algorithm, and application deep learning: concept, model,
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Deep Learning: Concept, Model,Algorithm, and Application
Oleh. Prof. Zainal A. Hasibuan, PhD.
Webinar Aptikom #3
20 Juni, 2020
AGENDA• Konsep: Apa itu Deep Learning?
• Mengapa Deep Learning Menjadi Penting?
• Bagaimana Cara Bekerja Deep Learning?
• Model: Apa Saja Jenis Deep Learning?
• Algoritma: Metode Apa Saja Yang DigunakanDeep Learning?
• Aplikasi Deep Learning
• Penutup
Ilustrasi….Penggunaan Deep Learningfb notifications Kusrini
added 1 photo that
might include you
• Smart city bisa menjadi enableruntuk mewujudkan visi ini. Teknologidan TIK dapat digunakan untukmempertinggi efisiensi,memperbaiki pelayanan publik, danmeningkatkan kesejahteraan warga.Teknologi juga bisa membantuprogram-program kami sepertimempromosikan produk Banjarbarusehingga bisa dikenal hingga di luarnegeri.
• Smart city can be an enabler to realizethis vision. Technology and ICT can beused to enhance efficiency, improvepublic services, and improve the welfareof citizens. Technology can also help ourprograms such as promoting Banjarbaruproducts to be known abroad.
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• Notifikasi dari facebook• Terjemahan dari Google• Mau kemana hari ini• Dll….
Masalah 2 yang bisa
diselesaikan oleh
Deep Learning
Definisi Deep Learning…• Deep learning adalah bagian dari Machine
Learning yang bisa “berpikir” seperti manusia,dan bisa membantu manusia membuatkeputusan dalam hal identifikasi, prediksi, danprekripsi (Zainal A. Hasibuan, 2020).
• Deep learning is a subset of machinelearning where artificial neural networks,algorithms inspired by the human brain, learn from large amounts of data.
• Deep learning allows machines to solve complexproblems even when using a data set that is verydiverse, unstructured and inter-connected.
Kenapa Dibilang “Deep” Learning?
“Dangkal”, karena hanya 1lapisan tersembunyi (1 hiddenlayer)
“Dalam”, karena banyak lapisantersembunyi (many hidden layers)
Bis
a Ju
ga D
ibila
ng
“ H
igh
Lea
rnin
g”?
“Ren
dah
”, karena
hanya 1 lap
isantersem
bu
nyi (1h
idd
en layer)
“Dalam
”, karena
banyak lap
isantersem
bu
nyi (many
hid
den
layers)
Deep Learning Untuk Mengatasi KeterbatasanMachine Learning dalam Memberikan Solusi
• Banyak masalah disekitar kita yang perludiotomasi
• Kemampuan mengolah data yang lebih besar danmulti-dimensi
• Kemampuan “belajar” yang lebih rinci danberulang-ulang secara otomatis dengan fitur2yang lebih banyak
• Kemampuan “belajar” yang lebih mirip dengankonstruksi cara berpikir manusia, bertahap,banyak pilihan, tapi harus membuat keputusan.
Hampir disetiap dimensi hidup kita, ada permasalahanyg solusinya perlu diotomasi, dan bisa diselesaikan
oleh Deep Learning
What Makes DL Become More Possible?
• Big Data (Large Data Sets)
• IoT and Sensor to Capture All Kinds of Data toProduce Big Data
• Computing Power Increased: Graphics ProcessingUnit (GPU) dan Central Processing Unit (CPU)
• The Emergence of Platforms and Software Tools
• Broadband to Guarantee Speed of Data Transfer
• Cloud Technology
• Large Memory Handling Capability
Zainal A. Hasibuan, 2020
Implementation #2: Uncover Ethnic and Sub-EthnicRelationships Using Deep Learning: Preserving
Indonesian Cultural Heritage
FeatureExtractor
FeatureExtractor
FeatureExtractor
Mid-LevelFeatures
Mid-LevelFeatures
High-LevelFeatures
Trainab
le Classifier
Deep
Learnin
g
Zainal A. Hasibuan, 2018
Suku Batak
Batak Toba, Batak Angkola
Batak Toba=Tobasa, SimalungunBatak Angkola=Sipirok, Pdg Bolak
Bagaimana Deep Learning Mengidentifikasinya?
