kurang teliti.

Semarang, April 2020 Reviewer 2

Prof. Dr. St. Budi Waluya, M.Si NIP. 196809071993031002

Unit kerja : Matematika FMIPA UNNES

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Document details

7 of 36

Application of Robust Linear Quadratic Control for Inventory System with Unknown Demand: Single Product Case (Conference Paper)

, ,

Dept. of Mathematics, Diponegoro University, Semarang, Indonesia

Abstract

In this paper, a dynamical model of single product inventory system with unknown demand in a linear state space equation with unknown parameter for inventory control purposes was formulated. An existing control method, robust linear quadratic regulator (RLQR), was applied to control the inventory level by generating the optimal purchasing product volume so that the product stock follows a reference trajectory with minimal cost. The result of the performed numerical experiments showed that the optimal purchasing product volume was determined for every time period and the product stock was closed to the given trajectory level desired by the decision maker. © IEEE.

SciVal Topic Prominence

Topic:

Prominence percentile: 85.301

Author keywords

inventory control inventory system robust LQR supply chain management unknown demand unknown parameter

Indexed keywords

Engineering controlled terms:

Decision making Equations of state Linear control systems Supply chain management

Engineering uncontrolled terms

Inventory systems Linear quadratic regulator Numerical experiments Reference trajectories Robust linear quadratic control State space equation unknown demand unknown parameter

Engineering main heading:

Inventory control

Funding details

Funding sponsor Funding number Acronym

Universitas Diponegoro 474-89/UN7.P4.3/PP/2018 UNDIP

Funding text

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2018 2nd International Conference on Informatics and Computational Sciences, ICICoS 2018 22 January 2019, Article number 8621666, Pages 174-178

2nd International Conference on Informatics and Computational Sciences, ICICoS 2018; Santika Premiere HotelSemarang; Indonesia; 30 October 2018 through 31 October 2018; Category numberCFP18N15-ART; Code 144517

Sutrisno  Widowati  Tjahiana, R.H. 

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Inventory control | Inventory | Optimal policies

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COMMITTEES

Honorary Chair:

 Prof. Widowati, Universitas Diponegoro, ID

 Prof. W. Jatmiko, Universitas Indonesia, ID Section Chair:

 FY. Zulkifli, IEEE Indonesia Section General Chair:

 DMK. Nugraheni, Universitas Diponegoro, ID

General Co-chairs:

 I. Waspada, Universitas Diponegoro, ID

Secretary:

 S. Adhy, Universitas Diponegoro, ID

 SN. Endah, Universitas Diponegoro, ID Finance:

 B. Noranita, Universitas Diponegoro, ID

 Khadijah, Universitas Diponegoro, ID Program Chair:

 P. W. Wirawan, Universitas Diponegoro, ID

 R. Saputra, Universitas Diponegoro, ID Publication Chair:

 R. Kusumaningrum, Universitas Diponegoro, ID

 Rismiyati, Universitas Diponegoro, ID

 Sutikno, Universitas Diponegoro, ID

 H. A. Wibawa, Universitas Diponegoro, ID

Technical Program Committee Chairs:

 A. Wibowo, Universitas Diponegoro, ID

 SM. Isa, Binus University, ID

Technical Program Committee Member:

