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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
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Rancangan Arsitektur Sistem Informasi E-Customer Relationship Management Menggunakan Metode Enterprise Unified Process Retno Wulandari; Kristoko Dwi Hartomo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4324

Abstract

Customer Relationship Management (CRM) is a management that discusses the handling of the relationship between customers and the company. In the Power Kitchen where one of the company's equipment in the city of Surabaya has problems related to handling complaints from customers. So we need a CRM system to build relationships with customers. Therefore, the Power Kitchen company needs a CRM system to make it easier for companies to handle complaints from customers. This research uses the Enterprise Unified Process method which is a framework for the software process used. This research has resulted in the form of recommendations for system architecture models to achieve company goals and service relationships between companies and customers.
Implementasi Metode SMART (Simple Multi Attribute Rating Technique) Pada Sistem Pendukung Keputusan Pemberian Kredit Pinjaman Wildan Muhammad Ardana; Irma Rofni Wulandari; Yuli Astuti; Lilis Dwi Farida; Wiwi Widayani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4333

Abstract

Koperasi Simpan Pinjam (KSP) Tamanmartani Sejahtera is an operational savings and loan cooperative that utilizes funds from members in the form of savings and then flows back to members in the form of credit or loans. KSP Tamanmartani Sejahtera's main problem is arrears or customer credit payment delays. If it is allowed to continue, it will make it difficult for cooperatives to develop. One way to reduce delays in credit payments is to select prospective customers selectively. When choosing prospective customers, managers often experience difficulty making credit request decisions. Managers must consider and analyze the background of the credit application. The provision of credit to prospective customers must meet the standard criteria the cooperative sets. Based on these problems, to provide maximum recognition, it is necessary to have a system to help managers determine customer credit decisions. A Decision Support System (DSS) is a system that can provide information and manipulation data to assist decision-makers. One can use many choices, one of which is the Simple Multi-Attribute Rating Technique (SMART). The SMART method can help solve complex problems based on the difficulties encountered in KSP Tamanmartani Sejahtera. The results of this study are The SMART method was successfully implemented into a website-based system and displayed the ranking of customers who deserved to be given a loan. The test results with Black Box testing show the system can run according to a predetermined design.
Comparative Analysis of Multinomial Naïve Bayes and Logistic Regression Models for Prediction of SMS Spam Pradana Ananda Raharja; Muhammad Fajar Sidiq; Diandra Chika Fransisca
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4019

Abstract

This research was conducted based on a report from the United States Federal Trade Commission regarding fraud through electronic text messages via SMS that fraudsters use to manipulate potential victims. Usually, scammers spread SMS spam as an intermediary for the crime. The development of a supervised learning algorithm is applied to predict SMS spam into three categories, such as SMS spam, SMS fraud, and promotional SMS. The prediction system is dividing into several stages in the development process, including data labelling, data preprocessing, modelling, and model validation. The known accuracy based on modelling using Logistic Regression using a test size of 15% is 99%, using a test size of 20% is 99%, and using a test size of 25% is 98%. The Multinomial Naïve Bayes algorithm's accuracy with a test size of 15%, 20%, 25% is 97%. So, the SMS spam prediction approach uses the logistic regression method, which has the highest accuracy.
Sistem Pakar Deteksi Penyakit Bawang Merah dengan Metode Case Based Reasoning Yohani Setiya Rafika Nur; Auliya Burhanuddin; Dasril Aldo; Widya Lelisa Army
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4180

Abstract

Shallots are tubers commonly consumed by Indonesians. Shallots are one of the three members of the Allium genus that are much-loved and have high economic value. In the process of cultivating shallots susceptible to pests and diseases. Onion caterpillars, leaf flies, earthworms, purpura, fusarium wilt, onion mosaic and leaf spot are some of the pests and diseases that often attack shallot plants. Farmers will immediately give pesticides or methods that are sometimes not suitable for pests and diseases that attack. As a result, maintenance is not optimal and new pests or diseases often arise. This study aims to help farmers find early symptoms of shallot pests and diseases, so that pest and disease control is more optimal and on target. Processed as many as 10 attack data using Case Based Reasoning method. This method will process data in the form of symptoms seen in shallots, so that they can detect types of pests and diseases of shallots and their handling steps with 100% accuracy. Therefore, this method is relevant for the identification of shallot disease
Analisa Efektifitas Kebijakan PPKM terhadap Pertumbuhan Kasus COVID-19 Menggunakan Algoritma Naïve Bayes Regiolina Hayami; Yulia Fatma; Okta Tri Antoni; Harun Mukhtar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4356

Abstract

The pandemic that is being experienced by Indonesia, namely the outbreak of the COVID-19 virus, has led to the implementation of large-scale social restrictions in order to accelerate the handling of the spread of the virus. As a result of the increasing number of COVID-19 cases in Indonesia, including in Riau Province, where every City and Regency has increased, especially Pekanbaru, the implementation of Community Activity Restrictions (PPKM). This study tries to answer this question by using the Naïve Bayes Algorithm. Naïve Bayes Classifier is a probabilistic and statistical method of classification technique that predicts future opportunities based on previous experience. The use of the Naïve Bayes algorithm to predict the growth of Covid-19 cases in Pekanbaru obtained a good performance score with 90.00% accuracy, 90.24% precision and 91.90% recall. Based on the results of the classification of the datasets used in this study, it can be concluded that the implementation of the Policy for Enforcement of Community Activity Restrictions (PPKM) in the city of Pekanbaru has proven to be effective where there has been a decline in the category of growth of Covid-19 cases with a high category from 70% to 25%. Vice versa, the growth of Covid-19 cases in the low category has increased from 30% to 65%.
Perbandingan Metode AHP dan TOPSIS untuk Pemilihan Karyawan Berprestasi Musri Iskandar Nasution; Abdul Fadlil; Sunardi Sunardi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4194

