Bahan Multimedia

Desember 30, 2011

2.4 Audio Compression
2.4.1 Pulse Code Modulation
Audio signals are analog waves. The acoustic perception is determined by the
frequency (pitch) and the amplitude (loudness).
For storage, processing and transmission in the computer audio signals must con-
verted into a digital representation. The classical way to do that is called pulse code
modulation (PCM). It consists of three steps: sampling, quantization and coding.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-1
Technology Ralf Steinmetz Part 2.4
Sampling
The analog signal is sampled periodically. At each sampling interval the analog value
of the signal (e.g., the voltage level) is recorded as a real number.
After sampling the signal is no longer continuous but discrete in the temporal dimen-
sion.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-2
Technology Ralf Steinmetz Part 2.4
Sampling Theorem of Nyquist
In order to reconstruct the original analog signal without loss we obviously need a
minimum sampling frequency. The minimum sampling frequency fA is given by the
sampling theorem of Nyquist (1924):
For noise-free channels the sampling frequency fA must be twice as high as the
highest frequency occurring in the signal:
fA = 2 fS
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-3
Technology Ralf Steinmetz Part 2.4
Example: Sampling a Signal at Different Rates
input
waveform
clock
sampled
output
(a) Sampling rate is much higher than signal frequency
input
waveform
clock
sampled
output
(b) Sampling rate is lower than signal frequency
input
waveform
clock
sampled
output
(c) Sampling rate is at the Nyquist limit
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-4
Technology Ralf Steinmetz Part 2.4
Quantization
The range of values occurring in the analog signal is subdivided into a fixed number of
discrete intervals. Since all analog values contained in an interval will be mapped to
the same interval number we introduce a quantization error. If the size of the quantiz-
ation interval is a then the maximum quantization error is a/2.
a/2
a
a/2
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Technology Ralf Steinmetz Part 2.4
Binary Coding
We now have to determine a unique binary representation for each quantization
interval. In principle any binary code does the job. The simplest code (which is in fact
often used in practice) is to encode each interval with a fixed-size binary number.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-6
Technology Ralf Steinmetz Part 2.4
PCM: The Complete Process
7
6
5
4
3
2
1
001 010 101 011 110 101 111 100
Sampling interval
The combination of the steps sampling, quantization and binary coding is called
Pulse Code Modulation (PCM).
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Technology Ralf Steinmetz Part 2.4
CODECs
The devices performing A/D conversion and D/A conversion are called CODECs
(Coders/Decoders).
Analog- Analog-
PCM-Signale
signale signale
CODEC CODEC
Note: A modem is used to transmit digital signals over analog links, a
codec is used to transmit analog signals over digital links.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-8
Technology Ralf Steinmetz Part 2.4
PCM Telephone Channel
Sampling Rate
Starting point: an analog CCITT telephone channel
Frequency range: 300 – 3400 Hz, i.e., audio
bandwidth: 3100 Hz (sufficient for speech)
Sampling frequency: fA = 8 kHz
Sampling period: TA = 1/ fA = 1/8000 Hz
= 125 μs
The sampling frequency chosen by CCITT is higher than the Nyquist limit: for a
maximum frequency of 3400 Hz in the signal a sampling frequency of 6800 Hz would
be sufficient. This has technical reasons (noise, influence of filters, channel
separation, etc.)
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-9
Technology Ralf Steinmetz Part 2.4
Quantization of the Amplitude
The minimum number of quantization intervals is determined by the understandability
of speech at the receiver. Based on experimental experience CCITT has chosen 256
quantization intervals.
With standard binary coding we thus need 8 bits per sample.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-10
Technology Ralf Steinmetz Part 2.4
Bit Rate of the PCM Channel
We conclude that the bit rate of a standard PCM channel is
8 bits * 8000/s = 64 kbit/s
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-11
Technology Ralf Steinmetz Part 2.4
Non-Linear Quantization
With linear quantization all intervals have the same size, they do not depend on the
amplitude of the signal. However it would be desirable to have a smaller amount of
quantization noise at small amplitude levels because quantization noise is more
disturbing in “quiet times“.
This goal can be reached with non-linear quantization. We simply chose larger quan-
tization intervals at higher amplitude values.
Technically this can be done by a “signal compressor“ which preceeds the coding
step. At the receiver side an expander is used to reconstruct the original dynamics.
Many compressors use a logarithmic mapping. In digital electronics this is often
approximated by a piecewise-linear curve. The 13-segment compressor curve is a
typical example.
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Technology Ralf Steinmetz Part 2.4
13-Segment Compressor Curve
113 … 128
number of the
quantisation
interval
16-
49 … 64
33 … 48
17 … 32
-1 -1/2 1_
1⁄4 1⁄2
1/8
1/16
1/32
normalised amplitude
of the signal
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-13
Technology Ralf Steinmetz Part 2.4
Quantization Intervals
Delta Modulation
Instead of coding the absolute values of the amplitude, the difference to the value in the previous
interval is coded in one bit. Only steps of +1 or –1 are possible.
15
14
Signal changes too fast,
13 coding error
12
11
10
9
8
7
6
5
4
3
2
1
0
Coding: 1 = increasing signal
1 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1
0 = decreasing signal
time
Sampling
interval
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-14
Technology Ralf Steinmetz Part 2.4
Differential PCM (DPCM)
