scholarly journals Klasterisasi Tingkat Masa Studi Tepat Waktu Mahasiswa Menggunakan Algoritma K-Medoids

2021 ◽  
pp. 162-168
Author(s):  
Fahmi Firzada ◽  
Y Yuhandri
Keyword(s):  
Bachelor's Degree ◽  
Evaluation Of Performance ◽  
Study Programs ◽  
Student Graduation ◽  
Predetermined Time

The period of study on time is one of the parameters of a student's success in completing college to obtain a bachelor's degree. A student is said to have completed his studies on time if he is able to complete his studies less than or equal to the predetermined time. Academic Provides facilities to find out the estimated time of student graduation. By providing information on which students are included in the cluster, they can complete their studies on time and which students do not complete their studies on time. In this study, the data processed were data from students who had graduated in the previous year. Then the data is processed using rapidminer software. This study applies the K-Medoids algorithm in clustering. The result of testing this method is to determine the student clusters who can complete the study period on time and the student clusters who cannot complete the study period on time. This research is expected to contribute to the campus in evaluating the tendency of students to complete their studies on time or not. The results of the evaluation of performance can produce information for study programs, lecturers and students in making policies

Temporal Prediction on Students’ Graduation using Naïve Bayes and K-Nearest Neighbor Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 682
Author(s):  
Ahmad Marzuqi ◽  
Kusuma Ayu Laksitowening ◽  
Ibnu Asror
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Temporal Prediction ◽  
Study Programs ◽  
Level 3 ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm

Accreditation is a form of assessment of the feasibility and quality of higher education. One of the accreditation assessment factors is the percentage of graduation on time. A low percentage of on-time graduations can affect the assessment of accreditation of study programs. Predicting student graduation can be a solution to this problem. The prediction results can show that students are at risk of not graduating on time. Temporal prediction allows students and study programs to do the necessary treatment early. Prediction of graduation can use the learning analytics method, using a combination of the naïve bayes and the k-nearest neighbor algorithm. The Naïve Bayes algorithm looks for the courses that most influence graduation. The k-nearest neighbor algorithm as a classification method with the attribute limit used is 40% of the total attributes so that the algorithm becomes more effective and efficient. The dataset used is four batches of Telkom University Informatics Engineering student data involving data index of course scores 1, level 2, level 3, and level 4 data. The results obtained from this study are 5 attributes that most influence student graduation. As well as the results of the presentation of the combination naïve bayes and k-nearest neighbor algorithm with the largest percentage yield at level 1 75.40%, level 2 82.08%, level 3 81.91%, and level 4 90.42%.

Perpaduan Metode Certainty Factor dan Forward Chaining untuk Menentukan Tingkat Stres Mahasiswa Tingkat Akhir Berbasis Android

2021 ◽  
Vol 5 (3) ◽  
pp. 267
Author(s):  
Nurholis Nurholis ◽  
Fauziah Fauziah ◽  
Novi Dian Natashia
Keyword(s):  
Expert System ◽  
System Application ◽  
Confidence Level ◽  
Bachelor's Degree ◽  
Certainty Factor ◽  
Forward Chaining ◽  
Final Project ◽  
Level Of Confidence ◽  
Stress Levels ◽  
Predetermined Time

Students who are taking the final semester are students who are completing all their subjects, can take the final project provided they have met the number of credits set to obtain a bachelor's degree. In the process of making a final project, students are required to complete it within a predetermined time, this demand causes students to be depressed, causing stress that affects them in completing their final project and study period. Based on this problem, an Expert System Application was made to Diagnose Stress Levels in Final Year Students by Combining Certainty Factor Methods and Android-Based Forward Chaining Techniques to find out more clearly the level of stress experienced by final year students. The results of the diagnosis on the expert system application and the results of manual calculations on one of the data which is representative of the 200 student data, which produce the same level of confidence, each of which produces a confidence level of 97.97% and was diagnosed with mild stress.Keywords:Android, Certainty Factor, Forward Chaining, Final Year Students, Expert System, Stress.

