Student Information System
RPS: Session, Learning Material, Reference, Assessment/Rubric
Info
Search Subject
Subject Name :
Landscape
Kode Matakuliah
Nama Matakuliah
sks
Creator
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
Matakuliah tidak ditemukan
RPS Detail
Matakuliah
Profile
History
Kode Matakuliah
IKA301
Nama Matakuliah
Kecerdasan Buatan
sks
3.00
Semester Name
Subject Code
Subject Name
Group Name
Group Code
Portofolio
Review History
RPS Review History
No
Review
Review By
Review Date
Capaian Pembelajaran (CP) terkait
Capaian Pembelajaran (CP)
SIKAP
2
Dapat menunjukkan etika dan moral komunal : Asah, Asih dan Asuh (S.b)
Ability to demonstrate communal ethics and morals: Asah, Asih and Asuh(S.b)
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Memiliki etika dalam pemanfaatan Kecerdasan Artifisial.
Have Ethics in using AI
(3,4)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Menunjukkan etika dalam penerapan teknologi Kecerdasan Buatan.
have ethic in using the Artificial Intelligence technologies.
(3,4)
PENGETAHUAN
1
Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Mahasiswa memahami teknik pencarian untuk mensimulasikan kecerdasan.
Students are able to understand the searching techniques to simulate intelligence
(2,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa memahami perkembangan sejarah Kecerdasan Artifisial dan aplikasinya.
Studenta are able to understand the historical development of AI and its applications
(2,2)
2
Show/Hide
Mahasiswa memahami Representasi pengetahuan dan inferensi.
Studnet are able to understand knowledge representation and inference.
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa memahami Logika proposisi.
Student understand Propositional logic
(2,2)
3
Show/Hide
Mahasiswa memahami jaringan syaraf tiruan
Student understand the neural network technology
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa memahami konsep dan cara kerja jaringan syaraf tiruan
Student understand and know how the neural network work
(2,3)
4
Show/Hide
Mahasiswa memahami metode pembelajran supervised learning
Student understand supervised learning method
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa memahami algoritma klasifikasi
Student understand the classification algorithm
(2,2)
2
Memahami perbedaan antara Supervised Learning dan Unsupervised Learning.
Understand the difference between Supervised Learning and Unsupervised Learning
(2,2)
KETRAMPILAN UMUM
1
Kemampuan menganalisis persoalan komputasi yang kompleks serta menerapkan prinsip-prinsip computing dan disiplin ilmu relevan lainnya untuk mengidentifikasi solusi, dengan mempertimbangkan wawasan perkembangan ilmu transdisiplin (KU.a)
Ability to analyze complex computational problems and apply the principles of computing and other relevant disciplines to identify solutions, taking into account the insights of the development of transdisciplinary science (KU.a)
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Mahasiswa memahami Representasi pengetahuan dan inferensi.
Student are able to understand knowledge representation and inference
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa dapat menerapkan Logika proposisi.
Student can implement Propositional logic
(3,3)
2
Show/Hide
Mahasiswa memahami Logika predikat.
Student understand Predicate logic
(2,2)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa Memahami Kuantor eksistensial dan universal.
Mahasiswa memahami Existential and universal quantifier.
(2,3)
3
Show/Hide
Mahasiswa memahami Penalaran Bayesian.
Student understand Bayesian reasoning
(2,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa memahami peluang bersyarat.
Student understand Conditional probability
(2,2)
KETRAMPILAN KHUSUS
1
Mampu mengimplementasikan dan mengintegrasikan komponen-komponen komputasi pada bidang data science (KK.a)
Able to implement and integrate computational components in the field of data science (KK.a)
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Mahasiswa menggunakan teknik pencarian untuk mensimulasikan kecerdasan.
