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INDONESIA
JURNAL MEDIA INFORMATIKA BUDIDARMA
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 26145278     EISSN : 25488368     DOI : http://dx.doi.org/10.30865/mib.v3i1.1060
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Decission Support System, Expert System, Informatics tecnique, Information System, Cryptography, Networking, Security, Computer Science, Image Processing, Artificial Inteligence, Steganography etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,182 Documents
 87   88   89   90   91 
Sentiment Analysis of Simobi Plus Mobile Application Using Naïve Bayes Classification Stevan Hamonangan Hardi; Kristoko Dwi Hartomo
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.v7i3.6300

Abstract

Sinar Mas Bank is one of many banks operating in Indonesia. Quite a few people use Sinar Mas Bank's services as their bank of choice for their day-to-day transactions. By popular demand, Sinar Mas Bank serves users of banking services by creating an M-banking application. The M-banking application created by Bank Sinar Mas is called Simobi Plus Mobile Banking. There are already 52.3 thousand reviews regardings this application on the Google Play Store platform. Among these are positive and negative reviews from customers who use the application for their daily transactions. In reviews that use 1-5 star ratings, many people are misled by giving different ratings than the given stars. Many customers who leave 5-star app reviews, but comments on these reviews contain negative words. As a result, the application developer becomes confused because the comments given do not match the rating given by the user. Comments that are not in accordance with the rating given can involve the developer of the application to make improvements or development for the application. Therefore, Research should be conducted using techniques and analytics to categorize the user comments into several groups. This study uses sentiment analysis using the Naive Bayes method to capture positive and negative sentiments for comments on the Simobi Plus mobile banking application on the Google Play store, so that these sentiments have the appropriate value. The accuracy scores for the negative class, positive class, recall, and mood analysis are used to evaluate the test. The resulting value has an accuracy of 99%, which is almost perfect. The precision value was 100%, whereas the recall class produced a value of 98% (positive class: negative). And the AUC value is 0.980.
Klasifikasi Jenis Mangga Menggunakan Algoritma Convolutional Neural Network Risma Yati; Tatang Rohana; Adi Rizky Pratama
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.v7i3.6445

Abstract

The name of the mango is Mangnifera IndicaL. It originated in India and spread to Indonesia. There are various types of mango variations with different shapes and colors according to the type. To distinguish each mango is seen by its shape and color. However, if in the harvest process mango farmers have to choose manually it takes a long time and potentially mistaken in determining the type. So it needs technology that can make it easier to differentiate the type of mango based on its shape. The study aims to create models with the best accuracy on the process of classifying 5 types of mango based on its shape. The data used in the research this time there are 5 types of mango that will be classified, namely Mangga Apel, Arumanis mango, Mangga Gedong Gincu, Golek mango and Mangga Manalagi. Used 375 images of mango as data sets. The data set before entering the previous training process is undergoing a pre-processing phase that includes the augmentation and resize process. The number of images increased to 2250. The data set is divided into three parts: 70% training data, 20% validation data, and 10% test data. Next is the process of segmentation, the segmentation used in this research is otsu segmentation. The classification process uses the Convolutional Neural Network (CNN) architecture with 3 layers of convolution 16,32 and 64, also using the Adam optimizer. 4 experimental scenarios were performed to find the best accuracy value by distinguishing between learning rate and batch size. From the confusion matrix test results, the best accuracy values were obtained from the input hyperparameter size100x100, epoch 100, learning rate 0,001 and batch size 15 with accurate values of 99.56%, precision 100%, recall 100%, and f1-score 100%.
Classification of Medicinal Wild Plant Leaf Types Using a Combination of ELM and PCA Algorithms Dedy Alamsyah; Farli Rossi; Ri Sabti Septarini; Mohammad Imam Shalahudin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6817

Abstract

Despite their detrimental nature, it turns out that wild plants have many benefits for human health. Wild plants with a form of herbaceous vegetation contain ingredients that can be used as medicine, especially in their leaves. However, because the information is very similar and the form is similar, people don't know about it. For this reason, the aim of this research is to implement an artificial neural network algorithm using Extreme Learning Machine (ELM) and the Principal Component Analysis (PCA) algorithm to classify images of wild plant leaves with medicinal properties, especially in herbaceous vegetation. The feature extraction used in this research involves morphological features by considering the shape of the object. The PCA algorithm will reduce data complexity and identify hidden patterns in the data by changing the original feature space to a new and more concise feature space. Next, the ELM algorithm is used to recognize class grouping patterns when solving classification problems. Accuracy test results show a value of 90.667%.
Implementasi Data Mining dengan Algoritma Apriori dalam Menentukan Pola Pembelian Aksesoris Laptop Gatot Soepriyono; Agung Triayudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6555

