ABSTRAKPencarian pada database yang biasa dilakukan mahasiswa hanya mampu mencari judul yang sesuai berdasarkan kata kunci yang diinputkan, misalnya, jika kata kunci yang dimasukkan adalah "sistem cerdas" maka akan ditampilkan semua dokumen yang mengandung kata "sistem cerdas" namun sistem tidak bisa mengukur mana dokumen yang paling mirip. Untuk dapat melakukan pencarian berdasar substansi yang paling mirip, terdapat teknologi yang disebut information Text Retrieval. Dalam penelitian ini akan dikembangkan suatu sistem temu kembali informasi judul tugas akhir dan perhitungan kemiripan dokumen menggunakan vector space model. Sistem secara otomatis akan melakukan indexing secara offline dan temu kembali (retrieval) secara real time. Proses retrieval dimulai dengan mengambil query dari pengguna, menerapkan stop word removal sehingga dihasilkan keyword yang compaq tetapi dapatmewakili query tersebut, kemudian sistem menghitung kemiripan antarakeyword dengan daftar dokumen yang diwakili oleh term-term di dalam index. Dokumen akan ditampilkan diurutkan berdasarkan dokumen yang paling mirip.Dari hasil pengujian terlihat ketika keyword "android" dimasukkan maka akan tampil empat dokumen yang diurutkan sesuai tingkat kemiripannya, yaitu docId 3 dengan tingkat kemiripan 0.9512, docId 4 dengan tingkat kemiripan 0.5020, docId 2 dengan tingkat kemiripan 0.2671, docId 8 dengan tingkat kemiripan 0.1522.Kata kunci: temu kembali, vector space model, stopword. ABSTRACTQuerying data from the database with SQL syntax will produce results that exactly match with the condition specified in where clause. It will not be able to determine the other result that does not contain the condition specified in where clause, even it may be subtantially similar with the condition. It will not be able to determine the value of similarity of each result with the condition searched. Information retrieval brings the solution to overcome this problem.This research is aimed to develop information text retrieval for the title of the final project and calculate the similarity between document using vector space model. The system will automatically indexecah terms inside document on offline mode and will retrieve information on real time mode. The retrieveal process will begin by taking query from user, then removingthe stop word . Then the system will calculate the similarityof query with the documents represented by index value of each term.. The output will show the the most relevant documentthat has the biggest simalirity value, followed by the other adocuments that have smaller similarity value. From the testing by applying "android" keyword, the system shows four documents ordered by simalirty value descending. Document with docId 3 has simalirity value 0.9512, docId 4 has simalirity value 0.5020, docId 2 has simalirity value 0.2671 and docId 8 has simalirity value 0.1522.
The savings and loans cooperative are a cooperative that has an important purpose in its reach and services, such as the Permata Ngijo Savings and Loans Cooperative. Activities within the cooperative itself include managing member data as well as savings and loan transactions. In systems that are still manual, such as Microsoft Excel, there is a risk of inaccurate data and takes a long time. This manual system is still less effective because the number of members and transactions continues to grow. Therefore, an application is needed as a more effective data processing and information system for members that is easier to access. This study aims to build an android-based savings and loan application using the hypertext preprocessor (PHP) programming language and my structured query language (MySQL) as data storage. Based on the android application that has been made, it shows that the black box functionality of the application runs as planned, the success rate for users reaches 90%.
Remunerasi dosen erat kaitannya dengan Tri Dharma Perguruan Tinggi yang meliputi tiga kriteria yaitu Pengajaran, Penelitian dan Pengabdian. Dari data tersebut akan dilakukan clustering untuk menganalisa kelompok dosen. Data yang digunakan adalah data dosen teknik telekomunikasi, dengan 7 atribut yaitu pengajaran, pelatihan dan buku ajar, penelitian, pengabdian, jabatan, kehadiran dan penunjang. Metode yang digunakan adalah Fuzzy c-means, berbeda dengan k-means dimana satu data hanya masuk pada satu cluster saja, pada Fuzzy c-means data akan masuk pada setiap cluster dengan derajat keanggotaan yang berbeda-beda berkisar diantara 0-1. Berdasarkan hasil pengujian, terdapat 3 cluster yang terbentuk dengan jumlah dosen yang masuk cluster 0 sebanyak 4 dosen, Cluster 1 sebanyak 10 dosen, dan cluster 2 sebanyak 14 dosen. Berdasarkan analisa data hasil pengujian, cluster 0 memiliki nilai yang lebih baik dari cluster lainnya karena memiliki titik pusat cluster tertinggi sehingga nilai kinerja dosen yang masuk dalam cluster 0 juga tinggi mendekati nilai titik pusat cluster.
