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RPS Detail

Matakuliah

Profile	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. 1. Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann. Business Analytics. Cengage. 2021
2. 2. Winston, Wayne L., . Marketing analytics: data-driven techniques with Microsoft Excel. John Wiley & Sons. 2014

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