scholarly journals Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model

2020 ◽  
Vol 10 (10) ◽  
pp. 723-727
Author(s):  
Ace C. Lagman ◽  
◽  
Lourwel P. Alfonso ◽  
Marie Luvett I. Goh ◽  
Jay-ar P. Lalata ◽  
...  
Keyword(s):  
Classification Algorithm ◽  
Bachelor's Degree ◽  
Ensemble Model ◽  
Full Time ◽  
Accuracy Improvement ◽  
Student Graduation ◽  
Education Statistics ◽  
First Time

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between

scholarly journals PEDAGOGICAL FACILITATION FROM VIEW OF UNIVERSITY STUDENTS

2020 ◽  
pp. 47-56
Author(s):  
Hana Vavříková
Keyword(s):  
Academic Success ◽  
University Students ◽  
Bachelor's Degree ◽  
Degree Programs ◽  
Full Time ◽  
Study Success ◽  
Study Programs ◽  
Long Time ◽  
Faculty Of Education ◽  
The University

The professional public has been discussing for a long time the reasons why more than 2/5 of Czech students in bachelor’s degree programs are unsuccessful in their first study. The paper outlines the possible causes of this phenomenon, mentions partial factors influencing academic success or failure, and also lists the effects of the phenomenon on the life of individuals and society. Last but not least, this paper deals with pedagogical facilitation, ie interpersonal activity, which could eliminate some of the causes of study failure (low internal motivation, frustration), and which could set a favorable educational environment, and thus increase the study success of university students. As part of the preparation of this paper, its author conducted a survey, the aim of which was to look at partial aspects of study failure and pedagogical facilitation from the perspective of students of two forms (full-time, combined) of bachelor’s study programs at the Faculty of Education of the University of Ostrava.

scholarly journals ARCHIVAL SCIENCE - TODAY

Atlanti + ◽  
2019 ◽  
Vol 29 (1) ◽  
pp. 10-18
Author(s):  
Peter Pavel Klasinc
Keyword(s):  
Social Sciences ◽  
Information Science ◽  
Bachelor's Degree ◽  
Archival Science ◽  
Records Management ◽  
Alma Mater ◽  
Basic Definition ◽  
Interdisciplinary Science ◽  
Study Programs ◽  
Definition Of

In this paper the author is convinced that today is the time when archival science can be defined in detail or even redefined. In professional archival literature we can find many definitions of archival science, which we can accept or take knowledge of without problems. If we analyze these definitions, we will, as a result, determine whether these definitions are still really appropriate for present time.The new definition of archival science was primarily referred to by the results of the preparation of materials for the accreditation of study programs in archival science (Ist degree - Bachelor's degree), archival science and records management (2nd degree - master of archival science and documentology) and archival science (3rd degree-doctor of archival science) at Alma Mater Europaea - European Center Maribor.The author in this paper is trying to redefine the basic definition of archival science. Therefore, the author makes the following statement: "Archival science is an independent, academic, multidisciplinary and interdisciplinary science".Historical overview of definitions is interesting because of the prespectives it gives on archival science. Often, archival science relies on historical or social sciences, and recently to information science.

scholarly journals Sistem Informasi Tugas Akhir Program Studi Informatika

2020 ◽  
Vol 2 (1) ◽  
pp. 19-29
Author(s):  
Mustianti Mustianti ◽  
Ida Bagus Ketut Widiartha ◽  
Moh Ali Albar
Keyword(s):  
Information System ◽  
Black Box ◽  
Bachelor's Degree ◽  
Graduation Requirements ◽  
Study Program ◽  
Study Programs ◽  
Long Time ◽  
Title Registration ◽  
Black Box Testing

The thesis is one of the graduation requirements that students must take to obtain a bachelor's degree. Each student informatics study program who will carry out the Thesis will do several processes, including the process of registration of titles, guidance, registration of Thesis seminars, Examination and judiciary sessions. The thesis administration process is still running manually, where at the time of title registration, students must fill in the form of thesis title. After that, students will wait for the announcement to find out their supervisor as well as the registration of the Thesis Seminar, the examination, and the Judiciary sessions. Students must come directly to the office with the registration requirements. The process requires a long time so that thesis administration services become less effective. Therefore a website-based Thesis Information System was built which aims to facilitate the thesis administration process of Informatics study programs. Based on black box testing, this system was declared to have run well while testing using the MOS method showed that 95.34% of informatics student respondents, 99.33% of non-informatics student respondents, and 100% of lecturers and admin respondents agreed. Therefore this system is suitable for use. Keywords: Thesis, website-based, black box, MOS.

