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Similar to TIK_5. pembangunan knowledge
Similar to TIK_5. pembangunan knowledge (20)
TIK_5. pembangunan knowledge
- 1. DUH1A2 - Literasi TIK Penggunaan informasi baru untuk membangun konsep baru atau membuat pemahaman baru Dana S. Kusumo Semester Ganjil – TA 2016/2017
- 2. Pembahasan: 1. Model Data-Information-Knowledge-Wisdom (DIKW) 2. Membangun pengetahuan baru Tujuan Pembelajaran : 1. Mampu memahami data, informasi, pengetahuan 2. Mampu memodelkan pengetahuan (yang ada) 3. Mampu membangun pengetahuan baru berdasar pengetahuan yang sudah ada
- 3. Model Data-Information-Knowledge- Wisdom (DIKW) Gene Bellinger (2004), Systems Thinking, Knowledge Management - Emerging Perspectives
- 4. Kategori pikiran manusia (Russell Ackoff) Data: simbol (misal: angka, huruf, gambar) Informasi: data yang sudah diproses dan berguna; menjawab pertanyaan "who", "what", "where", dan "when" Pengetahuan (Knowledge): aplikasi/pengunaan data dan informasi; menjawab pertanyaan "how" Pemahaman (Understanding): pemahaman dan menjawab pertanyaan "why" Kebijakan (Wisdom): pemahaman yang dievaluasi Empat kategori pertama merupakan masa lalu/masa kini dan Kebijakan menyangkut masa depan
- 5. Data Berupa fakta atau pernyataan Tidak mempunyai relasi dengan lainnya Contoh: sekarang hujan
- 6. Informasi Terdapat relasi antar data, misalnya: sebab-akibat Contoh: Terjadi musim kemarau berkepanjangan maka beberapa titik api (hot spot) berisiko menyebabkan kebakaran Pertanyaan, apakah pernyataan berikut data atau informasi: Harga saham TLKM naik dari Rp 4360 menjadi Rp 4390 di sesi pembukaan 8-8-2016?
- 7. Mengubah data menjadi infomasi Data Possible methods of converting data into information Sales figures Plot charts and identify trends Market and competition data Find average or typical values Financial performance Present complex data as a chart or graph Production output Monitor changes over time and forecast future values Costs of resources or other inputs Compare figures and identify similarities or differences Staff absences, holidays or sick leave Assess whether a result is significant or occurred by chance Accident records Assess whether one thing is related to another How to define data, information and knowledge, http://searchdatamanagement.techtarget.com/feature/Defining-data- information-and-knowledge
- 8. Pengetahuan (Knowledge) Pola/pattern informasi yang saling terhubung/muncul dari informasi Pola ini bersifat berulang-ulang dan dapat memprediksi (apa yang terjadi kemudian) Contoh: Jika kelembaban sangat tinggi dan temperatur turun maka atmosfir menampun uap air sehingga akan turun hujan
- 9. Kebijakan (Wisdom) Terdiri dari pengetahuan-pengetahuan Prinsip-prinsip, yang terjadi dan menjelaskan kenapa pola-pola tersebut terjadi Contoh: Hujan terjadi karena terjadinya interaksi antara curah hujan, penguapan, arah udara, perubahan suhu dsb Wisdom hanya dimiliki manusia karena menyangkut memahami dan menilai antara baik-buruk dan benar-salah
- 10. Pemodelan Pengetahuan Beberapa pemodelan pengetahuan (bisa digunakan oleh manusia dan komputer) Aturan (rules) Semantic Network Lainnya: Frames Concept Diagram
- 11. Aturan (rules) Knowledge direpresentasikan sebagai pasangan atribut yang bernilai (E.H. Shortliffe) Bentuk umum rules: if attribute A1 has value V1 and attribute A2 has value V2 then attribute A3 has value V3
- 12. Semantic Network Node adalah item-item khusus dan link menunjukkan hubungan antar item Dengan menelusuri link memungkinkan (mesin) menjawab pertanyaan sbb: Bagaimana power sampai ke elemen pemanas (heating)? Apa tujuan dari lamp?
