| Sesi Ke |
KAD |
Bahan Kajian |
Metoda Pembelajaran |
Waktu Belajar (Menit) |
Pengalaman Belajar Mahasiswa |
Referensi |
Kriteria Penilaian (Indikator) |
| 1 |
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Perkembangan teknologi Kecerdasan Artifisial dan metode-metode yang telah dikembangkan. |
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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. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| 2 |
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Intelligent agent dan Search space problem. |
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150.00 |
Mahasiswa melakukan eksplorasi simulasi kecerdasan menggunakan teknik pencarian. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Mensimulasikan kecerdasan menggunakan algoritma-algoritma pencarian.
Simulating intelligence using search algorithms. |
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| 3 |
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Logika proposisi dan inferensi logika. |
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150.00 |
Mahasiswa menerapkan inferensi logika pada permainan Wumpus. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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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)
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KAD: Mahasiswa dapat menerapkan Logika proposisi.
Student can implement Propositional logic (3,3) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Merepresentasikan pengetahuan menggunakan logika proposisi.
Representing knowledge using propositional logic |
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| 4 |
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Logika Proposisi dan Logika Predikat. |
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150.00 |
Mahasiswa memahami kekurangan Logika Proposisi dalam merepresentasikan pengetahuan dan bagaimana Logika Predikat mengatasi kekurangan tersebut. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Merepresentasikan pengetahuan menggunakan logika proposisi.
Representing knowledge using propositional logic |
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Penerapan kuantor yang tepat untuk merepresentasikan pengetahuan.
Correct application of quantifiers in representing knowledge. |
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| 5 |
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Ketidakpastian dalam representasi pengetahuan. |
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150.00 |
Mahasiswa melakukan eksplorasi kelemahan teknik logika proposisi dan logika predikat untuk merepresntasikan pengetahuan yang tidak fully-observable. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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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)
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CPMK: Mahasiswa memahami Penalaran Bayesian.
Student understand Bayesian reasoning |
KAD: Mahasiswa memahami peluang bersyarat.
Student understand Conditional probability (2,2) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Menggunakan peluang bersyarat dengan benar.
Apply conditional probability accurately. |
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| 6 |
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Inferensi Naive Bayes. |
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150.00 |
Mahasiswa menerapkan proses inferensi menggunakan data historis dan memahami perlunya teknik smoothing. |
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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)
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CPMK: Mahasiswa memahami Penalaran Bayesian.
Student understand Bayesian reasoning |
KAD: Mahasiswa memahami peluang bersyarat.
Student understand Conditional probability (2,2) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Menggunakan peluang bersyarat dengan benar.
Apply conditional probability accurately. |
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| 7 |
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Pembelajaran Mesin. |
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150.00 |
Mahasiswa melakukan eksplorasi pengembangan teknik simulasi kecerdasan. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Menerjemahkan pohon keputusan sebagai pengetahuan berbasis aturan.
Translate decision tree as rule-based knowledge. |
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| 8 |
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Supervised Learning. |
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150.00 |
Mahasiswa melakukan eksplorasi menggunakan scikit-learn. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning |
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| 9 |
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Unsupervised Learning. |
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150.00 |
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically. |
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning |
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| 10 |
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Neural Networks |
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150.00 |
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically. |
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Membedakan Supervised Learning dengan Unsupervised Learning.
Differentiate between Supervised Learning and Unsupervised Learning |
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| 11 |
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Deep learning. |
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150.00 |
Mahasiswa melakukan eksplorasi menggunakan library scikit-learn. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Mensimulasikan model perseptron secara manual dan menggunakan program.
Simulating perceptron model manually and programmatically. |
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| 12 |
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Pengolahan Bahasa Alami. |
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150.00 |
Mahasiswa melakukan eksplorasi dasar-dasar pengolahan bahasa alami menggunakan library SpaCy. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Implementasi fungsional pengolahan teks menggunakan Python.
Functional implementation of a text processing tool in Python using NLP techniques. |
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| 13 |
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Computer Vision. |
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150.00 |
Mahasiswa melakukan eksplorasi menggunakan library TensorFlow dan OpenCV. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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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 |
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Etika dalam penggunaan Kecerdasan Artifisial. |
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150.00 |
Mahasiswa membuat kajian sederhana mengenai etika penggunaan teknologi Kecerdasan Artifisial dalam kehidupan. |
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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)
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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) |
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| Daftar Kriteria Penilaian (Indikator) |
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| PI Description | PI Assessment Methods |
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Essay reflektif yang sistematis dalam mempertimbangkan aspek etika Kecerdasan Buatan.
Well-written reflective essay on ethical considerations in AI. |
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