 A Hardjoko, Universitas Gadjah Mada, ID

 A Purwarianti, Institut Teknologi Bandung, ID

 A Sujiwo, Institut Pertanian Bogor, ID

 AA Gunawan , Binus University, ID

 AA Krishnadhi, Universitas Indonesia, ID

 AF Huda, UIN Sunan Gunung Djati, ID

 Afiahayati, Universitas Gadjah Mada, ID

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3

 A Musdholifah, Universitas Gadjah Mada, ID

 AK Sari, Universitas Gadjah Mada, ID

 AN Hidayanto, Universitas Indonesia, ID

 AP Widodo, Universitas Diponegoro, ID

 AS Nugroho, BPPT , ID

 Azhari, Universitas Gadjah Mada, ID

 B Hardjono, Universitas Pelita Harapan, ID

 B Surarso, Universitas Diponegoro, ID

 D Fitrianah, Universitas Mercu Buana, ID

 D Napitupulu, LIPI, ID

 D Octaviani, HELP University, MY

 DD Vries, Flinders University, AU

 E Soediyono, Universitas Kristen Satya Wacana, ID

 EM Imah, Universitas Negeri Surabaya, ID

 F Makhrus, Universitas Gadjah Mada, ID

 F Syafar, Universitas Negeri Makasar, ID

 Farikhin, Universitas Diponegoro, ID

 G Dougherty, California State University Channel Islands, USA

 G Rampersad, Flinders University, AU

 H Wei , University of Reading, UK

 HA Nugroho, Universitas Gadjah Mada, ID

 I Ardiyanto, Universitas Gadjah Mada, ID

 I Nurhaida, Universitas Mercu Buana, ID

 I Wasito, Universitas Diponegoro, ID

 I Yusri, Politeknik Negeri Ujung Pandang, ID

 IA Sirodjuddin, Universitas Trunojoyo, ID

 IM Shofi, UIN Syarif Hidayatullah, ID

 K Mustofa, Universitas Gadjah Mada, ID

 K Sekiyama, Nagoya University, JP

 K Surendro, Institut Teknologi Bandung, ID

 L Fang, Nanyang Technological University, SG

 LL Hong, Quest International University, MY

 Lukman, LIPI, ID

 M Guarracino, Italian National Research Council, ITA

 M Kankanhalli, National University of Singapore, SG

 M Riasetiawan, Universitas Gadjah Mada, ID

 M Yusuf, Universitas Trunojoyo, ID

 ME Wibowo, Universitas Gadjah Mada, ID

 Meyliana, BINUS University, ID

 MI Fanany, Universitas Indonesia, ID

 ML Khodra, Institut Teknologi Bandung, ID

 MR Ahmad, Universiti Teknologi Malaysia, MY

 MR Widiyanto , Universitas Indonesia , ID

 Mustafid, Universitas Diponegoro, ID

 OD Nurhayati, Universitas Diponegoro, ID

 P Mursanto, Universitas Indonesia, ID

 PI Santoso, Universitas Gadjah Mada, ID

 R Ferdiana, Universitas Gadjah Mada,ID

 R Kusumaningrum, Universitas Diponegoro, ID

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4

 R Munir, Institut Teknologi Bandung, ID

 R Sarno, Intitut Teknologi Surabaya, ID

 S Bressan, National University of Singapore, SG

 S Hartati, Universitas Gadjah Mada, ID

 S Rohajawati, Universitas Bakrie, ID

 S Wahjuni, Institut Pertanian Bogor, ID

 S Wibirama, Universitas Gadjah Mada, ID

 T Mantoro, Universitas Sampoerna, ID

 Tarno, Universitas Diponegoro, ID

 TD Susanto, Institut Teknologi Surabaya, ID

 UA Rachmawati, Universitas YARSI, ID

 W Jatmiko, Universitas Indonesia, ID

 X li, University of Queensland, AU

 Y Heryadi, BINUS University, ID

Reviewer:

 A Abdullah, Universiti Kebangsaan Malaysia, MY

 A Alsobeh, Yarmouk University, JOR

 A Arisal, Indonesian Institute of Sciences, ID

 A Baharum, Universiti Malaysia Sabah, MY

 A Farea, Taiz University, YEM

 A Ghasempour, ICT Faculty, USA

 A Gunawan, Bina Nusantara University, ID

 A Hidayanto, University of Indonesia, ID

 A Jain, Jaipur Engineering College & Research Centre, IND

 A Mubarak-Ali, Universiti Malaysia Pahang, MY

 A Mukherjee, Jiangsu University, CHN

 A Najmurrokhman, Universitas Jenderal Achmad Yani, ID

 A Puji Widodo, Diponegoro University, ID

 A Septiarini, Univeristas Mulawarman, ID

 A Serrat, USTO MB, DZA

 A Shibghatullah, UCSI University, MY

 A Suganda Girsang, Bina Nusantara University, ID

 A Sunyoto, Universitas AMIKOM Yogyakarta, ID

 A Wibowo, Diponegoro University, ID

 B Alhaji, Nigerian Defence Academy, NER

 B Hardjono, Universitas Pelita Harapan, ID

 B Jiang, The City University of New York, USA

 B Rintyarna, Sepuluh Nopember Institute of Technology, ID

 B Warsito, Diponegoro University, ID

 D Agustriawan, Indonesia International Institute for Life Sciences, ID

 D Andriana, Indonesian Institute of Sciences, ID

 D Ariatmanto, Universitas Amikom Yogyakarta, ID

 D Hooshyar, Korea University, KOR

 D Koukopoulos, University of Patras, GRC

 D Nugraheni, Universitas Diponegoro, ID

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20

KEYNOTE SPEAKER 1

The Shifting Paradigm in Computational Science Research Toward Industry 4.0: Issues and Challenges

Prof. Zainal A. Hasibuan, PhD.

