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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) KLIK: Kajian Ilmiah Informatika dan Komputer
Muhammad Irsyad
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.
Optimasi Convolutional Neural Network NASNetLarge Menggunakan Augmentasi Data untuk Klasifikasi Citra Penyakit Daun Padi Afiana Nabilla Zulfa; Jasril Jasril; Muhammad Irsyad; Febi Yanto; Suwanto Sanjaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6056

Abstract

Diseases that attack rice are one of the elements that can reduce rice production. Rice diseases include Blast, Brown Spot, Leaf Smut, and so on. Distinguishing rice disease from sight has a weakness because rice disease has similar symptoms and characteristics. Farmers lack knowledge in identifying rice disease types so that technology is needed that can help distinguish rice diseases. The method used for rice image classification in this study is the Convolutional Neural Network NASNetLarge architecture. There are two classification processes, namely the classification process using data augmentation and without data augmentation. The data consists of 4 classes, namely Healthy, Leaf Smut, Blast, and Brown Spot with a total of 440 original images and 1320 augmented images. This study uses data augmentation, namely Horizontal Flips, Vertical Flips, and Contrast. The results for the classification process without data augmentation obtained the highest accuracy, namely 94.31%, 100% precision, 100% recall, and 100% f1-score at a ratio of 80:20, learning rate 0.1, dense 256, batch size 32, and optimizer Adam. While the accuracy obtained in the classification process using data augmentation is 98.73%, 96.11% precision, 100% recall, and 98.01% f1-score at a ratio of 70:30, learning rate 0.1, dense 16, batch size 128, and the Adagrad optimizer. The accuracy results show that the data augmentation and hyperparameters used can increase the accuracy in classifying rice leaf disease images.
Klasifikasi Citra Stroke Menggunakan Augmentasi dan Convolutional Neural Network EfficientNet-B0 Nadila Handayani Putri; Jasril Jasril; Muhammad Irsyad; Surya Agustian; Febi Yanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5981

Abstract

A stroke is a sudden onset of brain dysfunction, lasting for 24 hours or longer, resulting from clinically focal and global brain dysfunction. As many as 15 million people die from stroke each year. The stroke patients need an immediate treatment to minimize the risk of brain damage. One of the proponents for the stroke diagnosis is through a computed tomography (CT) image. In recent years, the image processing techniques capable to detect stroke patterns in a brain image, it can be useful for doctors and radiologists in doing diagnosis and treatment. This study aims to compare the level of accuracy using augmentation and without augmentation and hyperparameters using the Convolutional Neural Network in the EfficientNet-B0 architecture to classify ischemic, hemorrhagic, and normal brain stroke images. The data augmentation is produced by rotating, horizontal flipping, and contrast tuning of the original data. Testing data is provided as much as 20% of the portion of the original and augmented data, and the other 80% is used for the training process to find the optimal model. The model search is based on the composition of the training and validation data with a ratio of 70:30, 80:20 and 90:10. The experimental results show that the best performance is obtained for the combined original and augmented images, with accuracies of 97%, 93%, and 94%, respectively, for the three types of data-test: original, augmented, and combined. The merging of original and augmentated images for training data has shown that the model is robust enough in producing high accuracy results.
Desain Sistem Pemasaran Produk UMKM dengan Konsep UI/UX Menggunakan Metode Design Thinking Bebi Oktaviani; Reski Mai Chandra; Muhammad Irsyad; Pizaini Pizaini
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3387

Abstract

The contribution of UMKM to the Indonesian economy has a major role in increasing economic growth in Indonesia. The development of UMKM in Indonesia has always been a special concern of many Indonesian citizens, including the government itself. Therefore, we conducted this research with the hope that the resulting product can provide solutions according to the needs and desires of users. This research focuses on system development for marketing UMKM products by designing UI and UX using the Design Thinking method. This Design Thinking method is a comprehensive thinking process that focuses on finding solutions with an empathetic process for a particular human-centered need. This method has 5 stages, namely Empathize, Define, Ideate, Prototype, and Testing. The testing method used is the SUS (System Usability Scale) method. System recommendations built in the form of a prototype using the Figma application. The feature that distinguishes this application from other applications is the AR feature to clearly see the product to be purchased. In addition, users can also choose souvenirs typical of an area by pressing directly from the interactive map provided. The prototype that was built was successfully tested on 10 respondents with an application prototype worth 87.
Analisis Pola Asosiasi Data Transaksi Penjualan Minuman Menggunakan Algoritma FP-Growth dan Eclat Risna Lailatun Najmi; Muhammad Irsyad; Fitri Insani; Alwis Nazir; Pizaini .
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3592

Abstract

Every day transaction activities between companies and consumers continue to be carried out. This makes transaction data more and more and accumulate. This transaction data can be processed into more useful information using technology. Data mining is a technology that can work on a collection of transaction data into information that can be taken by companies as decision makers. The association rule method is used as a method to see the relationship between items in a transaction data. To analyze transaction data, researchers used the FP-Growth and Eclat algorithms. There are three stages of association in this study which are distinguished from the confidence value. The results in the first stage have a minimum confidence value of 0.4, the FP-Growth algorithm produces 41 association pattern rules, while the Eclat algorithm produces 32 association pattern rules. Then in the second stage the minimum trust value is 0.5, the FP-Growth algorithm produces 40 association pattern rules, for the Eclat algorithm it produces 32 association pattern rules. In the third stage, the minimum trust value is 0.6, the FP-Growth algorithm generates 32 association pattern rules, while the Eclat algorithm generates 30 association pattern rules. The results of the association pattern rules show that the Eclat algorithm is more efficient in determining the association pattern rules than the Fp-Growth algorithm
Estimasi Hasil Panen Ayam Pedaging Menggunakan Algoritma Regresi Linear Berganda Ahyani Junia Karlina; Muhammad Irsyad; Fitri Insani; Jasril; Eka Pandu Cynthia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.920