In differential PCM we encode the actual difference between the signal values in two
adjacent intervals with a small number of bits. This leads to a bit rate and precision
between that of encoding the absolute values and delta modulation.
Adaptive DPCM (ADPCM)
The dynamics in real audio signals are often such that we have quiet periods and loud
periods. In quiet periods (i.e., periods with low variance of the amplitude) we can en-
code the signal with fewer bits than in loud periods. This is called Adaptive Pulse
Code Modulation (ADPCM).
For example, ADPCM allows us to compress a HiFi stereo audio signal from 1.4
Mbit/s to 0.2 Mbit/s without loss of quality.
Well-known ADPCM algorithms are μ-law and A-law.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-15
Technology Ralf Steinmetz Part 2.4
Typical Sampling and Quantization Parameters
Sampling Rate
8 kHz telephony, μ-law encoding, SUN audio
32 kHz Digital Radio Broadcast
44,1 kHz Audio CD
48 kHz Digital Audio Tape (DAT)
Quantization
8 bits 256 amplitude levels: speech
16 bits 65536 amplitude levels: HiFi music
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Technology Ralf Steinmetz Part 2.4
2.4.2 Audio Compression with Psycho-Acoustic
Models
Compression Based on Semantic Irrelevance
We remove those parts of the signal at the source that the receiver will not be able to
hear anyway.
Example: The Masking Effect
A high-amplitude signal masks out a low-amplitude signal at an adjacent frequency
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Technology Ralf Steinmetz Part 2.4
Psycho-Acoustic Models
Audio
500 Hz
Level
Masker
Masking
Threshold
200 Hz
Masker
2 kHz
5 kHz
Masker
Masker
kHz
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Technology Ralf Steinmetz Part 2.4
Example: MPEG Audio
Characteristics
Compression to 32, 64, 96, 128 or 192 kbit/s
Audio channels
• Mono or
• Two independent channels or
• “Joint Stereo”
Techniques
• Sampling rates: 32 kHz, 44,1 kHz or 48 kHz
• 16 bits per sample
• Maximum encoding and decoding delay: 80 ms at 128 kbit/s
A psycho-acoustic model controls the quantization.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-19
Technology Ralf Steinmetz Part 2.4
Two Techniques in MPEG-1 Audio
MUSICAM ASPEC
Masking Pattern Universal Sub-band
Advanced Spectral Entropy Coding
Integrated Coding and Multiplexing
Institut für Rundfunktechnik,
FhG Erlangen
München
Overlapping dynamic frequency
Sub-band Coding
bands, entropy coding (Huffman)
Simple and easy to implement Very good quality at very low bit rates
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-20
Technology Ralf Steinmetz Part 2.4
MPEG Audio Encoder and Decoder
Encoder ISO/MPEG/AUDIO
Bitstream
Digital Audio
Signal (PCM) Time/ Quantizer
Frame
Frequency and
Packing
Coding
Mapping
Psychoacoustic
Model
Decoder
Digital Audio
ISO/MPEG/AUDIO
Signal (PCM)
Bitstream
Frequency/
Frame
Reconstruction Time
Unpacking
Mapping
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Technology Ralf Steinmetz Part 2.4
Three Layers in MPEG Audio
1. Sub-band coding with 32 bands with the MUSICAM technique
• High data rate
• mono, stereo, 48 kHz, 44.1 kHz, 32 kHz
2. Sub-band coding with MUSICAM, more complex psycho-acoustic model
• Intermediate
• Better sound quality at low bit rates
3. Transformation-based compression with the ASPEC technique
• Lowest data rate
• Stereo Audio in CD quality at less than 128 kbit/s!
• Mono Audio in telephone quality at 8 kbit/s
MPEG audio layer 3, encoded with ASPEC, is also called MP3 (!)
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-22
Technology Ralf Steinmetz Part 2.4
MP3 – History (1)
As early as 1987 the Fraunhofer Institut für Integrierte Schaltkreise (Institute of Integ-
rated Circuits) in Erlangen (Germany) began with the development of audio compres-
sion techniques that took the specific properties of the human perceptual system into
account. Their technique was included into the MPEG Audio standard of ISO (IS-
11172-3 and IS 13818-3) as MPEG Audio Layer 3 (MP3).
The original goal was a reduction of the data rate by a factor of 12 compared to an
audio CD, with no audible difference.
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Technology Ralf Steinmetz Part 2.4
MP3 – History (2)
As usual, ISO only standardizes the technical parameters and the decoder. The inner
workings of the encoder remain unspecified. This gives developers significant freedom
to develop specific encoding techniques, and even get patents for their encoding
algorithms.
As a consequence we know very little about the exact implementation of the MP3 en-
coder written by the Fraunhofer Institute. Exact details on their psycho-acoustic model
are not published. The Fraunhofer Institute also holds a patent on its optimized enco-
ding mechanism for MP3.
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Technology Ralf Steinmetz Part 2.4
MPEG Audio Layers (1)
MP3 subdivides the data stream into frames. Each frame corresponds to the audio
signal in a certain time period. It contains 384 samples. The samples represent values
out of 32 frequency sub-bands. There are 12 values from each sub-band.
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Technology Ralf Steinmetz Part 2.4
MPEG Audio Layers (2)
Layer 1
Frequency masking: Usage of a DCT-based filter. At any given time the algorithm
only considers one frame. The frequences occurring in this frame are subdivided into
the frequency bands and then filtered.
Layer 2
Temporal masking: At any given time the algorithm looks at three adjacent frames,
the current, the previous and the next frame. This allows to take advantage of tem-
poral masking effects as perceived by the human ear.
Layer 3
Non-linear masking: The frequencies are subdivided into bands of different widths.
Also, stereo channels are encoded differentially, i.e., the difference between the two
channels rather than the absolute values are encoded. The last step is a Huffman
coding of the coefficients.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-26
Technology Ralf Steinmetz Part 2.4
Layer 1: Psycho-Acoustic Effect
