Assignment 9: Social Network Analysis (30 Accounts Instagram)

SOCIAL NETWORK ANALYSIS

The 30 accounts Instagram are:
1. Narisha
2. Fifin
3. Sandi
4. Lathiefa
5. Eko
6. Ilham
7. Akbar
8. Biagi
9. Adit
10. Meta
11.Abdullah
12. Vhio
13. Anun
14. Ayta
15. Syifa
16. Ica
17. Niken
18. Firdha
19. Naomi
20. Adrian
21. Fauzi
22. Faza
23. Nadya P.
24. Karina
25. Arlinda
26. Sabrina
27. Andini
28. Melati
29. Calvin
30. Laudza

To describe this matrix, we use number 1 and 0. For example:

• Narisha is following Fifin. So, use number 1 to describe this relationship
• The same account cannot follow themselves. So, use number 0 to describe this relationship
• Narisha does not follow Adrian. So,  use number 0 to describe this relationship

This is the matrix of "following relationship" between 30 accounts Instagram:

from the matrix above, the plots is:

CLOSENESS

 

From the picture above, can conclude that the largest closeness value is ilham with a score of 0.9354839 because he followed 28 from 30 Instagram accounts and people also give followed back to Ilham.

BETWEENNESS

Betweenness can be assumed as a symbol of "strength"  or  "influence" of a node in social networks.  This is because the node is a bridge between one node to another node. So, from the picture above, can conclude that the largest betweenness score is Ilham with a score of  28.2577011.

KEY PLAYER

Based on this social network analysis, Ilham is the key player because he has the highest score of closeness and betweenness.

OUTDEGREE

INDEGREE

FREEMAN

Assignment 8 Big Data: Social Network Analysis

SNA
Social Network Analysis

What is Social Network Analysis?
Social network analysis (SNA) is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities.

SNA provides both a visual and a mathematical analysis of human relationships.

Potential of Social Network Analysis
a. In Inteligence = Social Network Analysis, as an analytic method, has inarguable applicability to the field of intelligence and is progressively reshaping the analytic landscape in terms of how analysts understand networks. For example, analysts currently use SNA to identify key people in an organization or social network, develop a strategic agent network, identify new agents and simulate information flows through a network. Beyond this, SNA can be easily combined with other analytic practices such as Geographic Information Systems (GIS), gravity model analysis or Intelligence Preparation of the Battlefield (IPB) to create robust, predictive analyses.

b. Tool for monitoring and evaluation of capacity building interventions
The project exploited existing monitoring and
evaluation data to: identify elements of AWARD’s interventions, strategies and theories of change
that intentionally (and unintentionally) facilitate networking; understand which factors within and outside of the fellowship (related to geographical proximity, social interactions, etc.) facilitate
networking; examine how networking is associated with empowerment outcomes; and to discuss how AWARD should measure network mechanisms and benefits better in the future.

Assignment 7 Big Data: Association Rules Ao

ASSOCIATION

Definition
Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a relational database or another information repository.

Part of Association
An association rule has two parts, there is an antecedent (if) and a consequent (then).
a. An antecedent is an item found within the data.
b. A consequent is an item found in combination with the antecedent.

Uses of Association
In data mining, association rules are useful for analyzing and predicting customer behaviour. They play an important part in customer analytics, market basket analysis, product clustering, catalogue design and store layout.

Programmers use association rules to build programs capable of machine learning. Machine learning is a type of artificial intelligence (AI) that seeks to build programs with the ability to become more efficient without being explicitly programmed.

Example of Association
Association rule mining (ARM) algorithms are used to extract associations among a large set of items or events. This example analyzes the association of outcome of the match with opponent team e.g., Australia, Pakistan, Sri Lanka, type of venue (home ground, opponent ground or neutral venue), inning order (setting the target, chasing the target) and outcome of the toss.

This is Dataset 1 = All ODI matches played by team India till 05- June-2010.

The performance of team India against Australia is dismal. The toss outcome, innings order and venue do not have any impact on the performance of team India against Australia. This finding clearly shows the superiority of Australia over team India. Further, Australia is one of the top-ranked teams in world cricket for a long period of time.

