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RPS: Session, Learning Material, Reference, Assessment/Rubric
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Subject Name :
Landscape
Kode Matakuliah
Nama Matakuliah
sks
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Reviewer
Action
Kurikulum Sistem Informasi 2023/2024
IKA6301
Kecerdasan Buatan
3.00
2554 Anung Barlianto Ariwibowo, M.Kom.
2641 Binti Solihah, S.T., M.Kom.
RPS
|
Assessment Map
Kurikulum Teknik Informatika 2019/2020
IKA301
Kecerdasan Buatan
3.00
2641 Binti Solihah, S.T., M.Kom.
2641 Binti Solihah, S.T., M.Kom.
RPS
|
Assessment Map
RPS Detail
Matakuliah
Course Profile
History
Kode Matakuliah
IKA6301
Nama Matakuliah
Kecerdasan Buatan
sks
3.00
Semester Name
Subject Code
Subject Name
Group Name
Group Code
Portfolio
Evidence
Gasal 2023/2024 (R)
IKA6301
Kecerdasan Buatan
SI-01
01
Portofolio
Tidak Diijinkan
Gasal 2024/2025 (R)
IKA6301
Kecerdasan Buatan
SI-01
01
Portofolio
Tidak Diijinkan
Review History
RPS Review History
No
Review
Review By
Review Date
1
learning outcome seharusnya menggunakan kalimat yang menyatakan hasil belajar bukan materi yang dipelajari. CPnya ketrampilan tetapi level bloom yang digunakan adalah pengetahuan
Binti Solihah
2024-11-06 17:34:52
2
CPL yang dibebankan sudah dipetakan ke Mata Kuliah dan pemetaan komponen nilai sudah sesuai
Binti Solihah
2024-11-06 17:36:13
Capaian Pembelajaran (CP) terkait
Capaian Pembelajaran (CP)
KETRAMPILAN KHUSUS
7
Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Mahasiswa menggunakan teknik pencarian untuk mensimulasikan kecerdasan.
Students use search techniques to simulate intelligence
(3,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Memahami representasi masalah dalam ruang pencarian
Understand how to represents problems as search space
(2,2)
2
Memahami perkembangan sejarah Kecerdasan Artifisial dan aplikasinya.
Understand the historical development of AI and its applications.
(2,1)
2
Show/Hide
Representasi pengetahuan dan inferensi.
Knowledge representation and inference.
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Logika proposisi.
Propositional logic.
(3,3)
2
Inferensi logika proposisi.
Inference in propositional logic.
(3,3)
3
Show/Hide
Logika predikat.
Predicate logic.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Kuantor eksistensial dan universal.
Existential and universal quantifier.
(2,2)
4
Show/Hide
Penalaran Bayesian.
Bayesian reasoning.
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Peluang bersyarat.
Conditional probability.
(3,3)
5
Show/Hide
Sistem pakar berbasis aturan.
Rule-based Expert Systems.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Pohon keputusan dan aturan.
Decision tree and rules.
(2,2)
6
Show/Hide
Ragam teknik-teknik Machine Learning.
Variety of Machine Learning techniques.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Memahami perbedaan antara Supervised Learning dan Unsupervised Learning.
Understand the difference between Supervised Learning and Unsupervised Learning.
(2,2)
7
Show/Hide
Supervised Learning.
Supervised Learning.
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mengembangkan keahlian penerapan Supervised Learning pada masalah klasifikasi.
Develop skills in supervised learning and its applications in classification problems.
(3,3)
8
Show/Hide
Perseptron dan Jaringan Syaraf Tiruan.
Perceptron and Artificial Neural Networks.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Memahami dan menerapkan cara kerja jaringan syaraf tiruan untuk pemodelan prediktif.
Understand and apply the fundamental workings of neural networks for predictive modeling.
(3,3)
9
Show/Hide
Deep learning.
Deep learning.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Menerapkan teknik-teknik Deep Learning untuk tugas-tugas prediktif yang kompleks.
Apply Deep Learning techniques for complex predictive tasks.
(3,3)
10
Show/Hide
Pengolahan Bahasa Alami.