Universitas Indonesia, Indonesia

Abstract

Fast growing ICT usage creates issues and challenges in research and development. Old paradigm in research: laboratory scaled experiment, sampled and inference data, hypotheses and prototyping, are shifted toward a new paradigm in research: pervasive and open, big data and pattern discovery, analytic and intrinsic. The new paradigm of research produces discovery and innovation that creates disruptive knowledge and technology for Industry 4.0. These disruptive knowledge and technology are driven by a big volume of data, unthinkable variety of data, a velocity of big data processing, and veracity of data validity. Everything is a life, intelligent and connected that blurring the lines between the physical, digital, and biological spheres. It’s all possible cause of the internet of things, cloud computing and cognitive computing. The distance between producer and consumer are abridged.

The integration of physical, digital, connected, and intelligent are prominent in the emergence technology, such as integrated waste management, intelligent health information system, smart energy conservation, optimizing transport management, and so forth. Putting in the Indonesian context, the new paradigm of research must evolve as opposed to revolve, from the range of industry 1.0 to industry 4.0.

(12)

22

KEYNOTE SPEAKER 2

A Big Data Interconnected World in the Era of Industry 4.0

Professor Wenny Rahayu La Trobe University, Australia

Abstract

Big Data and Internet of Things (IoT) have received considerable interest in the development of smart technologies in today's interconnected world. The rapid advancement of such internet technologies has enabled myriad systems and applications to generate data of massive volume, variety, and velocity. Traditional databases and systems are no longer suitable to manage and integrate this high data volume effectively. In this era of Industry 4.0, a growing number of industries/organisations have to deal with such big datasets. These datasets have different data types (e.g., dynamic streaming data, static data) in different formats (e.g., structured, semi-structured, unstructured) from multiple sources.

In this talk, a number of areas surrounding integrating big data from distributed environments in the context of Industry 4.0 will be discussed. These areas include data integration approach to deal with static and streaming data from multiple sources, as well as multiple privacy and access control techniques surrounding big data interconnectivity.

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24

KEYNOTE SPEAKER 3

Enhanced Support Vector Machines Classification with Low Dependency on Kernel and Parameters

Assoc Prof. Dr. Lee Lam Hong

Quest International University Perak, Malaysia

Abstract

This research introduces an enhanced Support Vector Machines (SVM) classification framework for discovering knowledge in different data of varying characteristics. Since the beginning of this decade, Industry 4.0 has been revived and vast amount of data are generated continuously from the interactions of the communities of modern world. These data which are large in size, complex, noisy and of varying characteristics, have gained great importance as they embed valuable information to various sectors of the world, hence lead to big challenges in identifying the effective processes for knowledge discovery. SVM has been reported as one of the highly accurate classification models, and has been increasingly used in a multitude of domains.

However, the good classification performance of SVM is only guaranteed when the model is fine-tuned with appropriate kernel function and parameters’ values, which requires convoluted process and additional data for validation. An enhancement for the conventional SVM is proposed to overcome the high dependency of its performance on kernel and parameters. The optimal separating hyper-plane, which its construction is mainly relying on the configuration of kernel and parameters, has been substituted with distance/similarity measure, to serve as the classification decision making function. The conventional SVM training algorithm has been utilized to reduce training data points by retaining only the Support Vectors (SVs) of different categories. In classification phase, distance/similarity measure is used to make the decision based on the average distance/similarity between the testing data point to each group of SVs from different categories. By eliminating the optimal separating hyper-plane as the decision surface, the impact of kernel and parameters on the accuracy could be minimized. The ultimate goal of this research is to develop a prominent classification framework which is able to handle different datasets of varying characteristics for successful knowledge discovery in various domains.

(14)

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EffectofSynthetic Minority Oversampling Technique (SMOTE), Feature Representation, and Classification Algorithm on Imbalanced Sentiment Analysis

Widi Satriaji (Universitas Diponegoro, Indonesia), Retno Kusumaningrum (Diponegoro University, Indonesia) 99

Hierarchical Sentence Sentiment AnalysisofHotel Reviews Using The Naive Bayes Classifier

Sandy Kurniawan (Universitas Diponegoro, Indonesia), Retno Kusumaningrum (Diponegoro University, Indonesia), Melnyi Timu (Dili Institute of Technology, Timor-Leste) ""••"" " •••"" " •••••" ""•••"" " " " ,.•.•.•.•.•.•.•.•.•.•.•.•.,... 104 Real-time DetectionofData Completeness Degree for Traffic Simulation Using Text Similarity and Time RelevanceofData from Social

