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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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Prototipe Sistem Monitoring Rumah Walet Berbasis IoT Muhamad Ariandi; Jerry Alvinser
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5897

Abstract

Swallow's nest is one of the areas of business carried out by residents of the Tulung Selapan area, where many residents of the area make their houses part of the swallow's house, so that the houses where people live and the swallow's house merge. Problems often occur in swiftlet houses which cause unstable temperatures, poor air quality, and the sound of swallow call speakers that often turn off without a known cause. Where to carry out maintenance of the swallow house cannot be done at any time, only certain times that can be done by the swallow entrepreneur in checking the swallow house, namely when the swallow comes out. From these problems, the researcher aims to make a prototype of a swallow house monitoring tool which has several components such as temperature so that the condition of the swallow house is maintained at a temperature of 260C - 290C using a DHT22 sensor and a mistmaker. The heater automatically turns on at temperatures below 260C to keep the swiftlet house hot and if the temperature is above 290C a mistmaker is used as a cooler which will automatically turn ON. To maintain air quality using the MQ-2 sensor, and monitoring the current flowing to the swiftlet calling speaker using a DC voltage sensor with the help of Esp32 as a microcontroller. So with the existence of this swiftlet house prototype tool, swallows will feel comfortable and produce good quality swallow nests for entrepreneurs to make it easier to monitor swallows in their swallow houses. From the results of measurements and percentage calculations performed on each component of the tool sensor, nothing exceeds the tolerance value of 5% and the components used in this tool can work properly according to their functions. This monitoring system prototype tool can help swallow house entrepreneurs more easily control the condition of swallow houses and produce good quality swallow nests than before, so they have quite expensive selling prices.
Sentiment Analysis of Practo Application Reviews Using Naïve Bayes and TF-IDF Methods Rizal Adi Putranto; Mahendra Dwifebri Purbolaksono; Widi Astuti
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.v7i3.6311

Abstract

Entering the 4.0 era, it seems that the healthcare industry is the one most likely to benefit from the combination of physical, digital and biological systems. Digital health applications or telemedicine have experienced significant growth in recent years. In the current era, the development of telemedicine is accelerating, one of which is the Practo application. As the number of users using this app increases, it is important to get their opinions in order to improve the health services provided by the app. Therefore, sentiment analysis of the comments regarding the health services on the app is necessary to find out the users' opinions. By utilizing sentiment analysis, it is possible to use the sentiment analysis results obtained as a sample that corresponds to both positive and negative comments. In addition, it can be revealed that there is a mismatch between the ratings and comments given by users. This information has the benefit of being able to improve the Practo application and improve the health services provided to more effectively meet the needs and expectations of users. This research employs the Naïve Bayes approach for sentiment analysis, utilizing TF-IDF feature extraction. Naïve Bayes was chosen because it is known as an efficient classification algorithm but has a high level of accuracy. This approach involves utilizing the Bayes rule formula to calculate probabilities and make classifications. It is applicable for solving classification problems that involve either numeric or nominal feature data. Meanwhile, TF-IDF was chosen because it can associate each word in a document with a numerical value that reflects its level of relevance to the document. TF-IDF is used to measure the weighting of words as features in the summary. In this study, the best model achieved a performance with an f1-score of 85.50%.
Penerapan Data Mining Dengan Metode K-Nearest Neighbor Terhadap Klasifikasi Sarang Walet Muhammad Ismail; Renaldi Yulvianda
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.v7i3.6431

Abstract

Sungai Benuh Village is one of the areas where many swallow houses are made because it is able to produce a large number of swallow nests so that the purpose of this study was to make a classification by applying data mining to see the quality level of swallow nests, so that later it will become a reference in helping buyers and the seller obtains appropriate results and maintains the selling power of the swallow's nest. This is also based on a problem that is often encountered, namely sellers and buyers do not have a fixed standard of evaluation when a transaction takes place, so that unilateral judgments appear. In addition, there are differences in quality and quantity in different seasons. During the rainy season, swallow nests are larger, white, clean and numerous, while during the dry season, the opposite results are obtained. Classification results using the k-nearest neighbor method with Weka software show 90% accuracy for 45 out of 50 data samples, including comparative data samples or new data samples using a value of k = 7 with categorized attributes of cleanliness, color, size, shape and harvest time “Good” or “Bad”. Evaluation of the results with the confusion matrix results obtained accuracy of 80%, precision 80.49, recall 94.29% and F1 score 86.84%. So, this research was successfully carried out with high classification results so that it can be a reference to help buyers and sellers obtain a mutual agreement during transactions and maintain the selling power of the swallow's nest.
Perbandingan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Memprediksi Curah Hujan M Devid Alam Carnegie; Chairani Chairani
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.v7i3.6213