Abstract

Consumer purchasing patterns are an important factor in the business world, which influences marketing strategies, stock management and company profits. In the context of the laptop accessories business, a deep understanding of consumer purchasing patterns is very necessary to increase operational efficiency and customer satisfaction. Data mining, as a powerful data analysis method, has become an effective tool in uncovering these patterns. One of the data mining algorithms that is often used to analyze association patterns is the Apriori algorithm. This research applies the Apriori algorithm to identify and analyze purchasing patterns for laptop accessories from transaction data obtained from a retail store. By analyzing this data, we can identify items that are frequently purchased together and purchasing patterns that may not be immediately apparent to humans. The results of this analysis provide valuable insight into consumer preferences, helping retail stores to design more effective marketing strategies. The results of this research can also be used to manage stock more efficiently. By knowing deeper purchasing patterns, retail stores can predict stock needs more accurately, reduce the risk of excess inventory, and optimize operational expenses. Thus, this research can help increase company profits and satisfy customers by providing accessories that suit their preferences. In the increasingly developing information era, the use of data mining and algorithms such as Apriori is becoming increasingly important. This research is an example of how data analysis can be used in the real world to support smarter and more efficient decision making in the laptop accessories business. As a result, a better understanding of consumer behavior and purchasing patterns can provide a strong foundation for developing successful business strategies.
Market Basket Analysis Menggunakan Algoritma Apriori dan FP Growth untuk Menentukan Pola Pembelian Konsumen Reski Noviana; Arief Hermawan; Donny Avianto
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.v7i3.6304

Abstract

An increase in sales transaction data every day will cause a very large amount of product sales transaction data to be stored. In most cases, data relating to very large sales transactions are only stored and used for archival purposes, not exploited adequately. In retail marketing, association data mining is used to investigate product purchasing patterns. However, if the company does not know the customer's purchasing patterns, it can have impacts such as inappropriate marketing strategies, decreased customer retention, lost business opportunities, lack of personalization, tough competition, stock/production inefficiencies, loss of customer trust. Knowing consumer purchasing patterns, the company can develop sales strategies and make the right decisions. In this study using Market Basket Analysis using the Apriori Algorithm and FP-Growth to determine consumer buying patterns. The results of this study resulted in two itemset combinations. The first combination is that if the buyer buys yogurt and sausage, the buyer also buys whole milk. The resulting support value is 0.00147 (0.0147%), the confidence value is 0.255814 (25.58%) and the lift value is 1.61986. the second combination, namely if the buyer buys sausage (sausages) and rolls/buns (bread rolls), then also buys whole milk (milk), this combination produces a support value of 0.001136 (0.0113%), a confidence of 0.2125 (21.25%) and a lift of 1.34559 . In addition to the combination of the itemset produced in this study, it also measures computational speed in processing Groceries data for Market Basket Analysis. The computational speed produced by the Apriori Algorithm is 3.1765 seconds, while the FP-Growth algorithm is 0.15892 seconds. The difference in computational speed between the Apriori Algorithm and FP-Growth is 3.0176 seconds.
Analisis Penerapan Metode MOORA dan WASPAS dalam Keputusan Pengajuan Kredit Pemilikan Rumah (KPR) Rizka Tri Alinse; Venny Novita Sari; Achmad Fikri Sallaby
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.v7i3.6174

Abstract

To fulfill the necessities of life, humans need a place to shelter from the hot sun and the dark of the night even if it rains, the role of residence is very important. Not only that, the house can also be a place for gathering and carrying out family activities such as carrying out daily activities and communicating with each other. A house is an asset whose price is not cheap, so not a few people buy a house by credit. Therefore, the bank created a mortgage program to make it easier for people with limited economic conditions. Mortgages are made with payments in installments so it is likely that the bank will lose if the mortgage is given to the wrong person. So we need a system that facilitates the bank in making a decision. The Decision Making System (DSS) can be carried out using several methods such as MOORA, MOOSRA, WASPAS, TOPSIS, PSI, VIKOR, WP, SAW and others. In this study, the method used in determining the recipient of a Home Ownership Loan is the MOORA and WASPAS methods. These two methods will be applied separately based on existing criteria and alternatives. The final result after applying this method is that the best alternative is obtained by A7 with a Qi value of -0.04131 (MOORA method). Likewise with the application of WASPAS, the highest value was obtained for alternative A7 with a Qi value of 2.91829.
Peningkatan Algoritma C4.5 Berbasis PSO Pada Penyakit Kanker Payudara Rudi Nurcahyo; Ahmad Zainul Fanani; Affandy Affandy; Mochammad Ilham Aziz
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6841