OutputInput Process
Zainal A. Hasibuan, 2020Inputlayer
N HiddenLayers
Outputlayer
Representasigambar digantike angka-2pantulanwarna RGB
Beberapa Istilah dalam Deep Learning• A node, also called a neuron or Perceptron, is a computational
unit that has one or more weighted input connections, a transferfunction that combines the inputs in some way, and an outputconnection. Nodes are then organized into layers to comprise anetwork
• Tensors, defined mathematically, are simply arrays of numbers, orfunctions, that transform according to certain rules under achange of coordinates.
• An artificial neuron (also referred to as a perceptron) is amathematical function. It takes one or more inputs that aremultiplied by values called “weights” and added together. Thisvalue is then passed to a non-linear function, known as anactivation function, to become the neuron's output.
• the perceptron is an algorithm for supervised learning of binaryclassifiers. ... It is a type of linear classifier, i.e. a classificationalgorithm that makes its predictions based on a linear predictorfunction combining a set of weights with the feature vector.
Convolutional Neural Networks (CNN)• Jaringan Neural Konvolusional (Convolutional Neural Networks)
pada dasarnya adalah jaringan saraf standar yang telahdiperluas melintasi ruang menggunakan bobot bersama.
• CNN dirancang untuk mengenali gambar dengan memilikibelokan di dalamnya, yang melihat ujung-ujung objek yangdikenali pada gambar.
• CNNs adalah jaringan saraf yang menggunakan operasikonvolusi sebagai salah satu lapisannya.
35Sumber: https://ramprs.github.io/2017/01/21/Grad-CAM-Making-Off-the-Shelf-Deep-Models-Transparent-through-Visual-Explanations.html
Recurrent Neural Networks (RNN)
• Recurrent Neural Networks (Jaringan Syaraf Berulang)pada dasarnya adalah jaringan syaraf standar yang telahdiperluas sepanjang waktu dengan memiliki tepi yangdimasukkan ke dalam langkah waktu berikutnya
• RNN adalah jenis jaringan saraf yang memiliki loop internal
• RNN dirancang untuk mengenali urutan, misalnya, sinyalucapan atau teks.
38Sumber: https://datathings.com/blog/post/lstm/
Revisit: Ilustrasi…Penggunaan Deep Learningfb notifications Kusrini
added 1 photo that
might include you
• Smart city bisa menjadi enableruntuk mewujudkan visi ini. Teknologidan TIK dapat digunakan untukmempertinggi efisiensi,memperbaiki pelayanan publik, danmeningkatkan kesejahteraan warga.Teknologi juga bisa membantuprogram-program kami sepertimempromosikan produk Banjarbarusehingga bisa dikenal hingga di luarnegeri.
• Smart city can be an enabler to realizethis vision. Technology and ICT can beused to enhance efficiency, improvepublic services, and improve the welfareof citizens. Technology can also help ourprograms such as promoting Banjarbaruproducts to be known abroad.
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1
2CNN
RNN
10 Metode untuk Deep Learning
1. Back-Propagation
2. Stochastic Gradient Descent
3. Learning Rate Decay
4. Dropout
5. Max Pooling
6. Batch Normalization
7. Long Short-Term Memory
8. Skip-gram
9. Continuous Bag Of Words
10. Transfer Learning
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(sumber:James Lee, 2017)
1. Back-Propagation
• Back-propagationadalah metodesederhana untukmenghitungturunan parsial(atau gradien)dari suatu fungsi,yang memilikibentukkomposisi fungsi(seperti padaJaring Saraf).
42
sumber: http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html
2. Stochastic Gradient Descent
• Gradient descentbertujuan untukmencapai titikminimum darisuatu fungsi,namun kadangkalaterjebak dalamminimum lokalyang tergantungpada sifat medan.
