Prioritas Pengemudi Untuk Kenyamanan Layanan Penumpang Di Lingkungan Akademik Berbasis  K-Nearest Neighbor

Setiawan, Aries; Winarno, Agus

doi:10.20884/1.dr.2020.16.1.296

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Beberapa penelitian banyak menggunakan teknik k-nearest neighbor misalnya untuk riset bidang kesehatan, seperti klasifikasi penyakit jantung melalui seleksi fitur nilai Gain [1], penyakit difteri [3], klasifikasi penyakit diabetes melitus [4] dan klasifikasi kerusakan tulang belakang [5]. Selain dibidang kesehatan, k-nearest neighbor juga banyak dimanfaatkan untuk kebutuhan riset seperti klasifikasi fungsi senyawa aktif [6], klasifikasi dokumen twitter untuk mengidentifikasi karakter dari calon karyawan [7], klasifikasi artikel berbahasa indonesia [8], klasifikasi predikat prestasi mahasiswa [9], klasifikasi kualitas air bersih [10], serta masih banyak riset lainnya seperti yang dilakukan [11], [12], [13] dan [14]. Meskipun banyak pendekatan lain seperti decision tree dan naive bayes untuk klasifikasi jenis penyakit jantung, namun pada penelitian ini teknik k-nn digunakan sebab karakteristik data yang dianalisis tidak dapat diterapkan secara langsung pada decision tree maupun naive bayes.…”

Section: Pendahuluanunclassified

Implementasi K-Nearest Neighbor Untuk Prototype Sistem Pakar Identifikasi Dini Penyakit Jantung

Munandar¹,

Munir²

2022

jtir

View full text Add to dashboard Cite

INTISARIPenyakit jantung dapat menyerang siapa saja, di segala usia, jenis kelamin, pekerjaan maupun gaya hidup. Belum meratanya dokter spesialis penyakit jantung di berbagai wilayah, menyebabkan penyakit ini masih menjadi salah satu penyebab tingginya kematian pasien. Penelitian ini bertujuan untuk memberikan alternative lain dalam upaya identifikasi dini penyakit jantung akibat kurang meratanya dokter spesialis jantung di berbagai wilayah. Prototype system dikembangkan sebagai alat bantu sederhana identifikasi penyakit jantung. Data electrocardiogram pasien penyakit jantung digunakan sebagai data klasifikasi jenis penyakit jantung dan dianalisis menggunakan pendekatan k-nearest neighbor. Penelitian ini berhasil membuat prototype system pakar yang mampu mengkalsifikasi penyakit cardiovascular berbasis web sehingga diharapkan mampu meningkatkan layanan kesehatan khususnya untuk penanganan pasien penyakit jantung. Hasil penelitian juga memperlihatkan bahwa k-nearest neighbor memiliki tingkat akurasi sebesar 70.40%, lebih tinggi dibandingkan dengan teknik lainnya yakni naïve bayes, algoritma C45 dan multi class classifier meta..Kata kunci — penyakit jantung, electrocardiogram, k-nearest neighbor, prototype, sistem pakar ABSTRACTHeart disease can affect anyone of all ages, genders, occupations and lifestyles. The uneven distribution of specialists in heart disease in various regions has caused this disease still to be one of the causes of high patient mortality. This study aims to provide another alternative to determine the initial illness due to the uneven distribution of cardiologists in various regions. The prototype system was developed as a simple tool for heart disease. Electrocardiogram data of heart disease patients were used as data for the classification of disease types and analysis using the k-nearest neighbor approach. This study succeeded in making a prototype system capable of classifying web-based heart disease experts so that it is expected to be able to improve health services, especially for the treatment of heart disease. The results also explain that the k-nearest neighbor has an accuracy rate of 70.40%, higher than other techniques, namely naive Bayes, C45 algorithm and multi-classifier meta.Kata kunci — heart disease, electrocardiogram, k-nearest neighbor, prototype, expert system

show abstract

Section: Pendahuluanunclassified

Implementasi K-Nearest Neighbor Untuk Prototype Sistem Pakar Identifikasi Dini Penyakit Jantung

Munandar¹,

Munir²

2022

jtir

View full text Add to dashboard Cite

show abstract

“…Sebagai salah satu metode klasifikasi [8], K-Nearest Neighbor memiliki diagram tahapan sebegai berikut [9]:…”

Section: Metode K-nearest Neighborunclassified

Referensi Penugasan Tenaga Teknisi Pada Penanganan Varian Kerusakan Menggunakan Decision Support System

Setiawan

Ratnawati

Prihandono

2021

Self Cite

View full text Add to dashboard Cite

One type of asset managed by an agency is an asset that has a service life limit, if it is not properly maintained it will affect the quality of use. Maintenance can be in the form of routine and incidental maintenance carried out by technicians. Each technician has different abilities, so the assignment should be based on objectivity. The high quantity of maintenance on all assets in an agency and often lack of attention to the division of tasks on all technicians resulted in some technicians having to accept maintenance assignments on assets that were not in accordance with their fields. The right steps were taken to support the decision to refer technicians to an asset maintenance process using the K-Nearest Neigbor method. The final result shows that the percentage of occurrences of referenced names occupies the highest percentage, namely 66% with a value of k=3, so that k=3 is suitable for the process of reference for technicians.

show abstract

“…Pada penelitian sebelumnya, menggunakan perbandingan nilai tetangga terdekat (k) 3,5,7dihasilkan k optimal 7, hal ini memungkinkan pemakaian k dengan nilai k lebih tinggi akan menghasilkan nilai akurasi yang lebih baik (Setiawan & Winarno, 2020).…”

Section: Pemilihanunclassified

Implementasi K-Nearest Neighbor pada Decission Support System Pemilihan Satuan Pengamanan Event Perguruan Tinggi

Setiawan

Widjajanto

Kurniawan

et al. 2020

JIUP

Self Cite

View full text Add to dashboard Cite

Routine events required by tertiary institutions require escort from selected security guards. Elections based on personal subjectivity will lead to results that are not in accordance with the purpose of the security itself. However, if the selection is based on the objectives will give results that are in accordance with professionalism. Each security unit has a different level of importance, so that at the level of security the event needs a level of professionalism in accordance with the level of importance at the college level. In detail the selection of security units on several criteria, namely event, years of service, cooperation, service, personality, skills and responsibilities. The method used in this selection process is the K-Nearest Neighbor, with the final result approval rate of 0.88%

show abstract

Prioritas Pengemudi Untuk Kenyamanan Layanan Penumpang Di Lingkungan Akademik Berbasis K-Nearest Neighbor

Cited by 3 publications

References 0 publications

Implementasi K-Nearest Neighbor Untuk Prototype Sistem Pakar Identifikasi Dini Penyakit Jantung

Implementasi K-Nearest Neighbor Untuk Prototype Sistem Pakar Identifikasi Dini Penyakit Jantung

Referensi Penugasan Tenaga Teknisi Pada Penanganan Varian Kerusakan Menggunakan Decision Support System

Implementasi K-Nearest Neighbor pada Decission Support System Pemilihan Satuan Pengamanan Event Perguruan Tinggi

Contact Info

Product

Resources

About