1. Sensitivity of the human ear
2. The frequency masking effect
Experiment: Play a tone of 1 kHz (the masking tone) at a certain amplitude (e.g., 60 dB). Then
add a test tone (e.g., of 1.1 kHz) and increase its amplitude until the the test tone is heard. This
will happen at a much higher amplitude than in the quiet.
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Technology Ralf Steinmetz Part 2.4
Layer 1: Compression
Apply a sub-band filter to subdivide the signal into 32 bands (“critical bands“).
For each band, define a masking curve that indicates at which level the signal will be
masked by adjacent bands.
Algorithm:
• Compute the energy in each band.
• If the energy in a band is smaller than the masking threshold of a neighboring
band, do not encode the band.
• Otherwise encode the band. Quantize the coefficients with a quantization
factor. Choose the factor so that the quantization error is smaller than the
masking factor (one bit in the quantization corresponds to a noise of 6 dB).
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Technology Ralf Steinmetz Part 2.4
Layer 1: Example
The table shows the levels of the first 16 out of the 32 bands.
———————————————————————————————-
Band 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Level 0 8 12 10 6 2 10 60 35 20 15 2 3 5 3 1
———————————————————————————————-
The level of band 8 is 60 dB. We assume that is has a masking threshold of 12 dB for
band 7 and of 15 dB for band 9.
The level of band 7 is 10 dB ( 15 dB), thus we encode it. We choose the quantization
factor so that the quantization error will be less than 2 bits (12 dB).
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-29
Technology Ralf Steinmetz Part 2.4
Layer 2: Psycho-Acoustic Effect
Temporal masking: When we hear a loud sound that suddenly stops it takes a while
until we can hear soft sounds again.
Experiment: Play a masking tone of 1 kHz at 60 dB and a 1.1 kHz test tone at 40 dB
(the test tone is not heard, it is masked). Stop the masking tone and after a short
delay also the test tone. Vary the delay to find the time threshold at which the test
tone can just be heard.
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Technology Ralf Steinmetz Part 2.4
Layer 2: Compression
Repeat the experiment with other test tones:
In a way similar to layer 1, we take advantage of this temporal phenomenon to
mask out sub-bands, this time those of adjacent frames.
For simplification we assume that a sub-band can mask out its neighbors only in
one preceeding and one succeeding frame.
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-31
Technology Ralf Steinmetz Part 2.4
Layer 3: Psycho-Acoustic Effect
The contrast resolution of the human ear decreases with the frequency of the signal.
In layers 1 and 2 the frequency spectrum is subdivided into 32 critical bands of iden-
tical size. In layer 3, the frequencies are distributed in a non-linear fashion, in a way
so that all bands contribute equally to the perception by the ear.
The “Bark“
We introduce a new unit: the Bark (named after Barkhausen)
1 Bark = width of a critical band.
For frequencies 500 Hz: 1 Bark = 9+4 log(f/1000)
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Technology Ralf Steinmetz Part 2.4
Layer 3: Compression
Masking Thresholds on critical band scale:
Layer 3 comes closer to human perception by choosing a more appropriate
definition of the sub-bands, based on the Bark.
In addition to frequency masking and temporal masking, as in layers 1 and 2, layer 3
also introduces the differential coding of stereo signals, as well as an entropy
encoding of the coefficients based on the Huffman code.
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Technology Ralf Steinmetz Part 2.4
Performance of MP3
Layer Target bitrate Ratio Quality at Quality at Theor. min
64 kbit/s 128 kbit/s delay
Layer 1 192 kbit/s 4:1 — — 19 ms
Layer 2 128 kbit/s 6:1 2.1-2.6 4+ 35 ms
Layer 3 64 kbit/s 12:1 3.6-3.8 4+ 59 ms
Quality measure:
5 = perfect, 4 = just noticeable, 3 = slightly annoying, 2 = annoying, 1 = very annoying
Real delay is about three times the theoretical delay.
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Technology Ralf Steinmetz Part 2.4
2.4.3 Speech Coding
Special codecs optimized for the human voice can reach a very high speech quality at
very low data rates.They operate at the normal range of the voice, i.e. at 300 – 3400
Hz.
Such special codecs are most often based on Linear Predictive Coding (LPC).
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Technology Ralf Steinmetz Part 2.4
Linear Predictive Coding (1)
LPC models the anatomy of the human voice organs as a system of connected tubes of different
diameters.
refl[1]
refl[0]
tube p
tube 1 tube 2 …
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Technology Ralf Steinmetz Part 2.4
Linear Predictive Coding (2)
Acoustic waves are produced by the vocal cords, flow through a system of tubes, are
partially reflected at the transitions and interfere with the following waves.
The reflection rate at each transition is modeled by the reflection coefficient refl[0], …,
refl[p-1].
We can thus characterize the (speaker-dependent) production of the voice signal with
a very small number of parameters.
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Technology Ralf Steinmetz Part 2.4
LPC Encoder
The LPC Algorithm
• The audio signal is decomposed into small frames of fixed length (20 – 30 ms). For
each frame s[i] we compute p weights lpc[0], .. , lpc[p-1] so that s[i] is
approximated by
lpc[0] * s[i-1] + lpc[1] * s[i-2] + … + lpc[p-1] * s[i-p]
Popular values for p are 8 or 14.
• A synthetically generated source signal is used as input to the model. The gener-
ated source can be switched between two modes: voiced (for vowels) and noise
(voiceless, for consonants).
• The differences between the synthetically generated signal and the real voice
signal during the frame are detected and used to re-calculate the prediction
coefficients lpc[i].
For each frame the mode of excitation (voiced or voiceless) and the current values