Abstract

This study designed a system to determine outstanding employee selection using a Decision Support System (DSS) with the Analytical Hierarchy Process (AHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The purpose of this study is to analyze the accuracy in making decisions. The stages of this research are collecting employee data and criteria data, then weighting the criteria and assessment, after that the calculation uses the AHP and TOPSIS methods, and the last step is the analysis of the calculation results and the calculation of accuracy. The criteria used are attendance, years of service, permission, and discipline. Implementation for building applications using the PHP programming language and MySQL database. The results of the calculation of the accuracy obtained by the AHP method are 100%, as well as the TOPSIS method at 100%. The results of the AHP calculation show that the first rank results are obtained with a value of 0.02525, namely employees with code K8, while the results of the TOPSIS calculation show that the first rank results are obtained with a value of 0.955236913, namely employees with code K8. This shows that the two methods have the same results in determining the first rank of employees, however the TOPSIS method is better than AHP because the TOPSIS calculation process is carried out twice normalization so that it does not produce the same value.
Penerapan Metode Certainty Factor Dalam Diagnosa Hermatologi-Onkologi Nur Yanti Lumban Gaol; Lusiyanti Lusiyanti; Asyahri Hadi Nasyuha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4190

Abstract

Hematology oncology is a blood cancer that is quite worrying because it is identical to a condition that leads to death and this happens all over the world, where this disease not only affects adults but can also occur in children. Some experts suspect that the cause of blood cancer is due to changes in DNA that can trigger healthy blood cells to become cancerous. The World Health Organization (WHO) has stated that blood cancer is a very serious health problem because the number of sufferers increases by about 20% per year. This condition can certainly be prevented if patients who experience this disease can be detected early. To help overcome these problems, an Android-based system was developed that can diagnose blood cancer early based on the symptoms experienced by the patient, and this system was developed through a process of adopting expertise from experts into the form of a computer-based system known as the Expert System. . In order for the results of the diagnosis to have a high level of accuracy, an appropriate method is needed in its application, for that a method with a certainty factor algorithm is used. Based on the research results, in designing an expert system that adopts the Certainty Factor method, it can be used in solving problems related to the process of diagnosing hematological oncology diseases with an accurate level of certainty, this makes the diagnosis process easier and accuracy in determining oncological hematological diseases by utilizing the system.
Implementasi Electronic Data Processing Untuk meningkatkan Efektifitas dan Efisiensi Pada Text Mining Nofiyani Nofiyani; Wulandari Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4332

Abstract

Technological developments make the distribution of the amount of data more and more and continue to grow every day, these developments can be used to mine data which can later be processed into text/information needed for its use. Preprocessing is part of text mining where the process is divided into several stages, namely case folding, symbol removal, slangword conversion, stopword removal, stemming and tokenization. The news obtained is raw data from the xlm file from google alert which is then inputted into a system developed using the PHP programming language and mysql database. The data processing method in this research is Electronic Data Processing. The use of this system is expected to help the data preprocessing process where the process takes a long time, especially if a large sample of data is needed. The results of the study showed that a crawling process data processing information system for 20 data records only takes 0.0079004486401876 Mins and the data cleaning process or preprocessing for 88 data records only takes 0.012900729974111 Mins. In other words, data processing using the system is more effective and efficient for the next process.
Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor Siti Nur Arafah; Fathoni Fathoni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4434

Abstract

The LRT is a sustainable fast transportation system, which was built to overcome the congestion problem in the city of Palembang. In order to attract people's interest to switch to using public transportation compared to private transportation, one of them is by improving the quality of services provided. Sentiment analysis is used to classify positive and negative opinions on users of Palembang City LRT transportation services. In addition to retrieving data through crawling data on tweet data, the researchers also distributed questionnaires. In conducting the classification process of sentiment analysis, this study uses the Improved K-Nearest Neighbor method which is a modification of the K-Nearest Neighbor method. The results of this research are testing and training data on 1617 data records and the highest accuracy of 74.07% on 90% training data and 10% testing data, with 70% precision, 56% recall and 59% f-1 score, while the lowest accuracy with an accuracy of 63.04% on 50% training data and 50% testing data, with 44% precision, 42% recall and 42% f-1 score
Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search Rahmad Ubaidillah; Muliadi Muliadi; Dodon Turianto Nugrahadi; M Reza Faisal; Rudy Herteno
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4146

Abstract

Liver disease is a disorder of liver function caused by infection with viruses, bacteria or other toxic substances so that the liver cannot function properly. This liver disease needs to be diagnosed early using a classification algorithm. By using the Indian liver patient dataset, predictions can be made using a classification algorithm to determine whether or not patients have liver disease. However, this dataset has a problem where there is an imbalance of data between patients with liver disease and those without, so it can reduce the performance of the prediction model because it tends to produce non-specific predictions. In this study, classification uses the XGBoost method which is then added with SMOTE to overcome class imbalances in the dataset and/or combined with Bayesian search hyperparameter tuning so that the resulting model performance is better. From the research, the results obtained from the XGBoost model get an AUC value of 0.618, for the XGBoost model with Bayesian search the AUC value is 0.658, then for the XGBoost SMOTE model the AUC value is 0.716, then for the XGBoost SMOTE model with Bayesian search the AUC value is 0.767. From the comparison of the four models, XGBoost SMOTE with Bayesian search obtained the highest AUC results and has an AUC difference of 0.149 compared to the XGBoost model without SMOTE and Bayesian search.

Page 64 of 119 | Total Record : 1182

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