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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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Kombinasi Metode Multi-Attribute Utility Theory dan Pivot Pairwise Relative Criteria Importance Assessment Dalam Penentuan Lulusan Terbaik Ainaya Qintan Maharani; Temi Ardiansah
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.v7i4.6884

Abstract

The determination of the best graduates at SMK YPT Pringsewu is determined based on the average score, achievement, and report card score. By using this assessment, the school takes a long time with a large number of prospective graduates to complete and select all prospective graduates and determine the best graduates. So it requires a recommendation system in determining the best graduates, one of the recommendation systems for determining the best graduates is a decision support system. With the decision support system for determining the best graduates, it will speed up the process of determining the best graduates because it uses a computerized system. This study aims to develop a decision support system that applies the MAUT method and the PIPRECIA method in determining the best graduation in school and develop an objective system or approach to assess and rank the best graduates based on relevant criteria and preferences set by researchers or related parties. PIPRECIA and MAUT methods are used to overcome complexities in multi-criteria decision making and help select the best graduates effectively. The results of determining the best graduate students using the MAUT method and PIPRECIA recommended Mareta Amelia as the best graduate student in school because the final ranking results got Rank 1 with a total score of 0.642. The overall results for testing using the blackbox testing model with results are 100% in accordance with the functionality of the system for the determination of the best graduates.
Pengembangan Chatbot Kesehatan Mental Menggunakan Algoritma Long Short-Term Memory Fajarudin Zakariya; Junta Zeniarja; Sri Winarno
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7177

Abstract

Mental health has now become a crucial aspect of contemporary society, especially in Indonesia. This reflects the emotional, psychological, and social well-being of individuals, encompassing the ability to cope with stress in daily life. A comprehensive understanding of mental health has become highly important for the community to prevent the occurrence of mental health problems or disorders. The objective of this research is to design a chatbot as an information and solution hub for maintaining mental health, with the hope that the development of this chatbot can help reduce the risk of mental health-related issues. In the development process of this chatbot, the author applies the AI Project Cycle and utilizes a deep learning approach for the chatbot model. The development involves the Flask platform, and to achieve high accuracy, the model employs the Long Short-Term Memory (LSTM) architecturea type of recurrent neural network (RNN) specifically designed to handle long-term dependency issues common in complex mental health contexts. LSTM enables the model to store and access long-term contextual information, which can be highly beneficial in providing accurate solutions and understanding emotional condition changes. The trained LSTM model demonstrates an accuracy of 93%, validation accuracy of 82%, a loss of 0.3%, and validation loss of 1.6% after 200 epochs. Therefore, it can be concluded that using the LSTM algorithm for the chatbot model in this development is quite effective.
Decision Support System for Determining New Branch Locations Applying the Multi Attribute Utility Theory (MAUT) Method Achmad Fikri Sallaby; Agung Triayudi; Kelik Sussolaikah; Muhammad Zakaria Lubis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7049

Abstract

The location of new branches that are close to community activities and have adequate facilities makes it easier for consumers to get the services and products they need. Determining the feasibility of new branch locations from several product or service producers still uses a system that is not accurate, which can cause problems in determining the location of new strategic and targeted branches. However, there are several obstacles in the selection of new branch locations, so technological assistance is needed in determining the location, product analysis, marketing management, and other matters concerning the development of the business being carried out. Technology that is considered efficient, easy, and flexible and is used by entrepreneurs, especially in determining the location of new branches using a decision support system using the MAUT method, is expected to help the location of new branches that are efficient and strategic. The decision support system is a conclusion and determination of the best using some data and computerized testing in each criterion so as to get valid results. After calculating each criterion and alternative, the best ranking is obtained in alternative A1 with a value of 0.7925 on Pertahanan Street.
Perbandingan Algoritma SVM, KNN dan NBC Terhadap Analisis Sentimen Aplikasi Loan Service Dewi Nurmalasari; Teguh Iman Hermanto; Iman Ma'ruf Nugroho
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.v7i3.6427

Abstract

According to data on the number of credit card users in Indonesia, it has decreased from late 2019 to 2021 oneThe reason is because of the Covid-19 pandemic that hit. Because of this condition, many people are starting to switch to digital credit because they are considered to minimize transmission of viruses and the process is felt to be more efficient than having to use a credit card. This study aims to compare the level of accuracy between the three algorithms, namely the naïve Bayes classifier, k-nearest neighbor and support vector machine for digital credit applications or often called loan services, namely Kredivo. Akulaku, and Indodana in Indonesia by classifying it into two classes namely positive and negative by using the help of the Python programming language to analyze a sentiment by going through text preprocessing and weighting processes said TF-IDF. The results for the accuracy of the Kredivo application using K-NN get a score of 84%, Naïve Bayes 88%, and SVM get 89%. For the application of the K-NN method, it gets 79%, Naïve Bayes 86%, and SVM 87%. As for the indodana application, the K-NN method gets 81%, Naïve Bayes 88%, and SVM 88%. From the results of this accuracy it can be concluded that the Support Vector Machine method has better accuracy results compared to the K-Nearest Neighbor and Naïve Bayes methods.
Pengaruh Oversampling dan Cross Validation Pada Model Machine Learning Untuk Sentimen Analisis Kebijakan Luaran Kelulusan Mahasiswa Mufida Rahayu; Ardytha Luthfiarta; Lailatul Cahyaningrum; Alya Nurfaiza Azzahra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7012