Media

Eviana Tjatur Putri (Institut Teknologi Sepuluh Nopember&STMIK PPKIA Tarakanita Rahmawati, Indonesia), Joko Buliali (Institut Teknologi

Sepuluh Nopember, Indonesia), Myrna Ermawati (Institut Teknologi Sepuluh Nopember, Indonesia) ,... 109 Interpretive Flexibility in Using Mobile Applications to Handle Public Complaints by Government Officials

Dewinta Puristia (Universitas Indonesia, Indonesia), Irwansyah Irwansyah (Universitas Indonesia, Indonesia) ,.•.•.•.•.•.•.•.•.•.•.•.•.,.,... 115 Feature Selection for Music Emotion Recognition

Emilia Widiyanti (Diponegoro University, Indonesia), Sukmawati Nur Endah (Universitas Diponegoro, Indonesia) .•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.,.•.•.•.•.•.•.•.•.•.•.•.•.,.,.•.•.•.•.•.•.•.

Reducing Image Noises Using Genetic Algorithm's Uniform Crossover

Agnes Irene Silitonga (Universitas Sumatera Utara, Indonesia), Erna Budhiarti Nababan, MIT (Universitas Sumatera Utara, Indonesia), Opim Salim Sitompul (Universitas Sumatera Utara, Indonesia) ." " " " "" " " ,... 125 HOG and Zone Base Features for Handwritten Javanese Character Classification

Rismiyati Rismiyati (Universitas Diponegoro, Indonesia), Khadijah Khadijah (Universitas Diponegoro, Indonesia), Djalal Riyanto (Universitas Diponegoro, Indonesia) """"•••""""••••"""."••••"""""••"""""•••" ""••.••" ""•.•.•" " , ,.,... 131 Feature AnalysisofNormal Epithelial Cervical Cell Characteristics in Pap Smear Images

Rahadian Kurniawan (Universitas Islam Indonesia, Indonesia), Dhomas Hatta Fudholi (Universitas Islam Indonesia, Indonesia), Izzati Muhimmah (Universitas Islam Indonesia, Indonesia), Arrie Kurniawardhani (Universitas Islam Indonesia, Indonesia), Indrayanti Indrayanti

(Universitas Muhammadiyah Yogyakarta, Indonesia) " " "... 136 AComparisonofHandcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images

Kuntoro Adi Nugroho (Universitas Diponegoro, Indonesia) " " " " " ,... 141

Neural Network ImplementationofDivers Sign Language Recognition based on Eight Hu-Moment Parameters

Matt Ervin Mital (De La Salle University-Manila, Philippines, Philippines), Elmer P. Dadios (De La Salle University-Manila, Philippines,

Philippines), Herbert Villaruel (De La Salle University-Manila, Philippines) "" " " ,.•.•.•.•.•.•.•.•.•.•.•.•.•.,... 147

Machine Learning and Computational Sciences

The StudyofSynthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray Classification

Khadijah Khadijah (Universitas Diponegoro, Indonesia), Retno Kusumaningrum (Diponegoro University, Indonesia), Sukmawati Nur Endah

(Universitas Diponegoro, Indonesia), Rismiyati Rismiyati (Universitas Diponegoro, Indonesia) •.•.•.•." ,.•.•.•.•.•.•.•.•.•.•.•.•.•.,... 153 ImplementationofThe Binary Inclusion-Maximal Biclustering Algorithm on Adenoma Microarray Gene Expression Data

Syamira Merina (Universitas Indonesia, Indonesia), Alhadi Bustamam (Universitas Indonesia, Indonesia), Gianinna Ardaneswari (Universitas

Indonesia, Indonesia) .""•.•" ""••" " " " " ,.,... 159

Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for SelectionofMicroRNA Expression FeaturesofBreast Cancer Amazona Adorada (Diponegoro University, Indonesia), Ratih Permatasari (Diponegoro University, Indonesia), Panji Wirawan (Diponegoro

University, Indonesia), Adi Wibowo (Diponegoro University, Indonesia), Adi Sujiwo (Nagoya University & Institut Pertanian Bogor, Japan) ...,... 165 ADistributional ModelofSensitive Values on p-Sensitive in Multiple Sensitive Attributes

Widodo Widodo (Universitas Negeri Jakarta, Indonesia), Wahyu Catur Wibowo (Faculty of Computer Science, University of Indonesia,

