The adoption of e-learning in developing countries like Indonesian Universities have been focused in urban areas like the big cities, especially in Java island. There is a lack of development of e-learning in a remote city like Kupang East Nusa Tenggara Indonesia which is located far away from the capital city. This research aims to assess the effectiveness of e-learning by analyzing three factors in one of the higher institution in Kupang city, i.e. Sekolah Tinggi Kesehatan Citra Mandiri Husada Kupang (STIKes CHMK). The factors include culture, technology and infrastructure, and content satisfaction. The data were collected using questionnaires. Research shows that with proper preparation for e-learning, the acceptance of e-learning in rural areas is significantly high. This finding suggests that e-learning can greatly benefit the students like Kupang city in developing countries.

This research is made to implement the KNN (K-Nearest Neighbor) algorithm for sentiment analysis Twitter about Jakarta Governor Election 2017. The object is 2000 data tweets in Indonesia collected from Twitter during Januari 2017 using Python package called Twitterscraper. The methode used in sentiment analysis system is KNN with TF-IDF term weighting and Cosine similarity measure. As the test result, the highest accuracy is 67,2% when k=5, the highest precision is 56,94% with k=5, and the highest recall 78,24% with k=15.Keywords : K – Nearest Neighbor, Twitterscraper, TF-IDF, Cosine Similarity Penelitian ini dibuat untuk mengimplementasikan algoritma KNN (K - Nearest Neighbor) dalam analisis sentimen pengguna Twitter tentang topik Pilkada DKI 2017. Data tweet yang digunakan adalah sebanyak 2000 data tweet berbahasa Indonesia yang dikumpulkan selama bulan Januari 2017 menggunakan package Python bernama Twitterscraper. Menggunakan algoritma KNN dengan pembobotan kata TF-IDF dan fungsi Cosine Similarity, akan dilakukan pengklasifikasian nilai sentimen ke dalam dua kelas : positif dan negatif. Dari hasil pengujian diketahui bahwa nilai akurasi terbesar adalah 67,2% ketika k=5, presisi tertinggi 56,94% ketika k=5, dan recall 78,24% dengan k=15.Kata Kunci : K – Nearest Neighbor, Twitterscraper, TF-IDF, Cosine Similarity

Traditional learning approaches in teaching English assume that learners have same background, ability, and requirements. In this study, we propose an adaptive learning systems-knowledge level (ALS-KL) is a learning system that can personalize materials due to proficiency level of English language learners. A pre-test is given to measure the proficiency level of learners. The level is divided into elementary, intermediate and advanced. The ALS-KL provides the most suitable materials according to the needs of the learners. We applied the proposed system on 90 learners with different English proficiency level. The effectiveness of the system was evaluated using post-test. The results of the post-test showed that the proposed system was able to improve learners with intermediate-level and advanced level 1.5 times and 4 times respectively while reducing the number of learners with elementary-level by more than 50%. These indicate the effectiveness of the proposed system.

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Implementation of the Cosine Similarity Algorithm on Text Mining of Al-Qur'an Translations Based on the Relationship of Topics)M. DIDIK R. WAHYUDI ABSTRAK Al-Qur'an merupakan sumber hukum dan panduan dalam pemecahan berbagai masalah umat Islam dalam menjalani kehidupan beragama, bermasyarakat, dan bernegara. Pemecaham masalah di dalam Al-Qur'an tidak hanya mengacu pada satu atau dua ayat. Jumlah ayat dan surat Al-Qur'an yang sangat banyak menyebabkan pencarian suatu ayat Al-Qur'an menggunakan cara konvensional akan memerlukan waktu lama. Oleh karena itu, dibutuhkan sebuah sistem untuk mengenali, mencari topik, dan mengelompokkan suatu permasalahan. Pencarian topik dalam terjemahan Al-Qur'an merupakan salah satu penerapan dari metode klasifikasi pengelompokan teks yang melakukan proses secara otomatis menempatkan dokumen teks ke dalam suatu kategori berdasarkan isi teks tersebut. Pengelompokan terjemah ayat Al-Qur'an berbahasa Indonesia dapat dilakukan berdasarkan tingkat kemiripan antar ayat. Algoritma yang bisa dipergunakan dalam permasalahan ini adalah Cosine Similarity. Algoritma ini akan menghitung tingkat kemiripan antar ayat yang akan menghasilkan beberapa kelompok ayat yang diambil untuk dibandingkan dengan index Al-Qur'an. Hasil penelitian menunjukkan bahwa tingkat kemiripan antar ayat sebesar 20% memberikan hasil terbaik pada pengelompokan index Al-Qur'an ratarata sebesar 46,42%. Tingkat kemiripan antar terjemah ayat Al-Qur'an sebesar 40% memberikan rata-rata sebesar 15, 39% pada pengelompokan index Al-Qur'an. Untuk tingkat kemiripan antar ayat diatas 40%, ada kelompok similaritas ayat yang tidak masuk dalam index Al-Qur'an.

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The accuracy of a long study of college students at a university becomes very important in demonstrating the quality of the learning process in college. There are many things that affect a student's study time. Data Mining offers a way to know the various aspects that may affect a student's study time. To know the various aspects that influence the duration of the study based on data graduation students are available, then the implementation of a Data Mining algorithms can be used. In this study, Data Mining algorithms used to find aspects that affect student study duration is Apriori algorithm.Keywords: graduation analysis, long studying, data mining, apriori algorithms  Ketepatan lama studi mahasiswa pada suatu perguruan tinggi menjadi hal yang sangat penting dalam menunjukkan kualitas proses pembelajaran di perguruan tinggi. Ada banyak hal yang mempengaruhi lama studi mahasiswa. Data Mining menawarkan suatu cara untuk mengetahui berbagai aspek yang dapat berpengaruh terhadap lama studi mahasiswa. Untuk mengetahui berbagai aspek yang mempengaruhi lama studi mahasiswa berdasarkan data kelulusan yang tersedia, maka implementasi suatu algoritma Data Mining dapat dipergunakan. Dalam penelitian ini, algoritma Data Mining yang dipergunakan untuk menemukan aspek yang mempengaruhi lama studi mahasiswa adalah algoritma Apriori.Katakunci : analisis kelulusan, lama studi, data mining, algoritma apriori

This study aims to find out the collaboration between class teachers, special guidance teachers, and parents of children with special needs. In addition, it also knows the optimization of the developmental aspects of children with special needs which include academic attitudes, social skills, emotions, and independence. This study used qualitative research methods. The process of data collection is done using the method of interviews, observation, and documentation study. Data analysis using the model presented by Miles and Huberman includes the process of data reduction, data presentation, and drawing conclusions based on facts in the field. The results of the study show that good collaboration between classroom teachers, special guidance teachers, and parents in the process of education in schools strongly supports the development of children with special needs. The development of aspects of social skills is more prominent than the development of aspects of academic attitudes, emotional development, and independence.