Team India has performed well against England at home as well as away from home, except when team India bats first in both conditions, or wins toss in away conditions.

The performance of Team India against Pakistan is not encouraging at home and away conditions. The chance of Team India winning while chasing the target against Pakistan is 58% when playing away from home. Team India’s performance against South Africa is better in home condition than in away condition. India wins against South Africa at home but loses most of the time while playing away from home with a relatively high level of confidence.

The performance of team India is commendable against Sri Lanka. The toss outcome, innings order and venue have not impacted the performance of team India against. One of the interesting associations that came out of this study is the performance of the West Indies cricket team against team India. Team India’s chance of winning against West Indies is 72% when team India wins the toss and bats first, whereas other rules conclude that team India will lose against West Indies in all other conditions. 

This is Dataset 2 = ODI matched played by India during the last ten years (since 25-March-2001 to 05-June-2010). Dataset 2 is a subset of dataset 1.

Even in the last ten years, India’s performance has not improved against Australia. Team India has lost to Australia irrespective of playing conditions. A comparison between the two data sets shows that India’s chance of winning against Australia has dropped considerably over time; the confidence with which India loses has increased while the outcome does not depend on the pitch or innings order.

Against England, it is interesting to find the Team India’s record of chasing is more promising in away condition if Team India has lost the toss, whereas it is otherwise, even in the last ten years. In home conditions, if team India bats first, then the chance of team India losing the match against England is 86%.

Team India wins against Pakistan while playing away from home. Team India winning the toss matters at home as well as away from home.

The outcome of India vs. Sri Lanka matches has remained almost unchanged over time though confidence of both winning and losing increased.

In the last ten years, India has won more matches even while playing away from home than the overall scenario and the confidence of winning matches has also increased. In essence, India has adapted itself to the changing conditions outside the country, in the last ten years. Indian cricket team has won 102 matches out of 225 matches with a winning percentage of around 55%.

Assignment 6: Clustering & Eample

CLUSTERING

Clustering Analysis
Clustering analysis is finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters.

Two Categories of Clustering Analysis Methods:
1. Hierarchical Cluster Analysis Methods
a. Agglomerative Methods
In Agglomerative methods, all methods start in separate clusters till slowly similar objects are combined and this process is repeated till all objects are in a single cluster. Finally, the optimum number of clusters is chosen from among all options.

b. Divisive Methods
In Divisive Methods, all objects the same cluster and the reverse of the agglomerative method is used.

2. Non-hierarchical Cluster Analysis Methods
(known as k-means clustering methods)
K-means used when large data sets are involved. Further, these provide the flexibility of moving a subject from one cluster to another.

An example in daily life:

Clusters play an important role in "human development". For example, all natural objects are basically classified into groups: animals, plants, minerals. According to the biological taxonomy, all animals are grouped into categories of the kingdom, phylum, class, order, family, genus, species from the general to the specific. There are animals named tigers, lions, wolves, dogs, horses, sheep, cats, mice etc. 

Big Data Assignment 5: Modeller, Simulator, Optimizer

Modeller, Simulator, Optimizer

A. Definition

1. Modeller
• A modeller is a person who represents a construction and work of several systems and makes approach the estimates of actual systems.
• A model that makes by a modeller will be better if it is simple and real because it will make it easier to understand.
2. Simulator
• A simulator is a device which artificially creates to enables the operators to represent under test conditions phenomena likely to occur in actual performance.
• A simulator is usually used in training people such as pilots, and astronauts. a simulator also can use in car racing games.
3. Optimizer
• An optimizer is a program that uses linear programming to optimize a process.
• An optimizer will help to show an optimum process.

B. Example

In Indonesia, especially in Batu, Malang have Museum Angkut. Museum Angkut displays various transportation (land, sea and air transportation). Not only that there is a simulation vehicle for visitors. Simulation that available in there are flight and race car simulation. Beside to attract visitors, the simulation provides an opportunity to feel how the drivers drive a car and how the pilot operates the plane until it lands. This is a picture of race care simulation in Museum Angkut:

In addition car simulation very useful to improve driving training, train someone to be able to drive a car and conditions where the driver must be careful, ensure that he remains safe in potentially dangerous conditions in various choices of road conditions (Night mode, Fog mode and Highway experience). Driving simulators can also be used for exams to get a Driving License.