of the parameters are encoded and transmitted.
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Technology Ralf Steinmetz Part 2.4
LPC Variations
• CELP (Code Excited Linear Prediction): We not only distinguish “voiced“ and
“voiceless“ but many more types of excitation. These are pre-defined by the
developers and stored in the form of a “codebook“. For each frame we transmit
the index into the codebook and the lpc parameters.
• ACELP: like CELP, but with an adaptive codebook
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Technology Ralf Steinmetz Part 2.4
LPC Examples
G.723.1
Adaptive CELP Encoder (Code Excited Linear Predictor).
Bit rate for G.723.1: 5,3 kbit/s – 6.3 kbit/s
GSM 06.10
Regular Pulse Excitation – Long Term Prediction (RPE-LTP)
• LPC encoding
• The synthetically generated signal is based on earlier signal values.
• Bit rate for GSM 06.10: 13.2 kbit/s
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-40
Technology Ralf Steinmetz Part 2.4
Specialized Speech Coding vs. PCM Coding
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Technology Ralf Steinmetz Part 2.4
ITU-T Standards for Speech Coding
A selection from the G.7xx-Standards:
• G.711: 64 kbit/s (GSTN telephony, H.323 and H.320 Videoconferencing)
• G.728 LD-CELP: 16 kbit/s (GSM telephony, H.320 Videoconferencing)
• G.729 ACELP: 8 kbit/s (GSM telephony, H.324 Vi-deo-telephony)
• G.723.1 MPE/ACELP 5.3 kbit/s bis 6.3 kbit/s (GSTN Video-telephony, H.323
telephony)
A Graduate Course on Multimedia © Wolfgang Effelsberg, 2. Compression, 2.4-42
Technology Ralf Steinmetz Part 2.4