Natural Language Processing.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Menerapkan teknik Deep Learning untuk Pengolahan Bahasa Alami.
Apply Deep Learning techniques in Natural Language Processing.
(3,3)
11
Show/Hide
Computer vision.
Computer vision.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Menerapkan teknik Deep Learning untuk Computer Vision.
Apply Deep Learning techniques for Computer Vision problems.
(3,3)
12
Show/Hide
Etika dan perkembangan Kecerdasan Artifisial.
Ethics and the future of AI.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Memahami permasalahan etika yang muncul dari perkembangan teknologi Kecerdasan Buatan.
Understand the impacts of ethical problems from the advancement of Artificial Intelligence technologies.
(4,4)
RPS per Session
Sesi Ke
KAD
Bahan Kajian
Metoda Pembelajaran
Waktu Belajar (Menit)
Pengalaman Belajar Mahasiswa
Referensi
Kriteria Penilaian (Indikator)
1
Perkembangan teknologi Kecerdasan Artifisial dan metode-metode yang telah dikembangkan.
Diskusi
Tutorial
150.00
Mahasiswa menerima ceramah sejarah Kecerdasan Artifisial, berbagai teknik dan metode yang dikembangkan oleh para ahli. Mahasiswa melakukan persiapan lingkungan pemrograman menggunakan bahasa Python.
Stuart Russell and Peter Norvig(2009) (Bab 1 Introduction. Bab 2 Intelligent Agents.)
Tugas Individu - 2.00 %
Ujian Tengah Semester - 2.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Mahasiswa menggunakan teknik pencarian untuk mensimulasikan kecerdasan.
Students use search techniques to simulate intelligence
KAD
: Memahami perkembangan sejarah Kecerdasan Artifisial dan aplikasinya.
Understand the historical development of AI and its applications.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Setup environment pemrograman selesai.
Finished programming environment setup.
Tugas Individu 2.00 %
Ujian Tengah Semester 2.00 %
2
Intelligent agent dan Search space problem.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi simulasi kecerdasan menggunakan teknik pencarian.
Stuart Russell and Peter Norvig(2009) (Bab 3 Solving Problems by Searching.)
Tugas Individu - 3.00 %
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Mahasiswa menggunakan teknik pencarian untuk mensimulasikan kecerdasan.
Students use search techniques to simulate intelligence
KAD
: Memahami representasi masalah dalam ruang pencarian
Understand how to represents problems as search space
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan kecerdasan menggunakan algoritma-algoritma pencarian.
Simulating intelligence using search algorithms.
Tugas Individu 3.00 %
Ujian Tengah Semester 3.00 %
3
Logika proposisi dan inferensi logika.
Diskusi
Permainan
150.00
Mahasiswa menerapkan inferensi logika pada permainan Wumpus.
Stuart Russell and Peter Norvig(2009) (Bab 7 Logical Agents.)
Tugas Individu - 3.00 %
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Representasi pengetahuan dan inferensi.
Knowledge representation and inference.
KAD
: Inferensi logika proposisi.
Inference in propositional logic.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Melakukan inferensi logika proposisi.
Make inference using propositional logic.
Tugas Individu 2.00 %
Ujian Tengah Semester 2.00 %
KAD
: Logika proposisi.
Propositional logic.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Merepresentasikan pengetahuan menggunakan logika proposisi.
Representing knowledge using propositional logic
Tugas Individu 1.00 %
Ujian Tengah Semester 1.00 %
1.00(Pass)
Solusi akurat untuk memecahkan puzzle logika menggunakan logika proposisi dan logika predikat.
Accurate solutions to logic puzzles using propositional logic and predicate logic.
4
Logika Proposisi dan Logika Predikat
Tutorial
Diskusi
150.00
Mahasiswa memahami kekurangan Logika Proposisi dalam merepresentasikan pengetahuan dan bagaimana Logika Predikat mengatasi kekurangan tersebut.
Stuart Russell and Peter Norvig(2009) (Bab 8 First-Order Logic)
Tugas Individu - 3.00 %
Ujian Tengah Semester - 3.00 %
Tugas Kelompok - 10.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Representasi pengetahuan dan inferensi.