scholarly journals Pemanfaatan Data PDDIKTI sebagai Pendukung Keputusan Manajemen Perguruan Tinggi

2020 ◽  
Vol 7 (3) ◽  
pp. 555
Author(s):  
Ngatmari Ngatmari ◽  
Muhammad Bisri Musthafa ◽  
Cahya Rahmad ◽  
Rosa Andrie Asmara ◽  
Faisal Rahutomo
Keyword(s):  
Higher Education ◽  
Knowledge Discovery ◽  
Data Storage ◽  
Moving Average ◽  
Storage System ◽  
Rule Induction ◽  
Study Program ◽  
Tertiary Institutions ◽  
Study Programs ◽  
Student Graduation

<p>Pangkalan Data Pendidikan Tinggi (PDDIKTI) merupakan sebuah sistem penyimpan data yang dikelola Pusat Data dan Informasi (Pusdatin) Kementrian Ristek dan Pendidikan Tinggi. Data yang tersedia di PDDIKTI merupakan data yang akurat, karena proses pelaporan data akademik secara berkala dua kali setiap. Data yang telah berlimpah tersebut, tentu sangat disayangkan jika tidak digunakan untuk keperluan yang lebih bermanfaat, misal untuk mengetahui pola akademik kelulusan mahasiswa dan prestasi akademik mahasiswa. Untuk memperoleh informasi-informasi penting tersebut bisa dilakukan dengan cara penggalian informasi (<em>knowledge discovery</em>). Teknik dalam memberikan solusi masalah tersebut adalah teknik klasifikasi untuk membantu pengambilan keputusan, misalkan <em>Decission Tree</em> (C4.5, ID3, CHAID, <em>rule induction</em>) dan teknik peramalan (<em>forecasting</em>) menggunakan metode <em>simple moving average (SMA)</em>. Tujuan dari penambangan data PDDIKTI adalah untuk melakukan deteksi dini terhadap mahasiswa, sehingga dosen bisa memberikan masukan-masukan ketika mahasiswa tersebut telah diklasifikan sebagai mahasiswa yang lulus tidak tepat waktu serta memprediksi jumlah mahasiswa yang akan masuk pada perguran tinggi pada salah satu prodi X, sehingga manajemen baik tingkat program studi maupun universitas bisa melakukan langkah-langkah yang dianggap penting guna meningkatkan jumlah mahasiswa. Pengujian pada 2.601 <em>record</em> akademik mahasiswa dengan atribut ipk_sem1, ipk_sem2, ipk_sem3, ipk_sem4, pekerjaan_ortu, ket_lulus, rerata_ipk, penghasilan_ayah, untuk klasifikasi kelulusan mahasiswa menghasilkan nilai <em>accuracy</em> 86,54 % nilai <em>precission</em> 93,37% dan nilai <em>recall</em> 89,27% serta pengujian prediksi jumlah peminat program studi  diperoleh nilai <em>accuracy</em> 78,25 % dan <em>MAPE</em> sebesar 21,75 %.</p><p class="Judul2"><em><br /></em></p><p class="Judul2"><em><strong>Abstract</strong> </em></p><p class="Judul2"><em><br /></em></p><p class="Judul2"><em>The Higher Education Database (PDDIKTI) is a data storage system managed by the Center for Data and Information (Pusdatin) of the Ministry of Research and Technology and Higher Education. The data available at PDDIKTI is accurate data, because the process of reporting academic data regularly twice each. The abundant data is certainly unfortunate if not used for more useful purposes, for example to find out the academic patterns of student graduation and student academic achievement. To obtain important information can be done by extracting information (knowledge discovery). Techniques in providing solutions to these problems are classification techniques to assist decision making, for example Decission Tree (C4.5, ID3, CHAID, rule induction) and forecasting techniques using simple moving average (SMA) methods. The purpose of PDDIKTI data mining is to conduct early detection of students, so that lecturers can provide input when the students have been classified as students who graduate not on time and predict the number of students who will enter the tertiary institutions in one of the X study programs, so that management both the level of study program and university can take steps that are considered important to increase the number of students. Tests on 2601 student academic records with the attributes ipk_sem1, ipk_sem2, ipk_sem3, ipk_sem4, occupation_ortu, graduated, average_ipk, income_ayah, for the graduation classification of students resulted in an accuracy value of 86.54% a value of 93.37% and a recall value of 89.27% and a test of 89.27% and a test of graduation prediction of the number of study program enthusiasts obtained an accuracy value of 78.25% and MAPE of 21.75%.</em></p>