Abstract

Onenof the diseases innthe world that causes deathnin women isncancer. Cancernis a diseasencaused by uncontrolled enlargement of abnormal organs in the body. Cancer diagnosis is made using anthropometric data from routine blood analysis. The data used is the Breast Cancer Coimbra Data Set obtained from the UCI Machine Learning Repository. The C4.5 method is andecision treenalgorithm that is often used in the classification process. The selection of the right features, as well as the selectionnof the right method to overcome the class imbalance in the classification process cannimprove the performancenof the C4.5 algorithm. confusion matrix can benused in the Test to determine Classification accuracy. In this research, the application of PSO as a feature organization.
Implementasi Kontrol PID untuk Percepatan Rotasi pada Robot Hexapod Yovi Pratama; Muhammad Irwan Bustami; Afrizal Nehemia Toscany; Chindra Saputra; Xaverius Sika; Janu Hadi Susilo; Arahmad Taupiq; Cahyana Putra Pratama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6583

Abstract

In the development of robot technology, the hexapod robot has great potential to be used in post-disaster rescue because of its ability to adapt to difficult terrain. In this study the authors consider the effectiveness of PID (Proportional Integral Derivative) control based on related literature. Several previous studies have shown the successful use of PID in servo or robotics movement control in different contexts, such as approaching fire sources, controlling DC motor angles, controlling cleaning robot movements on glass, and so on. But the authors want to implement PID motion control with a different approach. The main objective of the hexapod robot is to save the victim by overcoming obstacles and delivering them to the safe zone. In the first test the authors use the PID control method in the middle position between the initial compass rotation value and the destination rotation. The results show that the time needed to reach the rotation goal is relatively fast. In contrast, the second test using the PID control method for all rotary motion resulted in a significant reduction in the error value compared to the first test. Although it requires more time, this method increases the robot's accuracy in achieving the rotation objective efficiently. The robot uses a CMPS03 compass sensor which can provide a direction value which will be input to the PID and servos that are installed as foot movers, each servo is controlled by a microcontroller with the inverse kinematic method to produce the appropriate movement. The results of this research are expected to be the basis for the development of more capable and reliable robots in the next Indonesian SAR robot contest.
Penerapan Metode Elimination Et Choix Traduisant la Realite (ELECTRE) dalam Proses Segmenting, Targeting, dan Positioning Layanan Akomodasi Yerik Afrianto Singgalen
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6677

Abstract

One of the challenges in marketing marketing strategy is determining the appropriate segments and target markets and increasing competitiveness through solid positioning. To determine the STP (Segmenting, Targeting, Positioning) marketing strategy in the accommodation service business, it is necessary to support decision-support models relevant to resource availability and the market environment. This study applies the Elimination et Choix Traduisant la Realite (ELECTRE) method in segmenting, targeting, and positioning hotel, homestay, and resort accommodation services. The stages in the ELECTRE method are as follows:  the stage of determining criteria and alternatives; the stage of normalization of the decision matrix; the weighting stage of the normalization matrix; the stage of determining the set of concordance and discordance on the index; the calculation stage of the concordance and discordance matrices; the calculation stage of the dominant matrix of concordance and discordance; Determination stage aggregate dominance matrix; the stage of elimination of less favorable alternatives. Based on the results of this study, it is known that the ELECTRE method can provide a systematic decision-making structure and framework, especially in segmenting, targeting, and positioning, to optimize results by business goals. Based on the results of implementing the ELECTRE method, it can be seen that A1 has an E value of 63.52, so it occupies the first position. In addition, A2 has an E value of 53.74, so it occupies the second position. Also, A3 has an E value of 49.74, occupying the third position. Based on the application of the ELECTRE method, hotels (A1), homestays (A2),  and resorts  (A3) need to optimize the 9P marketing mix (product, price, place, promotion, people, process, physical evidence, performance, partnership) in the process of Segmenting, Targeting, and Positioning for the sustainability of the accommodation service business. However, there is a challenge in this method, which is the integration of subjective preferences of capital owners in the decision-making process using ELECTRE, which can lead to distortion of results due to subjectivity and individual interests.
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.

Page 89 of 119 | Total Record : 1182

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