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RPS: Session, Learning Material, Reference, Assessment/Rubric
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Kode Matakuliah
Nama Matakuliah
sks
Creator
Reviewer
Action
Kurikulum Sistem Informasi 2023/2024
ISS6305
Analitik Bisnis
3.00
2612 Dr. Ir. Teddy Siswanto, M.Si.
2128 Dr. Dedy Sugiarto
RPS
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RPS Detail
Matakuliah
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History
Kode Matakuliah
ISS6305
Nama Matakuliah
Analitik Bisnis
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
1
Materi dari awal sd uts sebaiknya tidak tumpang tindih dengan mata kuliah probabilitas dan statistika (materi tipe data, distribusi data, ukuran statistika, pengenalan regresi serta mata kuliah analitik data dan analitik lanjut (regresi dengan prediktor kualitatif, time series data) serta mata kuliah machine learning (predictive modelling). Disarankan topik lebih terkait studi kasus implementasi spt supply chain analytics, marketing analytics dll
Dedy Sugiarto
2024-06-09 13:31:57
2
Terlihat saran reviewer sudah ditindaklanjuti dengan konten terkait marketing analytics
Dedy Sugiarto
2024-07-02 14:47:36
Capaian Pembelajaran (CP) terkait
Capaian Pembelajaran (CP)
KETRAMPILAN KHUSUS
1
Mampu memahami, menganalisis, menilai konsep dasar dan peran sistem informasi dalam mengelola data yaitu pemfilteran, agregasi dan pengorganisasian dalam analisis dan visualisasi data untuk memberikan rekomendasi pengambilan keputusan pada proses dan sistem organisasi. (CPL01 (KK.a))
Able to understand, analyze, and evaluate the basic concepts and role of information systems in managing data, including filtering, aggregation, and organization in data analysis and visualization, to provide decision-making recommendations in organizational processes and systems. (CPL01 (KK.a))
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Analisis dan Visualisasi Data
Data Analysis and Visualization
(4,3)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mampu menganalisis bisnis
Able to analyze business
(4,3)
2
Mampu mensimulasikan Analisis Keputusan
Able to simulate Decision Analysis
(4,3)
3
Mampu memproyeksikan Tugas Kelompok
Able to project Group Assignments
(3,3)
2
Mampu merancang dan menggunakan database, serta mengolah dan menganalisa data dengan alat dan teknik pengolahan data. (CPL02 (KK.b))
Able to design and use databases, as well as process and analyze data using data processing tools and techniques. (CPL02 (KK.b))
No
Detail
Course Learning Outcomes
Action
1
Show/Hide
Teknik Olahan Data
Data Processing Techniques
(6,4)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mampu menerapkan tools olahan data
Able to apply data processing tools
(3,3)
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
Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
(4,4)
No
Session Learning Outcomes - Description (Cognitive Level,Knowledge Level)
Action
1
Mampu mensimulasikan Spreadsheet untuk olah data marketing
Able to simulate Spreadsheets for marketing data processing
(3,3)
2
Mampu mensimulasikan Data mining
Able to simulate Data mining
(3,3)
RPS per Session
Sesi Ke
KAD
Bahan Kajian
Metoda Pembelajaran
Waktu Belajar (Menit)
Pengalaman Belajar Mahasiswa
Referensi
Kriteria Penilaian (Indikator)
1
Decision Making, Business Analytics Defined, A Categorization Of Analytical Methods And Models, Big Data, Business Analytics In Practice (ch01)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann(2021)
Ujian Tengah Semester - 2.00 %
CAPAIAN PEMBELAJARAN
: Mampu memahami, menganalisis, menilai konsep dasar dan peran sistem informasi dalam mengelola data yaitu pemfilteran, agregasi dan pengorganisasian dalam analisis dan visualisasi data untuk memberikan rekomendasi pengambilan keputusan pada proses dan sistem organisasi. (CPL01 (KK.a))
Able to understand, analyze, and evaluate the basic concepts and role of information systems in managing data, including filtering, aggregation, and organization in data analysis and visualization, to provide decision-making recommendations in organizational processes and systems. (CPL01 (KK.a))
CPMK
: Analisis dan Visualisasi Data
Data Analysis and Visualization
KAD
: Mampu menganalisis bisnis
Able to analyze business
(4,4)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Kinerja bisnis dapat dianalis
Business performance can be analyzed
Ujian Tengah Semester 2.00 %
2
Spreadsheet Models,Building Good Spreadsheet Models,What-If Analysis, Some Useful Excel Functions For Modeling, Auditing Spreadsheet Models, Predictive And Prescriptive Spreadsheet Models (ch10)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Mampu merancang dan menggunakan database, serta mengolah dan menganalisa data dengan alat dan teknik pengolahan data. (CPL02 (KK.b))
Able to design and use databases, as well as process and analyze data using data processing tools and techniques. (CPL02 (KK.b))