Abstract

The Minister of Education, Culture, Research and Technology issued a new policy on graduation standards for undergraduate and postgraduate students. This policy was delivered on August 29, 2023, on live streaming YouTube Kemendikbudristek at the Merdeka Belajar seminar episode 26: Transformation of National Standards and Higher Education Accreditation. The policy has caused various kinds of positive and negative responses in the community. Based on this problem, this research analyzes the sentiment of how the attitude and response of the community regarding this matter, so that it can be useful for the community in the future. This research uses two algorithms Nave Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) with data collection done through YouTube video comments getting a total dataset of 1085 data. After that, enter the data pre-processing which is then labeled using the Lexicon-based method with the stemming Sastrawi method. Datasets are grouped into positive sentiment and negative sentiment where the labeling results show unbalanced label data. Then the oversampling method Synthetic Minority Over-sampling Technique (SMOTE) is performed so that the data can be balanced and produce good accuracy. The test results after the SMOTE technique show that the NBC algorithm has the highest accuracy compared to KNN. The accuracy results are 74%, precision 74.6%, recall 74% and f1-score 73.9%. While KNN produces an accuracy of 50.2%, precision of 75.2%, recall of 50.2%, and f1-score of 34.5%.
Algoritma Jaccard Similarity untuk Deteksi Kemiripan Judul Disertasi dengan Pendekatan Variasi Stop Word Removal Liga Mayola; M. Hafizh; Deri Marse Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7109

Abstract

Choosing an unique dissertation title is a challenge. The number of dissertation titles rises as the number of students increases. The title of the dissertation must differ between students. Anticipation that can be done is to adopt a similarity algorithm to detect similarities in dissertation titles. The similarity algorithm chosen is the Jaccard Similarity Algorithm. Jaccard algorithm can be used to detect document similarities. Analysis process begins with preprocessing text. The stages of preprocessing text are case folding, tokenizing, stop word removal and stemming. In this study, variations of stop word removal were tested and the accuracy results obtained were tested after being analyzed using Jaccard Similarity. Researchers call it Stop Word Removal Version One (SWR1) and Stop Word Removal Version Two (SWR2). In SWR1 only prepositions and conjunctions are deleted. Meanwhile SWR2; what was done was the deletion of words in SWR1 plus the deletion of words that were often used in the title but did not make a significant contribution to the meaning of the title. The aim of this approach is to test the accuracy produced by Jaccard against these two stop word removal approaches. The research results show that Jaccard accuracy with SWR2 has an accuracy of 97.8% and SWR1 accuracy is 57.7%. stop word removal , is a critical stage in determining similarity and has a significant influence on the results of the Jaccard Algorithm.
Seleksi Fitur Information Gain untuk Optimasi Klasifikasi Penyakit Tuberkulosis Ardi Caesar Kurniawan; Abu Salam
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7122

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a global health threat that spreads through the air. Factors such as gender, age, and geographical location influence its spread. Indonesia, the country with the second-highest number of TB cases globally, recorded a significant increase in TB cases from 2020 to 2022, especially in Semarang City. To minimize TBs impact, its crucial to identify the factors influencing its progression. Machine Learning techniques like feature selection (Information Gain) and classification algorithms (Random Forest) can be utilized. Feature selection helps determine which factors most influence TB by ranking attribute weights, while Random Forest is used for classification. Oversampling techniques like Synthetic Minority Oversampling Technique (SMOTE) are used to handle data imbalance and improve classification performance. The study concluded that the Random Forest classification model showed the best performance using all features or attributes from the highest to the lowest weight namely; tipe_diagnosis, jenis_fasyankes, usia, kelurahan_kecamatan, riwayat_dm, riwayat_HIV, tahun, paduan_OAT, status_pekerjaan, jenis_kelamin, tipe_TBC, riwayat_TBC, bulan and sumber_obat on the original TB disease dataset in Semarang City. The recall and accuracy rate reached 75%. This result is better than the TB classification model in Semarang City that uses the oversampling dataset with SMOTE and only uses the top 10-12 attributes, with a recall and accuracy rate of 74%. This research shows that certain techniques in Machine Learning can help understand the factors influencing TB treatment outcomes.
Prototipe Alat Pendeteksi Detak Jantung, Saturasi Oksigen, dan Suhu Tubuh Berbasis Arduino Mega Menggunakan Fuzzy Sugeno Annisa Dwita Aurum; Endah Fitriani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7249