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3. Learning Rate Decay• prosedur optimalisasi gradien stokastik untuk
menyesuaikan tingkat pembelajaran(learning rate) dapat dilakukan denganmeningkatkan kinerja dan mengurangi waktupelatihan atau dikenal dengan tingkatpembelajaran adaptif.
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4. Dropout• Dropout adalah teknik untuk mengatasi masalah
overfitting.
• Dropout telah terbukti meningkatkan kinerja jaringansaraf pada tugas pembelajaran yang diawasi dalam visi,pengenalan suara, klasifikasi dokumen dan biologikomputasi, memperoleh hasil canggih pada banyakdataset benchmark (S. H. Khan, Hayat, & Porikli, 2019;Poernomo & Kang, 2018).
45
5. Max Pooling• Max pooling adalah proses diskritisasi berbasis sampel
yang bertujuan untuk mengambil sampel representasiinput (seperti: gambar, matriks keluaran lapisantersembunyi, dll.), mengurangi dimensinya danmemungkinkan asumsi dibuat tentang fitur-fitur yangterdapat dalam sub-wilayah yang di-binned.
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6. Batch Normalization
• Batch normalisasimembantu sedikitproses penyetelaninisialisasi bobot danparameterpembelajaran yangcermat denganmengurangi jumlahdengan apa nilai unittersembunyibergeser di sekitar(pergeserankovarians).
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7. Long Short-Term Memory
Jaringan LSTM memiliki tiga aspek yangmembedakannya dari neuron biasa dalamjaringan saraf berulang yaitu memiliki kontroluntuk memutuskan kapan:• membiarkan input masuk ke neuron.• mengingat apa yang dihitung pada langkah
waktu sebelumnya.• membiarkan output meneruskan ke cap
waktu berikutnya.
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9. Continuous Bag Of Words
• Dalam modelContinuous BagOf Words,tujuannyaadalah untukdapatmenggunakankonteks yangmengelilingikata tertentudanmemprediksikata tertentu.
50
10. Transfer Learning
• Transferpembelajaranadalah saatmenggunakanCNN yang dilatihuntuk satu setdata, memotonglapisan terakhir,melatih ulangmodel lapisanterakhir pada setdata yangberbeda.
51
Aplikasi #1Deep Learning for Students’ Academic
Performance
Oleh: Ariana Yunita, Harry Budi Santoso, Zainal A Hasibuan
4,69% students Drop Out from 2013/2014 to 2014/2015 (Ristekdikti, 2016).
The biggest number is in Jakarta.
4,69% Banten (14.87%), North Sumatera (11.11%), BangkaBelitung (11.32%), Lampung (10.11%).
Several provinces with high rate of students DO morethan 10%
43,073Number of students Drop Out from period 2013/2014 to
2014/2015 in Jakarta
55
◉ This phenomenon poses significant challenges tohigher degree institutions, and they have toanalyze the way to address those issues andvarious factors that affect graduates’ success rate,as well as to predict successful graduates basedon their behaviors while studying at a university.
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Objectives◉ To propose a predictive model for students’
academic performance
◉ To do a preliminary study using log datafrom a course to predict whether studentsget an excellent score, get an intermediatescore, or fail.
57
Research Framework
58
Demographicdata
Academicdata
Behavioral
data input
Machine
Learning
DeepLearnin
g
process
Learning
Analytics
Prediction
output
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Type of Data Description of DataStructured Socio-demographic, including
∙ gender,∙ age,∙ residential area,∙ financial status,∙ type of jobs,∙ salary
Academics, including∙ pre-test (Academic Potential Test),∙ mathematics score,∙ GPA of every semester∙ GPA when graduating
Behavior, including∙ Frequency of borrowing book at
library∙ Log data from LMS (total page
view, total posting, totaldownloads)
Unstructured Log Data from LMS (discussion forum)Type of Data for Learning Analytics
Research Framework
Dataset Building Approach
Data Pre-processing
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Log data are groupedbased on the type of
activity in LMS. Missingvalue, inconsistent data,
noise and outlier areexamined in this step.
