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INSTALASI KABEL JARINGAN (UTP)

1. Tujuan

a. Mahasiswa memahami koneksi dalam kabel UTP

b. Mahasiswa dapat membuat kabel UTP straight dan crossover.

2. Dasar Teori

2 Komputer dapat dihubungkan satu dengan lainnya membentuk suatu jaringan. Kabel adalah media transmisi yang biasa digunakan dan jenis yang sering digunakan adalah kabel twisted pair dan coaxial.

Twisted Pair (Shielded and Unshielded)

Kabel twisted pair adalah kabel tembaga yang dililitkan secara berpasangan dengan tujuan untuk menutupi kelemahan kabel terhadap noise electrics yang berasal dari dalam kabel (pair to pair coupling atau crosstalk) dan dari luar kabel (interferensi electromagnetic dan interferensi radio). Jadi semakin banyak lilitan kabel setiap inci maka semakin kuat daya tahan kabel terhadap interferensi elektromagnetik dan crosstalk.

Kabel twisted pair terdiri dari dua jenis, yaitu terbungkus / shielded (shielded twistwed pair/STP) dan tak terbungkus / unishielded (unshielded twisted pair / UTP). Karekteristik yang dimiliki kabel ini adalah :

• Sepasang kabel yang di_twist, yang jumlah pasangannya bisa dua,empat,atau lebih
• Kecepatan transfer data yang dapat di layani sampai 100 Mbps.
• Konektor yang bisa digunakan RJ-11 atau RJ-45.

Untuk membuat kabel UTP membutuhkan crimping tool yang digunakan untuk melebarkan kabel dan memasangnya ke conector. Jangan memotong kabel UTP lebih dari setengah inci. Jangan menggores kabel karena akan mengurangi kinerja kabel.

Coaxial

Kabel coaxial memiliki ukuran yang beragam. Diameter yang besar memiliki transmisi panjang dan menolak noise. Nama lain dari kabel ini adalah thicknet. Kabel ini sangat popular untuk LAN karena memiliki brandwith yang lebar sehingga dapat digunakan untuk komunikasi broadband (multiple chanel).

Kabel coaxial memilki karakteristik sebagai berikut:

• Kecepatan data dapat mencapai 10 Mbps.
• Sering digunakan untuk kabel TVARCnet,thickethernet dan tihin Ethernet.
• Thick coaxial /10Base5 /RG-8 sering digunakan untuk backbone untuk instalasi jaringan antar gedung. Kabel ini secara fisik berat dan tidak fleksibel,namun ia mampu mecapai jarak 500m bahkan lebih.
• Thin coaxial / 10Base2 / RG-58 / cheapernet sering digunakan untuk jaringan antar workstation. Kabel ini secara fifik lebih mudah ditangani dari pada RG-8 secara fleksibel lebih ringan.

Kabel ini sangat mudah untuk topologi bus dan topologi kabel ring. Kabel thin coaxial paling banyak digunakan dalam LAN, lebih mahal dari twisted pair dan lebih sukar.

Fiber Optik (Serat Optik)

Kabel serat optic adalah kabel jaringan computer yang dapat mentransmisikan cahaya (gelombang elektromagnetik). Cahaya ini memiliki sifat dualisme, yaitu cahaya bersifat gelombang (efek Compton, panjang gelombang de broglie, computer optic dan lainnya) dan cahaya bersifat partikel (efek fotolistrik, eksperimen davisson-germer, dan lainnya).

Kabel ini memiliki daya jelajah 550 meter sampai ratusan kilometer. Keunggulan kabel ini adalah kebal terhadap interferensi elektromagnerik, sehingga mampu mentransmisikan data dengan kecepatan tinggi, tidak membawa sinyal listrik dan mengubah sinyal (bit) menjadi bentuk cahaya.

2

Kabel serat optic terbagi atas 2 bagian, yaitu single dan mode. Kabel single mampu menjelajah jarak sampai ratusan kilometer dan mampu mengirim satu sinyal dalam satu waktu. kabel multimode mampu menjelajah ± 500 meter, mengirim beberapa sinyal dalam waktu yang sama, dan mengirim data pada sudut bias (refraction) yang berbeda.