Abstract

One of the impacts of the threat caused by heavy rain is flooding, which can have negative effects on human life. There are many factors that contribute to heavy rain, and predicting the intensity of rainfall issued by BMKG (Meteorology, Climatology, and Geophysics Agency) is an initial solution for planning and taking actions to mitigate the impacts of natural disasters. Machine learning methods can be used to predict weather parameters, especially time series rainfall. Deep learning, a branch of machine learning that can understand patterns and make weather parameter predictions with high accuracy, includes several algorithms commonly used for analyzing and predicting weather parameters, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This research aims to compare both algorithms and determine which one performs best in predicting rainfall at the North Lampung Geophysics Station. From the evaluation results with RMSE (Root Mean Square Error) value of 16.81, MSE (Mean Square Error) value of 282.55, and MAD (Mean Absolute Deviation) value of 10.43, it is known that the LSTM model 1 with a dataset split of 7:3 has the best performance in predicting rainfall. As for the rain prediction, the GRU model 1 with a dataset split of 7:3 performs best with an accuracy value of 62%, precision of 58%, recall of 66%, and f1score of 62%.
Analisis Sentimen Terhadap Isu Resesi Tahun 2023 di Indonesia menggunakan Metode Naïve Bayes Naufal Fakhri Zakaria; Merlinda Wibowo; Novanda Alim Setya Nugraha
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.v7i3.6386

Abstract

Recession is a phenomenon in which the real GDP (gross domestic product) decreases for two consecutive quarters, meaning that economic activities such as distribution, investment, consumption, production will decrease, causing a domino effect that is detrimental to various parties, one of which is layoffs (termination of employment). The recession was initiated by the weakening of the global economy which had an impact on the domestic economy and countries in the world. The stronger the dependence of a country's economy on the global economy, the faster a recession will occur in that country. Indonesian President Joko Widodo predicts that in 2023 Indonesia will be a dark year due to the economic and energy crisis due to COVID-19 and the war between Russia and Ukraine Therefore a sentiment analysis is needed to see public opinion regarding the issue of the 2023 recession in Indonesia. The method used in this study is the Naïve Bayes classification method. Naïve Bayes is a classification algorithm that is widely used in Data Mining or Text Mining. This study aims to search for negative, positive, and neutral comments and to find out the accuracy of the Naïve Bayes method. Sentiment analysis was obtained by means of data cleaning, labeling, TF-IDF, split, Naïve Bayes classification, and evaluation. It is hoped that after making sentiment analysis using the Naïve Bayes method, negative, positive and neutral comments will be obtained and the accuracy of Naïve Bayes will reach 70%.
Penerapan Algoritma Apriori dan FP-Growth Untuk Market Basket Analisis Pada Data Transaksi NonPromo Andrew Aquila Chrisanto Pabendon; Hindriyanto Dwi Purnomo
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.v7i3.6153

Abstract

This research aims to find association rules based on the transactions of Aksesmu members on non-promo items. The method in this study uses Association rules using the a priori algorithm and FP-Growth to obtain Frequent Itemsets. The data analysis phase is carried out starting with Exploratory Data Analysis, Pre-Processing Data, Transformation Data, and Data Mining, to evaluate the results of the formed association rules. Researchers conducted 4 experiments with a minimum support of 0.02 and a minimum confidence of 0.25 on a priori and FP-Growth was the best by producing 52 frequent itemsets and 17 association rules. With a dataset of 379,635, a priori is faster in processing frequent itemsets with a time of 1.10 seconds while FP-Growth is with 1.86 seconds. Apriori and FP-Growth produce the same frequent itemset, namely the highest category is obtained by SKT with a support of 0.32 and SKM with a support of 0.26, but the best association rules are produced by the Extruded & Pellet and Sweetened Condensed Milk categories with a confidence of 0.47, which if items in the Extruded & Pellet category are purchased together with Sweetened Condensed Milk category items have a success rate of 47%.
Prediksi Curah Hujan Bulanan Berdasrkan Parameter Cuaca Menggunakan Jaringan Saraf Tiruan Levenberg Marquardt Setiyaris Setiyaris; Mokhamad Amin Hariyadi; Cahyo Crysdian
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.v7i3.6328