FeatureEngineering
EDA is essential Thisstep allows to
visualize data, andshow numerical
summary in statistics,such as mean,
median, and quartile.
Feature engineering allowsto select, correlate and
examine which importantfeatures for model. In mostcases, feature selection is
needed to increase theperformance of prediction.
Exploratory Data Analysis (EDA)
List of Features
61
Features Description Type of Data DetailsGender Student gender Nominal Female/Male
Level_discussion Whether a studentposted less than theaverage of students’sposting
Ordinal High, Medium, Low
Post_in_Discussion_w1
Whether a studentposted in discussion inweek 1 or not
Ordinal 1: Yes0 : No
Post_in_Discussion_w2
Whether a studentposted in discussion inweek 2 or not
Ordinal 1: Yes0 : No
Post_in_Discussion_w3
Whether a studentposted in discussion inweek 3 or not
Ordinal 1: Yes0 : No
Post_in_Discussion_w4
Whether a studentposted in discussion inweek 4 or not
Ordinal 1: Yes0 : No
List of Features (2)
62
Features Description Type of Data DetailsRead_Discussion_w1
Whether a studentread forum discussionin week 1 or not
Ordinal 1: Yes0 : No
Read_Discussion_w2
Whether a studentread forum discussionin week 2 or not
Ordinal 1: Yes0 : No
Read_Discussion_w3
Whether a studentread forum discussionin week 3 or not
Ordinal 1: Yes0 : No
Read_Discussion_w4
Whether a studentread forum discussionin week 4 or not
Ordinal 1: Yes0 : No
Read_Discussion_w1
Whether a studentread forum discussionin week 1 or not
Ordinal 1: Yes0 : No
Post_in_Discussion_w4
Whether a studentposted in discussionin week 4 or not
Ordinal 1: Yes0 : No
List of Features (3)Features Description Type of Data DetailsView_syllabus Whether a student
viewed syllabus or notOrdinal 1: Yes
0 : NoAccess_audio Whether a student
accessed audio or notOrdinal 1: Yes
0 : NoView_announcement Whether a student
viewed announcementor not
Ordinal 1: Yes0 : No
View_lecture_slides Whether a studentviewed lecture slides ornot
Ordinal 1: Yes0 : No
Number_of_viewing_lecture_slides
How many times astudent viewed lectureslides
Interval 0-maximum numberof viewing
Level_viewing_lectures
Level of viewing lectureslides
Ordinal High: More than 15timesMedium 11-15Low: Less Than 11times
Target Whether studentachieved final scoremore than 80(class 2),between 65 and 80( l 1 l th 65
Ratio 0: less than 651: 65-802: more than 80
Architectures and How toSplit Data
64
Data Training 54Data
Validation13
Data Testing 17
TABLE . Data for Training, Validating andTesting
Inputlayer
N HiddenLayers
Outputlayer
Fig . Feedforward Neural Network with NHidden Layers
DL architectures were built usingKeras as the frontend andTensorFlow as the backend [23].This preliminary study used asupervised technique to predictthree classes. Activation functionsthat were used were relU andSoftmax. RelU was used as ahidden layer and Softmax was usedas an output layer. Furthermore,Adam [24] algorithm was used foroptimization in this study.
Place your screenshot here
Results
Using DL architecture with 2 hiddenlayers, input = 18 nodes, hidden units= 12 and 10, output = 3 nodes, themodel has been validated as can beseen in Figure at the right. It hastraining and validation accuracymore than 85%. However, when themodel is tested the accuracy is stillaround 35%.
Several reasons why the accuracy isstill low, probably because thenature of DL needs larger data toperform high accuracy.
Another reason probably becausethe features used in this study hasnot represented SAP.
65
Conclusion and Future Research◉ Firstly, this study built a dataset for SAP in the course level.
Second, this paper conducted a preliminary study using DLto predict SAP, but the results is still low accuracy becauseprobably the amount of data is not enough for DL andfeatures used in this study has not represented SAP.