And flight simulator useful when apply the tools to simulate control or function like real aeroplanes, as a pilot training vehicle in terms of pilot education about increasing agility in "operating" the aircraft used later, and pilot readiness will be more tested and honed in what circumstances the pilot will make decisions in the best, ordinary or worst situations.

Big Data Assignment 4: Complexity & Metrics

1. Identify complex systems in daily life that can be managed by a computational system (e.g. information system, DSS, ERP, etc)

The picture above is one example of complex systems in daily life that can be managed and the picture explains the payroll information of a company ranging from employees, directors, and personnel into an information system and further divided into data flows such as the picture above. An arrow indicates the relationship between the actor and the system that will govern it.

Examples of DDS in business that we meet every day to increase the productivity of 3-star hotels in Surabaya using AHP and OMAX productivity or comparison between input and output is one of the tools that influence the determination of profitability and competitiveness in a company. Hotels need to measure work productivity in order to survive and compete in efficiency and effectiveness with other hotels. Based on the problems faced, it is necessary to have a system that can help in measuring work productivity from existing departments. The application of the system is a DSS application using Analytical Hierarchy Process (AHP) method for weighting and Objectives Matrix (OMAX) for productivity measurement. The results of the application made are in the form of information about what criteria affect the performance of the hotel.

2. Differentiate between 4 types of problem contexts.

Cynefin Framework is basically identifying problems based on the relationship between cause and effect. The problem is an effect, so based on the relationship with the root of the problem/cause, here are 4 types of problems based on Cynefin Framework.

• Simple context. This problem is defined as a problem whose relationship between cause and effect is very clear and tight. Sources of knowledge are everywhere and can be done by anyone. To overcome it does not require meticulous analysis and thinking. The approach taken is Sense--> Categorize--> Respond. Also, the estimated time needed is also easily calculated. You can estimate whether this will be completed 1 hour, 1 day or 1 week. 

• Complicated context. This type of problem is defined as a problem whose relationship between causes and effect is unclear. So to find out the cause, further investigation is needed, further analysis is needed. The cause/root of the problem can be more than one possibility and can be interrelated. Sources of knowledge are available and can be taught. Because it requires inspection and analysis, the approach taken is Sense--> Analyze--> Respond. The estimated time for this type of problem is rather difficult to calculate. We can only estimate based on the maximum time we have ever done before. Maybe between 1 day or 2 days. It may also be 1 week to 2 weeks. 

• Complex context. This type of problem is defined as a problem whose relationship between cause and effect is unclear. So to overcome it requires reflection, contemplation, asceticism, seeking guidance, asking for enlightenment. Knowledge sources may be available but are very limited. Because it requires contemplation, the approach taken is Probe--> Sense--> Respond. Also, the estimated time for this type of problem is very difficult to calculate. We cannot predict when the problem will be solved. If it works, we will let you know. When it’s done, it’s done. But if it doesn't work, then try again. 

• Chaos context. This type of problem is defined as a problem that has no relationship between cause and effect. So as to overcome it not by thinking or contemplation. But immediately act without much thought. The approach taken is Act--> Sense--> Respond, that is, we act first and then see the situation can only make decisions. If you think about it first, it will break down first. For this type of problem, there is Novel Practice, which is improvising, doing something new, strange or foreign. The estimated time will also not be measured. 

3. Case Study of Mc Donald manage their big data

Case: Mc Donald not only provides chicken menus for his customers. But also provides a variety of new and unique menus in the world. With over 37.000 worldwide locations, daily customer traffic of over 60 million people, and sales of more than 75 hamburgers every second. Customers also can buy in Mc Donald by dining in, drive-thru, and take away.

Mc Donald can manage the data using big data. Based on my opinion Mc Donald case study company is included in the simple context because the cause and effect are clear.