Abstract

Data mining is the process of collecting and managing information that aims to extract important data from data. Currently data mining is used by companies to manage data but there are still many companies engaged in the livestock sector that have not used data mining to manage data. One of these companies is PT.PX which is a broiler company located in Riau, precisely in Sungai Pagar. The ever-increasing need for broiler chickens makes it difficult for chicken breeders to produce chicken according to market demand in each period. Unpredictable demand for broiler chickens makes breeders confused to determine how many chicks to produce. PT.PX still manages data using Microsoft Excel so the process is still very long and it is not certain to get accurate results. PT.PX also does not have a system for predicting broiler yields to find out how many chicken populations there will be in the next period. The existence of this data mining can help breeders to find out the number of populations to be produced for the next period. In predicting broiler yields, estimation methods can be used using multiple linear regression algorithms. Multiple linear regression was used to determine the relationship between feed, weight and age of chickens and chicken population. The information used in this research is information on harvested chickens obtained from 2019 to 2022. The results of multiple linear regression calculations at PT.PX obtained broiler yields of 12,217 populations
Klasifikasi Sentimen Masyarakat Di Twitter Terhadap Prabowo Subianto Sebagai Bakal Calon Presiden 2024 Menggunakan M-KNN Abdul Halim; Yusra Yusra; Muhammad Fikry; Muhammad Irsyad; Elvia Budianita
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4054

Abstract

Presidential elections are held every five years and each presidential candidate will get support from several political parties to run for candidacy in the election. In a multi-party system, the number of parties participating in the election is very large, so that the perspectives of voters on political actors, including presidential candidates who will advance in the 2024 elections, are varied. The survey results from Polling Indonesia (SPIN) conducted from 7 to 16 October 2022 show that Prabowo Subianto has the highest electability with a score of 31.6%, based on a national leadership survey. In this study, a test was carried out by classifying tweet data from the public collected on the Twitter application from January to December 2022 using the Modified k-Nearest Neighbor method to analyze public sentiment regarding the upcoming election. Data collected as many as 2,100 data with positive and negative categories related to "Presidential Candidate" and "Prabowo Subianto" and the implementation of the Modified k-Nearest Neighbor classification was carried out using Google Colab. Based on the results of the confusion matrix test from the Modified k-Nearest Neighbor classification with three comparisons made (ie comparisons 70%:30%, 80%:20% dan 90%:10%) and using K=3, 5, 7, 9, 11 when testing a comparison of 90:10 at K=3 the highest accuracy results were obtained with a value of 93,3%.
Penetration Testing Information System Security Assessment Framework (ISSAF) Zul Azis Khan; Nazruddin Safaat H; Muhammad Irsyad; Teddie Darmizal
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1507

Abstract

The development of information technology has had a positive impact on various fields, including the field of web technology. Information technology has now become a necessity in improving the performance of organizations and educational institutions in achieving goals. Websites are a tool for institutions to promote to the general public. The https://kekampus.umri.ac.id/ website is an information system owned by the Umri campus which is used for PKKMB and Umri Masters, as a website that functions in storing data, it is necessary to increase security to prevent hacker attacks, there are several methods used, one of which is The ISSAF framework is a penetration testing standard used to test the resilience of websites. The aim of this research is to determine the security gaps of the https://kekampus.umri.ac.id/ website by using the penetration testing method with the ISSAF Framework. The ISSAF framework includes nine test assessments which include Information Gathering, Network Mapping, Vulnerability Identification, Penetration, Gaining Access and Privilege Escalation, Enumerating Further, Compromising Remote Users/Sites, Maintaining Access, and Covering Tracks. In this study, examiners only carried out four stages of the nine stages in the ISSAF framework. This research uses a black box strategy where testers are only given access to the target website domain. This research was conducted because of the problems that often occur in gacor slots in one of UMRI's information systems. The results of the analysis carried out found that there were several vulnerabilities that were lacking on the website, namely SQL injection attacks, cross JavaScript, cookie secure flags on the https://kekampus.umri.ac.id/ website. and provide suggestions or recommendations to improve security on the https://kekampus.umri.ac.id/ website.
Co-Authors Abdul Halim Afiana Nabilla Zulfa Ahyani Junia Karlina Alwis Nazir Ana Komaria Baskara Bebi Oktaviani Eka Pandu Cynthia Elvia Budianita Febi Yanto Fitri Insani Jasril Jasril Jasril Muhammad Fikry Nadila Handayani Putri Nazruddin Safaat H Pizaini . Pizaini Pizaini Reski Mai Chandra Risna Lailatun Najmi Surya Agustian Suwanto Sanjaya Teddie Darmizal Yusra Yusra Zul Azis Khan