Along with the increasing number of websites circulating on the Internet, the security holes that arise are also increasing. The Electrical Engineering Department's website is no exception, especially on the Electrical Engineering Department's website which has never been audited to scan for security holes on the Electrical Engineering Department's website so the level of reliability of the Electrical Engineering Department's website cannot be known. On this basis, a study entitled "Analysis and Implementation of the ISSAF Framework for OSSTMM in Testing Website Security Gaps at Polynema" will be carried out. In this study, the authors tested security holes on the website at Polinema using the ISSAF and OSSTMM frameworks to scan for security holes on the Electrical Engineering Network website. Then from the test results, recommendations will be given to website managers to overcome existing security holes. Before giving recommendations, the author will try to update website security and re-test the updated website. This is done to prove whether updates made to website security can work effectively in overcoming security holes that were previously found. Based on the research that has been done, it is known that on the Electrical Engineering Department's website there are 21 security holes with 7 of them at medium level when testing for security holes using the ISSAF framework. And there are 17 security holes when testing security holes using the OSSTMM framework. The security holes include 10 open ports, DoS, brute-force, and there are security holes in the library used.
AbstrakNilai akhir mahasiswa dapat ditentukan dengan berbagai cara, beberapa diantaranya menggunakan range nilai, standart deviasi, dll. Dalam penelitian ini akan ditawarkan sebuah metode baru untuk menentukan nilai akhir mahasiswa menggunakan clustering dalam hal ini adalah Fuzzy C-Means.Fuzzy C-Means digunakan untuk mengelompokkan sejumlah data dalam beberapa cluster. Tiap data memiliki derajat keanggotaan pada masing-masing cluster antara 0-1 yang diukur melalui fungsi objektif. Pada Fuzzy C-Means ini fungsi objektif diminimumkan menggunakan iterasi yang biasanya terjebak dalam optimum lokal. Algoritma genetika diharapkan dapat menangani masalah tersebut karena algoritma genetika berbasis evolusi yaitu dapat mencari individu terbaik melalui operasi genetika (seleksi, crossover, mutasi) dan dievaluasi berdasarkan nilai fitness. Penelitian ini bertujuan untuk mengoptimasi titik pusat cluster pada Fuzzy C-Means menggunakan algoritma genetika. Hasilnya, bahwa dengan menggunakan GFS didapatkan fungsi objektif yang lebih kecil daripada menggunakan FCM, walaupun membutuhkan waktu yang relative besar. Meskipun selisih antara FCM dan GFS tidak terlalu besar namun hal tersebut berpengaruh pada anggota cluster Kata kunci— clustering, Fuzzy C-Means, algoritma genetika AbstractThe final grade of students could be determined in various ways, some of which use a range of values, deviation standard, etc. In this study will be offered a new method for determining final grades of students by using the clustering method. In this research the clustering method that will be used is the Fuzzy C-Means (FCM).Fuzzy C-Means is used to group a number of data in multiple clusters. Each data has a degree of membership (the range value of membership degree is 0-1). Membership degree is measured through the objective function. In Fuzzy C-Means, objective function is minimized by using iteration and is usually trapped in a local optimum. Genetic algorithm is expected to handle these problems. The operation of genetic algorithm based on evolution that is able to find the best individuals through genetic operations (selection, crossover and mutation) and evaluated based on fitness values.This research aims to optimize the cluster center point of FCM by using genetic algorithms. The result of this research shows that by combining the Genetic Algorithm with FCM could obtained a smaller objective function than using FCM, although it takes longer in execution time. Although the difference of objective function that produced by FCM and FCM-Genetic Algorithm combination is not too big each other, but it takes effect on the cluster members. Keywords— clustering, fuzzy c-means, genetic algorithm
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