Indonesia) .."""••••"".""••""•.•."" " " " ••••""" " ,... 169 ApplicationofRobust Linear Quadratic Control for Inventory System with Unknown Demand.' Single Product Case

Sutrisno Sutrisno (Diponegoro University, Indonesia), Widowati Widowati (Diponegoro University, Indonesia), R. Heru Tjahjana (Diponegoro

University, Indonesia) .""••••"".""••" ••" ••" ""•••"""""••"."" " " " " " " •.•.•.•.•.•.•.•.•.•.•.•.•.•.•.••.•.•.•.•.•.•.•.•., ,... 174 Implementationofe-New Local Search based Multiobjective Optimization Algorithm and Multi-objective Co-variance based Artificial Bee

Colony Algorithm in Stocks Portfolio Optimization Problem

Rizki Ramadhiani (Universitas Indonesia, Indonesia), Michael Yan (Universitas Indonesia, Indonesia), Gatot Hertono (Universitas Indonesia,

Indonesia), Bevina Handari (Universitas Indonesia, Indonesia) " " " " ,... 179

AColumn Generation Approach for Personnel Scheduling with Discrete Uncertain Requirements

Pattarapong Pakpoom (Kasetsart University, Thailand), Peerayuth Charnsethikul (Kasetsart University, Thailand) .•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.,.•.•.•.•.•.•.•.•.•.•.•.•.•.,... 185

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Dominant Criteria and Its Factor Affecting Student Achievement Based on Rough-Regression Model

Riswan Efendi

Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif

Kasim Riau, 28293 Pekanbaru, Indonesia

riswan.efendi@uin-suska.ac.id Susnaningsih Mu’at Faculty of Economics and Sosial, Universitas Islam Negeri Sultan Syarif

susnaningsih@uin-suska.ac.id

Novi Yanti

Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif

novi_yanti@uin-suska.ac.id Noor Azah Samsudin Faculty of Science Computer and Information Technolgy, Universiti Tun

Hussein Onn Malaysia 86400 Batu Pahat, Malaysia

azah@uthm.edu.my

Alex Wenda

Faculty of Economics and Sosial, Universitas Islam Negeri Sultan Syarif

alexwenda@uin-suska.ac.id Mustafa Mat Deris Faculty of Science Computer and Information Technolgy, Universiti Tun

Hussein Onn Malaysia 86400 Batu Pahat, Malaysia

mmustafa@uthm.edu.my

Abstract— the ordinary least square model has been widely considered to estimate the significant factors which influence the student achievement. Some factor is qualitative type and measured using criteria or categories. However, the decisive criteria for each factor which affect to the cumulative grade point average of student cannot be determined by this model. In this paper, we are interested to build a new procedure using rough-regression model in determining the dominant criteria from each factor based on generalization of dependency attribute. Based on result, the proposed procedure is capable to investigate the dominant criteria and factors affecting student achievement, such as, language spoken with dominant criteria is

“many-many”, FB friend with dominant criteria is “many” and fast food with dominant criteria is “never”. This proposed procedure is very appropriate to implement for handling categorical data.

Keywords—rough-regression, dominant criteria, FB friend, fast food, language spoken, CGPA.

I. INTRODUCTION

In education and psychology applications, regression models have been widely applied to investigate the inter- relationships between explanatory and response factors (attributes) which influence the academic performance of the universities students. For example, there was a negative association between times spent on Facebook and GPA (grade point average) for freshmen, sophomores, and juniors [3, 4]. Moreover, the increasing of cell phones use was associated with decreasing of academic performance for college students in U.S [17]. However, the implementation of rough sets into regression model is still limited to investigate the education and psychology attributes. Additionally, the attributes from both domains are categorical values.

Moreover, there was an association between language proficiency and multilingualism to academic performance, especially international students by using statistical approach [1, 2]. Based on [5-7], the high academic performance have been achieved significantly when the student never consumed fast food using adjusted odds ratio. Motivated by three different factors, namely, number of FB friend, number of language spoken, and fast food per week, we are interested to determine the dominant criteria from each factor which affect to academic performance of university students using rough sets and regression models.

Recently, rough-regression model has been introduced in medical applications, especially in prediction cholesterol level, flu diagnosis and cancer diagnosis [13-15]. However, the discussion between rough set and regression models has been presented separately in the previous studies for education domains. In this paper, we discuss the generalization of dependency attribute of rough sets in determining dominant criteria for each significant factor in regression model.