Kabel serat optic merupakan media LAN yang paling baik kualitasnya, tetapi harganya cukup mahal. Kecepatan transfer datanya sangat tinggi, yaitu 100 mbps dan bebas noise (error) kecuali jika medianya mengalami kerusakan.

UTP (unshielded Twisted pairs)

Jenis kabel UTP yang terbaik saat ini adalah jenis kategori 5 (CAT5),sehingga sering banyak digunakan, dari pada jenis katagori sebelumnya. UTP CAT5 akan tamapak seperti gambar1. yaitu terdiri dari 4 pasang kabel yang tiap-tiap kabel mempunyai warna yang berbeda (white/blue, blue, white/orange, orange, white/green, green, white/brown, brown).

Standart Internasional untuk urusan kabel UTP adalah seperti tampak pada table 1

Table 1. Standart Urutan Kabel UTP

Urutan Kabel	Warna
1	White/orange
2	Orange
3	White/green
4	Blue
5	White/blue
6	Green
7	White/brown
8	Brown

Gambar 2 (a). Meratakan dan menyusun kabel UTP

(b). memasukkan kabel UTP ke konektor RJ 45 dan mengkaitkan

Ada 2 jenis hubungan kabel UTP yaitu straight dan crossover, hubungan straight umumnya digunakan untuk menghubungkan PC ke HUB sedangkan crossover untuk menghubungkan PC ke PC atau HUB ke HUB. Hubungan straight dibuat dengan membuat hubungan kabel yang sama di kedua ujung kabel UTP, sedangkan crossover dibuat dengan menukar pin 1 dengan pin 2 dari sisi awal kabel ke pin 3 dan pin 6 dari sisi akhir kabel.

4

3. Peralatan

1. Kabel UTP dengan panjang sesuai ketentuan.

2. LAN Tester

3. Crimping tool

4. Konektor RJ 45

4. Procedure Percobaan

1. Siapkan peralatan dan kabel UTP dengan panjang sesuai ketentuan

2. Tentukan jenis hubungan yang akan dibuat (straight atau crossover).

3. Urutkan kabel UTP sesuai jenis hubungan yang akan dibuat dan dirapikan.

4. Masukkan kabel UTP ke konektor RJ 45, pastikan urutannya benar dan rata sampai ke ujung, ulangi jika salah.

5. Setelah memasukkankabel UTP ke RJ 45 sesuai dengan ketentuan, kaitkan kabel UTP dengan konektor RJ 45 menggunakan crimping tool dengan menekan sekeras-kerasnya sampai terlihat tembaga pada RJ 45 menembus kekabel UTP.

6. Tes kabel LAN yang sudah dibuat menggunakan LAN tester, dan catat konektivitas tiap-tiap pin ke kabel data percobaan, sesuaikan dengan hubungan kabel yang anda buat (straight atau crossover).

Landasan Teori

Jaringan merupakan pada dasarnya adalah jaringan kabel, menghubungkan satu sisi dengan sisi lain, namun bukan berarti kurva tertutup, bisa jadi merupakan kurva terbuka. Seiring dengan perkmbangan teknologi, penghubung antar komputer mengalami perubahan serupa. Mulai dari teknologi telegraf yang memanfaatkan gelombang radio hingga teknologi serat optik dan laser menjadi tumpuan perkembangan jaringan komputer. Serta bentuk dan fungsi dari jaringan tersebut menentukan pemilihan jenis kabel.demikian juga sebaliknya ketersediaan kabel dan harga menjadi pertimbangan utama untuk network.

