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All Journal Infotekmesin Journal of Innovation Information Technology and Application (JINITA)
Ali Sofyan
Politeknik Harapan Bersama
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering

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Metode Naive Bayes Dalam Menentukan Program Studi Bagi Calon Mahasiswa Baru Wildani Eko Nugroho; Ali Sofyan; Oman Somantri
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.491

Abstract

In a university, determining a study program for prospective students is something that is often done to focus on prospective students so that they are in accordance with their competencies. This is a very important hope, because prospective students can develop self-competence according to their academic abilities. This research method uses several stages, including data cleaning, data collection, determining criteria, determining probability, and final testing. The Naïve Bayes method with a case study at the Private Madrasah Aliyah PAB 6 Helvetia and testing of 100 student data with an accuracy rate of 90% is a previous research. The purpose of this study was to make a classification of majors based on the criteria, while in this study the aim of making a classification of study programs for prospective new students. In this study, the same method was used but the number of data records was different, the test data was 1671 student data records, the data was obtained from 2256 data records.From the total data records were 2256, after data cleaning and data collection were carried out, 1671 test data were obtained. In the test data, there are several probability values that contain various criteria and attributes used to determine the classification of study programs for prospective new students. The number of data records is divided into 2 parts, the first is used for training data with 1158 data with a percentage of 70%, and testing data with 513 data records with a percentage of 30%. From the test results with the same method with different number of data records, the accuracy rate is from 90% to 96% with an accuracy value of 96.68%. From this accuracy value shows that the classification results obtained show the Pharmacy DIII study program.
Klasifikasi Kinerja Pengajaran Dosen Menggunakan Metode ANFIS Sebagai Upaya Peningkatan Sistem Penjaminan Mutu Internal (SPMI) Ida Afriliana; Arfan Haqiqi Sulasmoro; Ali Sofyan
Journal of Innovation Information Technology and Application (JINITA) Vol 1 No 1 (2019): JINITA, December 2019
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (507.148 KB) | DOI: 10.35970/jinita.v1i1.89

Abstract

A college is a place for education providers that aim to produce quality human resources and be able to face increasingly fierce work competition. Lecturers become one of the important elements in producing competent human resources. The task of the lecturer is to carry out the Tri Dharma of Higher Education which includes teaching, research and community service. The purpose of this study was to obtain a teaching performance classification of lecturers using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method as an effort to improve the internal quality assurance system. Where in this learning process planning, implementation, evaluation, control and improvement have been carried out as a form of implementation of the Internal Quality Assurance System (SPMI). The data used in this study are data from the questionnaire sheets of lecturers' reaction sheets from students to the teaching of lecturers who support courses. The data used is data for 1 academic year, namely data for the 2017 2018 academic year. The results of this study are that ANFIS is suitable for classifying lecturers' performance.
Co-Authors Ida Afriliana Oman Somantri Sulasmoro, Arfan Haqiqi Wildani Eko Nugroho, Wildani Eko