scholarly journals Korelasi antara persepsi pengelolaan dan layanan pustaka dengan motivasi belajar di digital library UNY

Epistema ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 59-67
Author(s):  
Muh. Ihwan Arosyadi ◽  
Suyantiningsih Suyantiningsih
Keyword(s):  
Digital Library ◽  
Pearson Correlation ◽  
Correlation Method ◽  
Bachelor's Degree ◽  
Library Services ◽  
Library Management ◽  
Study Programs ◽  
Faculty Of Education ◽  
The Relationship ◽  
Very High

AbstrakPenelitian ini bertujuan untuk: (1) mengetahui tingkat pengelolaan pustaka di Digital Library, (2) mendeskripsikan tingkat layanan pustaka di Digital Library, (3) menginvestigasi tingkat motivasi belajar mahasiswa FIP terhadap Digital Library, (4) mengetahui korelasi antara pengelolaan pustaka dengan motivasi belajar di Digital Library, dan (5) mengetahui korelasi antara layanan pustaka dengan motivasi belajar di Digital Library. Jenis penelitian yang dilakukan dalam penelitian ini adalah kuantitatif korelasional, menggunakan metode korelasi product moment. Sampel dalam penelitian ini adalah pemustaka (mahasiswa) yang sedang menempuh gelar sarjana S1 dengan target presentase 15% dari program studi yang ada di Fakultas Ilmu Pendidikan UNY. Hasil penelitian menunjukkan bahwa: (1) tingkat pengelolaan pustaka di Digital Library senilai 3,2 kategori sangat tinggi, (2) tingkat layanan pustaka di Digital Library senilai 3,1 termasuk dalam kategori sangat tinggi, (3) tingkat motivasi belajar mahasiswa FIP terhadap Digital Library senilai 3,1 kategori sangat tinggi, (4) korelasi pengelolaan pustaka dengan motivasi belajar berdasarkan teori Pearson Corelation nilai significancy menunjukan (p = 0,000 <0,05). Hal ini membuktikan bahwa hubungan kedua variabel adalah positif. (5) korelasi layanan pustaka dengan motivasi belajar berdasarkan teori Pearson Corelation nilai significancy menunjukan (p = 0,000 <0,05), dengan demikian hal ini membuktikan bahwa hubungan kedua variabel adalah positif. Abstract This study aims to: (1) determine the level of library management in the Digital Library, (2) describe the level of library services in the Digital Library, (3) investigate the motivation level of student learning towards the Digital Library, (4) find out the correlation between library management and motivation of learning in the Digital Library, and (5) knowing the correlation between library services with motivation of learning in the Digital Library. This type of research conducted in this study is quantitative correlational, using the product moment correlation method. The sample in this study is the user (student) who is pursuing a bachelor's degree with a target percentage of 15% of the existing study programs at the Faculty of Education UNY. The results showed that: (1) the level of library management in the Digital Library worth 3.2, categories is very high, (2) the level of library service in the Digital Library worth 3.1 is included in the very high category, (3) the level of students’ motivation of learning towards Digital Library worth 3.1 categories is very high, (4) correlation of library management with learning motivation based on the Pearson Corelation theory significance value shows (p = 0,000 <0.05), this proves that the relationship between the two variables is positive, (5) correlation of library services with motivation of  learning based on the Pearson Correlation theory significance value shows (p = 0,000 <0.05), thus this proves that the relationship between the two variables is positive.

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