◉ Based on exploratory data analysis, this study shows thatlog data could not reflect cognitive activities, especially ifthe rate of feature usage was only calculated based on thetotal hits of every activity on LMS. However, several insightscan be gained, such as students in the average achievergroup seems to have high motivation study than studentswith high achiever.
66
◉ For future research:
◉ Revise feature engineering
◉ Using larger data from students, such as social,demographic and financial factors. It is alsosuggested to build a predictive model thatinclude other data as listed in the Table “Typeof Data of Learning Analytics”.
67
Conclusion and Future Research
Aplikasi #2:UNIFIED CONCEPT-BASED MULTIMEDIA
INFORMATION RETRIEVAL SYSTEMOleh: Ridwan Andi Kambau & Zainal A. Hasibuan
Membangun Model(Training, Validasi dan Testing CNN dan RNN)
Valida
si
Training CNN& RNN
Testing CNN &RNN
Pengindeksan Terpadu dengan ModelPengklasifikasi Multimedia
Terms/Concepts
Model PengklasifikasiMultimedia
ModelPengklasifikasi Citra
ModelPengklasifikasi VIdeo
ModelPengklasifikasi Audio
ModelPengklasifikasi Teks
0-Toraja1-Batak2-Bali
3-tongkonan4-rambusolo5-rumahbolon6-Tari tortor7-PuraUluwatu8-TariPendet
DataMultimedia
Pengujian Model PengklasifikasiImage dengan kueri Tongkonan.jpg
Pada Pengindeksan Terpadu
Hal yang sama dilakukan untuk Sembilanobjek yang diklasifikasi dengan empat formatMedia
Akurasi Pengujian ModelPengklasifikasi Multimedia
Objek-objekSuku
KlasifikasiCitra
KalsifikasiAudio
KalsifikasiVideo
KlasifikasiTeks
KlasifikasiMultimedia
Akurasi Akurasi Akurasi Akurasi Akurasi Rata2
Toraja 0.74 0.67 0.74 0.96 0.78
Tongkonan 0.64 0.56 0.62 0.75 0.64
Rambusolo 0.65 0.54 0.60 0.73 0.63
Batak 0.78 0.67 0.78 0.98 0.80
Rumah Bolon 0.66 0.53 0.58 0.76 0.63
Tari Tortor 0.62 0.52 0.52 0.74 0.60
Bali 0.80 0.67 0.80 0.98 0.81
Pura Uluwatu 0.60 0.50 0.58 0.77 0.61
Tari Pendet 0.59 0.53 0.58 0.76 0.61
Rata-rata perMedia
0.68 0.58 0.64 0.82
Which Tool is Best for Solving DeepLearning Problems?
• TensorFlow, PyTorch, Keras, Caffe are some ofthe popular tools which are being widelyused to carry out Deep Learning algorithms.
• TensorFlow is one of the best deeplearning frameworks and has been adoptedby several giants such as Airbus, Twitter, IBMdue to its highly flexible system architecture.
Deep Learning Approach to Multi-Discipline Research
DeepLearning
R&D
Zainal A. Hasibuan, 2019
Dosen Berlatar BelakangEkonomi, dan Bisnis,Sistem Informasi dll
Dosen berlatar belakangSistem Informasi, Data Science
Dosen berlatarBelakang IlmuKomputer/TI/SE
Business A
nalyst
System Analyst
Pro
gram
mer
Tech
nica
l Ana
lyst
Dosen berlatarBelakang IlmuKomputer/TI/SE
Dosen2 Doktor dan Professor dariEkonomi, Bisnis, Komputer, dan berbagaiSektor: pertanian, pariwisata, dll
Research Objectives
• Tersedia dataset dari big data untuk berbagaisektor
• Menghasilkan algoritma baru dalam DeepLearning
• Terbangunnya berbagai sistem otomasiberdasarkan Deep Learning:– Identifikasi dan klasifikasi perilaku pelanggan– Prediksi produksi pangan– Deteksi berbagai penyakit– Dan lain-lain
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