Knowledge representation and inference.
KAD
: Logika proposisi.
Propositional logic.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Merepresentasikan pengetahuan menggunakan logika proposisi.
Representing knowledge using propositional logic
Tugas Individu 2.00 %
Ujian Tengah Semester 2.00 %
Tugas Kelompok 8.00 %
1.00(Pass)
Solusi akurat untuk memecahkan puzzle logika menggunakan logika proposisi dan logika predikat.
Accurate solutions to logic puzzles using propositional logic and predicate logic.
CPMK
: Logika predikat.
Predicate logic.
KAD
: Kuantor eksistensial dan universal.
Existential and universal quantifier.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Penerapan kuantor yang tepat untuk merepresentasikan pengetahuan.
Correct application of quantifiers in representing knowledge.
Tugas Individu 1.00 %
Ujian Tengah Semester 1.00 %
Tugas Kelompok 2.00 %
5
Ketidakpastian dalam representasi pengetahuan.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi kelemahan teknik logika proposisi dan logika predikat untuk merepresntasikan pengetahuan yang tidak fully-observable.
Stuart Russell and Peter Norvig(2009) (Bab 13 Quantifying Uncertainty)
Tugas Individu - 3.00 %
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Representasi pengetahuan dan inferensi.
Knowledge representation and inference.
KAD
: Inferensi logika proposisi.
Inference in propositional logic.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Melakukan inferensi logika proposisi.
Make inference using propositional logic.
Tugas Individu 1.00 %
Ujian Tengah Semester 1.00 %
CPMK
: Penalaran Bayesian.
Bayesian reasoning.
KAD
: Peluang bersyarat.
Conditional probability.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Menggunakan peluang bersyarat dengan benar.
Apply conditional probability accurately.
Tugas Individu 2.00 %
Ujian Tengah Semester 2.00 %
6
Inferensi Naive Bayes.
Tutorial
Diskusi
150.00
Mahasiswa menerapkan proses inferensi menggunakan data historis dan memahami perlunya teknik smoothing.
Stuart Russell and Peter Norvig(2009) (Bab 13 Quantifying Uncertainty)
Tugas Individu - 3.00 %
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Penalaran Bayesian.
Bayesian reasoning.
KAD
: Peluang bersyarat.
Conditional probability.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Menggunakan peluang bersyarat dengan benar.
Apply conditional probability accurately.
Tugas Individu 3.00 %
Ujian Tengah Semester 3.00 %
7
Pembelajaran Mesin.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi pengembangan teknik simulasi kecerdasan.
Stuart Russell and Peter Norvig(2009) (Bab 18 Learning from Examples)
Tugas Individu - 3.00 %
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Sistem pakar berbasis aturan.
Rule-based Expert Systems.
KAD
: Pohon keputusan dan aturan.
Decision tree and rules.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Menerjemahkan pohon keputusan sebagai pengetahuan berbasis aturan.
Translate decision tree as rule-based knowledge.
Tugas Individu 1.50 %
Ujian Tengah Semester 1.50 %
CPMK
: Ragam teknik-teknik Machine Learning.
Variety of Machine Learning techniques.
KAD
: Memahami perbedaan antara Supervised Learning dan Unsupervised Learning.
Understand the difference between Supervised Learning and Unsupervised Learning.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning
Tugas Individu 1.50 %
Ujian Tengah Semester 1.50 %
8
Supervised Learning.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan scikit-learn.
Stuart Russell and Peter Norvig(2009) (Bab 18 Learning from Examples)
Tugas Individu - 2.00 %
Ujian Akhir Semester - 2.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Ragam teknik-teknik Machine Learning.
Variety of Machine Learning techniques.
KAD
: Memahami perbedaan antara Supervised Learning dan Unsupervised Learning.
Understand the difference between Supervised Learning and Unsupervised Learning.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning
Tugas Individu 1.00 %
Ujian Akhir Semester 1.00 %
CPMK
: Supervised Learning.
Supervised Learning.
KAD
: Mengembangkan keahlian penerapan Supervised Learning pada masalah klasifikasi.
