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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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Clustering Prevalensi Stunting Balita Menggunakan Agglomerative Hierarchical Clustering Maulina Rizky Anggraeni; Uky Yudatama; Maimunah Maimunah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5501

Abstract

Indonesia is a country that still has a high stunting prevalence rate of around 36%, ranking 5th with the highest stunting prevalence rate. According to the WHO (World Health Organization) this figure has not reached the expected rate, which is below 20%. Out of 180 countries in the world, nutrition problems in Indonesia are ranked 117th which is still far behind neighboring countries, such as Malaysia which is ranked 44th, Vietnam is ranked 58th, Thiland is ranked 64th, and Singapore is ranked 12th. have a stunting prevalence rate above 20%. Clustering is the process of analyzing data to group similar data into one class and different from other classes. This research was conducted using the Agglomerative Hierarchical Clustering Average Linkage method with a bottom-up approach. The data used is the prevalence of stunting in Tegalrejo using data totaling 2397 in January for toddlers and 3256 in February. The results of the clustering form 3 levels of stunting prevalence each month which can be translated into low prevalence, moderate prevalence, high prevalence, these labels are obtained based on the mean value in each cluster. From the clustering results, there were 5 villages with a low prevalence of stunting in January and February. In villages with a moderate prevalence of stunting, there were 12 villages in January and 10 villages in February. In villages with a high prevalence of stunting in January there were 4 villages and in February 6 villages. This means that there are additional villages with a high prevalence of stunting.
Analisis Metode Ensemble Pada Klasifikasi Penyakit Jantung Berbasis Decision Tree Mochammad Ilham Aziz; Ahmad Zainul Fanani; Affandy Affandy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5169

Abstract

Heart disease is one disease that is not easy to predict early on and maybe some people are not aware that they have the disease. Data obtained by WHO More than 17 million people worldwide died of heart attacks in 2016. If thesymptoms of heart disease or heart attack are known, prevention of heart disease can be anticipated and even minimized mortality. Analysis of heart disease aims to reduce mortality from the disease. In writing this research, a decision tree algorithm method is used, the algorithm still has weaknesses in making prediction accuracy. So we need a way to improve the accuracy of the classification learning outcomes. This study aims to improve the learning outcomes of heart disease classification by using ensemble learning methods, namely Boostrap Aggregating (Bagging) and Adaptive Boosting (Adaboost). Both methods were tested by predicting deaths caused by heart disease.
Penerapan Metode SMARTER pada Sistem Pendukung Keputusan Pemilihan Lahan Kayu Putih Ratih Hafsarah Maharrani; Prih Diantono Abda’u; Hety Dwi Hastuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5293

Abstract

The productivity of eucalyptus in Indonesia is included in the low productivity group, the lack of raw materials resulting in a deficit in eucalyptus production is the cause of this. Meanwhile market demand is increasing. The large area of land in the Cilacap Regency area is not matched by its utilization, this is due to a lack of knowledge about potential land uses. There are 5 criteria for land, namely land "S1 (Highly Suitable), S2 (Quite Suitable), S3 (marginally suitable), N1 (currently not suitable) and N (not suitable forever). “Decision support methods can be used in determining land suitability, one of which is the SMARTER method. The method was chosen because determining the suitability of eucalyptus land was processed by "rank order centroid (ROC) weighting" where the existing criteria have weight based on the priority level adjusted to the ranking process. Of the 84 processed data, it was found that around 98% or 83 of the data stated that the Cilacap district was included in the S2 suitability range (Quite Appropriate). In addition, based on the results of the accuracy testing carried out, the percentage of accuracy was 80.95%. Seeing the high percentage, the decision support system using the SMARTER method is appropriate to be used to determine the suitability of eucalyptus land. “
Implementasi Data Mining Tingkat Kepemimpinan Siswa dengan K-Nearest Neighbor, Decision Tree, dan Naïve Bayes Didin Sayhidin; Gendhi Haris; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5351