Students use search techniques to simulate intelligence
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa dapat membuat representasi masalah dalam ruang pencarian
Student Able to represents problems as search space
(3,3)
2
Show/Hide
Mahasiswa dapat menerapkan beberapa metode Computer vision.
student are able to implement some computer vision method
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa dapat menerapkan teknik Deep Learning untuk Computer Vision.
Student can apply Deep Learning techniques for Computer Vision problems
(3,3)
3
Show/Hide
Mahasiswa dapat menerapkan beberapa metode pada Pengolahan Bahasa Alami.
Student are able to implement some method in Natural Language Processing
(3,3)
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)
4
Show/Hide
Mahasiswa dapat menerapkan teknik klasifikasi
STudent can implement the classification method
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Menerapkan Supervised Learning pada masalah klasifikasi.
implement supervised learning in classification problems
(3,3)
5
Show/Hide
Mahasiswa dapat menerapkan Sistem pakar berbasis aturan.
Student able to implement Rule-based Expert Systems.
(3,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mahasiswa dapat mengimplementasikan Pohon keputusan dan aturan.
Student are able to implement Decision tree and rules
(3,3)
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.
Tutorial
Diskusi
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.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami teknik pencarian untuk mensimulasikan kecerdasan.
Students are able to understand the searching techniques to simulate intelligence
KAD
: Mahasiswa memahami perkembangan sejarah Kecerdasan Artifisial dan aplikasinya.
Studenta are able to understand the historical development of AI and its applications
(2,2)
Daftar Kriteria Penilaian (Indikator)
2
Intelligent agent dan Search space problem.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi simulasi kecerdasan menggunakan teknik pencarian.
CAPAIAN PEMBELAJARAN
: Mampu mengimplementasikan dan mengintegrasikan komponen-komponen komputasi pada bidang data science (KK.a)
Able to implement and integrate computational components in the field of data science (KK.a)
CPMK
: Mahasiswa menggunakan teknik pencarian untuk mensimulasikan kecerdasan.
Students use search techniques to simulate intelligence
KAD
: Mahasiswa dapat membuat representasi masalah dalam ruang pencarian
Student Able to represents problems as search space
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan kecerdasan menggunakan algoritma-algoritma pencarian.
Simulating intelligence using search algorithms.
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3
Logika proposisi dan inferensi logika.
Diskusi
Permainan
150.00
Mahasiswa menerapkan inferensi logika pada permainan Wumpus.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami Representasi pengetahuan dan inferensi.
Studnet are able to understand knowledge representation and inference.
KAD
: Mahasiswa memahami Logika proposisi.
Student understand Propositional logic
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Melakukan inferensi logika proposisi.
Make inference using propositional logic.
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CAPAIAN PEMBELAJARAN
: Kemampuan menganalisis persoalan komputasi yang kompleks serta menerapkan prinsip-prinsip computing dan disiplin ilmu relevan lainnya untuk mengidentifikasi solusi, dengan mempertimbangkan wawasan perkembangan ilmu transdisiplin (KU.a)
Ability to analyze complex computational problems and apply the principles of computing and other relevant disciplines to identify solutions, taking into account the insights of the development of transdisciplinary science (KU.a)
KAD
: Mahasiswa dapat menerapkan Logika proposisi.
Student can implement Propositional logic
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Merepresentasikan pengetahuan menggunakan logika proposisi.
Representing knowledge using propositional logic
Notice
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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.
CAPAIAN PEMBELAJARAN
: Kemampuan menganalisis persoalan komputasi yang kompleks serta menerapkan prinsip-prinsip computing dan disiplin ilmu relevan lainnya untuk mengidentifikasi solusi, dengan mempertimbangkan wawasan perkembangan ilmu transdisiplin (KU.a)
Ability to analyze complex computational problems and apply the principles of computing and other relevant disciplines to identify solutions, taking into account the insights of the development of transdisciplinary science (KU.a)
CPMK
: Mahasiswa memahami Representasi pengetahuan dan inferensi.