Additionally, the proposed procedure will be implemented to calculate and determine the decisive criteria for student CGPA.

II. ROUGH SET AND REGRESSION MODELS A. Rough Sets and Applications

Pawlak has been initiated in 1982 the rough sets theory for uncertainty and categorical attributes analysis [8], there are some components in this theory related with information system 𝑆 = (𝑈, Ω, 𝑉𝑞, 𝑓_𝑞) [8-10].

A students, patients and observations are called as objects in rough sets. While, the factors, variables, and characteristic information are denoted as attributes. A decision table is addressed for information systems which organized using the row and column correspond to objects and attributes, respectively [8-10]. Based on [10], there are some parameter should be determined in the rough sets model, such as, the indiscernibility, lower-upper approximations, and boundary region as expressed in Equations (1) – (5) accordingly.

𝐼𝑁𝐷(𝑀) = {(𝑥, 𝑦) ∈ 𝑈²: ∀𝑎 ∈ 𝑀, 𝑎(𝑥) = 𝑎(𝑦)} (1)

𝑀(𝑋) = {{𝑥 ∈ 𝑈|[𝑥]_𝐵⊆ 𝑋}}, (2) 𝑀̅(𝑋) = {𝑥 ∈ 𝑈|[𝑥]𝐵∩ 𝑋 ≠ ∅}, (3)

BND(𝑋) = 𝑀(𝑋) − 𝑀̅(𝑋),

(4) 𝑏(𝑋) =^𝑀(𝑋)

𝑀̅(𝑋), (5)

while, the association value among variables or factors is written as [11]:

𝑙 =^∑^{𝑥∈𝑈/𝐷}^|𝐶(𝑋)|

|𝑈| ; 𝐶, 𝐷 ⊆ 𝐴 ∧ 𝐶 ∩ 𝐷 = ∅. (6) 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)

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Semantic Service Description and Compositions:

A Systematic Literature Review

Kabul Kurniawan Multimedia and Information

System Group University of Vienna, Wahringerstrasse 29, 1190

Vienna, Austria kabulk87@univie.ac.at

Fajar J. Ekaputra Institute of Information

Systems Engineering, TU Wien,

Favoritenstrasse 9-11, 1040 Vienna, Austria fajar.ekaputra@tuwien.ac.at

Peb R. Aryan Institute of Information

Systems Engineering, TU Wien,

Favoritenstrasse 9-11, 1040 Vienna, Austria peb.aryan@tuwien.ac.at

Abstract—For decades, researchers and practitioners develop various approaches such as Web Service technologies (e.g., UDDI, WSDL, SOAP) to address application integration problems. In particular, Web Service composition methods can solve complex service integrations. However, in highly dynamic environments, these manual service compositions still requires a lot of effort.

To address this challenge, researchers have recently introduced semantic web service composition methods. The growing interest in this topic of semantic web service composition has led to an increasing number of approaches, which has not been systematically surveyed so far. Researchers have reported several surveys in the related areas such as web service composition or semantic web service search. However, to the best of our knowledge, none of them provides a survey about these semantic web service compositions in particular. Hence, this review aims to address this issue by identifying existing efforts on semantic web service compositions. The survey focuses on two aspects (i) semantic web service description, as it is an essential aspect for semantic service composition, (ii) semantic web service composition, to identify methods and their implementations on the real world problem.

Index Terms—semantic service description; semantic service composition; semantic web services; semantic APIs

I. INTRODUCTION

Web services play an important role as mediators between applications and facilitate information exchange between them. Researchers and practitioners proposed various web service technologies (e.g., WSDL, SOAP, and UDDI) to address the problem of application integration. However, these conventional web services mainly focus on the operational and syntactical description for web service discovery, composition, and execution. Furthermore, its usability remains to require full human intervention (e.g., developers, operators, users, etc.) There is a need to manually inspect service integrations, which tends to be time-consuming and error-prone. This requirement of manual task is the bottleneck of automation and scaling.

Therefore, the conventional web services lacking technologies required to cope with the scale envisioned for web service.

The Semantic Web vision aims to make information on the web to have clear meaning so that the computer and human can be better to work in cooperation [1]. It has triggered

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* Main Author

researchers in both communities (semantic web and web service) to improve its usability by combining them into semantic web services. Semantic web services extend conventional web services capability by adding a semantic markup on top of web service descriptions to make it more machine-readable and well-defined meaning. While services are easy to read and understand by a computer, it can facilitate automated service discovery, composition, execution, and monitoring.

