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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer
M Afdal
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisa Manajemen Risiko Sistem Informasi Perpustakaan Menggunakan Metode Failure Mode Effect and Analysis (FMEA) Maisarah Assa'diyah; Tengku Khairil Ahsyar; M Afdal
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.v3i6.867

Abstract

Public libraries that have implemented information technology in their business processes have a great responsibility in service and management because the visiting users are the general public. In implementing information technology, maintaining the security of user data, members, resources, and library information is very important. Digital libraries must have consideration of the risks and threats that may occur. The risks and threats that can occur are as follows server damage, hardware damage, staff negligence, loss, and natural disasters. The purpose of this research is to analyze risk management by identifying risks and measuring the level of risk from one of the Riau Province Library and Archives Services which already uses a library automation system, namely INLISLite. The method used to identify and assess risk is the Failure Mode Effect and Analysis (FMEA) method. Risk assessment is based on calculating the value of the Risk Priority Number (RPN) resulting from multiplying the level parameters severity, occurrence, and detection. Risk assessment is carried out based on the category list of asset components that support the running of the system, namely, hardware, software, data, people, and network. From the calculations that have been carried out, there are six categories of RPN levels, namely 1 score at a very high level, 3 scores at a high level, 2 scores at a medium level, 16 scores at a low level, and 1 score at a very low level. From the results of the RPN value that needs to be given recommendations for action, namely the RPN value which is at very high and high levels
Vector Tile Server In Geographic Information System In Bapenda Pekanbaru City Desy Herlina Citra; Muhammad Jazman; Anofrizen; M Afdal; Arief Rahman
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.v3i6.907

Abstract

The Regional Revenue Agency (Bapenda) is an organization primarily focused on regional taxation matters. Bapenda City of Pekanbaru used WebGIS to view spatial data related to taxes. The WebGIS system used in Bapenda is desktop-based. The users of this system must engage directly with GIS hardware in order to get information about taxes, which is a disadvantage of this system. Furthermore, the WebGIS utilized by Bapenda currently relies on WMS (Web Map Service). However, due to the extensive data processing requirements, WMS is not deemed suitable. WMS may result in broken labels, causing confusion among users, and it also has limitations when it comes to styling capabilities. This study's goal is to recommend the use of GIS, specifically the GeoServer and PostGIS programs, for efficient data storage table administration and map digitalization. A Geographic Information System using a Vector Tile Server is the end result of this system. Gaining access to a spatial data infrastructure that works with Vector Tile Server is considered to be beneficial since it will allow spatial data to be presented and styled without placing a heavy load on the client. Implementing GIS, especially GeoServer and PostGIS applications, may make it easier for Pekanbaru City to audit and collect taxes, which will make these responsibilities easier for Pekanbaru City Bapenda.
Analisis Sentimen pada Ulasan Aplikasi Maxim di Google Play Store dengan K-Nearest Neighbor Restu Ramadhan; M Afdal; Inggih Permana; Muhammad Jazman
JURIKOM (Jurnal Riset Komputer) Vol 10, No 3 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i3.6396

Abstract

Online transportation is an innovation in emerging technology to solve various problems that arise in conventional public transportation such as in the ease of ordering, availability, and digitization of payments. Maxim is an online transportation company that has been operating since 2018 in Indonesia. As the number of users of the maxim application increases, demands for the quality of application service also increase. In the Google Play Store, reviews and information about an app are stored in text form. One of the processes of extracting text mining information in the text category is Sentiment Analysis to see the tendency of a sentiment or opinion whether it is positive, neutral, or negative at the Maxim application user reviews. The sentiment classification process using the K-NN algorithm produces accuracy, precision, and recall of 90.23%; 90.23%; and a recall value of 72.38% with an experiment using 90% training data, 10% test data, and a value of k = 5.
Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors Dedi Pramana; M Afdal; Mustakim Mustakim; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6523

Abstract

The relocation of Indonesia's capital city is a hot topic of discussion at the moment. So that this government policy reaps a lot of reactions from various parties, especially the general public in Indonesia. Various reactions were shown with various expressions on various social media. One of the social media that has become a place for people to express themselves in responding to this government policy is Instagram. The comments poured by the community on posts on Instagram are very diverse ranging from positive, negative, and neutral comments. If these comments are processed properly, they can be used as evaluation material for the relocation of the State capital. Seeing this, a sentiment analysis is needed which is intended to classify the various comments so that they can be presented into information which will be intended to help the government make considerations in carrying out policies towards moving the national capital. In this study, data processing was carried out with the Naive Bayes Classifier and K-Nearest Neightbors algorithms with Instagram comment data on posts related to moving the national capital. Where the amount of data used is 2,404 comments. It was found that the accuracy of the NBC algorithm was 63.09% and K-Nearest Neightbors was 69.23% so it can be concluded that KNN is better than NBC. In addition, the popularity of public sentiment towards the relocation of the National Capital was also obtained with a positive sentiment of 28% totaling 643 comments, a neutral sentiment of 42% totaling 1025 comments, and a negative sentiment of 30% totaling 730 comments.
Penerapan Algoritma Fuzzy C-Means Pada Segmentasi Pelanggan B2B dengan Model LRFM Aufa Zahrani Putri; M Afdal; Siti Monalisa; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6150

Abstract

PT. XYZ is one of the major pharmaceutical industries in Indonesia by marketing its products through B2B (Business to Business) customers. PT. XYZ doesn't understand what customers need. PT. XYZ also implements a cashback system for B2B customers. This study aims to determine customer segmentation, analysis of customer characteristics, firmgration and proposed strategies provided by researchers to PT. XYZ. Loyalty and customer characteristics are very influential on a company. To show which customers are loyal to the company, the Fuzzy C-Means algorithm is used to cluster and the Davies Bouldien Indeks (DBI) is used for the clustering algorithm results. The algorithm used is according to the Length, Recency, Frequency and Monetary (LRFM) model to classify purchasing behavior. It can be seen from the frequency variable which customers are loyal to which companies are not. Then determine the firmography using the attributes of business entity type, customer type, and location. After determining loyal and non-loyal customers, the analysis of customer characteristics is divided into 4 parts, namely the Superstar Segment or the best customer, which is located in cluster 2 where customers in cluster 2 can have a long-term relationship with the company, then the Golden Segment or which has the second highest value (monetary) is located in cluster 4, then the Average Value Segment or the customer who has the average value of all segments is located in cluster 5 and the Dormant Segment or the lowest customer is located in cluster 3 where customer 3 has little relationship with the company.
Co-Authors Anofrizen Arief Rahman Aufa Zahrani Putri Dedi Pramana Desy Herlina Citra Inggih Permana Maisarah Assa'diyah Muhammad Jazman Mustakim Mustakim Restu Ramadhan Siti Monalisa Tengku Khairil Ahsyar