Student are able to understand knowledge representation and inference
KAD
: Mahasiswa dapat menerapkan Logika proposisi.
Student can implement Propositional logic
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Merepresentasikan pengetahuan menggunakan logika proposisi.
Representing knowledge using propositional logic
Notice
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/var/www/html/sis/application/views/scripts/index/rps-view.phtml
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CPMK
: Mahasiswa memahami Logika predikat.
Student understand Predicate logic
KAD
: Mahasiswa Memahami Kuantor eksistensial dan universal.
Mahasiswa memahami 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.
Notice
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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.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami Representasi pengetahuan dan inferensi.
Studnet are able to understand knowledge representation and inference.
KAD
: Mahasiswa memahami Logika proposisi.
Student understand Propositional logic
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Melakukan inferensi logika proposisi.
Make inference using propositional logic.
Notice
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CAPAIAN PEMBELAJARAN
: Kemampuan menganalisis persoalan komputasi yang kompleks serta menerapkan prinsip-prinsip computing dan disiplin ilmu relevan lainnya untuk mengidentifikasi solusi, dengan mempertimbangkan wawasan perkembangan ilmu transdisiplin (KU.a)
Ability to analyze complex computational problems and apply the principles of computing and other relevant disciplines to identify solutions, taking into account the insights of the development of transdisciplinary science (KU.a)
CPMK
: Mahasiswa memahami Penalaran Bayesian.
Student understand Bayesian reasoning
KAD
: Mahasiswa memahami peluang bersyarat.
Student understand Conditional probability
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Menggunakan peluang bersyarat dengan benar.
Apply conditional probability accurately.
Notice
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6
Inferensi Naive Bayes.
Tutorial
Diskusi
150.00
Mahasiswa menerapkan proses inferensi menggunakan data historis dan memahami perlunya teknik smoothing.
CAPAIAN PEMBELAJARAN
: Kemampuan menganalisis persoalan komputasi yang kompleks serta menerapkan prinsip-prinsip computing dan disiplin ilmu relevan lainnya untuk mengidentifikasi solusi, dengan mempertimbangkan wawasan perkembangan ilmu transdisiplin (KU.a)
Ability to analyze complex computational problems and apply the principles of computing and other relevant disciplines to identify solutions, taking into account the insights of the development of transdisciplinary science (KU.a)
CPMK
: Mahasiswa memahami Penalaran Bayesian.
Student understand Bayesian reasoning
KAD
: Mahasiswa memahami peluang bersyarat.
Student understand Conditional probability
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Menggunakan peluang bersyarat dengan benar.
Apply conditional probability accurately.
Notice
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7
Pembelajaran Mesin.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi pengembangan teknik simulasi kecerdasan.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami metode pembelajran supervised learning
Student understand supervised learning method
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
Notice
: Undefined index: ASSESSMENT in
/var/www/html/sis/application/views/scripts/index/rps-view.phtml
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CAPAIAN PEMBELAJARAN
: Mampu mengimplementasikan dan mengintegrasikan komponen-komponen komputasi pada bidang data science (KK.a)
Able to implement and integrate computational components in the field of data science (KK.a)
CPMK
: Mahasiswa dapat menerapkan Sistem pakar berbasis aturan.
Student able to implement Rule-based Expert Systems.
KAD
: Mahasiswa dapat mengimplementasikan Pohon keputusan dan aturan.
Student are able to implement Decision tree and rules
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Menerjemahkan pohon keputusan sebagai pengetahuan berbasis aturan.
Translate decision tree as rule-based knowledge.
Notice
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8
Supervised Learning.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan scikit-learn.
CAPAIAN PEMBELAJARAN
: Mampu mengimplementasikan dan mengintegrasikan komponen-komponen komputasi pada bidang data science (KK.a)
Able to implement and integrate computational components in the field of data science (KK.a)
CPMK
: Mahasiswa dapat menerapkan teknik klasifikasi
STudent can implement the classification method
KAD
: Menerapkan Supervised Learning pada masalah klasifikasi.
implement supervised learning 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.
