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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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Prediksi Jumlah Perceraian Menggunakan Metode Multilayer Perceptron Ikhsanul Hamdi; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
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.v7i3.6291

Abstract

Divorce is a situation when a married couple decides to end their relationship and separate legally. The increasing number of cases in divorce cases filed at the Bangkinang Religious Court every month has led to a gradual increase and decrease. This study uses the Multilayer Perceptron (MLP) method and evaluates using Mean Squared Error (MSE) to determine prediction accuracy. The data used is divorce data from the Bangkinang Religious Court from January 2014 to December 2022 collected and processed from the Religious Court office. A total of 102 data in the form of time series data. In this study using MLP which consists of three layers, namely the input layer, hidden layer, and output layer. And using architectural testing consisting of 6-7-1, 6-9-1, and 6-12-1 with learning rate parameters: 0.01, 0.03, 0.09 with a comparison of training and test data 70:30, 80:20, 90 :10. Based on the test results using MSE, the best architecture was obtained, namely by comparing data 90:10 with 6-9-1 architecture, learning rate: 0.03, Epoch: 300, Alpha fixed value: 0.1, MSE results were successfully obtained: 0.01144 and the pattern of the number of splits from January until May 2023 has decreased, thus, this MLP can provide predictive results that help in predicting the number of divorces.
Analisis Sentimen Opini Pengguna Twitter Terhadap Tragedi Kanjuruhan Malang dengan Metode Support Vector Machine Fahri Putra Herlambang; Donny Avianto
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.v7i4.6332

Abstract

The Kanjuruhan tragedy on October 1, 2022, strongly impacted Indonesian football stadium safety. At the Kanjuruhan Stadium, a Persebaya vs. Arema FC match resulted in the deaths of 135 supporters. Due to the significant number of fatalities, there is ongoing debate regarding the responsible parties for the tragedy. Since there are expected to be 18.45 million active users in Indonesia by 2022, Twitter research helps determine popular attitudes. Support Vector Machine is used in this work to evaluate tweets and identify whether they include positive or negative emotions. The categorization outcomes may influence how the public views those responsible for the tragedy. On October 6, 2022, specific Twitter data on tear gas riots, oppressive government, rivalry between supporters, and violence against authorities were taken into account. The sentiment classes are negative, neutral, and positive. The study attained a 95.55% f1-score, 95.16% accuracy, 97.56% precision, and 95.16% recall.
Sistem Pakar Deteksi Penyakit Otitis dengan Perbandingan Metode Certainty Factor, Teorema Bayes, dan Dempster Shafer Abdul Karim; Shinta Esabella; Mhd Ali Hanafiah; Muhammad Bobbi Kurniawan Nasution; Andi Ernawati
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.v7i4.6595

Abstract

Otitis, especially otitis media, otitis externa, and otitis interna, is a common health issue in humans. Accurate identification and diagnosis are crucial for effective treatment. Expert systems have become an effective solution in this field as they can leverage medical knowledge to support decision-making processes. This research aims to develop an Expert System for Otitis Disease Detection using three different methods: Certainty Factor (CF), Bayes' Theorem, and Dempster-Shafer Method. The CF method is used to measure the level of confidence in decision-making, Bayes' Theorem utilizes probabilities to support diagnosis, and Dempster-Shafer matches previous cases with current symptoms to provide diagnostic recommendations. In this study, otitis symptom data were collected from previously diagnosed patients to train the expert system. The system was then tested with new cases to analyze the performance and accuracy of each method. The research results indicate that all three methods have the potential to detect otitis disease with varying levels of accuracy. In some situations, one method may outperform the others, suggesting that using a combination or integration of methods can enhance diagnostic accuracy. This research makes a significant contribution to the development of expert systems in the healthcare and medical services field. It is expected to assist doctors in the faster and more accurate diagnosis of otitis diseases. The calculation results show that the Certainty Factor method has the highest confidence level in diagnosing otitis media (86%), otitis externa (79%), and otitis interna (87%). While Dempster-Shafer has lower confidence levels in all cases, it still provides a significant contribution in certain situations. 
Model Clustering Zona Kesesuaian Lahan menggunakan Kombinasi Algoritma Fuzzy C-Means dan Partition Coefficient Index Yerymia Alfa Susetyo
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.v7i3.6097

