Download Training Presentation - Pusat Penelitian Biomaterial LIPI

1
TINJAUAN PUSTAKA
Ilmu Komputer IPB - MPTP Ganjil 2011/2012
Tinjauan Pustaka
2


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
Kapan?
3
Kapankah kita melakukan tinjauan pustaka?


Sebelum atau setelah percobaan?
Keterampilan Terkait
4

Pencarian informasi:
 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
Bukan Sekadar Daftar!
5




Disusun sesuai permasalahan yang dibahas
Mensintesa hasil secara ringkas
Menunjukkan kontroversi dalam topik terkait
Memformulasikan pertanyaan-pertanyaan yang
perlu didalami
Pertanyaan Penting dalam Penulisan
Tinjauan Pustaka
6







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?
Pertanyaan Penting untuk Pustaka
7






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?
Tahapan (Levy & Ellis 2006)
8
Input
Proses
• Primer
• Sekunder
• Tersier
•
•
•
•
•
•
Ketahui
Pahami
Terapkan
Analisa
Sintesa
Evaluasi
Output
Input
9


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
Proses: Ketahui (Levy & Ellis 2006)
10


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.
Proses: Pahami (Levy & Ellis 2006)
11


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.

Proses: Terapkan (Levy & Ellis 2006)
12

Demonstrating, illustrating, solving, relating,
classifying
Proses: Analisa (Levy & Ellis 2006)
13


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).

Proses: Sintesa (Levy & Ellis 2006)
14


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).
Proses: Evaluasi (Levy & Ellis 2006)
15


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.
Logical Fallacies (Weber & Brizee
2011)
16






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
Slippery slope
17

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
Hasty Generalization
18

Generalisasi tanpa bukti cukup. Contoh:
 Even
though it's only the first day, I can tell this is going
to be a boring course.
Post hoc ergo propter hoc
19

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.
Genetic Fallacy
20

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.
Begging the Claim
21

Kesimpulan ditetapkan oleh klaim. Contoh:
 Filthy

and polluting coal should be banned.
Bukti polusi belum disajikan.
Circular Argument
22

Menyatakan ulang argumen. Contoh:
 George
Bush is a good communicator because he speaks
effectively.

Tidak menambahkan keterangan.
Ad hominem
23

Serangan pribadi. Contoh:
 Green
Peace's strategies aren't effective because they are
all dirty, lazy hippies.

Tidak jelas strategi yang dimaksud dan
kekurangannya.
Ad populum
24

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.
Red herring
25

Pengalihan perhatian dari inti masalah. Contoh:
 The
level of mercury in seafood may be unsafe, but what
will fishers do to support their families?
Straw man
26

Terlalu menyederhanakan argumentasi lawan agar
mudah dibantah. Contoh:
 People
who don't support the proposed state minimum
wage increase hate the poor.
Moral equivalence
27

Menyetarakan kesalahan kecil dengan kejahatan
besar. Contoh:
 That
parking attendant who gave me a ticket is as bad as
Hitler.
Sumber
28





Booth WC, Colomb GG, Williams JM. 1995. The Craft
of Research. The University of Chicago Press.
Levy Y, Ellis TJ. 2006. A systems approach to conduct an
effective literature review in support of information
systems research. Informing Science Journal. 9: 181212.
Reed LE. Performing a literature review.
http://www.iris.ethz.ch/msrl/education/iris_studies/pdf/l
iterature_review.pdf
Taylor D. The literature review: A few tips on conducting
it. http://www.writing.utoronto.ca/advice/specifictypes-of-writing/literature-review
Weber R, Brizee A. Logical fallacies.
http://owl.english.purdue.edu/owl/resource/659/03/