Kisi Kisi Soal Interaksi Manusia dan Komputer (IMK)

April 7, 2011

Materi soal untuk ujian MID Semester 2011 meliputi Konsep IMK, Faktor Manusia pada perangkat lunak interaktif  serta Teori, Prinsip dan Pedoman

Contoh beberapa soal  Interaksi Manusia Komputer (IMK)

  1. Jelaskan konsep interaksi manusia komputer (Human Computer Interaction) yang  didefinisikan oleh ACM SIGCHI?
  2. Untuk membangun sistem yang interaktif diperlukan rekayasa perangkat lunak. Jelaskan tiga tujuan dari penggunaan rekayasa perangkat lunak hubungannya dengan pembangunan sistem yang interaktif?
  3. Jelaskan definisi dari antar muka pemakai dan tujuan dari perancangan antar pemuka pemakai?
  4. Tuliskan dan uraikan 8 (delapan) aturan emas perancangan antarmuka pemakai?
  5. Berikan penjelasan tentang tujuan tingkat tinggi data display dan data entry?
  6. Dalam pembangunan sistem yang interkatif, perlu memperhatikan keseimbangan antara automasi dan kendali manusia. Jelaskan tentang keseimbangan antara automasi dan kendali manusia?
  7. Panduan bagi perancang untuk membangun sistem interaktif memerlukan pemahman tentang prinsip-prinsip tingkat menengah. Jelaskan dan uraikan tentang prinsip-prinsip tingkat menengah tersebut?

