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Rekursif: Jurnal Informatika
Published by Universitas Bengkulu
ISSN : 23030755     EISSN : 27770427     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Rekursif adalah jurnal ilmiah yang diterbitkan oleh Program Studi Informatika, Fakultas Teknik, Universitas Bengkulu. Rekursif menerima artikel ilmiah dengan topik; Informatika, Sistem Informasi, dan Teknologi Informasi dari peneliti, dosen, guru, dan mahasiswa. Rekursif diterbitakan secara berkala setiap bulan Maret dan November berdasarkan hasil peer-reviewed. ISSN 2303-0755
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024" : 5 Documents clear
Implementasi Metode Naïve Bayes Dalam Sistem Pakar Diagnosis Penyakit Pada Itik Mojosari Andreswari, Desi; Suteky, Tatik; Epana Sari, Renti
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.32507

Abstract

Mojosari ducks are a type of duck that has potential as an egg producer, so many duck breeders are interested in it. Delays in treating disease in Mojosari ducks can result in various losses, such as decreased egg quality and quantity, disease transmission to other ducks, and can cause death of the ducks. Therefore, researchers built an expert system that can carry out quick and accurate diagnoses of duck diseases as well as correct countermeasures. This system was built by applying the Naïve Bayes method to an expert system to diagnose Mojosari duck disease by calculating the prior probability value of each disease based on input of symptoms that appear in the ducks. This system can detect 10 types of diseases and 40 symptoms that attack Mojosari ducks. This research was carried out with 30 test data and resulted in a system accuracy of 90%. Keywords: Mojosari Ducks, Diseases, Diagnose, Ducks, Expert Systems, Naïve Bayes.
Pengelompokan Kebutuhan Anggaran Negara Berdasarkan Indikator Ekonomi dan Kesehatan Menggunakan Fuzzy C-Means dan PCA Fachrurrozi, Muhammad; Muhammad, Fadzli; Sitepu, Delvi Nur Ropiq; Pratama, Reksi Hendra
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.38096

Abstract

Clustering the national budgets based on the economic and health indicators is another strategic approach that has been used to improve the effective planning of budget allocation. In this study, FCM will be applied for clustering budget data based on economic and health indicators across the regions. To reduce high-dimensional data complexity, in this paper, pre-processing data analysis will be done using PCA. Basically, PCA works by reducing data dimensions through the extraction of major factors that provide the greatest contribution to the variance of the data, thereby making the process of clustering using FCM feasible. The results derived from the analysis will indicate that the integration of PCA into FCM derives more accurate and informative clustering results and helps policymakers in devising appropriate strategies for budget allocations. Consequently, such findings are envisioned as adding to the positive development of enhancing efficiency and effectiveness in national budget allocation.
Pengelompokan Fuzzy Pengelompokan Pelanggan Pusat Perbelanjaan Menggunakan Algoritma Fuzzy C-Means Depriansyah, Yebi; Ardiansyah, M. Febri; Mezi; Rinaldi, M. Kevin
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.38100

Abstract

Customer segmentation is a crucial strategy for understanding consumer behavior and improving marketing efforts. This research aims to classify mall customers based on demographic data and shopping behavior using the Fuzzy C-Means (FCM) algorithm. The dataset employed is the "Mall Customer Segmentation Data," containing information such as age, gender, annual income, and spending score. The FCM algorithm groups customers into clusters based on data similarity, considering the fuzzy membership value for each customer. The results are expected to provide deeper insights into the characteristics of each customer group, assisting mall management in developing more targeted marketing strategies. The study will also discuss the interpretation of each cluster and the evaluation of the FCM algorithm's performance in the context of customer segmentation.
Analisis Klaster Negara Berdasarkan Indikator Sosial-Ekonomi Menggunakan Fuzzy C-Means dan K-Means Sulaiman, Davi; Afrian Al-Haritz, Rafi; Ahmad Saputra, Fahim; Irawan, Ade
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.38116

Abstract

This paper analyzes country clustering based on socio-economic indicators using Fuzzy C-Means (FCM) and K-Means algorithms. Each country has unique socio-economic characteristics that include aspects such as child mortality, exports, imports, per capita income, inflation, life expectancy, and gross domestic product. The dataset used includes 167 countries with 10 key indicators. After pre-processing and normalizing the data, clustering is performed using FCM and K-Means, where effectiveness is evaluated based on Sum of Squared Errors (SSE) and Silhoutte Score. This research aims to find the best algorithm in terms of accuracy and time efficiency in clustering countries based on socio-economic indicators. Keywords: Clustering, Fuzzy C-Means, K-Means, Socio-economic indicators, Sum of Squared Errors (SSE), Silhouttte Score.
Implementasi Metode Multi Objective Optimization on The Basis of Ratio Analysis (MOORA) Pada Sistem Pendukung Keputusan Prioritas Penanganan Genangan Banjir Salsabila, Elvina; Utama, Ferzha Putra; Sari, Julia Purnama
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.39532

Abstract

Banjir atau genangan merupakan peristiwa dimana air melimpah atau menggenangi daratan atau lahan yang semestinya kering yang menyebabkan kerugian ekonomi bagi penduduk. Salah satu tujuan penelitian ini yaitu mengetahui hasil dari implementasi metode MOORA pada sistem pendukung keputusan penentuan prioritas penanganan genangan banjir berbasis web. Kriteria yang digunakan pada penelitian ini meliputi kedalaman genangan, luas genangan, lama genangan, dan frekuensi genangan kriteria kerugian ekonomi, kriteria gangguan sosial dan fasilitas pemerintah, kriteria kerugian dan gangguan transportasi, kriteria kerugian pada daerah perumahan, serta kriteria kerugian hak milik dan pribadi. Metode MOORA adalah salah satu metode yang diperkenalkan oleh Brauers dan Zavadskas pada tahun 2006. Berdasarkan penelitian yang telah dilakukan disimpulkan bahwa Kelurahan Rawa Makmur mendapatkan rangking 1 dengan nilai Yi 0.235556 dan rangking 30 yaitu Kelurahan Betungan dengan nilai Yi 0.13033. Hasil pengujian keakuratan sistem yang mendapatkan akurasi sebesar 100% dan pengujian black box dengan akurasi 100%. Kata Kunci: Banjir, Sistem Pendukung Keputusan, Metode MOORA.

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