<p>Dokumen berita olahraga dalam bentuk web kini memiliki jumlah yang besar dalam kurun waktu singkat. Untuk kemudahan akses dokumen perlu melakukan pengelompokan dokumen berita kedalam beberapa kategori. Hal tersebut bertujuan agar berita olahraga tersusun sesuai dengan kategori yang ditentukan. Berita dapat dikelompokkan secara manual oleh manusia, akan tetapi hal tersebut membutuhkan waktu yang lama untuk melakukan kategorisasi. Metode klasifikasi diusulkan dalam penelitian ini untuk melakukan pengkategorian secara otomatis dokumen berita. Tujuan dilakukannya klasifikasi adalah untuk mempercepat dan mempermudah dalam pemberian kategori, sehingga dapat meningkatkan efisiensi waktu. Pada penelitian ini menggunakan metode klasifikasi Naïve Bayes Classifier. Sebelum dilakukan klasifikasi ada proses preprocessing dengan menggunakan Enhanced Confix Striping Stemmer. Hal ini bertujuan untuk mengembalikan ke bentuk kata dasar, sehingga data berkurang dan proses komputasi menjadi lebih efisien. Pengujian dilakukan menggunakan 18 berita olahraga yang dipilih secara acak oleh user atau tester, dari 18 berita yang diujikan terdapat 14 berita yang bernilai benar atau relevan dengan analisis yang dilakukan use atau tester pada berita uji. Dari penelitian ini dapat disimpulkan bahwa Aplikasi Klasifikasi Berita Olahraga menggunakan Metode Naïve Bayes dengan Enhanced Confix Striping Stemmer mampu mengklasifikasi berita olahraga sesuai dengan kategori masing-masing, seperti Sepak Bola, Basket, Raket, Formula 1, Moto GP dan olahraga lainnya dengan keakuratan sebesar 77%.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"> </p><p>Web-based sports news currently has a considerable amount of documents. News documents need to be grouped into multiple categories for easy access. The goal is that sports news is structured according to the specified category. News can be grouped manually by humans, but it takes a long time to categorize if it involves large documents. Classification method is proposed in this research to categorize automatically news document. The purpose of doing the classification is to accelerate and simplify the granting of categories, thereby increasing the efficiency of time. In this research using the Naïve Bayes Classifier classification method. Prior to classification there is a preprocessing process using Enhanced Confix Striping Stemmer. It aims to return to the basic word form, so the data is reduced and the computing process becomes more efficient. From the test using 18 sports news randomly selected by the user or tester, there are 14 news stories that are true or relevant to the analysis by the user or the tester on the test news. This study concludes that the Sports News Classification Application using the Naïve Bayes Method with Enhanced Confix Striping Stemmer is able to classify sports news according to their respective categories, such as Football, Basket, Racquet, Formula 1, Moto GP and other sports with accuracy of 77%.</p>
<div class="WordSection1"><p>KKN (Kuliah Kerja Nyata) merupakan salah satu mata kuliah wajib Universitas di Universitas Trunojoyo Madura (UTM). Selama ini proses penilaian KKN masih menggunakan cara manual sehingga memiliki beberapa kelemahan antara lain, objektivitas dan dasar penilaian kurang terjaga serta proses penilaian butuh waktu yang lama. Karena itu, keberadaan sebuah sistem penilalian KKN yang menerapkan teknologi informasi sangat dibutuhkan. Salah satu metode yang dapat diterapkan yaitu Metode Rating Scale karena mudah dan praktis untuk menilai mahasiswa yang mengikuti kegiatan KKN yang jumlahnya banyak. Metode Rating Scale dikenal dengan Skala Bertingkat, yaitu berupa suatu daftar yang berisi tentang sifat atau ciri-ciri tingkah laku yang ingin dinilai yang sudah sesuai dengan kriteria yang mau dinilai dan dicatat secara bertingkat dimulai dari nilai terendah hingga nilai yang tertinggi. Hasil dari penelitian ini yaitu, penggunaan Metode Rating Scale pada sistem penilaian KKN UTM dapat saja mempengaruhi perubahan nilai, dan perubahan nilai itu disebabkan oleh konversi dari hasil input penilaian manual ke Rating Scale. Selain itu, berdasarkan hasil pengujian aplikasi ini layak digunakan.</p><p>Kata Kunci: Kuliah Kerja Nyata, Penilaian KKN, Rating Scale.</p><p align="center">Comuunity Service Scoring Application in LPPM UTM Using Rating Scale Method</p><p><strong> </strong></p><p><strong>ABSTRACT</strong></p><p><em>KKN is one of compulsory lesson in the University of Trunojoyo Madura (UTM) for some majors. The scoring of KKN still uses manual scoring which has several weaknesses such as Objectivity and basic assessment is less secure also longer time needed for scoring. So that, the existence of KKN scoring system is needed. One of method that can be implemented is Rating Scale Method because of it’s easiness and efficiency to score a lot of students who enrolls KKN in a certain period of time. Rating Scale Method is known as graded scale, it’s like a list which comprises character or behavior will be scored with decided criteria and recorded gradually starts from the lowest to the highest score. The Results of this research is, the use of Rating Scale Method in KKN scoring system can influence the change of scores and it’s change is caused by the conversion from the manual scoring input to the Rating Scale Method. Besides, based on the testing this app is able to use properly</em><em>.</em><em></em></p><p><em>Keywords: Kuliah Kerja Nyata, </em><em>KKN Scoring, Rating Scale</em><em>.</em></p></div><em><br clear="all" /></em>
The scholarship is a financial aid given to candidate student who is eligible. This aid is aimed to help the students to pursue their study. In UTM, the scholarship system awards applied to select the right candidate of college students is still based on the principles of the proximity of the campus party. The scholarship given isn't for the right target. This research implemented a system of determining admission scholarships using the method of SAW and TOPSIS as a solution to support decision makers based on criteria that have been defined, including GPA, income of the parents, the number of dependant of the parents, tuition fees, semester and the involvement in association. The result of some trials had been shows that the average accuracy for five years is 88.4% compared to manual calculations or the equivalent of 6 to 8 days.
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