The growing interest in this topic of semantic web service composition has led to an increasing number of approaches, which has not been systematically surveyed so far. Researchers have reported several surveys in the related areas such as web service composition or semantic web service search. However, to the best of our knowledge, none of them focus on the semantic web service compositions in particular.

The survey of Tosi et al. [2] has identified several approaches available for semantically annotating functional and non-functional aspects of web services. Another study from Klusch et al. [3] provides an overview of state of the art to the semantic web service search. Sam Gini et al., [4] did a comparative study about web service composition by giving performance metrics to help researcher the best approach of it. In our survey, we aim to identify problems and the state- of-the-art concerning semantic service compositions.

We start the explanation with the survey about the existing semantic service descriptions, as it is an essential foundation on how the automatic service composition will be done.

Afterward, we continue with the survey about semantic service compositions including their methods, requirements of semantic service description and their implementations to find the application domains the semantic service composition. As a result, the survey report on 25 papers from both conferences and journals. The survey shows that the semantic service description is an essential aspect of semantic service compositions. Several methods based on semantic service composition have been developed to facilitate both semi-automatic and automatic service composition. It also shows that the semantic web service composition method has been applied in academic and industrial domains.

This paper is organized as follows. First, the research methodology is explained in Section II. Then, the result and 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)

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Neural Network Implementation of Divers Sign Language Recognition based on Eight Hu-Moment

Parameters

Matt Ervin G. Mital

Department of Electronics Engineering De La Salle University

Manila, Philippines matt_mital@dlsu.edu.ph

Herbert V. Villaruel Department of Electronics Engineering

De La Salle University Manila, Philippines herbert_villaruel@dlsu.edu.ph

Elmer P. Dadios

Department of Electronics Engineering De La Salle University

Manila, Philippines elmer.dadios@dlsu.edu.ph

Abstract— Improvement in the aspects of human-to- human and human-to-machine (and vice-versa) communication is still needed amidst the rapid development of technology.

Divers sign language, a type of communication usually done underwater is the primary focus of this paper. Human divers are always at risk due to the unpredictable and unstable condition of water. With the help of image processing and artificial neural network, recognition of 13 commonly used hand signals is implemented. The significance of this study is with regards to the extension of the capabilities of a machine to interpret commands or meanings of signals. This adds to the probability of assurance of safety of divers especially when their voice equipment fails. The aim is to show the conformity and effectivity of relating underwater communications, image processing utilizing Hu-Moments as feature extraction method, and neural network. The results are shown through graphical representations of correlation coefficients, errors and success rates of pattern recognition. This research serves as a solution, although indirect, to present technologies such that people may consider the possibility of incorporating a neural network attribute.

Keywords: Hu-Moments, Neural Network, Image Processing, Divers Sign Language

I. INTRODUCTION

In a general sense, being able to communicate is important. Its relevance can be applied at home, in one’s workplace and in the society. Communication can be in the forms: verbal (spoken), written (stated in paper) or non- verbal (body or hand gestures). The type to be employed depends on a specific situation.

As technology continues to develop, the means of having communication had been very extensive. Even if these ways are accessible to many people, the struggle of persons with hearing and speaking impairment remains. The most effective and convenient way for them is to use sign language thru hand gestures. Communication between a non-sign language speaker and a sign-language speaker is difficult as the presence of an interpreter is not always there [1].

The problem with communication does not stop there.

One of the emerging technologies is underwater communications which is used in various application such as monitoring and surveillance [2]. There are also times wherein human to human interaction underwater is necessary; the best example are scuba divers. Although voice equipment is readily present, the only way to communicate in case of emergency (voice equipment fails) is by the utilization of hand signals [3].

It is also possible to establish interaction between a robot and a human (and vice-versa) underwater. It is highlighted in [4] that the said interaction is limited to joysticks resulting to simple commands and basic data acquisition. The challenge of constructing an effective system is making it to have adaptability to unpredictable and dynamic behavior of water environments [5]. Correct reception of messages is evidently necessary [6].

Incorporating all the notions, stated is the problem statement: Underwater communications, being a developing form of technology, is an apparent target for further research and development. In order to enhance human-to-human (whether between impaired individuals or not) and human- to-machine (and vice-versa) interaction, hand-signal recognition may be employed as an aid to present technology.

As a reiteration, to ensure safety of human divers when voice equipment fail, the only way to communicate is thru hand gestures. Hence, divers’ sign language recognition utilizing artificial neural network and image processing is proposed.