CPMK
: Teknik Olahan Data
Data Processing Techniques
KAD
: Mampu menerapkan tools olahan data
Able to apply data processing tools
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Tools yang digunakan dapat menghasilkan output
The tools used can produce output
Ujian Tengah Semester 3.00 %
3
Monte Carlo Simulation, Risk Analysis For Sanotronics Llc, Simulation Modeling For Land Shark Inc., Simulation With Dependent Random Variables, Simulation Considerations (ch11)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Mampu memahami, menganalisis, menilai konsep dasar dan peran sistem informasi dalam mengelola data yaitu pemfilteran, agregasi dan pengorganisasian dalam analisis dan visualisasi data untuk memberikan rekomendasi pengambilan keputusan pada proses dan sistem organisasi. (CPL01 (KK.a))
Able to understand, analyze, and evaluate the basic concepts and role of information systems in managing data, including filtering, aggregation, and organization in data analysis and visualization, to provide decision-making recommendations in organizational processes and systems. (CPL01 (KK.a))
CPMK
: Analisis dan Visualisasi Data
Data Analysis and Visualization
KAD
: Mampu mensimulasikan Analisis Keputusan
Able to simulate Decision Analysis
(4,4)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Simulasi Analisis Keputusan dapat berjalan
Decision Analysis Simulation can work
Ujian Tengah Semester 3.00 %
4
Decision Analysis, Problem Formulation, Decision Analysis Without Probabilities, Decision Analysis With Probabilities, Decision Analysis With Sample Information, Computing Branch Probabilities With Bayes, Utility Theory (ch15)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Tengah Semester - 3.00 %
CAPAIAN PEMBELAJARAN
: Mampu memahami, menganalisis, menilai konsep dasar dan peran sistem informasi dalam mengelola data yaitu pemfilteran, agregasi dan pengorganisasian dalam analisis dan visualisasi data untuk memberikan rekomendasi pengambilan keputusan pada proses dan sistem organisasi. (CPL01 (KK.a))
Able to understand, analyze, and evaluate the basic concepts and role of information systems in managing data, including filtering, aggregation, and organization in data analysis and visualization, to provide decision-making recommendations in organizational processes and systems. (CPL01 (KK.a))
CPMK
: Analisis dan Visualisasi Data
Data Analysis and Visualization
KAD
: Mampu mensimulasikan Analisis Keputusan
Able to simulate Decision Analysis
(4,4)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Simulasi Analisis Keputusan dapat berjalan
Decision Analysis Simulation can work
Ujian Tengah Semester 3.00 %
5
Slicing and Dicing Marketing Data with PivotTables, Using Excel Charts to Summarize Marketing Data, Using Excel Functions to Summarize Marketing Data (ch01)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Winston, Wayne L., (2014)
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Spreadsheet untuk olah data marketing
Able to simulate Spreadsheets for marketing data processing
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data Advertising dapat dihasilkan melalui spreadsheet
Advertising data processing can be generated via spreadsheet
Ujian Tengah Semester 3.00 %
6
What do Customers Want? Conjoint Analysis, Logistic Regression, Discrete Choice Analysis (ch16)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Spreadsheet untuk olah data marketing
Able to simulate Spreadsheets for marketing data processing
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data Advertising dapat dihasilkan melalui spreadsheet
Advertising data processing can be generated via spreadsheet
Ujian Tengah Semester 3.00 %
7
Customer Value: Calculating Lifetime Customer Value, Using Customer Value to Value a Business, Customer Value, Monte Carlo Simulation, and Marketing Decision Making, Allocating Marketing Resources between Customer Acquisition and Retention (ch19)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Data mining
Able to simulate Data mining
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data mining dapat menghasilkan Internet dan Social Marketing value
Data mining can produce Internet and Social Marketing value
Ujian Tengah Semester 3.00 %
8
Market Segmentation: Cluster Analysis, Collaborative Filtering, Using Classification Trees for Segmentation (ch23)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Akhir Semester - 5.00 %
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Spreadsheet untuk olah data marketing
Able to simulate Spreadsheets for marketing data processing
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data Advertising dapat dihasilkan melalui spreadsheet
Advertising data processing can be generated via spreadsheet
Ujian Akhir Semester 5.00 %
9
