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All Journal JUSIFO : Jurnal Sistem Informasi Annual Research Seminar Indonesian Journal of Business Intelligence (IJUBI) Jurnal Generic Jurnal Pendidikan Sains dan Komputer
Rusdi Efendi
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Other

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Kombinasi Analytical Network Process (ANP) dan Technique For Others Reference by Similarity to Ideal Solution (TOPSIS) untuk Menentukan Penerima Bantuan Rastra Yunita Yunita; Rusdi Efendi; Evita Hardanita
JUSIFO : Jurnal Sistem Informasi Vol 7 No 1 (2021): JUSIFO (Jurnal Sistem Informasi) | June 2021
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v7i1.8440

Abstract

The problem of poverty is still a priority for the Indonesian government. The Indonesian government has launched several programs to assist in alleviating poverty, one of which is the rice program for prosperous families, called Rastra. In the implementation of the Rastra program, it should be carried out objectively so that the distribution of the program can be right on target, but there are still problems in determining the recipients and the accuracy of the distribution of Rastra program. This study aims to combine the ANP and TOPSIS methods in helping to provide recommendations for recipients of Rastra program, besides that in this study also measuring the level of accuracy between the implementation carried out with the real data. In this study, a combination of ANP and TOPSIS methods was used in decision making. The ANP method is used to determine the weight of the criteria used, while the TOPSIS method is used to determine the recommendations for recipients of the Rastra program. The accuracy test was carried out in 13 villages in the Gunung Megang, Muara Enim. The results of the tests carried out obtained an average of 84.10%. This shows that the combination of these two methods can be applied as a solution in providing recommendations for recipients of the Rastra program.
Implementation of Fuzzy Linguistic Quantifier and Analytic Hierarchy Process in Decision Support Systems (Case Study: Exemplary Employee Selection ) Rusdi Efendi; Gustian Aidil Fitri
Annual Research Seminar (ARS) Vol 1, No 1 (2015)
Publisher : Annual Research Seminar (ARS)

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Abstract

To Make the selection based on potential model employee that he had not an easy thing, especially if conducting assessment involving people (Group Decision Support System). Sometimes a leader or manager of the company only chose an exemplary employee based on the recommendation of other employees. What if this exemplary employee assessment to be done by more than one person, let alone carried out by a division manager or supervisor with the reason the parties that often interact directly with employees so as to provide a more objective assessment.In this study the variables that are used to perform calculations on model employee 4 pieces, namely: performance, discipline, loyalty, and presence. The assessment process begins weighting to each variable by using fuzzy linguistic quantifier levels while using Analytic Hierarchy Process (AHP). The end result of this research is in the form of an exemplary employee levels where persantase of each variable is not an average value as is commonly done, but based on the interest factor of each variable.
Perbandingan Metode Certainty Factor Dan Backpropagation untuk Mendiagnosis Penyakit Gangguan Tidur Oktaria Permata Sari; Arti Dian Nastiti; Rusdi Efendi; Desty Rodiah
Generic Vol 12 No 2 (2020): Vol 12, No 2 (2020)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Gangguan tidur merupakan salah satu penyakit yang sering diabaikan, kondisi ini jika terus diabaikan maka akan sangat berpengaruh pada kesehatan penderita. Sistem pakar untuk mendiagnosis gejala awal penyakit gangguan tidur sangat diperlukan. Sistem pakar yang dibangun menggunakan metode Certainty Factor dan Backpropagation dimana kedua metode ini akan memberikan informasi hasil diagnosis penyakit, confidance serta solusi awal dari penyakit yang diderita. Hasil Confidance dari metode Certainty Factor dan Backpropagation akan dibandingkan untuk melihat metode mana yang paling akurat dalam mendiagnosis penyakit gangguan tidur. Penelitian ini menggunakan 18 gejala penyakit gangguan tidur, 6 jenis penyakit gangguan tidur serta 24 kasus pengujian. Dari 24 kasus pengujian didapat hasil tingkat akurasi sebesar 100% pada metode Certainty Factor sedangkan untuk metode Backpropagation didapat hasil tingkat akurasi sebesar 70.83%.
PENGARUH REDUKSI DIMENSI TERHADAP METODE PENGKLASTERAN BERBASIS CENTROID DAN METODE PENGKLASTERAN BERBASIS DENSITY DALAM PENGKLASTERAN DOKUMEN TEKS Muhammad Ihsan Jambak; Rusdi Efendi
Indonesian Journal of Business Intelligence (IJUBI) Vol 4, No 2 (2021): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Program Studi S1 Sistem Informasi Fakultas Komputer dan Teknik Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v4i2.1918