Abstract

The agricultural sector is one of the vital supporters of national development. The planning of a good agricultural system needs to be supported by looking at the characteristics of each region. The diversity of agricultural areas in Indonesia needs to be simplified by classification according to their similar characteristics. This study aims to group the area of land suitability in an agricultural area. Clustering is obtained using the Fuzzy C-Means algorithm that is validated using the Partition Coefficient Index. Agriculture zone clusters are obtained from the identification of the characteristics of the slope, height, and rainfall of each region. It produced three clusters of land-compatibility zones with almost identical degree of membership. The Partition Coefficient Index algorithm is used to validate the resulting cluster. The results of these three clusters are valid, with PCI membership degrees already grouped according to each cluster. There are two points in the cluster 1, seven points in cluster 2, and eight points on cluster 3.The three clusters that have been generated can facilitate the identification of suitable agricultural land according to their respective characteristics.
Penerapan Metode CRISP-DM dalam Klasifikasi Data Ulasan Pengunjung Destinasi Danau Toba Menggunakan Algoritma Naïve Bayes Classifier (NBC) dan Decision Tree (DT) Yerik Afrianto Singgalen
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.v7i3.6461

Abstract

This study aims to implement a classification method using the Nave Bayes Classifier (NBC) algorithm on Lake Toba visitor review text data. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology comprises the following stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The findings of this study indicate that during the phase of business comprehension, the context of the discussion focuses on the tourism sector, specifically tourist perceptions of the quality of products and services at Lake Toba tourist destinations. At the data comprehension stage, the source of review data used was the Tripadvisor website, which contained as many as 858 reviews with the following rating classification: 8 reviews with abysmal ratings; 22 reviews with poor ratings; 81 reviews with neutral ratings; 304 reviews with good ratings; 443 reviews with excellent ratings. Data cleansing is performed at the data preparation stage so that 382 data are processed by dividing training data by 70 percent and test data by 30 percent. During the modeling phase, the performance of the NBC and DT algorithms was evaluated using and without SMOTE UPsampling operators. The comparison of NBC and DT algorithm values indicates that the model with the best performance is DT using SMOTE UPsampling operators with accuracy values (98.27 percent), precision values (98.83 percent), recall values (97.71 percent), f-measure values (98.26 percent), and AUC values (98.27 percent) (0.982). At the evaluation stage, the importance of excellent service (Quality Human Resources) and supporting infrastructure was highlighted by analyzing the results of ranking the five most frequently used terms in Lake Toba visitor review data (tourism facilities and infrastructure). At the deployment stage, it is necessary to balance the development of attractions, accessibility, lodging, and tourism-supporting amenities to generate visiting intention and revisit motivation to Lake Toba.
Prototipe Sistem Monitoring dan Kendali Suhu Box Kubikel 20 kV Berbasis Long Range (LoRa) Muhamad Ariandi; Yoza Risti Oktaria
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.v7i4.6796

Abstract

Electricity distribution often faces problems, one of which is interference with the 20 kV cubicle box caused by corona due to changes in temperature and condensation. This disturbance can cause arcing between the insulator and live parts. One solution is to use a heater and exhaust fan to maintain temperature and humidity. This study proposes a prototype to combine temperature and control heaters and exhaust fans automatically. Integrated sensors help monitor the temperature in the cubicle. Using a temperature sensor (DHT 22) shows high accuracy, and a current sensor (SCT 013) is suitable for exhaust fans and heaters. When the sensor is ready, the DHT 22 sensor will read the temperature in the room. If the reading temperature exceeds 40°C, then the exhaust fan will turn on to remove hot air in the cubicle box, whereas if the temperature is below 40°C the heater will turn on. If the detected temperature exceeds 43°C, and the exhaust fan does not turn on, a notification will appear on Blynk to immediately check the exhaust fan for errors. Meanwhile, if the temperature detects the cubicle box below the temperature of 37°C, a heater error notification will appear. The SCT 013 current sensor will detect the amount of current flowing in the exhaust fan and heater with the help of Esp32 as a microcontroller. This prototype utilizes LoRa technology for remote communication and sends notifications via the Blynk application. Tests show that this tool can help operators effectively monitor and control cubicle box temperature.
Weight-Based Hybrid Filtering in a Movie Recommendation System Based on Twitter with LSTM Classification Muhammad Nur Ilyas; Erwin Budi Setiawan
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.v7i4.6668

