Ilmu Komputer IPB - MPTP Ganjil 2011/2012

Presentasi berjudul: "Ilmu Komputer IPB - MPTP Ganjil 2011/2012"— Transcript presentasi:

1 Ilmu Komputer IPB - MPTP Ganjil 2011/2012
Tinjauan Pustaka Ilmu Komputer IPB - MPTP Ganjil 2011/2012

2 Tinjauan Pustaka Tinjauan kritis terhadap berbagai hasil publikasi dalam suatu topik Tujuan: menjelaskan capaian dalam topik tersebut menunjukkan kekuatan dan kelemahan yang ada menyediakan landasan teoretis yang kuat untuk penelitian yang diajukan menegaskan keberadaan masalah

3 Kapan? Kapankah kita melakukan tinjauan pustaka?
Sebelum atau setelah percobaan?

4 Keterampilan Terkait Pencarian informasi: Penilaian kritis
Menemukan pustaka terkait Menentukan peneliti-peneliti utama dalam bidang terkait Menemukan informasi relevan dalam pustaka Penilaian kritis Menganalisa metode dan hasil dalam pustaka Mengidentifikasi bias dan validitas penelitian

5 Bukan Sekadar Daftar! Disusun sesuai permasalahan yang dibahas
Mensintesa hasil secara ringkas Menunjukkan kontroversi dalam topik terkait Memformulasikan pertanyaan-pertanyaan yang perlu didalami

6 Pertanyaan Penting dalam Penulisan Tinjauan Pustaka
Apakah masalah atau pertanyaan penelitian yang didefinisikan? Apakah fokus tinjauan pustaka yang diperlukan? Teori? Metodologi? Dll. Apakah ruang lingkupnya? Apakah pustaka yang dicakup memadai? Apakah pustaka telah dianalisa secara kritis? Apakah pustaka yang bertentangan dengan sudut pandang kita telah dibahas? Apakah akan relevan dan berguna bagi pembaca?

7 Pertanyaan Penting untuk Pustaka
Apakah penulis memformulasikan suatu masalah? Apakah jelas? Apakah cukup penting? Apakah masalah tersebut lebih tepat diselesaikan dengan pendekatan lain? Apakah penulis telah menyajikan tinjauan pustaka? Apakah mencakup yang bertentangan dengan posisinya? Apakah metode dan bahan yang digunakan memadai? Bagaimana struktur argumentasi yang digunakan?

8 Tahapan (Levy & Ellis 2006) Proses Input Output Primer Sekunder
Tersier Proses Ketahui Pahami Terapkan Analisa Sintesa Evaluasi Output

9 Input Primer: data, program komputer, model
Sekunder: laporan penelitian yang menggunakan data primer, mis. artikel konferensi dan jurnal Menjadi primer jika termasuk data penelitian Tersier: hasil sintesis dan laporan sumber sekunder, mis. buku teks, kamus, dan ensiklopedi

10 Proses: Ketahui (Levy & Ellis 2006)
Listing, defining, describing, identifying Contoh: Other research also indicates that individual and group marks should be combined in-group activities (Buchy & Quinlan, 2000; Lim et al., 2003; Romano & Nunamaker, 1998). Buchy and Quinlan (2000) interviewed 36 students participating in tutorial groups. These interviews indicated that the students felt they were becoming more conscious of learning processes of both themselves and their peers.

11 Proses: Pahami (Levy & Ellis 2006)
Summarizing, differentiating, interpreting, contrasting Contoh: Han and Kamber (2001) suggest an evolution that moves from data collection and database creation, towards data management, and ultimately, data analysis and understanding. Han and Kamber (2001) suggest an evolution that moves from data collection and database creation, towards data management, and ultimately, data analysis and understanding. For example, data processing is a base function enabling manipulation and aggregation of data, thus facilitating searching and retrieval.

12 Proses: Terapkan (Levy & Ellis 2006)
Demonstrating, illustrating, solving, relating, classifying

13 Proses: Analisa (Levy & Ellis 2006)
Separating, connecting, comparing, selecting, explaining Contoh: Data mining is the analyzing and interpretation of large amounts of information. Through analyzing vast amounts of data it is possible to find patterns, relationships and from these discoveries it is possible to make correlations (Chen & Liu, 2005). Data mining is a process of discovering new knowledge by using statistical analysis to identify previously unsuspected patterns and clustering in large data sets (Chen & Liu, 2005).