- 13. Knowledge Structure Pengetahuan yang aktual dihasilkan dari pengetahuan yang sudah ada Identifikasi pengetahuan dasar untuk membangun struktur pengetahuan Pengetahuan dasar ini menjadi bagian struktur pengetahuan; dan dapat digunakan untuk menghasilkan pengetahuan aktual Contoh pengetahuan dasar dari pengetahuan merebus telur: Boiling an egg Boiling Water Obtaining an egg Chemical changes
- 14. Knowledge Map/Network Setelah pengetahuan dasar diketahui, relasi antar pengetahuan dipetakan yang sifatnya valid dan sesuai kebutuhan Pengetahuan manusia berkembang melalui pembelajaran dan berdasar pengetahan sebelumnya Pembelajaran bersifat hirarki karena pengetahua baru tergantung pada pengetahuan sebelumnya
- 15. Contoh Knowledge Map/Network Pengetahuan merebus telur membutuhkan pemahaman: Tipe telur rebus (matang, ½ matang), lama perbusan, aturan dan prosedur untuk merebus Untuk itu diperlukan pengetahuan (terurut berdasar tingkat kepentingan): memanaskan air (syarat merebus), perubahan air pada saat direbus (penentuan kematangan), kemudian pemilihan telur yang baik serta mendapatkan ukuran telur yang sesuai
- 16. Latihan Apakah ‘ini’ dengan deskripsi sebagai berikut: Biasanya berupa kotak Mempunyai pintu minimal 1 buah Berfungsi sebagai tempat penyimpanan Menghasilkan air
- 17. Daftar Pustaka Gordon, John L. "Creating knowledge maps by exploiting dependent relationships." Knowledge-based systems 13.2 (2000): 71-79. Data, Information, Knowledge, and Wisdom by Gene Bellinger, Durval Castro, Anthony Mills, internet 8-8-2016: 11:48 AM How to define data, information and knowledge, http://searchdatamanagement.techtarget.com/feature/Defining-data-information- and-knowledge, 8-8-2016: 11:52 AM
Editor's Notes
- Exception systems are similar to rules in that they can also be archived, understood by humans and used directly in automated reasoning systems. Exceptions may take the form: attribute A1 has value V1 unless attribute A2 has value V2 and attribute A3 has value V3
- In this simple network, nodes are specific items and links show relationships between items. It would be possible for an automated system to answer questions about items contained within the network by following links (provided that it could understand the questions). How does power get to the heating element? What is the purpose of the lamp? It would also be possible to a computer to construct a textual statement about the knowledge contained in the network.
- Within the context of this work, the actual knowledge is not directly part of the structure of knowledge but is indexed from it. In order to create a structure for knowledge , it is necessary to identify specific pieces or islands of knowledge and give them a unique name or identifier. These identifiers can then form part of a structural diagram for knowledge and can also be used to index to the actual knowledge implied by the identifier. The amount of knowledge that an identifier represents, or granularity, is an important consideration but should match the context within which the diagram will be used. If the knowledge concerned with boiling an egg is considered, then the associated identifiers may be. Boiling an egg Boiling Water Obtaining an egg Chemical changes Each of these identifiers can represent a piece of knowledge but are not that knowledge.
- Figure 2 shows what a knowledge network may look like. Understanding implies that the human expert knows why the supporting knowledge or empirical data is actually supportive of the higher knowledge item. Also contained within each link would be a measure of importance which shows how important each supportive piece of knowledge or empirical data is to the higher knowledge item. Each of these identifiers can represent a piece of knowledge but are not that knowledge. Similarly, the knowledge concerning the calculation of the gravitational attraction between two masses may include: Gravitational Attraction Mathematics Mass Force Distance Once knowledge identifiers can be derived, it is then necessary to consider a relationship between these identifiers that is both valid and useful within the context of knowledge mapping. At the present time, the acquisition of machine knowledge employs little dependent structure but human knowledge, particularly expert human knowledge, is acquired in a more rigorous way. Human knowledge is learned and the learning process is dependent on prior knowledge. That is, the learning process is hierarchical because the understanding of new knowledge often relies on the prior understanding of some existing knowledge.
- An incomplete example is shown above; that of boiling an egg. The knowledge concerning boiling an egg involves knowing about hard and soft boiled eggs and how long it takes to produce each. It also involves knowing the procedure and procedural rules for completing the operation. This in turn implies that the expert egg boiler already knows how to boil water, which is clearly an important part in egg boiling because it is water that is used to boil eggs in. Knowing how the inside of an egg changes with boiling is also very important if the expert is to understand what makes a cooked, rather than a raw egg and what causes the change to take place. Almost as important is obtaining the egg in the first place and which eggs are normally eaten. Clearly egg size would also affect the procedural rules. This example illustrates the point that an expert egg boiler can do more and knows more than simply how to boil an egg. A robot egg boiler however, may be able to boil adequate eggs but may abstain from engaging in a conversation about egg boiling.