Abstract

The process of monitoring and evaluating high school student leadership is deemed necessary because the level of student leadership is one of the prerequisites for high school students to face real challenges in the future. Data mining can be used to classify the level of leadership among high school students. The purpose of the research conducted in this case is to apply data mining using the K-NN, Decision Trees, and Naive Bayes models. This research is located in two different public high schools, namely SMA A as training data and SMA B as test data. This data was obtained in the same year, namely 2022. The data obtained were analyzed with the help of the Rapidminer application using K-NN, Decision Tree, and Naive Bayes. Student data that is processed is Basic Education Data (DAPODIK) in excel format. Before being analyzed, the text is processed first, namely tokenization, case folding, stop words, and details. The main goal of the steps above is also the main goal of this study to get the most accurate algorithm for classifying student leadership levels and knowing the results for comparison. The conclusion of this study is when measuring the performance of the three algorithms, the test results use confusion matrix validation. The K-NN algorithm was found to have the highest accuracy score compared to the Decision Tree and Naive Bayes. The accuracy value of the K-NN method using a dataset of high school students is 95.86%, the accuracy value of the Decision Tree algorithm is 94.65%, and the accuracy value of the Naïve Bayes algorithm is 79.55%.
Penerapan Metode Transportasi dan Transhipment Menggunakan Linear Programming dalam Efisiensi Biaya Distribusi Barang Fikri Husin Batubara; Rina Widyasari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5424

Abstract

Goods delivery services or expedition services are now increasingly in demand by people in Indonesia every day. One of the most popular shipping services is the company PT. Tiki Jalur Nugraha Ekakurir (JNE), delivery of goods cannot be separated from the important role of the distribution channel, so that problems that arise in the distribution of goods at PT. JNE are the very expensive cost of shipping goods, which causes the company to incur large costs and suffer losses if the problem occurs. This distribution is not complete. The transportation model is used to solve distribution problems experienced by PT. JNE by applying the north west corner, least cost, vogel's approximation and russell's approximation methods as a comparison solution for problem solving. Then the efficient method will be proven by the linear programming method with the help of LINDO software and a sensitivity test to prove the correctness of the transportation model. With the completion of this model, the aim is to streamline the cost of distribution of goods at PT. JNE, from the results of the research on the distribution of goods that is most efficient with the transportation model, namely the vogel's approximation method with a total cost of Rp. 1.018.500.
Analisis Sentimen Pengguna Twitter Terhadap Kenaikan Harga Bahan Bakar Minyak (BBM) Menggunakan Metode Logistic Regression Muhammad Raja Nurhusen; Jamaludin Indra; Kiki Ahmad Baihaqi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5491

Abstract

In Indonesia itself, fuel is a very important raw material for society, especially for the industrial sector. The fuel price hike policy sparked controversy on social media, one of which was Twitter. After the increase in fuel prices was passed, every day on Twitter was filled with tweets with the hashtag (#bbmnaik). The pros and cons that exist in the community regarding the increase in fuel prices is an interesting research material. This study aims to analyze public sentiment whether it is negative or supportive. The method used is Logistic Regression assisted by the Confusion Matrix for evaluation calculations. The advantage of this method compared to other methods is that the Logistic Regression method is often used to create a predictive model whose result values are in the form of yes/no, true/false, thus this method is very suitable for this research. The data used is 3000 data with keywords (increase in fuel prices). The results of the analysis that has been carried out show that positive sentiments get an accuracy value of 38% and negative sentiments of 80%. Classification performance of the Logistic Regression method gains 73%. The results of evaluation calculations with the Confusion Matrix using data testing as many as 600 data get an accuracy rate of 77%, a precision value of 95%, a recall value of 79%, and an f1 score of 86%. So it can be concluded from the results of the sentiment analysis that has been done that the public is more pro against the rejection of the increase in fuel prices.
Anti Cheat of Computer Based Test Application in Enterpreneurship Exams using The Multiplicative Random Number Generator Method Badrul Anwar; Ganefri Ganefri; Asmar Yulastri; Dicky Nofriansyah; Asyahri Hadi Nasyuha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5437