• Sense: the situation in Mc Donald drive-thru experience. McDonald's drive-thru transactions took an average of 189.5 seconds from order to pick-up.
• Categorize: By knowing three different factors: design of the drive-thru, information that is provided to the customer during the drive-thru and the people waiting in line to order at a drive-thru, McDonald's are more able to create a more enjoyable drive-thru experience.
• Respond: knowing what times of day customers are more likely to go through a drive-thru ( help McDonald prepare and improve efficiency for the spike in demand ahead of time); Finding the optimal solutions for the design, information and people is an ongoing process that changes over time, context and cultures; McDonalds collects includes in-store traffic, customer interactions, flow through in the drive-thru’s, ordering patterns, point-of-sales data, video data and sensor data. These factors impact every part of the McDonalds empire from refining their menu design to optimising their training programmes.

4. Number of the component in the system to identify size or space
Stakeholders: employees, customers, suppliers, environment, investors, government
Software: Apps called McDelivery Indonesia (can order from this apps)
Storage: Each branch of McD have storage to put all of the ingredients (food and beverages)

5. Length of time (Data timeline and process length of McD)

• Customer come and order to the cashier (2-3 minutes)
• Cashier get the order data from customer.
• Payment from customer to the cashier (2-3 minutes)
• Cashier will give the data order to cook depends on the customer wants (5-15 minutes)
• The waiters also the person who cook, serve the order on the tray and make sure all order is complete (2-3 minutes)

The duration of order in McD takes 11-23 minutes and the data move from customer to the cashier and then from cashier to the waiters and the last is from waiters to the customer.

Task 2 & 3 : China Unicom (Model, methodology, measurement, accuracy)

From the previous case study of China Unicom, here i would like to shows China Unicom model, methodology, measurement, and accuracy.

MODEL & METHODOLOGY
 1. Classification Model.

• Because they make some comparison between their database (HBase) and Oracle. They compare and identify the insertion rate of traffic records and data query experiments in concurrent query transactions are based on the data set.
• Hbase in China unicom classified as noSQL column.

MEASUREMENT & ACCURACY

• This is Classification of the insertion rate of traffic records in Oracle database.

• This is a classification of The insertion speed of records in China Unicom system.

• From the picture (Fig 4&5), we know that the Oracle insertion rate will decrease dramatically (by about 4 times) after 500 000 traffic records are inserted, and the more records inserted, the slower the insertion rate becomes. Compared with Oracle database, China Unicom HBase shows very consistent performance, and the peak insertion rate reaches approximately 100 000 records per second.
• Fig. 4 shows negative correlation because their rate of traffic record is decrease dramatically.

• This is data query experiments in concurrent query transactions with Oracle database:

• This is data query experiments in concurrent query transactions with China Unicom system.

• From the picture (Fig 6&7) For Oracle database, the higher the number of concurrent query transactions conducted, the slower the average response time becomes. The impact of the size of records in the database has a deleterious effect on the query performance. However, for China Unicom optimized HBase system, the latency of most responses is in milliseconds, and the impact of the records already in the database is quite low compared with Oracle database.
• Compared with the proprietary solution, the open source solution adopted by China Unicom offers us more advantages to optimize data storage, speed up database transactions, and achieve better performance. 

Case Study: Mobile Internet Big Data Platform in China Unicom

CASE STUDY
Mobile Internet Big Data Platform in China Unicom

ABSTRACT

China Unicom, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate (CAGR) of 135%. Currently China Unicom monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System (HDFS) as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data and considers that a telecom operator-centric ecosystem can be formed that is critical to reaching prosperity in the modern communications business.

The object of this case:

China Unicom wants to lead to embracing the Mobile Internet Explosion and builds a big data platform to solve the challenges of data acquisition, data analysis, and data value-added services.

The problem in this case:

1.  The client users of China Unicom increase rapidly with a Compound Annual Growth Rate (CAGR) of 135%.
2. China Unicom’s big data platform, starting from October 2009, has recorded monthly traffic of more than 2 trillion records, monthly data volume is over 525 TB and the maximum data volume recorded has reached a peak of 5 PB. Overall writing speed has reached 1.390.000 records per second, and the recorded retrieval time in the table that contains trillions of records is less than 100 ms.
3. Any mobile network operator even only recording network flow data, the resulting data repository could easily reach the Terabyte level on a yearly basis. However, if all mobile traffic data is recorded for forensic analysis, the volume of the data could easily reach the Petabyte level.

