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All Journal INFORMAL: Informatics Journal Ekasakti Jurnal Penelitian dan Pengabdian (EJPP) Jurnal Malikussaleh Mengabdi
Ar Razi
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Algoritma Dijkstra dan Bellman-Ford dalam Sistem Pemetaan Barbershop di Kota Lhokseumawe Rozzi Kesuma Dinata; Bustami Bustami; Ar Razi; Muhammad Arasyi
INFORMAL: Informatics Journal Vol 7 No 2 (2022): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v7i2.33303

Abstract

A Barbershop service provider is a company that provides hair care to the community. Many people are currently doing business in this field, and many business actors are opening Barbershops in a variety of locations, ranging from campuses to office districts to densely populated towns. In Lhokseumawe City, there are 12 Barbershops. The application's benefit is that it can identify the shortest path from the user's location to the selected Barbershop, as well as the Barbershop's location and a brief description of the Barbershops in Lhokseumawe City. Only the system's defined nodes can be used to find the fastest route to the Barbershop. Dijkstra's method was chosen because it works against all current alternative functions and provides the shortest path from all nodes, ensuring that the shortest path is produced optimally. Because the Bellman-Ford algorithm is a variant of the BFS (best-first-search) algorithm, which is also employed in the search for the closest distance when the search for the closest distance has a negative weight, it was chosen. The same results were obtained in picking the route based on the results of the route selection test. However, when the two techniques are compared in terms of program execution time, Dijkstra's algorithm is faster than the Bellman-Ford algorithm.
Penyuluhan Pengendalian Korosi Pipa pada Pelaku Industri Blasting Fikri, Ahmad; Khairul Anshar; Agam Muarif; Rizka Mulyawan; Ar Razi; Desvina Yulisda; Kurniawati; Dini Rizki; Syamsul Bahri
Jurnal Malikussaleh Mengabdi Vol. 4 No. 1 (2025): Jurnal Malikussaleh Mengabdi, April 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i1.22388

Abstract

Korosi merupakan fenomena yang sering terjadi pada komponen dan peralatan industri. Proses penanganan korosi salah satunya adalah dengan menggunakan blasting. CV Mitra Blastida Utama merupakan salah satu perusahaan blasting yang cukup baik untuk membantu mitranya dalam menghadapi korosi. Kemampuan dalam melakukan blasting setelah didapatkan oleh perusahaan tersebut sejak lama. Namun prinsip blasting Yang digunakan untuk mengendalikan polusi belum sepenuhnya dipahami oleh karyawan perusahaan tersebut. Kegiatan penyuluhan pengendalian korosi pada pipa merupakan kegiatan memberikan dasar-dasar pengendalian korosi dan blasting pada mitra. Metode yang digunakan dalam kegiatan ini adalah metode penyuluhan. Pemahaman pengendalian korosi pada pipa mengalami peningkatan dari mulai sebelum penyuluhan sampai setelah dilakukan penyuluhan. Metode tersebut yang memudahkan peningkatan pemahaman peserta sebelum penyuluhan sampai sesudah penyuluhan.
KLASIFIKASI TINGKAT KEBERHASILAN SURVIVAL RATE (SR) PADA PRODUKSI UDANG VANAME MENGGUNAKAN ALGORITMA NAÏVE BAYES Ar Razi; Desvina Yulisda
Ekasakti Jurnal Penelitian dan Pengabdian Vol. 4 No. 2 (2024): Ekasakti Jurnal Penelitian & Pegabdian (Mei 2024 - Oktober 2024)
Publisher : LPPM Universitas Ekasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/ejpp.v4i2.1080

Abstract

Data mining is the process of collecting and processing data with the aim of extracting important information from the data. This process can be done using software that uses mathematical calculations, statistics, or AI. Naive Bayes is the most common classification technique and has a high level of accuracy. Many studies on classification have used the Naive Bayes algorithm. Naive Bayes is a simple probability classification technique used to assume that the explanatory variables are independent. The focus of learning this algorithm is probability estimation. One of the advantages of the naive Bayes algorithm is that the resulting error rate is lower. In addition, this algorithm has a higher level of accuracy and speed when used on larger datasets. This research uses the Naïve Bayes algorithm to classify the Survival Rate (SR) of Vaname shrimp into three classes, namely high, medium and low. The number of sample data used was 200 data which was divided into 2 categories, namely 170 training data and 30 testing data. The variables used in this research are temperature, PH, DO (dissolved oxygen) and salinity. The classification was validated using a confusion matrix and produced an accuracy of 70.4%, precision of 98%, and recall of 79.7%.
Co-Authors Agam Muarif Ahmad Fikri Bustami Bustami Dini Rizki Khairul Anshar Kurniawati Muhammad Arasyi Rizka Mulyawan Rozzi Kesuma Dinata Syamsul Bahri Yulisda, Desvina