Abstract

STMIK Triguna Dharma is one of the best private universities in North Sumatra in the field of ICT. To realize an entrepreneurial university, it is necessary to have maximum efforts from all elements of management and the foundation. This reflects the quality of learning and good higher education governance. So far, there have been many test implementation techniques in several schools, such as conventional and CBT concepts. Based on the observed phenomena, the CBT that was implemented had problems including fraud in the implementation. Referring to this problem, STMIK Triguna Dharma innovates by building a web-based anti-cheat CBT application by adopting the Multi Random Number Generator Method. The advantage of this method is that it is able to randomize questions and answers with the available question packages so that this effort can properly reduce cheating, for example on semester exams. The results of this study are a web-based anti-cheat application which adopts the MRNG method which is expected to be a solution and can be used by STMIK Triguna Dharma in conducting semester exams, especially in entrepreneurship courses.
Penerapan Support Vector Machine dan FastText untuk Mendeteksi Hate Speech dan Abusive pada Twitter Afdhal Zikri; Surya Agustian
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5408

Abstract

Hate speech and abusive language are negative tendencies that often appear on social media recently. In addition, due to the advancement of technology and the rapid growth of the internet, anyone can now engage in hate speech or even offensive expression such as in Twitter, which eventually leads to fights on that social media platforms. Automatic detection of offensive contents and hate speech is recommended to be applied, especially on the user application’s side, to filter tweet contents which destruct social life in the real world. The purpose of this research is to create a classification model using Support Vector Machine with FastText word embeddings features, to classify if a tweet contains hate speech and/or offensive language. Our contribution in this research is an improvement in performance from the baseline SVM (support vector machine) with FastText word embeddings features input. The experiment results will also be compared with several machine learning method that have been reported using the same dataset of 13,167 tweets. The experiment using the most optimal SVM model, yields an average accuracy of 82.65%, with the accuracies of the hate speech class, abusive language class and hate speech level, are 84.92%, 86.60% and 76.43% respectively. These results are better than conventional machine learning, but cannot exceed the results achieved by deep learning.
People Entity Recognition for the English Quran Translation using BERT Retno Diah Ayu Ningtias; Moch. Arif Bijaksana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5586

Abstract

The Quran is a holy book for Muslims all over the world. Therefore, the Quran is not only translated into Indonesian but also into many other languages, including English. The contents of the Quran are a collection of thousands of verses, each verse having different topics and entities. Sometimes, someone may find it difficult to understand and study the contents of the Quran. Therefore, to make it easier, it is done by extracting information and identifying various entities in the Quran, such as human entities. An important thing to do in order to extract information on human entities is to extract information related to the human entity itself first. Because it can help in the search process, particularly the search for names of people in the Quran. The extraction of human entities is commonly known as Named Entity Recognition (NER). With NER, it can automatically recognize important entities such as people's names, group names, and other entities in a sentence or verse in the Quran. Currently, research on the Quran's English translation is not widely done. Therefore, in this research, we are building an information extraction system model for human entities based on a pre-trained deep learning model called Bidirectional Encoder Representations from Transformer (BERT). The dataset used is made up of 19473 tokens and 720 entities taken from the website tanzil.net. The development of the model shows that BERT can be used to extract information for NER on the Quran translation in English by obtaining a F1-score value of 53 %.
Internet of Things Sebagai Alat Penentuan Lokasi Budidaya Rumput Laut Gracilaria Sp Moh Thoriq Afif; Aulia Desy Nur Utomo; Anggi Zafia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5567

Abstract

Seaweed or commonly called seaweed is one of the abundant underwater biological resources in Indonesia, with around 8.6% of the total marine biota. Seaweed has various benefits such as for food ingredients, cosmetic ingredients and others. However, in seaweed cultivation, failure often occurs due to various factors, one of which is the presence of diseases or pests that attack seaweed. Gracilaria sp seaweed in Gerongan ponds in Pasuruan, East Java often experiences different responses when planted in different ponds. Existing environmental factors come from ponds where seaweed is planted to cause damage. This tool is made using a microcontroller to prevent crop failure and damage to seaweed in the Gerongan pond, Pasururan, East Java, namely the design of a tool for determining the location of Gracilaria Sp seaweed cultivation based on IOT. This tool uses the NodeMCU ESP8266 with the help of sensors, namely a distance sensor for water depth, a temperature sensor, a strong current sensor, and a pH sensor. The results of this study show that seaweed farmers can monitor seaweed by stabilizing the pond environment with data that has been obtained from sensor results that they can see on the Android application. This tool makes it easier for farmers to cultivate seaweed and helps improve the quality of the Grasilaria sp. seaweed harvest.

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