The solution to this case:

1. Use the principle of aggregation. The principle of the aggregation is that a user’s valid behaviour data should not be lost and that efficiency is required to reduce the invalid data. Then the file is produced in less than five minutes, and the volume of every file is less than 200 MB. If the size of one single file exceeds 200 MB, multiple files will be produced to guarantee that the size of the single file is below the threshold, and the additional related files are identified by appending a hexadecimal number.
2. Transmit file by FTP protocol to the twenty-four FTP servers located in Beijing and to reduce the bandwidth of transmission, all files are compressed by the bzip2 compression algorithm before the files are uploaded to Beijing from every province. After being decompressed, the files are written into an HBase by a native Java API supported by HBase. In HBase, an online record table will be generated for each month.
3. China Unicom compares the performance of the Oracle and HBase by querying the record of a specified telephone number. The results are: 
• Compared with Oracle database, China Unicom HBase shows very consistent performance.
• Oracle database, the higher the number of concurrent query transactions conducted, the slower the average response time becomes. The impact of the size of records in the database has a deleterious effect on the query performance. However, for our optimized HBase system, the latency of most responses is in milliseconds, and the impact of the records already in the database is quite low compared with Oracle database.
• China Unicom work is optimized based on the open source nature of HBase, while Oracle database is a proprietary one where China Unicom cannot optimize the code to speed up transactions in the traffic records repository.

LUAR BIASANYA GO BLASTER

Assalamualaikum teman, apa yang kalian tau tentang gambar diatas? Sebuah sekolah yang terpencil? atau sekolah yang biasa-biasa aja? Kali ini aku bakal cerita tentang madrasah yang luar biasa ini

Namaku Narisha Mega Aulia Mahdani, waktu bersekolah di sini, banyak yang memanggilku Mega. Aku bersekolah disini sejak tahun 2011 akhir dan lulus pada tahun 2014. Aku angkatan Excellent Class Program lebih tepatnya kelas B. Kelasku banyak berganti nama mulai dari MAXC Blass, Clabenonsanda, dan GO Blaster. Eh ada satu nama lagi yaitu EXBLAVERS yang entah dari mana yang jelas nama itu selalu nempel di baju kelas kami hehe

Di atas adalah foto waktu kami, masih unyu dan polos ^^  sayangnya aku ngga masuk saat foto itu di ambil

Gimana sama yang ini? haha itu before afternya kita. Kelihatan kan perbedaannya? foto frame atas di ambil pas kelas 7 nah yang bawah kelas 9 akhir untuk foto album kenangan 

Ini waktu Mr siapa gitu lupa ke MTsN Kediri II nyoba ngobrol pake bahasa inggris terus foto bareng 

Ini dia member GO Blaster

1. Ahmad Mujaddid Ahwali (didit)

Absen satu, SMADA Kediri. Ke luar biasaannya: ngga sombong, periang, tinggi, ngga gampang putus asa, hits, dll hehe

Kejadian terbesar yang pernah terjadi sama didit pas waktu Mts dia sakit polip sampe di rawat di RS Surabaya itu yang bikin dia jadi anak subhanallah kayak sekarang :) Salut sama semangatnya

2. Dendi Dwiki Cahyanto (Dendi)

Absen dua mas, namanya Dendi :D SMADA Kediri. Ke luar biasaannya: lucu, rapi, telaten, ngga kasar sama cewek

Kejadian terbesar yang pernah dialami selama sekelas pas waktu si Dendi ngomong "Lambe opo kelek" pas lagi hening-heningnya bikin satu kelas ketawa haha

terus Dendi juga hits pas di kelas dengan julukan Dendi Santo's :D

3. Isna Thoriqurrahman (Thoriq)

Absen tiga, MAN Kota Kediri 3. Ke luar biasaanya: jaim max, menang kalo ikut kontes tahan tawa mungkin ya :D (maaf Thoriq), konyol, selebihnya aku kurang tau 