Abstract

Accurate prediction of rainfall is very important for warning services for hydrometeorological disasters or disasters caused by rain, so high accuracy is required in making predictions of rainfall. Artificial Neural Networks are becoming a trend in the field of computers because they provide the best accuracy in making predictions. Artificial neural networks are very powerful in recognizing data patterns to model and predict rainfall. The purpose of this research is to predict rainfall using the Levenberg Marquardt algorithm artificial neural network method. The data used for analysis are 120 data consisting of temperature, humidity, pressure, wind speed and solar radiation. To get accurate predictions, calculations are carried out by varying the amount of input and output data as well as varying the number of neurons in the hidden layer. The best performance of a model is measured from the value of MSE or Mean Square Error. The result shows that the network with a data composition of 90% input data, 10% output data and 25 neurons in the hidden layer is the best architecture with an MSE value of 0.029.
Sistem Pakar Deteksi Dini Status Stunting Pada Balita Menggunakan Metode Naive Bayes Marina Indah Prasasti; Dwi Normawati
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.v7i3.6443

Abstract

A child is defined as stunted if their height for their age is less than -2 Standard Deviations (SD) below the WHO growth criteria Median, and Stunting is caused by inadequate nutrition, frequent illness, and other multidimensional factors. Based on this problem, a study was conducted to develop an early detection Expert System for Stunting status in toddlers using Naive Bayes Method. Data collection was done through a literature review and interviews at the Bantul Health Office. The system development stage includes knowledge acquisition, creating a knowledge base, determining rule bases, creating a decision model with Naive Bayes, and designing the implementation of the system into a computer program. The final stage is to test the feasibility of all functional and non-functional systems through black-box testing, expert judgment, and the system usability scale. The research conducted resulted in an expert system that can detect early Stunting in toddlers and provide appropriate handling solutions and provide probability values for the status of Stunting in toddlers. Based on black-box testing, all functional systems run according to requirements and accuracy with expert judgment produces an accuracy rate of 100% and a system usability scale score of 90, it can be concluded that the expert system that has been built is feasible to use.
Penerapan Metode Preference Selection Index (PSI) Dalam Menilai Kinerja Dosen Saat Pembelajaran Daring Dimasa New Normal Neni Mulyani; Zulfi Azhar; Jeperson Hutahaean; Fitri Hadanyani; Indah Kurnia
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.v7i3.6100

Abstract

The performance of lecture during brave learning has drastically decreased due to the lack of communivation between lectures and students. As a result of bold learning. Lecturers often do not do learning using zoom meetings but only provide material without explaining it. Then the risk that will arise for lecturers who do not provide student learning reports will result in students not being enthusiastic in lectures, so that they can be reviewed in an assessment of lecturer performance. Therefore a decision support system is made to facilitate the calculation process of all criteria for assessing lecturer performance. The purpose of this decision support system is to determine the criteria for a problem using the Preference Selection Index (PSI) method.
Perbandingan Algoritma NBC, KNN, dan C4.5 Untuk Klasifikasi Penerima Bantuan Program Keluarga Harapan Aulia Dina; Inggih Permana; Fitriani Muttakin; Idria Maita
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.v7i3.6316

Abstract

One of the strategic programs in Indonesia to tackle poverty is the Family Hope Program (PKH) which is carried out by the government by providing cash to very poor families. The problem that occurs in PKH is the distribution of aid that is still not on target. Therefore this study aims to create a classification model for PKH beneficiaries to overcome these problems. The algorithms used to create a classification model are the Naïve Bayes Classifier (NBC), K-Nearest Neighbor (K-NN), and C4.5. The validation method used is K-Fold Cross Validation (K = 10). The number of attributes used is 33 attributes. The data used to construct the classification model (data after pre-processing) is as much as 378 data on prospective PKH beneficiaries. Based on the experimental results the NBC algorithm produces an accuracy value of 77.51%, the K-NN algorithm (K = 3) produces an accuracy value of 76.72%, the C4.5 algorithm produces an accuracy value of 80.16%. In addition, the C4.5 algorithm succeeded in reducing the number of attributes, from 33 attributes to just 8 attributes, namely: number of household members, fasbab, other houses, gold, fridge, number of rooms, walls, and excreta disposal. This reduces the complexity of the classification model generated by the C4.5 algorithm.

Page 85 of 119 | Total Record : 1182

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