Develop skills in supervised learning and its applications in classification problems.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Analisis dan interpretasi yang akurat terhadap prediksi model.
Accurate analysis and interpretation of the model's predictions.
Tugas Individu 1.00 %
Ujian Akhir Semester 1.00 %
9
Unsupervised Learning
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn.
Stuart Russell and Peter Norvig(2009) (Bab 18 Learning from Examples Bab 20 Learning Probabilistic Models)
Tugas Individu - 3.00 %
Ujian Akhir Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Ragam teknik-teknik Machine Learning.
Variety of Machine Learning techniques.
KAD
: Memahami perbedaan antara Supervised Learning dan Unsupervised Learning.
Understand the difference between Supervised Learning and Unsupervised Learning.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning
Tugas Individu 1.00 %
Ujian Akhir Semester 1.00 %
CPMK
: Perseptron dan Jaringan Syaraf Tiruan.
Perceptron and Artificial Neural Networks.
KAD
: Memahami dan menerapkan cara kerja jaringan syaraf tiruan untuk pemodelan prediktif.
Understand and apply the fundamental workings of neural networks for predictive modeling.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically.
Tugas Individu 2.00 %
Ujian Akhir Semester 2.00 %
10
Neural Networks
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn.
Stuart Russell and Peter Norvig(2009) (Bab 18 Learning from Examples)
Tugas Individu - 3.00 %
Ujian Akhir Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Ragam teknik-teknik Machine Learning.
Variety of Machine Learning techniques.
KAD
: Memahami perbedaan antara Supervised Learning dan Unsupervised Learning.
Understand the difference between Supervised Learning and Unsupervised Learning.
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning
Tugas Individu 1.00 %
Ujian Akhir Semester 1.00 %
CPMK
: Perseptron dan Jaringan Syaraf Tiruan.
Perceptron and Artificial Neural Networks.
KAD
: Memahami dan menerapkan cara kerja jaringan syaraf tiruan untuk pemodelan prediktif.
Understand and apply the fundamental workings of neural networks for predictive modeling.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically.
Tugas Individu 2.00 %
Ujian Akhir Semester 2.00 %
11
Deep learning
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn.
Stuart Russell and Peter Norvig(2009) (Bab 18 Learning from Examples)
Andrew W. Trask(2019) (Bab 2 Fundamental Concepts: How do Machines Learn?)
Ian Goodfellow and Yoshua Bengio and Aaron Courville(2016) (Bab 5 Machine Learning Basics)
Tugas Individu - 3.00 %
Ujian Akhir Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Perseptron dan Jaringan Syaraf Tiruan.
Perceptron and Artificial Neural Networks.
KAD
: Memahami dan menerapkan cara kerja jaringan syaraf tiruan untuk pemodelan prediktif.
Understand and apply the fundamental workings of neural networks for predictive modeling.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically.
Tugas Individu 1.00 %
Ujian Akhir Semester 1.00 %
CPMK
: Deep learning.
Deep learning.
KAD
: Menerapkan teknik-teknik Deep Learning untuk tugas-tugas prediktif yang kompleks.
Apply Deep Learning techniques for complex predictive tasks.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Pengembangan perseptron dan MLP untuk menerapkan teknik Deep Learning.
Successful extension of the neural network and multi layer perceptron to incorporate deep learning techniques.
Tugas Individu 2.00 %
Ujian Akhir Semester 2.00 %
12
Pengolahan Bahasa Alami
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi dasar-dasar pengolahan bahasa alami menggunakan library SpaCy.
Andrew W. Trask(2019) (Bab 11 Neural Networks that Understand Language: king - man + woman == ?)
Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.(2023) (Bab 15 Natural Language Processing: Pretraining)
Tugas Individu - 3.00 %
Tugas Kelompok - 10.00 %
Ujian Akhir Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Deep learning.
Deep learning.
KAD
: Menerapkan teknik-teknik Deep Learning untuk tugas-tugas prediktif yang kompleks.
Apply Deep Learning techniques for complex predictive tasks.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Pengembangan perseptron dan MLP untuk menerapkan teknik Deep Learning.