14 Proses: Sintesa (Levy & Ellis 2006)
Combining, integrating, modifying, rearranging, designing, composing, generalizing Contoh: The Digital Object Identifier (DOI) is an Internet-based system for global identification and reuse of digital content (Paskin, 2003). It provides a tracking mechanism to identify digital assets (Dalziel, 2004). The DOI is not widely employed across LOR and databases and is not universally adapted by content owners (Nair & Jeevan, 2004). The DOI does not provide provision for assets to be tagged with copyright information (Genoni, 2004). One current DRM initiative, the Digital Object Identifier (DOI), is an Internet-based system for global identification and reuse of digital content, and provides a tracking mechanism to identify digital assets (Paskin, 2003; Dalziel, 2004). However, despite being integrated in learning object technologies, this DOI is not widely employed across LOR and databases, nor is it universally adapted by content owners (Nair & Jeevan, 2004). Similarly, while most metadata schema enables assets to be tagged with copyright information, this method lacks technological enforcement (Genoni, 2004).

15 Proses: Evaluasi (Levy & Ellis 2006)
Assessing, deciding, recommending, selecting, judging, explaining, discriminating, supporting, concluding Contoh: Data mining has applicability to education as well as business (Sanjeev, 2002; Ma et al., 2000; Glance et al., 2005; Abe et al., 2004; Liu et al, 2005). … the applications of data mining fall under the general umbrella of business intelligence. Case studies have reported implementation of data mining applications for: (1) Enrollment management (to help capture promising students) (Sanjeev, 2002); (2) Alumni management (to foster donations and pledges) (Ma et al., 2000); (3) Marketing analysis (to better allocate the marketing funds) (Glance et al., 2005); and (4) Mail campaign analysis (to judge its effectiveness and design new, better targeted mailings) (Abe et al., 2004). Based upon the similarity to applications within the business community, Liu et al (2005) speculated that data mining could also be used within the educational community for fraud analysis and detection.

16 Logical Fallacies (Weber & Brizee 2011)
Slippery slope Hasty generalizations Post hoc ergo propter hoc Genetic fallacy Begging the claim Circular argument Either/or Ad hominem Ad populum Red herring Straw man Moral equivalence

17 Slippery slope Asumsi bahwa jika A terjadi, maka B, C, …, X, Y, Z pasti akan terjadi juga. Pada prinsipnya menyamakan A dengan Z, sehingga jika Z tidak diinginkan, A juga tidak boleh terjadi. Contoh: If we ban Hummers because they are bad for the environment eventually the government will ban all cars, so we should not ban Hummers. Larangan Hummer disamakan dengan larangan terhadap semua mobil  TIDAK SAMA

18 Hasty Generalization Generalisasi tanpa bukti cukup. Contoh:
Even though it's only the first day, I can tell this is going to be a boring course.

19 Post hoc ergo propter hoc
Kesimpulan bahwa jika A terjadi setelah B, maka B menyebabkan A. Contoh: I drank bottled water and now I am sick, so the water must have made me sick.

20 Genetic Fallacy Menjadikan karakteristik yang tidak relevan untuk menilai sesuatu. Contoh: The Volkswagen Beetle is an evil car because it was originally designed by Hitler's army.

21 Begging the Claim Kesimpulan ditetapkan oleh klaim. Contoh:
Filthy and polluting coal should be banned. Bukti polusi belum disajikan.

22 Circular Argument Menyatakan ulang argumen. Contoh:
George Bush is a good communicator because he speaks effectively. Tidak menambahkan keterangan.

23 Ad hominem Serangan pribadi. Contoh:
Green Peace's strategies aren't effective because they are all dirty, lazy hippies. Tidak jelas strategi yang dimaksud dan kekurangannya.

24 Ad populum Ajakan emosional yang tidak relevan. Contoh:
If you were a true American you would support the rights of people to choose whatever vehicle they want.

25 Red herring Pengalihan perhatian dari inti masalah. Contoh:
The level of mercury in seafood may be unsafe, but what will fishers do to support their families?

26 Straw man Terlalu menyederhanakan argumentasi lawan agar mudah dibantah. Contoh: People who don't support the proposed state minimum wage increase hate the poor.

27 Moral equivalence Menyetarakan kesalahan kecil dengan kejahatan besar. Contoh: That parking attendant who gave me a ticket is as bad as Hitler.

28 Sumber Booth WC, Colomb GG, Williams JM The Craft of Research. The University of Chicago Press. Levy Y, Ellis TJ A systems approach to conduct an effective literature review in support of information systems research. Informing Science Journal. 9: Reed LE. Performing a literature review. iterature_review.pdf Taylor D. The literature review: A few tips on conducting it. types-of-writing/literature-review Weber R, Brizee A. Logical fallacies.