Personality is one of the important variables for predicting student academic success. The purpose of the research is to examine the Big Five personality test as a predictor on the academic achievement of State Islamic Senior High School students in Indonesia. This research used a quantitative method which used a survey of the Big Five Personality Test and learning achievement on 5 subjects. The subjects of this study were the 2145 sample students of 23 State Islamic Senior High School of Insan Cendekia (SISHS-IC) around Indonesia. The results of this study indicate that all dimensions of Big Five Personality traits; openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability have a significant effect as predictors of students' academic achievement. While in parts of each dimension, the most significant predictor of students' academic achievement is the emotional stability and openness to experience. These findings are very important for teachers and schools to pay much more attention to emotional stability and openness to experience as predictors of student academic achievement.
Abstrak Perkembangan teknologi semakin memudahkan kegiatan manusia dan hampir semua kalangan memiliki ponsel. Sehingga ponsel menjadi alat yang penting dalam berkomunikasi bagi kebanyakan orang terutama SMS. Banyaknya pesan yang masuk bisa tidak memungkinkan untuk mengklasifikasikan SMS spam secara manual. Untuk itu dilakukan pengklasifikasian SMS spam menggunakan teknik klasifikasi dalam data mining. Banyaknya algoritma yang tersedia memungkinkan kita untuk menggunakan salah satunya sebagai algoritma terbaik untuk klasifikasi SMS spam. Untuk itu dilakukan pengujian beberapa algoritma klasifikasi dengan dataset SMS yaitu algoritma Naïve Bayes, Decision Tree dan SVM. Dari hasil Analisa pengujian didapatkan bahwa algoritma Naïve Bayes memiliki kemampuan yang lebih baik dibandingkan algoritma SVM dan Decision Tree. Karena nilai recall algoritma Naïve Bayes sebesar 0.93 pada kelas SMS fraud dan 0.92 pada kelas SMS promo, sedangkan f1-score algoritma Naïve Bayes lebih tinggi dibanding algoritma lainnya dan nilai accuracy Naïve Bayes sebesar 0.94.
Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately. Making a machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics: graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.
<span lang="EN-US">Madrasa (Islamic boarding school) in Indonesia have a strategic role in character building. At present madrasa education is still considered second class education. Besides, to improve the quality of madrasas can be started by improving the quality of the student national admission to all madrasas in Indonesia. This study aimed to trace the potential errors in the measurement results of Students National Admission of Madrasah Aliyah Negeri (SNPDB MAN-IC) 2020. Tracing was carried out on two aspects: i) Equality between test sets used based on evidence of test responses; and ii) Further tests on equality between question sets based on evidence of relationship between variables, taking into account the origin of the participating schools (MTs/JHS) and the origin of the participating regions (West, Central and East of Indonesia). This study involved 13,115 participants in 23 MAN-ICs throughout Indonesia in 2020. The materials tested comprised learning potential and academic ability (Mathematics, Natural Sciences, Social Studies, English, Arabic, and Islamic Religious Education). The study used achievement test with mathematics as a sample of test subjects. Based on the test response evidence, it was found that seven of the 15 questions were thought to have an indication of inequality between item sets. The results of tracing the evidence between variables indicated that it was the participants' origin of institutions that influenced the inequality between item sets. On the other hand, regional origin did not affect the inequality between item sets because the majority of participants came from the western region of Indonesia.</span>
ABSTRAK Dokumen dengan jumlah data yang besar dan bervariasi seringkali mempersulit proses klasifikasi. Hal ini dapat diperbaiki dengan mengatasi variasi data untuk menghasilkan akurasi yang lebih baik. Penelitian ini mengusulkan sebuah metode baru untuk kategorisasi dokumen teks berbahasa Inggris dengan terlebih dahulu melakukan pengelompokan menggunakan K-Means Clustering kemudian dokumen diklasifikasikan menggunakan multi-class Support Vector Machines (SVM PENDAHULUANBanyaknya dokumen yang ditemui di berbagai media memungkinkan orang untuk mendapatkan segala jenis informasi. Akan tetapi kebanyakan dokumen tidak diklasifikasi atau digolongkan sesuai dengan kelompoknya sehingga dokumen-dokumen yang berhubungan sulit ditemukan. Untuk itu perlu dilakukan kategorisasi dokumen agar dokumen yang bertopik sama bisa ditemukan dengan mudah.Variasi data terkadang dapat menyulitkan proses klasifikasi sehingga dapat dikelompokkan terlebih dahulu [1]. Hal yang sama dapat ditemukan dalam klasifikasi dokumen. Dokumen yang umumnya memiliki data dengan jumlah besar dan bervariasi dapat menyulitkan dalam membuat model klasifikasi. Oleh karena itu dokumen-dokumen tersebut dapat dikelompokkan menurut kemiripan satu sama lain agar dapat dengan mudah diklasifikasi, dicari dan ditemukan sesuai dengan permintaan yang ada.Dalam proses klasifikasi dokumen, seringkali ditemukan hasil yang kurang baik dikarenakan jumlah data dokumen yang besar dan bervariasi. METODESecara umum penelitian ini terdiri atas tiga tahapan. Tahap pertama ialah persiapan atau praproses. Tahap pra-proses mempersiapkan teks yang ada didalam dokumen agar siap untuk digunakan untuk proses selanjutya.Tahap yang kedua melakukan proses pengelompokan atau kategorisasi dokumen. Proses pengelompokan dilakukan terhadap hasil pra-proses yang merupakan representasi data dalam bentuk model ruang vektor. Metode pertama ialah pengelompokan dokumen yang ada dengan K-Means Clustering. Kemudian setiap kelompok dokumen tersebut akan diklasifikasi dengan Multi-Class SVM. PraprosesTahap awal sebelum melakukan proses pengelompokan dokumen adalah mempersiapkan teks yang ada didalam dokumen. Pada tahap praproses ini dilakukan beberapa subproses agar dokumen dapat dipakai untuk melakukan proses pengelompokan.Subproses yang pertama ialah tokenizer, yakni proses yang bertujuan untuk memisah teks menjadi beberapa token berdasarkan pembatas berupa spasi atau tanda baca. Proses selanjutnya adalah menghilangkan teks yang bersesuaian dengan teks
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