Interaksi Manusia dan Komputer

Maret 19, 2011

Materi kuliah ini mengulas tentang bagaimana mendisain perangkat lunak yang berfungsi live seperti layaknya manusia berkomunikasi dengan komunitasnya dalam konteks “interaktif” saling memahami dan membutuhkan akan data dan informasi yang disajikan

KISI-KISI SOAL ILMU ALAMIAH DASAR

Maret 31, 2009

Berikut diberikan beberapa contoh-contoh soal ujian Ilmu Alamiah Dasar

  1. Tuliskan dan jelaskan tentang sifat unik manusia?
  2. Manusia dibandingkan dengan makhluk hidup yang lain memiliki suatu perbedaan yang mendasar. Tuliskan dan jelaskan perbedaan manusia dengan makhluk hidup yang lain?
  3. Dalam perkembangan hidup, rasa ingin tahu manusia terhadap dirinya dan alam lingkungan juga berkembang. Tuliskan serta jelaskan dengan cara bagaimana manusia memuaskan rasa ingin tahunya berkaitan dengan pembuktian suatu kebenaran?
  4. Akibat keterbatasan kemampuan manusia dalam membuktikan sesuatu, manusia menciptakan mitos.
    1. Jelaskan penjelasan tentang mitos?
    2. Secara garis besar mitos dibagi dalam tiga kelompok. Jelaskan masing-msaing dan berikan contoh?
  5. Pada zaman prasejarah mitos dapat diterima dan dipercaya kebenarannya. Jelaskan berdasarkan alasan apa mitos dapat diterima dan dipercaya kebenarannya?
  6. Terdapat beberapa cara untuk memperoleh kesimpulan atau pengetahuan yang tidak berdasarkan penalaran. Tuliskan dan jelaskan cara untuk memperoleh kesimpulan atau pengetahuan yang tidak berdasarkan penalaran?
  7. Tuliskan dan jelaskan syarat-syarat yang harus dipenuhi agar suatu pengetahuan dapat disebut ilmu atau ilmiah?
  8. Dalam konsep ilmiah, cara berfikir dapat dibedakan menjadi cara berfikir deduktif dan cara berfikir induktif. Tuliskan dan jelaskan serta masing-masin berikan contoh dari cara berfikir tersebut?
  9. Dalam metode ilmiah, pendekatan rasional digabungkan dengan pendekatan empiris. Ini berarti bahwa semua teori ilmiah harus memenuhi dua syarat utama. Tuliskan dan jelakan dua syarat utama tersebut?
  10. Tuliskan dan jelaskan langkah-langkah metode ilmiah?
  11. Jelaskan dan gambarkan diagram skematis langkah-langkah metode ilmiah?
  12. Berikan penjelasan tentang lahirnya Ilmu Pengetahuan Alam?
  13. Apakah yang membedakan antara IPA Klasik (tradisional) dengan IPA Modern?
  14. Tuliskan serta jelaskan secara garis besar sumbangan bangsa Arab dalam perkembangan pengetahuan alam?

Ilmu Alamiah Dasar

Maret 28, 2009

Salah satu bahasan tentang ilmu alamiah dasar adalah yang berkaitan dengan benda mati dan benda hidup. Bagaimana benda hidup melakukan aktivitasnya dibandingkan dengan aktivitas yang dilakukan benda mati.

Salah satu jenis makluk hidup adalah manusia.  Manusia dibandingkan dengan makhluk hidup yang lain memiliki keunikan dan akal pikiran yang tidak dimiliki oleh makhluk hidup yang lain.

Hello world!

Maret 28, 2009

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