Kejadian yang sampe sekarang yang paling gokil dan aku inget pas waktu suruh nyanyi kedepan bareng-bareng lagunya coboy junior judulnya eeaa :D terus pas dia goyang ulet bulu bawa penggaris masya Allah lentur banget jogetnya wkwk (maaf lagi Thoriq)

Sekarang di MAN 3 dia jago basket lho sama anggota PKS ^^ 

4. M. Ahsanul Taqwim (Taqwim)

Absen empat, SMADA Kediri. Ke luar biasaanya: pintar sastra khususnya bikin puisi, diem aja udah lucu, sopan, ikut MPK pas MTs.

Kejadian yang paling ngga bisa dilupain waktu Taqwim bikin puisi, subhanallah kata-katanya ngena, pas, pokoknya beda lah dari yang lain. Sayangnya udah jarang ketemu, beda sekolah :(

5. M. Ainur Rizal (Rizal)

Ini nih absen lima, Smas't punya :) Akselerasi pula. Ke luar biasaannya: care, ngga sombong, mancung, tinggi, kalo memperjuangkan sesuatu ngga main-main, suka njarak, lele

Kejadian yang paling berkesan pas Rizal udah diterima di akselnya smas't, dia ngasih aku saran + bimbingan buat tes IQ nya aksel MAN 3 biar sama sama aksel, alhamdulillah akhirnya berkat usaha dan sarannya aku masuk aksel MAN 3. Makasih masee calon dokter

6. M. Hafid Mubarok (Hafid)

Absen enam lanjut, MAN Kota Kediri 3. Ke luar biasaannya: jago alat musik, drum khususnya, sabar, unyu, lucu, ngga kasar sama cewek, paulito

Kejadian yang berkesan pas dia tampil band next neyeng sama temen temen :D personilnya sama Rizal juga, Hafid juga anak PMR lhoo 

7. M. Hanif Alfarisi (Hanif)

Absen tujuh, SMAPTA Kediri. Ke luar biasaannya: mancung, ngga sombong, suka njarak, periang, gampang negosiasi, dll

Kejadian sama Hanif...... Aku lupa :D maafin 

8. M. Nur Wais Al Qorni (Wais)

Absen delapan, SMAS'T punya :) Ke luar biasaanya: gampang semuanya, ketawa, diem, kalo ada sesuatu yang ga jelas dia harus tau sampe bener-bener jelas, tinggi

Kejadian yang aku inget, biasa panggil wawil :D hehe (maaf wais) sama pas hujan dia suka main di depan kelas gaya prosotan tapi pake kaki

9. M. Fajar Militan (Mili)

Absen sembilan, SMKN 1 Kediri. Ke luar biasaannya: diam-diam menghanyutkan, lainnya kurang tau hehe

Kejadian yang aku inget dari SD sampe SMA paling kocak pas itu ada temenku yang usil tanya dia "Mil Mili, Shampomu apa?" dijawab "Sunlight" terus ditanya lagi "Warna apa?" terus dijawab "item" terus tanya lagi "bintangmu apa?" dijawab sama Mili "Princess" haha :D padahal yang bener kan Sunsilk warna item bintangnya pisces eh dia jawabnya begitu 

10. Rahmatullah Ramadhani (Rahmat)

Absen sepuluh, SMAS'T punya :) Ke luar biasaannya: sabar, pemberani, suka kasi solusi, enak diajak ngobrol, ngga kasar, dll

Kejadian yang aku inget, pas MTs aku suka berangkat pagi. jam 6 aku udah di sekolah, kebetulan Rahmat juga dateng pagi terus aku cerita masalah apa gitu lupa, sama dia dikasi solusi, ngebantu banget lah. Makasih meenn  

11. Shidiq Yusuf Satria (Shidiq)

Absen sebelas, MAN Kota Kediri 3 :b akselerasi juga. Satu-satunya anak GO BLASTER yang sekelas + sesekolah sama aku hehe. Ke luar biasaannya: pinter nutupin sesuatu, periang, konyol, gamers, dll

