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proposal tugas akhir – suatu dilema

Menunggu inisiatif mhs (s1) mengajukan tema dan menyiapkan proposal riset, kebanyakan akan sangat mengganggu dan memperlambat proses tugas akhir (TA). Proses ini tak akan masalah kalau kita berhadapan dengan mahasiswa kreatif dan pintar, apalagi ini S1 sedangkan kan mahasiswa S2 dan S3 saja masih sulit untuk mencari tema riset yang asli.  Di beberapa universitas luar negeri dan dalam negeri, ada yang tak mengharuskan mahasiswa membuat proposal riset. Bahkan dosen bisa menawarkan ide ide risetnya ke para mahasiswa, walau dosen juga bisa menerima usulan tema
dari mahasiswa.

TA merupakan media membantu program riset dosen dan prodi/pusat, dan salah satu butir penilaian borang BAN-PT adalah keterlibatan mahasiswa dalam riset dosennya. Makin banyak mahasiswa ikut riset dosen makin besar nilainya. Riset dan TA ini bisa menjadi modal untuk pengajuan proposal riset di tingkat lokal dan nasional.

Seperti yagn pernah sy sampaikan dibeberapa rapat dengan kolega, riset tak harus langsung membuat program/coding, ada tahap tahap tertentu yang harus dilalui dulu, Jika masing masing dosen / kelompok dosen sudah punya roadmap riset, maka insya Allah akan mudah membagi bagi bagian riset untuk dikerjakan kapan dan oleh siapa.

Memacu dosen untuk ikut terupdate keilmuannya, dengan paling tidak membaca dan mereview beberapa paper tingkat internasional, dan beberapa riset questionnya Insya Allah bisa muncul di situ dan ditindaklanjuti menjadi tema/topik riset TA/proposal pendanaan lokal/nasional.

wallahu’alam, God knows best !


big data may save lives

Using Big Data to Save Lives
UCR Today (10/22/12) Sean Nealon

University of California, Riverside researchers have developed a method for mining data derived from pediatric intensive care units to help doctors treat children and cut health care costs.  “This data has the potential to be a gold mine of useful–literally life saving–information,” says Riverside professor Eamonn Keogh.  He notes that modern pediatric care units are equipped with a variety of sensors that record up to 30 measurements.  The researchers developed a technique that makes it possible to search the sensor datasets, which can contain more than one trillion objects.  The researchers also are exploring ways to capture and store data from five or more sensors, and capture multiple data points per second.  In the next few years, the researchers plan to study archived pediatric intensive care unit data to find common patterns that can help doctors in diagnosing and predicting medical episodes.  The researchers also want to incorporate those patterns into intensive care unit sensors.  However, the difficulty is in finding medically useful patterns because there are an infinite number of trivial patterns.  “We have to find those that aren’t known but are useful and that can benefit from intervention,” Keogh says.

New Open-Source Python Textbook

I got an email from NA Digest and found the following site is very interesting, you may try to take alook at it


The book is aimed at high school students, but college
level students who are new to programming can benefit
from it as well. The book contains elements of computational
math and companion Exercise Book and Review Book
are provided.

installing GTX 560 driver and Cuda on Windows 7 and Ubuntu 12.04

I just would like to share my small experience, that the following sites are very helpful





Writing Graphics Software Gets Much Easier

MIT News (08/02/12) Larry Hardesty

Massachusetts Institute of Technology (MIT) researchers have developed Halide, a programming language they say creates software that is easier to read, write, and revise than image-processing programs written in a conventional language.  Halide also automates code-optimization procedures, making the coding process much faster than with other languages.  The researchers used Halide to rewrite several common image-processing algorithms that had already been optimized by professional programmers.  They say the Halide versions saw as much as six-fold performance gains.  A Halide program has one section for the algorithms and another for the processing schedule, which can specify the size and shape of the image chunks that each core needs to process at each step in the schedule.  After the schedule has been developed, Halide automatically handles the accounting.  “When you have the idea that you might want to parallelize something a certain way or use stages a certain way, when writing that manually, it’s really hard to express that idea correctly,” notes MIT’s Jonathan Ragan-Kelley.  University of California, Davis professor John Owens says Halide “really has all the pieces you want from a completed system, and it’s in a really important application domain.”

Computers Can Predict Effects of HIV Policies

Brown University (07/27/12) David Orenstein

Brown University researchers have developed software that can model the spread of HIV in New York City over several years to make specific predictions about the future of the epidemic under different intervention plans.  “What we’re trying to do is identify the ideal combination of interventions to reduce HIV most dramatically in injection drug users,” says Brown University professor Brandon Marshall.  The program projects that with no change in New York City’s current HIV programs, the infection rate among injection drug users will be 2.1 percent per 1,000 by 2040.  However, strategies such as expanding HIV testing, increasing drug treatment, and providing earlier delivery of antiretroviral therapy could cut the rate by more than 60 percent, to 0.8 per 1,000.  The model creates a virtual reality of 150,000 agents who engage in drug use and sexual activity like real people.  “With this model you can really look at the microconnections between people,” Marshall says.  The researchers calibrated the program until it reproduced the infection rates among injection drug users that were known to occur in New York City between 1992 and 2002.

AI Predicts When You’re About to Get Sick

New Scientist (07/26/12) Michael Reilly

University of Rochester’s Adam Sadilek and colleagues were able to predict when individuals in New York City were about to come down with the flu up to eight days before they showed symptoms, using artificial intelligence and Twitter data.  The team analyzed 4.4 million tweets tagged with global positioning system location data from more than 630,000 users in the New York City area over one month in 2010.  The researchers trained a machine-learning algorithm to distinguish between tweets such as “I’m so sick of this traffic!” and those by people who were actually sick and showing signs of the flu.  They were able to predict when someone was about to fall ill–and then tweet about it–with about 90 percent accuracy up to eight days in the future.  Still, Sadilek says the system is limited because people do not reliably tweet about their symptoms and because getting sick is not limited to who they come in contact with.  Nonetheless, the data from the system could potentially be used for a smartphone app that warns users when they are entering a public place with a high incidence of flu.