Abstract

With the era of digitalization, movie-watching has gained immense popularity, with platforms like Disney+ offering easy access to a variety of films. After watching, users frequently share their opinions on social media platforms such as Twitter, because of it is freedom of expression. With numerous movies available, users frequently encounter challenges in deciding what to watch. To address this, a recommendation system is proposed to streamline the decision-making process for users. Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering are common techniques used in recommendation systems. However, CF and CBF techniques face issues like cold start, sparse data, and overspecialization. To overcome these, this research constructs a Hybrid Filtering recommendation system, with a weight-based of CF-CBF coupled with Long Short-Term Memory (LSTM) classification. The classification uses various optimizers, including Adam, SGD, Nadam, RMSprop, and Adamax. Dataset is sourced from Kaggle website, which includes movie-related tweets linked to the Disney+ platform. The results indicate that Weight-Based Hybrid Filtering utilizing Adamax optimizer in LSTM classification yields superior performance metrics, by having 78% Precision, 79% Recall, 79% Accuracy, and 77% F1-Score value.
Penerapan Data Mining Untuk Klasifikasi Penerima Kredit Dengan Perbandingan Algoritma Naïve Bayes dan Algoritma C4.5 Dison Librado; Asyahri Hadi Nasyuha
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.v7i4.6907

Abstract

Credit is the process of borrowing money from customers to be paid over a certain period of time and with a payment agreement. In general, credit is provided by companies operating in the financial sector such as banks, cooperatives, business credit and finance. In the implementation process, providing credit to customers must be appropriate. In reality, the process of granting credit is still given to the wrong people. The problems faced must be resolved immediately and well, if the problems continue and giving credit not to the right customers will be very detrimental to the company. The settlement process can be done by looking at customer data that has previously received credit. Data mining is a technique that can be used to help solve these problems. In the process of resolving credit granting problems, data mining can be used to process previous credit customer data to obtain a pattern of which customers are eligible for credit. Classification is a method used in data mining to solve various kinds of problems. In this research, research will be carried out using the Naïve Bayes algorithm and the C4.5 algorithm. The method comparison process carried out in the research was carried out to obtain more definite results. This is based on the importance of giving credit to the right person so that there are no problems in the process of completing credit bill payments. Completion of data mining by applying the Naïve Bayes and C4.5 algorithms has been successfully carried out and classification can be carried out for decision making, both algorithms have the same decision making result, namely "Accepted". However, there are differences in the level of accuracy obtained. In the Naïve Bayes algorithm the accuracy level is 86.67%, while in the C4.5 algorithm the accuracy level is 100%.
Penerapan Deep Learning Dalam Pengenalan Endek Bali Menggunakan Convolutional Neural Network Theresia Hendrawati; Dewa Ayu Putri Wulandari; I Gde Swiyasa Surya Dharma; Ni Luh Wiwik Sri Rahayu Ginantra, M.Kom; Christina Purnama Yanti
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.v7i4.6721

Abstract

Endek Bali has been recognized as one of the Intellectual Property of Traditional Cultural Expressions, with registration number EBT 12.2020.0000085 on December 22, 2020. In the present era, many people find it difficult to distinguish between endek fabric and batik fabric because their patterns are quite similar. This research aims to help identify Bali's Endek fabric based on digital images. One of the approaches used is the Convolutional Neural Network method with ResNet50, which is a deep learning method used to recognize and classify objects in digital images. Evaluation result from testing the best model with new testing model using confession matrix get result of 90,69% accuracy, 90,69% recall, 90,60% precision and 90,68% f1-score. Thus, the model developed in this research demonstrates optimal performance in classifying images of Bali's Endek.
Implementasi Metode MAUT dengan Menerapkan Pembobotan ROC Dalam Pemilihan Ketua Himpunan Mahasiswa Jhiro Faran; Rima Tamara Aldisa
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.v7i3.6471

Abstract

The election of the head of the student association is an important process in maintaining the sustainability and progress of the organization. However, determining the best association chairperson can be a complex and subjective task. Therefore, we need an effective decision support system to assist in the selection process. This study aims to develop a Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method with ROC weighting in selecting the best association chairman. The MAUT method is one of the methods used to overcome complexity in decision making by considering several relevant attributes. The results of this study are expected to provide objective recommendations in the process of selecting the best chairman of the association. The results of this study using the MAUT method are with a value of 0.775 as the highest alternative chosen as chairman of the association. By utilizing the MAUT method, this system can assist the relevant election committee in obtaining better and more informed decisions. This proposed decision support system can also be used as a guide for other schools facing similar challenges in selecting association leaders

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