Successful extension of the neural network and multi layer perceptron to incorporate deep learning techniques.
Tugas Individu 1.00 %
Tugas Kelompok 2.00 %
Ujian Akhir Semester 1.00 %
CPMK
: Pengolahan Bahasa Alami.
Natural Language Processing.
KAD
: Menerapkan teknik Deep Learning untuk Pengolahan Bahasa Alami.
Apply Deep Learning techniques in Natural Language Processing.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Implementasi fungsional pengolahan teks menggunakan Python.
Functional implementation of a text processing tool in Python using NLP techniques.
Tugas Individu 2.00 %
Tugas Kelompok 8.00 %
Ujian Akhir Semester 2.00 %
13
Computer Vision
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library TensorFlow dan OpenCV.
Andrew W. Trask(2019) (Bab 10 Neural Learning about Edges and Corners: Intro to Convolutional Neural Networks)
Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.(2023) (Bab 14 Computer Vision)
Tugas Individu - 3.00 %
Ujian Akhir Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Deep learning.
Deep learning.
KAD
: Menerapkan teknik-teknik Deep Learning untuk tugas-tugas prediktif yang kompleks.
Apply Deep Learning techniques for complex predictive tasks.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Pengembangan perseptron dan MLP untuk menerapkan teknik Deep Learning.
Successful extension of the neural network and multi layer perceptron to incorporate deep learning techniques.
Tugas Individu 1.00 %
Ujian Akhir Semester 1.00 %
CPMK
: Computer vision.
Computer vision.
KAD
: Menerapkan teknik Deep Learning untuk Computer Vision.
Apply Deep Learning techniques for Computer Vision problems.
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Implementasi pengolahan citra sederhana menggunakan Python.
Correct implementation of basic image processing tasks in Python.
Tugas Individu 2.00 %
Ujian Akhir Semester 2.00 %
14
Etika dalam penggunaan Kecerdasan Artifisial
150.00
Mahasiswa membuat kajian sederhana mengenai etika penggunaan teknologi Kecerdasan Artifisial dalam kehidupan.
Stuart Russell and Peter Norvig(2009) (Bab 26 Philosophical Foundations Bab 27 AI: The Present and Future)
Tugas Individu - 3.00 %
Ujian Akhir Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Memiliki kemampuan dalam melakukan fungsi klasifikasi, klasterisasi, regresi, deteksi anomali, pemfilteran, aggregasi, pembelajaran aturan asosiasi, perangkuman, baik secara deskriptif maupun prediktif di dalam memahami masalah data secara tepat dengan memahami konsep, metode, teknik dan tahapan data mining serta visualisasi data sebagai pengetahuan. (CPL09 (KK.g))
Possess the ability to perform classification, clustering, regression, anomaly detection, filtering, aggregation, association rule learning, summarization, both descriptively and predictively, to accurately understand data problems by understanding the concepts, methods, techniques, and stages of data mining and data visualization as knowledge. (CPL09 (KK.g))
CPMK
: Etika dan perkembangan Kecerdasan Artifisial.
Ethics and the future of AI.
KAD
: Memahami permasalahan etika yang muncul dari perkembangan teknologi Kecerdasan Buatan.
Understand the impacts of ethical problems from the advancement of Artificial Intelligence technologies.
(4,4)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Essay reflektif yang sistematis dalam mempertimbangkan aspek etika Kecerdasan Buatan.
Well-written reflective essay on ethical considerations in AI.
Tugas Individu 3.00 %
Ujian Akhir Semester 3.00 %
Assessment Component
Assessment Detail
No
Component Name
Weightage
1
Tugas Individu
40
2
Tugas Kelompok
20
3
Ujian Akhir Semester
20
4
Ujian Tengah Semester
20
Total
100
Daftar Referensi
1. Stuart Russell and Peter Norvig. Artificial Intelligence: a Modern Approach. Pearson. 2009
2. Andrew W. Trask. Grokking Deep Learning. Manning Publications. 2019
3. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press. 2016
4. Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.. Dive into Deep Learning. Cambridge University Press. 2023