Kejadian yang aku inget pas MTs musikalisasi puisi satu kelompok, dia ngga begitu hafal kunci gitar tapi pas tampil dia berusaha maksimal buat kelompoknya. Makasih :D

12. Yuan Pradana Al fajar (Yuan)

Absen dua belas, SMANAM Kediri. Ke luar biasaannya: cantik bangeett wkwk (Becanda), putihh, bulu mata lentik badai. unyu pas MTs sekarang ngga tau

Kejadian yang aku inget pas Yuan kecelakaan terus sekelas bareng-bareng jenguk dia ke rumahnya ternyata udah bisa kesana kemari tapi belum masuk sekolah-_-

Buat para cowok GO BLASTER, salut banget sama kalian ({}) kalian sahabat sekaligus keluarga buat aku. Pokoknya semua tentang kalian itu berharga. Sukses sama-sama ya. Semangat!

Ini ciwiciwi cantikku

13. Aditya Maharani Putri Pertiwi (Rani)

Absen tiga belas, SMADA Kediri :) Putri Pertiwinya GO BLASTER hihi. Ke luar biasaannya: cantik ++, hits, sabar pas ditikung wkwk, enak diajak curhat, wah subhanallah deh

Kejadian yang aku inget pas Mbak Rani kasih semangat ke aku, kasih solusi, tetep senyum & ketawa meskipun masalah selalu ada. Makasih mbaakk :)

14. Ardhana Reswari (Ardha)

Absen empat belas, SMAS'T punya :) paralel satuuuu. Ke luar biasaannya: jujur apa adanya, gokil, terbuka, langganan ke kosku, baik ga ketulungan, manis, big hug buat Ardha ({})

Semua kejadian yang aku lakuin sama Ardha dari mulai MTs sampe SMA semua berkesan, dari mulai aku dibarengi berangkat sekolah, nginep di rumahnya, di traktir mi ayam, jajan, hang out ke toko buku, makan-makan, tidur di rumahku semuanya terlalu banyak kalo disebutin. Makasih ya Ardhaa 

15. Dyah Permata Ardyani (Dyah)

Absen lima belas, SMADA Kediri :) Ke luar biasaannya: pembela kebenaran, giginya subhanallah rapi sekali, baik, enak diajak negosiasi, suka kasi solusi

Kejadian yang paling aku inget pas MTs pas lomba senam SKJ aku sama Ardha ada salah gerakan ato posisi terus Dyah agak marah gitu tapi akhirnya aku dipeluk sama Dyah ({}) terus dia bilang "wes gaopo lain kali ojo diulangi neh"

16. Etika Norma Utami (Tika)

Absen enam belas, MAN Kota Kediri 3 :) Ke luar biasaannya: sholehah, pintar mengaji, care, lembut, dll hehe

Kejadian yang paling aku inget pas tika pernah nangis aku takut banget rasanya merasa bersalah mbe dia ya walaupun bukan gara-gara satu orang tapi rasanya merinding gimana gitu

17. Laila Nur 'Aini (Laila)

Absen tujuh belas, MAN Kota Kediri 3 :) Ke luar biasaannya: sabar, periang, ramah, suka nyapa orang, care
Kejadian yang paling aku inget pas waktu pramuka Laila care banget sama aku, aku gampang alergi, kedinginan, dll dia mesti ngebantu aku biar aku fit terus pas lomba pramuka. Makasih ya laa :)

18. Nada Farah Amrudhia (Nada)

Absen delapan belas, SMADA Kediri :) miceeennggg hehe. Ke luar biasaannya: cantik, mancung, baik, care banget, miss curhat, hits, wanita subhanallah
Semua kejadian sama miceng itu amazing, dari awal kenal sampe sekarang rasanya kayak udah deket banget ({}) Mbak Nada tau apa yang tak rasain jadi pas kasih solusi bisa pas, ngena. Makasih ceng :)

19. Naila Labibahasna Naharin (Naila)

Absen sembilan belas, MAN Kota Kediri 3 :) ke luar biasaannya: care, pembela kaum lemah, hits, unyu, ++ pokoknya

Kejadian yang aku inget pas lomba pramuka kita pernah marah gara-gara lainnya beli es kita panas-panasan bongkar tenda wkwk

20. Narisha Mega Aulia Mahdani (Mega)

Absen dua puluh, SMAS'T Kediri ups salah :D MAN Kota Kediri 3 alhamdulillah akselerasi, di skip aja ya hehe harapanku semoga SNMPTN bisa dipertimbangkan lagi ya Allah. aamiin..