Abstract

Health is a highly sought-after aspect for all living beings on Earth. By health checkups can prevent health disturbances that may develop into diseases. Various health checks include heart rate, oxygen saturation, and body temperature checks. These checks can be conducted independently at home using various health monitoring devices available in the market. However, these health devices only display numerical values without providing detailed information about the results, requiring users to seek further information. With the development technological and sensors, three health parameters can be combined into a single detection device capable of storing results. This detection device is designed using an Arduino Mega microcontroller with the Fuzzy Logic Sugeno classification method. Fuzzy input is obtained from the MAX30102 sensor for heart rate and oxygen saturation, as well as the MLX90614 sensor for body temperature. The detection results are displayed based on their classifications along with recommendations and can be stored on the device using a MicroSD card and sent to a Telegram application. The device operates on batteries for increased practicality.This research an accuracy of 96.11% for the MAX30102 sensor and 98.83% for the MLX90614 sensor. The Fuzzy Sugeno method was successfully implemented in the device and met expectations, producing outputs consistent with calculations. The data storage process using the SDCard Module was successful with a delay of 6.17 seconds, allowing review previous results. The battery testing indicated that the device can operate for up to 4 hours.
Prediksi Retensi Pengguna Baru Shopee Menggunakan Machine Learning Wahyu Fajrin Mustafa; Syarif Hidayat; Dhomas Hatta Fudholi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7074

Abstract

Shopee has evolved into one of the leading e-commerce platforms connecting sellers with consumers. However, the challenge of keeping users active and engaged on the platform has become increasingly complex. User retention, the ability of a platform to sustain and enhance user presence, is a key factor in the long-term success of an e-commerce platform. Understanding the factors influencing users' decisions to remain active or cease interactions with the platform involves analyzing various variables, including user behavior, preferences, shopping experiences, and interactions with the platform. This research is designed to develop an effective user retention prediction model using data from new Shopee users. By analyzing the data and applying machine learning techniques using Logistic Regression, Decision Tree, Gaussian Naive Bayes, Random Forest, KNN (K-Nearest Neighbors), MLP (Multi-Layer Perceptron), AdaBoost, and XGBoost methods, this study predicts user retention within a 14-day period after registration on Shopee. The results of this research indicate that the Random Forest model performs the best with an Accuracy value of 0.733677, Precision of 0.702161, Recall of 0.811626, and F1-Score of 0.752936. Cross-validation values demonstrate the model's consistency with an Accuracy of 0.727626, Precision of 0.698143, Recall of 0.801884, and F1-Score of 0.746328. The Random Forest model becomes a model with a high recall value, indicating good sensitivity in identifying users who retain. Consequently, the results of this research provide valuable insights for Shopee in developing retention strategies for new users, which is an important aspect in the growth and sustainability of the e-commerce business.
Sentimen Analisis Terhadap Fitur Tiktok Shop Menggunakan Nave Bayes dan K-Nearest Neighbor Sandi Saputra Hasibuan; Angraini Angraini; Eki Saputra; Megawati Megawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7238

Abstract

One of the features introduced by the TikTok platform is TikTok Shop, where sellers and buyers can interact easily through the live broadcast feature and promote products within the TikTok application without having to switch to other applications, as well as offering products at very cheap prices. However, this causes dissatisfaction from small traders who find it difficult to compete. Opinions about the TikTok Shop feature have created various responses from the public. This research aims to analyze public sentiment towards the TikTok Shop feature using Twitter as a data source. Applying the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms and dividing data using 10-Fold Cross Validation. Labeling was carried out using the clustering method using the K-Means algorithm, divided into three categories positive, negative and neutral. The addition of qualitative data analysis techniques with thematic analysis method in this research aims to find patterns or themes in the data. The labeling results show that 75% of the total data expresses negative sentiment towards the TikTok Shop feature. And the results of the thematic analysis found that the main theme, namely "Inappropriate regulations", covers 32% of the data, with a total of 754. This research concludes that the KNN method is superior to NBC, with better accuracy, precision and recall. This is in line with previous research that KNN is superior to nave Bayes, but other research shows the opposite where nave Bayes is superior to KNN. Further research can be carried out to improve the performance of these two algorithms in sentiment analysis, for example by using more sophisticated preprocessing methods, more representative feature extraction, or more efficient optimization techniques.

Page 97 of 119 | Total Record : 1182

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