Abstract

Density-based clustering is usually more effective when processing data of different densities. This method is pioneered by the Density-based Applied Noise Spatial Clustering (DBSCAN) algorithm. There is a significant difference in behavior between k-Means and DBSCAN, which is processing data that contains noise. To this end, this research studies the impact of dimensionality reduction on high-dimensional data on the clustering results of the k-Means algorithm represented by the centroid method and the clustering results of the DBSCAN algorithm represented by the density method. Although the quality of the clustering results on k-Means has been improved after the numerical reduction by Singular Value Decomposition (SVD), from the initial average distance of 1.04136 to 0.003, the statistical change is not significant or considered to be the same. Therefore, it can be concluded statistically that SVD has no effect on the quality of k-Means clustering results. On the other hand, in DBSCAN, the effect of SVD dimensionality reduction is very significant. It can change the quality of the clustering results from the initial average intra-cluster distance of 76.13480 to 13.71130 or improve the quality by 555.27%. The significant impact of SVD on SVD + k-Means optimization and SVD + DBSCAN optimization cluster calculation time changes is also shown. SVD optimization can accelerate k-Means calculation time from 3.68182 seconds to 2,09091 seconds or 1.76 times. At the same time, SVD optimization accelerates the DBSCAN calculation time from 19.40000 seconds to 0.97500 seconds or 19.89 times.
Analisis Komparatif Metode Weighted Product (WP) dan Metode Simple Additive Weighting (SAW) Yoppy Sazaki; Rusdi Efendi; M. Ihsan Jambak
Jurnal Pendidikan Sains dan Komputer Vol. 3 No. 02 (2023): Artikel Riset Periode Oktober 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v3i02.3347

Abstract

Dalam banyak kasus informasi ini kurang memadai untuk membuat sebuah keputusan yang spesifik guna memecahkan permasalahan tertentu. Oleh karena itulah Sistem Pendukung Keputusan dibuat sebagai suatu cara untuk memenuhi kebutuhan ini. Konsep sistem pendukung pengambilan keputusan yang berbasis komputer (computer based decision support system) saat ini sedang berkembang sangat pesat. Didalam proses pengambilan keputusan terdapat benyak kriteria yang digunakan, serta banyak juga metode yang bisa dipakai untuk mendapatkan solusi dari permasalahan tersebut. Ketika terdapat banyak metode yang bisa dipakai, maka kita akan menemukan masalah baru lagi yaitu metode mana yang akan memberikan hasil yang lebih akurat , oleh karena itulah penelitian ini melakukan analisa terhadap dua metode yang sering digunakan yaitu metode weighted product (WP) dan metode simple additive weighting (SAW) baik metode tersebut dijalankan secara sendiri – sendiri atau di lakukan pendekatan hybrid pada kedua metode tersebut. Dalam kedua metode Sistem Pendukung Keputusan ini terdapat dua bagian utama yang akan dibahas, yaitu proses pembobotan dan proses perengkingan. Sedangkan data uji yang akan digunakan sebanyak 200 buah data. Hasil perengkingan yang didapatkan dari metode ini nantinya akan dibandingkan dengan data awal, dimana data awalnya disusun atau direngkingkan dengan mengalikan nilai kepentingan data dengan data itu sendiri.
Co-Authors Arti Dian Nastiti Desty Rodiah Evita Hardanita Gustian Aidil Fitri Jambak, Muhammad Ihsan Oktaria Permata Sari Yunita Yunita