Forecasting New Product Sales: Using S Curves to Forecast Sales of a New Product,The Bass Diffusion Model,Using the Copernican Principle to Predict Duration of Future Sales (ch26)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Akhir Semester - 5.00 %
CAPAIAN PEMBELAJARAN
: Mampu merancang dan menggunakan database, serta mengolah dan menganalisa data dengan alat dan teknik pengolahan data. (CPL02 (KK.b))
Able to design and use databases, as well as process and analyze data using data processing tools and techniques. (CPL02 (KK.b))
CPMK
: Teknik Olahan Data
Data Processing Techniques
KAD
: Mampu menerapkan tools olahan data
Able to apply data processing tools
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Tools yang digunakan dapat menghasilkan output
The tools used can produce output
Ujian Akhir Semester 5.00 %
10
Retailing: Market Basket Analysis and Lift,RFM Analysis and Optimizing Direct Mail Campaigns,Using the SCANPRO Model and Its Variants, Allocating Retail Space and Sales Resources,Forecasting Sales from Few Data Points (ch29)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Akhir Semester - 5.00 %
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Data mining
Able to simulate Data mining
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data mining dapat menghasilkan Internet dan Social Marketing value
Data mining can produce Internet and Social Marketing value
Ujian Akhir Semester 5.00 %
11
Advertising: Measuring the Effectiveness of Advertising,Media Selection Models,Pay Per Click (PPC) Online Advertising (ch34)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Akhir Semester - 5.00 %
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Spreadsheet untuk olah data marketing
Able to simulate Spreadsheets for marketing data processing
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data Advertising dapat dihasilkan melalui spreadsheet
Advertising data processing can be generated via spreadsheet
Ujian Akhir Semester 5.00 %
12
Marketing Research Tools: Principal Component Analysis (PCA),Multidimensional Scaling (MDS),Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis,Analysis of Variance: One-way & Two-way ANOVA (ch37)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Akhir Semester - 5.00 %
CAPAIAN PEMBELAJARAN
: Mampu merancang dan menggunakan database, serta mengolah dan menganalisa data dengan alat dan teknik pengolahan data. (CPL02 (KK.b))
Able to design and use databases, as well as process and analyze data using data processing tools and techniques. (CPL02 (KK.b))
CPMK
: Teknik Olahan Data
Data Processing Techniques
KAD
: Mampu menerapkan tools olahan data
Able to apply data processing tools
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Tools yang digunakan dapat menghasilkan output
The tools used can produce output
Ujian Akhir Semester 5.00 %
13
Internet and Social Marketing: Networks,The Mathematics Behind The Tipping Point, Viral Marketing,Text Mining (ch42)
Tutorial
Kolaborative
170.00
Memanfaatkan berbagai sumber belajar. memberi dan menerima umpan balik melalui diskusi dan tanya jawab
Ujian Akhir Semester - 5.00 %
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
: Deskriptif, Prediktif, Prescriptif
Descriptive, Predictive, Prescriptive
KAD
: Mampu mensimulasikan Data mining
Able to simulate Data mining
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Olah data mining dapat menghasilkan Internet dan Social Marketing value
Data mining can produce Internet and Social Marketing value
Ujian Akhir Semester 5.00 %
14
Presentasi Tugas
Presentasi
170.00
Mempresentasikan semua tugas yang diberikan pada sesi perkuliahan
Tugas - 50.00 %
CAPAIAN PEMBELAJARAN
: Mampu memahami, menganalisis, menilai konsep dasar dan peran sistem informasi dalam mengelola data yaitu pemfilteran, agregasi dan pengorganisasian dalam analisis dan visualisasi data untuk memberikan rekomendasi pengambilan keputusan pada proses dan sistem organisasi. (CPL01 (KK.a))
Able to understand, analyze, and evaluate the basic concepts and role of information systems in managing data, including filtering, aggregation, and organization in data analysis and visualization, to provide decision-making recommendations in organizational processes and systems. (CPL01 (KK.a))
CPMK
: Analisis dan Visualisasi Data
Data Analysis and Visualization
KAD
: Mampu memproyeksikan Tugas Kelompok
Able to project Group Assignments
(3,3)
Daftar Kriteria Penilaian (Indikator)
PI Description
PI Assessment Methods
Tugas dapat dipresentasikan
Assignments can be presented
Tugas 50.00 %
Assessment Component
Assessment Detail
No
Component Name
Weightage
1
Tugas
50.00
2
Ujian Akhir Semester
30
3
Ujian Tengah Semester
20
Total
100
Daftar Referensi
1. Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann. Business Analytics. Cengage. 2021
2. Winston, Wayne L., . Marketing analytics: data-driven techniques with Microsoft Excel. John Wiley & Sons. 2014
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