Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Perempuan & Anak
Audi Bayu Yuliawan
Unknown Affiliation
Humanities Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Contraception Recommendations With Analytical Hierarchy Process (AHP) and Weighted Product Methods (WP) Audi Bayu Yuliawan; Nur Hayatin; Yufis Azhar
Jurnal Perempuan dan Anak Vol. 4 No. 1 (2021): Februari
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.796 KB) | DOI: 10.22219/jpa.v1i1.16337

Abstract

Planning Program (KB) as one way to reduce the high rate of pregnancy. Contraceptives used in family planning programs have various types. In addition to the presence of contraception for women, contraception is also available for men. It's just that the problem at this time, lack of knowledge will choose contraception in accordance with health conditions. The limited time, place and expertise of experts to always provide information is one of the obstacles to getting complete information. Decision Support System is a knowledge-based computer information system that is used to support decision making in a problem. This system will later use the Analytical Hierarchy Process method, this method is a method that makes decision makers to get priority scale or consideration of experience, views, intuition and original data. Not only that this system will also use the Weighted Product (WP) method to maximize the performance of AHP in ranking the final results. This application is made using the Android programming language with Android Studio as the platform. In this application will later display recommendations for selecting contraceptives that are suitable for a patient.
PENILAIAN KINERJA PEGAWAI DENGAN METODE TOPSIS DAN BACKPROPAGATION NEURAL NETWORK Audi Bayu Yuliawan; M. Amin Hariyadi; Ririen Kusumawati; Cahyo Crysdian; Fresy Nugroho
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7826

Abstract

Transformasi digital melalui penerapan Industri 4.0 dan e-Government telah mengubah paradigma administrasi publik, sehingga menuntut sistem evaluasi kinerja pegawai yang lebih adaptif dan objektif. Penelitian ini bertujuan untuk mengklasifikasikan kinerja pegawai ke dalam lima kategori, yaitu "sangat baik", "baik", "cukup", "buruk", dan "sangat buruk", dengan menggunakan pendekatan Neural Network Backpropagation. Metodologi yang digunakan mencakup beberapa tahapan utama, dimulai dari proses preprocessing data yang menge-lompokkan kriteria penilaian ke dalam empat aspek: kualifikasi, kom-petensi, kinerja, dan disiplin. Selanjutnya, dilakukan seleksi fitur menggunakan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), dan hasilnya digunakan sebagai data pelatihan pada model Neural Network Backpropagation. Hasil pelati-han menunjukkan performa model yang cukup baik, dengan nilai loss dan Mean Squared Error (MSE) sebesar 0,000465, Mean Absolute Per-centage Error (MAPE) sebesar 19,59%, dan akurasi mencapai 80,41%. Sementara itu, hasil eksperimen dengan metode TOPSIS secara terpisah mencatat akurasi sebesar 81% dan nilai loss sebesar 0,377. Kombinasi metode TOPSIS dan Neural Network Backpropagation ter-bukti efektif dalam mengklasifikasikan kinerja pegawai secara konsis-ten. Temuan ini memberikan kontribusi terhadap pengembangan sis-tem evaluasi kinerja berbasis kecerdasan buatan yang lebih akurat dan adaptif terhadap tantangan administrasi publik modern.
Co-Authors Cahyo Crysdian Fresy Nugroho M. Amin Hariyadi Nur Hayatin Ririen Kusumawati Yufis Azhar