Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Billy Hendrik
Universitas Putra Indonesia “YPTK” Padang, Indonesia
Computer Science & IT Control & Systems Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Penerapan Metode Naïve Bayes Dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen Putri Ramadani; Gunadi Widi Nurcahyo; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.361

Abstract

Student satisfaction in higher education is the main focus in improving the quality of education. In the Tridharma paradigm, satisfaction is measured through a comparison of expectations and teaching realization as the main indicator of learning effectiveness. This research method uses Naïve Bayes classification, through the steps of reading training data, calculating prior probabilities, training data probabilities for each category, reading testing data, and calculating final probabilities. This research aims to evaluate student satisfaction with lecturers' teaching at the LP3I Polytechnic, Padang Campus. The data used in this research was 574. The results of research with 574 data (516 training and 58 testing) showed that 52 data (89.648%) stated "Very Satisfied", while 6 data (10.344%) stated "Satisfied". Prediction accuracy reached 98.28%. However, when using the Naïve Bayes method with 574 data (574 training and 574 testing), 397 data (69.078%) stated "Very Satisfied" and 177 data (30.798%) stated "Satisfied". Without the Naïve Bayes method, 402 data (69.948%) stated "Very Satisfied" and 172 data (29.928%) stated "Satisfied". An improvement of 0.87% occurred for the "Very Satisfied" category and -0.87% for "Satisfied". There are no differences in percentages for other categories. From the comparison of results, it can be seen that the Naïve Bayes method is superior in predicting student satisfaction levels compared to calculations without this method. Therefore, it can be concluded that the Naïve Bayes process model is suitable for use as a method for determining good decisions in predictions
Implementasi K-Means Clustering Dalam Analisa Soal Ujian CBT Universitas Baiturrahmah Rico Anggara; Sarjon Defit; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.367

Abstract

Computer-based exams (CBT) are a type of exam where participants take the exam using a computer or digital device. CBT has become a common choice in exam administration. Exam question management is important for CBT success. Participants answer digital questions via a computer interface, and the results are processed automatically by the computer system. The results of this test can be used to assess student understanding and as a learning evaluation. This research aims to group exam questions based on participants' answers. The method used in this research is K-Means Clustering. This method has 5 stages, namely cluster center initialization, data grouping, calculation of new cluster centers, convergence and evaluation of results. This process repeats until the cluster center does not change any more or convergence has been achieved. Next, the K-Means Clustering algorithm is applied to group exam questions into appropriate clusters. This grouping process is carried out by considering the similarities between the exam questions based on the number of correct answers and the number of incorrect answers. Dataset source from UPT CBT, Baiturrahmah University. The question dataset consists of 100 exam questions that have been tested on students at the Faculty of Medicine, Baiturrahmah University. The results of this research can group exam questions into groups of difficult questions, medium questions and easy questions. This research can be a reference for academics in evaluating exam questions created by lecturers and can evaluate the level of understanding of students at Baiturrahmah University.
Sistem Pakar Identifikasi Jurusan Yang Sesuai Dengan Minat Bakat Siswa Nella Novrina Doni; S Sumijan; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.373

Abstract

The Certainty Factor method can help overcome this complexity by providing a level of confidence regarding major recommendations. Certainty Factor allows an expert system to measure the level of confidence or uncertainty associated with each knowledge rule or fact used. Each rule contributes to the overall confidence level, and this can provide an idea of the extent to which the system is confident in the recommendations it produces. In complex decision making, various knowledge rules can influence each other. The Certainty Factor method allows the integration of belief values from interrelated rules, providing a holistic picture of the extent to which evidence supports a conclusion. In some cases, knowledge rules may provide contradictory or conflicting information. Certainty Factor can be used to handle information conflicts by assigning a weight or level of confidence to each piece of information, so that the system can produce more accurate recommendations. Certainty Factor provides a mechanism for measuring the level of uncertainty in a decision. This is important when the available information is incomplete or there is uncertainty in the values used in the knowledge rules. By taking into account the level of uncertainty, the system can provide recommendations that are more realistic and appropriate to complex situations. The Certainty Factor method can be applied dynamically, allowing the system to adjust confidence levels over time or with the addition of new information. This is useful when students experience changes in interest or talent, so the system can provide more accurate and relevant recommendations..
Penerapan Teorema Bayes Pada Sistem Pakar Untuk Mendeteksi Dini Penyakit Tuberkulosis (Studi Kasus Di Rs. Tentara Dr. Reksodiwiryo Padang) Fadil Idensia; Y Yuhandri; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.369

Abstract

Tuberculosis (TB) is an infectious disease that is still a global health problem, including in Indonesia. Early detection of this disease is crucial for effective treatment. In order to improve early detection of TB, this research aims to apply the Bayes Theorem method to the development of an expert system. The case study was conducted at Dr. Reksodiwiryo, Padang, where the percentage of Tuberculosis based on the method has been identified. The Bayes Theorem method is implemented in an expert system to provide early diagnosis to patients suspected of having TB. Expert system testing was carried out to evaluate the accuracy of the diagnosis, with an average calculation result using Bayes' theorem of 80%. The results of this research indicate that the application of Bayes' Theorem in an expert system can be an effective tool in early detection of Tuberculosis. The practical implication of this research is to increase the capabilities of the Dr. Army Hospital. Reksodiwiryo Padang in treating TB early and accurately, as well as contributing to efforts to prevent and control this disease more efficiently.
Co-Authors Fadil Idensia Gunadi Widi Nurcahyo Nella Novrina Doni Putri Ramadani Rico Anggara S Sumijan Sarjon Defit Y Yuhandri