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CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami metode pembelajran supervised learning
Student understand supervised learning method
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
Notice
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9
Unsupervised Learning.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami jaringan syaraf tiruan
Student understand the neural network technology
KAD
: Mahasiswa memahami konsep dan cara kerja jaringan syaraf tiruan
Student understand and know how the neural network work
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically.
Notice
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CPMK
: Mahasiswa memahami metode pembelajran supervised learning
Student understand supervised learning method
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
Notice
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10
Neural Networks
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami jaringan syaraf tiruan
Student understand the neural network technology
KAD
: Mahasiswa memahami konsep dan cara kerja jaringan syaraf tiruan
Student understand and know how the neural network work
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically.
Notice
: Undefined index: ASSESSMENT in
/var/www/html/sis/application/views/scripts/index/rps-view.phtml
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Warning
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CPMK
: Mahasiswa memahami metode pembelajran supervised learning
Student understand supervised learning method
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
Notice
: Undefined index: ASSESSMENT in
/var/www/html/sis/application/views/scripts/index/rps-view.phtml
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11
Deep learning.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn.
CAPAIAN PEMBELAJARAN
: Pemahaman intelektual dan kemampuan untuk menerapkan matematika dan teori informatika (P.a)
Intellectual understanding and ability to apply mathematics and informatics theory (P.a)
CPMK
: Mahasiswa memahami jaringan syaraf tiruan
Student understand the neural network technology
KAD
: Mahasiswa memahami konsep dan cara kerja jaringan syaraf tiruan
Student understand and know how the neural network work
(2,2)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically.
Notice
: Undefined index: ASSESSMENT in
/var/www/html/sis/application/views/scripts/index/rps-view.phtml
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12
Pengolahan Bahasa Alami.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi dasar-dasar pengolahan bahasa alami menggunakan library SpaCy.
CAPAIAN PEMBELAJARAN
: Mampu mengimplementasikan dan mengintegrasikan komponen-komponen komputasi pada bidang data science (KK.a)
Able to implement and integrate computational components in the field of data science (KK.a)
CPMK
: Mahasiswa dapat menerapkan beberapa metode pada Pengolahan Bahasa Alami.
Student are able to implement some method in 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.
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13
Computer Vision.
Tutorial
Diskusi
150.00
Mahasiswa melakukan eksplorasi menggunakan library TensorFlow dan OpenCV.
CAPAIAN PEMBELAJARAN
: Mampu mengimplementasikan dan mengintegrasikan komponen-komponen komputasi pada bidang data science (KK.a)
Able to implement and integrate computational components in the field of data science (KK.a)
CPMK
: Mahasiswa dapat menerapkan beberapa metode Computer vision.
student are able to implement some computer vision method
KAD
: Mahasiswa dapat menerapkan teknik Deep Learning untuk Computer Vision.
Student can apply Deep Learning techniques for Computer Vision problems
(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.
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14
Etika dalam penggunaan Kecerdasan Artifisial.
Tutorial
Diskusi
150.00
Mahasiswa membuat kajian sederhana mengenai etika penggunaan teknologi Kecerdasan Artifisial dalam kehidupan.
CAPAIAN PEMBELAJARAN
: Dapat menunjukkan etika dan moral komunal : Asah, Asih dan Asuh (S.b)
Ability to demonstrate communal ethics and morals: Asah, Asih and Asuh(S.b)
CPMK
: Memiliki etika dalam pemanfaatan Kecerdasan Artifisial.
Have Ethics in using AI
KAD
: Menunjukkan etika dalam penerapan teknologi Kecerdasan Buatan.
have ethic in using the Artificial Intelligence technologies.
(3,3)
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.
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Assessment Component
Assessment Detail
No
Component Name
Weightage
Total
0
Daftar Referensi
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