II. REVIEW OF RELATED LITERATURE

Divers are exposed in underwater hazardous environments; carrying special hand-held devices in order to communicate to their robot diving buddies is just a hindrance for them to finish their goal and tasks. Hand detection and interpretation is a prerequisite – meaning, a requirement, in this world where human-robot interaction has become more and more prevalent [7].

It is supported in [8] that the use of auxiliary devices such as gloves for the collection of data is the commonly employed method. Although it provides high speed in terms of data transfer, it also has its drawback as a diver/operator needs to put on special devices where in fact there are ways to communicate without carrying such.

In order to enhance the processing of image, several studies are done which employ additional attributes such as sonar and filters [7]. The convex hull method and support vector machine have been implemented which produced a precision result of 92% and 94% respectively. The former is usually applied in finger counting by detecting the contour shape through a convexity defect. The latter is related to the seven Hu-invariant moments which is one of the main references of this paper. Moreover, fusing the two produces a result of 99%.

Other studies also utilized sonar; but this time, with Hidden Markov Models (HMM) [9][10]. Initially, HMMs are successfully applied in speech recognition; but later on, 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)

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A Column Generation Approach for Personnel Scheduling with Discrete Uncertain

Requirements

Pattarapong Pakpoom Department of Industrial Engineering

Kasestsart University Bangkok, Thailand pattarapong.p@ku.ac.th

Peerayuth Charnsethikul Department of Industrial Engineering

Kasestsart University Bangkok, Thailand

fengprc@ku.ac.th

Abstract—In this work, we model a personnel scheduling problem with uncertain demand as a two-stage stochastic integer program. The model is a large integer program with a large number of columns and constraints which creates dif- ficulty for optimization process. We apply column generation method and Benders’ decomposition technique to solve the problem. We test our proposed algorithm on some generated instances and obtain satisfying results showing improvement in obtaining good solutions quickly over solving MIP on GAMS with CPLEX solver.

Index Terms—personnel scheduling, stochastic program- ming, column generation, Benders’ decomposition

I. INTRODUCTION

In many business organizations, much of operation expense composes of labor cost which could fluctuate depending on employee scheduling. Effective scheduling could result in high cost reduction while facilitate in maintaining high level of job satisfaction. Task of scheduling is usually a recurring process. Workforce management assigns workers to shifts within certain period of time. This can be considered as repeating events which give rise to cyclic nature of the problem.

We can presume that the period after the last one of a planing horizon is considered as the first period. Variety of research focusing on personnel scheduling has been done in the past especially in service sectors such as nursing, call centers, or toll booths. This includes [1], [2], and [3]. Reference [1] simulates data with uncertainty of personnel scheduling of retail outlets as a mixed-integer linear programming problem and shows improvement over scheduling done by deterministic package used at several other stores. Reference [2] focuses their work on nurse staffing and scheduling under demand uncertainty. They develop multicut approach with branch- ing priority and show comparison with a deterministic model. Reference [3] applies Benders’ decomposition, Lagrangian relaxation with Benders’ decomposition, and heuristic based on nested Benders’ decomposition to nurse staffing and assignment. The effectiveness of their

algorithms is shown by comparison with collected data from hospital units. References [4] and [5] represent their problems with mathematical models having too many rows and too many columns for an optimization soft- ware to handle. They apply column generation method to resolve the difficulties.

References [6] and [7] demonstrate that when employ- ees have more choices in their schedule, their moral are higher which helps in supporting favorable attitude and increasing productivity. Nevertheless, freedom of choices comes at a price. By allowing more shift selections, mathematical model requires more variables. In the other aspect, demand uncertainty brings complication to an already challenging problem. Each possible demand variation in each time period factors in the number of constraints. Altogether, we have a mathematical model with large number of rows and columns. We also put back to back shifting restriction on our model. This requirement can be seen in some jobs such as nursing, where handling over information from one shift to the next is beneficial and sometimes even mandatory.

Reference [8] handles problems with large number of rows. In this work, we tackle a personnel scheduling problem with too many row and columns when back to back shifting requirement and cyclic time period restriction are imposed. Additionally, more columns are allowed to be added to possibly reduce the total cost. To overcome this obstacle, column generation method and Benders’ decomposition are proposed to resolve such problems. The column generation approach will alleviate problems arising from large amount of the columns and Benders’ decomposition algorithm will ease problems arising from large number of rows.

II. METHODOLOGY

In this section, we describe our proposed methodology. We first introduce our model formulation and how describe how general stochastic programming problems 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)