21. Nur Asfarina Frida Safitri (Farin)

Absen dua puluh satu, MAN Kota Kediri 3 :) ke luar biasaannya: hits, unyu, penyiar radio ulala, kocak, periang, suka korea
Kejadian yang aku inget sama Farin pas MTs waktu seni budaya atau praktek bikin apa gitu, kita selalu satu kelompok. Pernah bikin masakan sampe tidur di rumahku, pernah bikin tapai pas ujian praktik biologi hehe serulah sama dia

22. Oksa Nodyayu Satiti (Oksa)

Absen dua puluh dua, SMADA Kediri :) Ke luar biasaannya: cantik, unyu, perfekto, masternya buat lukisan, pintar nari, aduhh ga kuku sama Oksaaa
Kejadian yang paling aku inget pas lomba lagu motivasi kelas 9, GO BLASTER nampilin paduan suara pake almamater terus nyanyi lagunya Teach us something please :D yang ngajarin Oksa oiya satu lagi Oksa suka panggil aku Mbak 

23. Roisatul Islahiyah (Rois)

Absen dua puluh tiga, SMADA Kediri :) Ke luar biasaannya: ramah, baik, kalem, murah senyum, dll hehe
Kejadian yang sampe saat ini inget dan mungkin gaakan pernah lupa, ulang tahun kita selisih 1 hari :D jadi aku tanggal 9 Maret kalo Rois tanggal 10 Maret. Gara-gara itu, Rois suka panggil aku Mbak Mega sampe sekarang hehe

24. Salsabila Aisyi Rasikhah (Ais)

Absen dua puluh empat, MAN Kota Kediri 3 :) Ke luar biasaannya: supel, care, enak diajak ngobrol, bahasa jawanya nyenengne hehe, kocak, wah bikin ketawa pokoknya
Kejadian yang aku inget sama Ais dia itu fans beratnya One Direction. Suaranya bagus banget, selalu mewakili GO BLASTER kalo pas ada lomba berkaitan dengan nyanyi. Unyunya ga nahan

25. Vila Nur Fadliana (Vila)

Absen dua puluh lima, SMADA Kediri :) Pilaaaaaaa. Ke luar biasaannya: mancung, care, periang, suka kasi solusi, ga bikin tegang, penghiburku ini ({})
Kejadian yang paling aku inget sama vila pas kita pernah marah dan pingsan di UKS berdua hahaha rasanya gokil itu mah, cuma gara-gara posisi paduan suara ujian praktek. Tapi akhirnya baikan dan ga pernah marahan sampe sekarang. Makasih pilaaa

26. Yan Rachel Prastiwi (Tiwi)

Absen terakhiiirrr, dua puluh enam, SMADA Kediri :) Ke luar biasaannya: mancung, badannya pas, kalo ketawa mesti nangis wkwk, jago nari, care, baik, dll

Kejadian yang aku inget kita pernah satu kelompok seni budaya masak tidur di rumahku sama Farin terus sampe bangun awal tidur akhir, aku suka panggil Tiwi dengan Bobin soalnya wajahnya mirip sama Mr Bean hehe

Yak itulah beberapa ke luar biasaan teman-temanku :) Mereka yang membuatku semangat sampai sekarang, dan mereka yang mengajariku arti dari keterbukaan karena acara "blak-blakan" yang diadakan setiap Jumat di kelas. Apa arti kekompakan, kebersamaan, keceriaan tanpa kalian teman :) Terima kasih. Sebentar lagi kita akan sukses bersama ({}) Semangat!