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All Journal INFORMAL: Informatics Journal Ekasakti Jurnal Penelitian dan Pengabdian (EJPP) Jurnal Malikussaleh Mengabdi Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Ar Razi
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Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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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%.
Implementation Of Support Vector Regression In Prediction Air Quakity Index In Banda Aceh City Rizky Fasya Ramdhani; Rozzi Kesuma Dinata; Ar Razi
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Air quality is one of the important aspects in maintaining environmental balance and public health. Increasing air quality in the environment is a matter of concern. Therefore, a method that can predict the Air Quality Index (AQI) effectively is needed to be able to monitor and support decision making on environmental impacts. This study aims to predict the Air Quality Index in Banda Aceh City using the Support Vector Regression algorithm, with five main parameters used in the study, namely particulate matter , Sulfur dioxide, Nitrogen dioxide, Carbon monoxide , and Ozone . In this research, the Support Vector Regression algorithm was chosen because of its ability to handle non-linear data and also because it can provide accurate predictions on data. The prediction system designed will be web-based using the flask framework and MySQL database, while the Support Vector Regression modeling will be done on google colab for the media used. In the process of modeling the data will be divided into 80% training data and 20% test data to ensure the model can capture long and short-term patterns. The results of the prediction will be compared using the Root Mean Squarred Error (RMSE) and Mean Squarred Error (MSE) evaluation metrics. The results of the evaluation using both metrics yielded RMSE values of 1.9001 and MSE of 3.6015. These values indicate good performance of the model in predicting the data. This research is expected to provide insight for future similar research in terms of prediction using the Support Vector Regression algorithm.
Application Of Data Mining For Classification Of BLT-DD Recipients Using The Support Vector Machine Method Meliza Putri; Bustami; Ar Razi
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This study focuses on the application of the Support Vector Machine (SVM) algorithm for classifying recipients who are eligible to receive Direct Cash Assistance from the Village Fund (BLT-DD) in Nurussalam District, East Aceh Regency. The background of this research is the difficulty in identifying households eligible for BLT-DD due to increasing poverty and economic inequality exacerbated by the COVID-19 pandemic. This study aims to address this issue by utilizing the SVM algorithm, which can separate household data into two categories: "Eligible" and "Not Eligible." A total of 550 data points from Nurussalam District were used in this study, with 400 data points for training and 150 data points for testing. In the training data, 322 households (80.5%) were classified as "Eligible," while 78 households (19.5%) were categorized as "Not Eligible." The data collected includes variables such as household income, type of employment, education level, history of chronic disease, and home ownership status. After preprocessing the data, such as normalization and encoding, the SVM model was trained to classify BLT-DD recipients. In the testing data, 128 data points (85.33%) were classified as "Eligible," while 22 data points (14.67%) were classified as "Not Eligible." Further analysis of village distribution in Nurussalam District shows that some villages have a high percentage of eligible recipients, such as Blang Rambong and Alue Jagat, with 100% of recipients classified as "Eligible." Other villages, such as Arul Pinang and Alue Dua Muka O, show more varied eligibility rates, with 71.43% and 72.73% classified as "Eligible," respectively. In conclusion, the SVM algorithm provides an effective approach in determining the eligibility of BLT-DD recipients, helping the government to distribute assistance more accurately and efficiently in Nurussalam District.
Co-Authors Agam Muarif Ahmad Fikri Bustami Bustami Bustami Bustami Dini Rizki Khairul Anshar Kurniawati Meliza Putri Muhammad Arasyi Rizka Mulyawan Rizky Fasya Ramdhani Rozzi Kesuma Dinata Syamsul Bahri Yulisda, Desvina