5. Data percobaan

a) Percobaan pertama (gagal)

Crossover

Start		End		Konektivitas
PIN	Warna	PIN	Warna
1	White – Orange	1	White – Green	√
2	Orange	2	Green	√
3	White – Green	3	White – Orange	√
4	Blue	4	Blue	_
5	White – Blue	5	White – Blue	√
6	Green	6	Orange	√
7	White – Brown	7	White - Brown	_
8	Brown	8	Brown	_

b) Percobaan Kedua ( Berhasil)

Straight

Start		End		Konektivitas
PIN	Warna	PIN	Warna
1	White – Orange	1	White - Orange	√
2	Orange	2	Orange	√
3	White – Green	3	White – Green	√
4	Blue	4	Blue	√
5	White – Blue	5	White – Blue	√
6	Green	6	Green	√
7	White - Brown	7	White – Brown	√
8	Brown	8	Brown	√

6. Analisa

1. Identifikasi Masalah

Dalam praktikum pembuatan kabel UTP terdapat kendala – kendala dalam pembuatannya, seperti Kabel tidak conect, seperti yang terdapat pada table diatas. Terdapat beberapa kemungkinan penyebab tidak connect nya kabel UTP tersebut, diantaranya sebagai berikut:

· Kabel tidak tesusun secara berurutan sesuai ketentuan

· Tidak ratanya kabel UTP, biasanya ini terjadi pada saat pemotongan kabel. Sehingga kabel tidak sampai atau tidak terhubung dengan connector.

· Pemotongan kabel induk yang kurang panjang ,sehingga menghalangi masuknya kabel yang delapan.

· Pemotongan kabel yang terlalu dalam pada kabel delapan hingga mengakibatkan luka pada kabel tersebut,sehingga mengakibatkan tidak connectnya kabel tersebut..

· Penjepitan connector dengan menggunakan crimping tool kurang kuat, sehingga kabel tidak terpasang(menempel) dengan baik pada conector..

2. Pemecahan Masalah

· Untuk penyusunan dan pemotongan kabel yang benar dapat dilakukan dengan cara:

o Ketika pemotongan kabel induk, potonglah kabel denan sangat benar dan teliti, sehingga tidak mengkibatkan: kelukaan pada kabel(karena terlalu dalam), terlalu panjang &tidak terlalu pendek

o Urutkan kabel delapan sesuai ketentuan, kemudian dipegang kuat agar kabel tidak terpisah.

o Ketika pemotongan kabel delapan, pastikan kabel dalam posisi yang benar dan teliti kerataan kabel.

· Untuk penjepitan connector yang benar dilakukan dengan langkah-langkah sebagai berikut:

o Sebelum menjepit connector pastikan kabel terpasang dengan benar pada connector, teliti kembali kertaan,jika ada kesalahan keluarkan kembali kabel dari conector.

o Setelah kabel terpasang dengan benar, jepit connector dengan kuat menggunakan crimping tool.

· Jepit kabel dengan krimping, kemudian uji pada LAN tester, apabila ada kabel yang tidak connect( tidak nyala ). Maka ulang kembali pembuatan kabel UTP.

· Gambar berikut menunjukkan kabel UTP yang benar

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Data Mining

Data Mining: What is Data Mining?

Overview

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Continuous Innovation

Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.

Example

For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.

Data, Information, and Knowledge

Data

Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes:

• operational or transactional data such as, sales, cost, inventory, payroll, and accounting

• nonoperational data, such as industry sales, forecast data, and macro economic data

• meta data - data about the data itself, such as logical database design or data dictionary definitions

Information

The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when.

Knowledge

Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.

Data Warehouses

Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining.

What can data mining do?

Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.

WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.

The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.

By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick's defense and then finds Williams for an open jump shot.

How does data mining work?

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:

• Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.

• Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.

• Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.

• Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.

Data mining consists of five major elements:

• Extract, transform, and load transaction data onto the data warehouse system.

• Store and manage the data in a multidimensional database system.

• Provide data access to business analysts and information technology professionals.

• Analyze the data by application software.

• Present the data in a useful format, such as a graph or table.

Different levels of analysis are available:

• Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.

• Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.

• Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.

• Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.

• Rule induction: The extraction of useful if-then rules from data based on statistical significance.

• Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.

What technological infrastructure is required?

Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR has the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:

• Size of the database: the more data being processed and maintained, the more powerful the system required.

• Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.

Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.

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