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Bahrin Dahlan
Universitas Pohuwato
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Sistem Pendukung Keputusan Pemberian Insentif Berdasarkan Penilaian Kinerja Aparat Desa Menggunakan Metode Multi Attribute Utility Theory Bahrin Dahlan; Betrisandi
Jurnal Teknik Vol 20 No 1 (2022): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v20i1.199

Abstract

The purpose of This research is to generate incentives based on performance assessment, which creates a good and transparent village government. Therefore, the awarding of incentives is based on performance appraisal, in such assessment is required decision support System (SPK) that can take into account all criteria to help facilitate the decision making process. By Implementing the method multy Attribute Utility Theory (MAUT) with the criteria that have been applied to theoffice of Puncak Jaya village namely Performance Assessment, attendance, discipline, responsibility on the service. The resulting system is the calculation value ofincentives for incentive based on the performance assessment of the village apparatus with the method of multy Attribute Utility Theory (death). Based on research the decision support system that has been made can help decision makers in determining the incentive grant of village apparatus. This is evidenced by the test result done by the Black Box method and The testing base Path that generates the value V (G) = CC, where V (G) = 4 and CC = 4, so it is obtained that the logic ofa flowchart calculation of the normalization and Perankingan are correct and based on Black Box testing which includes the test input process and output with reference to the draft software has been fulfilled with the results according to the design.
Klasifikasi Pasien Persalinan Caesar Menggunakan Metode Nave Bayes Berbasis Forward Selection Muh Faisal; Bahrin Dahlan; Rahmat Thaib
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 6, No 6 (2023): Desember 2023
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v6i6.7143

Abstract

Abstrak - Hasil observasi di lingkungan rumah maupun kantor yakni cukup banyak ibu hamil yang akhirnya melakukan operasi caesar. Ada beberapa penyebab seorang ibu hamil melakukan caesar. Pertama, faktor kesehatan ibu. Kedua, faktor janin. Ketiga adalah faktor gabungan dari faktor ibu dan janin. Faktor-faktor tersebut menjadi indikasi apakah persalinan akan dilakukan dengan mutlak atau mungkin juga bisa menjadi relatif. Pada penelitian ini akan menerapkan metode Naive Bayes dengan optimasi forward selection untuk mendapatkan klasifikasi persalinan caesar dengan lebih optimal dimana hasil yang didapatkan penelitian sebelumnya terhadap prediksi ibu melahirkan hanya mendapat akurasi 88%. Nave Bayes merupakan pengklasifikasian dengan metode probabilitas dan statistik. Dari sembilan atribut yang digunakan yaitu Gravid Aterm, Riwayat SC, Posisi Bayi, Bayi Besar, Plasenta, Ketuban, Penyakit Ibu, Gemelli, dan Inpatu lalu dengan menggunakan algoritma Naive Bayes berbasis Forward Elimination didapatkan empat atribut weight yaitu Gravid Aterm, Bayi Besar, Ketuban dan t Gemelli dalam mengklasifikasi partus atau persalinan caesar. Secara mandiri tingkat akurasi yang dihasilkan algoritma Naive Bayes adalah 93,33 %. Sedangkan dengan menambahkan seleksi fitur Forward Elimination menghasilkan akurasi 94% dalam klasifikasi pasien persalinan Caesar. Dengan demikian, metode Naive Bayes berbasis Forward Elimination dapat digunakan sebagai metode yang lebih optimal dari penelitian sebelumnya.Kata Kunci: Nave Bayes, Forward Selection, CaesarAbstract -The results of observations in the home and office environment are quite a lot of pregnant women who end up doing cesarean sections. There are several causes of a pregnant woman doing a cesarean. First, the maternal health factor. Secondly, fetal factors. Third is the combined factor of maternal and fetal factors. These factors are an indication of whether labor will be done absolutely or maybe it can also be relative. This study will apply the Naive Bayes method with forward selection optimization to get a more optimal classification of cesarean delivery where the results obtained by previous studies on the prediction of childbirth only got 88% accuracy. Nave Bayes is a classification by probability and statistical methods. Of the nine attributes used, namely Gravid Aterm, SC History, The position of the Baby, Big Baby, Placenta, Amniotic, Maternal Disease, Gemelli, and Inpatu then using the Naive Bayes algorithm based on Forward Elimination obtained four weight attributes, namely Gravid Aterm, Big Baby, Amniotic and Gemelli t in classifying partus or cesarean delivery. Meanwhile, by adding the Forward Elimination feature selection resulted in 94% accuracy in the classification of Cesarean delivery patients. Thus, the Naive Bayes method based on Forward Elimination can be used as a more optimal method than previous studies.Keywords : Nave Bayes Forward Selection Caesar
Co-Authors Betrisandi Muh Faisal Rahmat Thaib