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Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
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mib.stmikbd@gmail.com
Editorial Address
Jalan sisingamangaraja No 338 Medan, Indonesia
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Kota medan,
Sumatera utara
INDONESIA
JURNAL MEDIA INFORMATIKA BUDIDARMA
ISSN : 26145278     EISSN : 25488368     DOI : http://dx.doi.org/10.30865/mib.v3i1.1060
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)
Articles 46 Documents
Search results for , issue "Vol 5, No 3 (2021): Juli 2021" : 46 Documents clear
Sistem Pendukung Keputusan Penentuan Merek Smartphone Terbaik Dalam Mendukung Belajar Online Mahasiswa Era Covid-19 Menggunakan Metode PSI (Preference Selection Index) Kifti, Wan Mariatul; Hasian, Irene
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.2994

Abstract

The era of the Covid-19 pandemic has resulted in people having to carry out activities by keeping their distance from one another in order to achieve the termination of the spread of the Covid-19 virus, this activity resulted in the government issuing a decree, especially to the education office to conduct online learning because schools and universities is one of the places that has great potential for easy transmission, online learning makes all students obliged to have a smartphone or android so that it makes it easier for students to access online learning, it is necessary to determine the best smartphone brand with the most purchase criteria, price, capacity owned by smartphone and year. output which will later become a literature review of students and parents of students to be more selective in choosing according to the wishes and needs of each user with the help of a decision support system and the application of the PSI method (Preference Selection Index). rence selection index), and the results obtained with the highest score on the xiaomi smartphone with the highest score of 0,327332, the assessment process is fairer and the decision-making process is fast.
Prediksi Tingkat Penjualan Sepeda Motor dengan Metode Rough Set Mandala, Eka Praja Wiyata; Putri, Dewi Eka
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3057

Abstract

The high level of motorcycle sales makes the showroom have difficulty in procuring variants of motorcycles to be sold. The many variants of motorcycles in one manufacturer, make different sales of each motorcycle variant, there are variants with high sales and some with low sales. So it is necessary to predict the level of motorcycle sales. This study uses data from one of the Honda motorcycle showrooms, namely the Hayati showroom, Pasaman branch. The data used is a recapitulation of motorcycle sales data in the second quarter of 2020. This study uses 24 data samples as a decision system. From the test results obtained 13 equivalence classes, then a reduction process is carried out to obtain 7 reducts and a rules generation process is carried out, then 41 rules are obtained with Motorcycle Prices as the dominant attribute in influencing the Sales Level attribute decisions with an incidence of 42% and min. support = 5.
Analisis Perbandingan Kinerja Algoritma Naïve Bayes, Decision Tree-J48 dan Lazy-IBK Rukmana, Indra; Rasheda, Arvin; Fathulhuda, Faiz; Cahyadi, Muh Rizky; Fitriyani, Fitriyani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3055

Abstract

This research is focused on knowing the performance of the classification algorithms, namely Naïve Bayes, Decision Tree-J48 and K-Nearest Neighbor. The speed and the percentage of accuracy in this study are the benchmarks for the performance of the algorithm. This study uses the Breast Cancer and Thoracic Surgery dataset, which is downloaded on the UCI Machine Learning Repository website. Using the help of Weka software Version 3.8.5 to find out the classification algorithm testing. The results show that the J-48 Decision Tree algorithm has the best accuracy, namely 75.6% in the cross-validation test mode for the Breast Cancer dataset and 84.5% for the Thoracic Surgery dataset.
Perancangan Web Marketplace Toko Sepatu Akshara.co dengan Sistem Rekomendasi Menggunakan Perhitungan Algoritma Apriori Manoppo, Dennise Gibran; Wahyudin, M Iwan; Winarsih, Winarsih
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3091

Abstract

In this advanced period, shoes arsse an essential requirement for practically all circles, a great deal of Online business organizations are arising and filling quickly in Indonesia, in addition to the quantity of dynamic web clients in Indonesia is expanding quickly from one year to another. Making Online business organizations contend to explore different techniques as far as advertising to draw in more individuals to purchase the items they offer to get by in the online businss market rivalry in this country, one illustration of a promoting methodology to draw in and increment public premium buys is the execution of the merchandise proposal framework in Online business. Consequently, in this investigation, an electronic Web based business will be made that can help Internet business organizations anticipate purchaser premium in a thing and afterward prescribe it to draw in more purchasers who come. This Web based business utilizes the Apriori Calculation way to deal with get more exactness in the information handling measure. The outcomes acquired from that examination are the making electronic Web based business by executing the suggestion technique showed on the "Akshara.co" framework include
Pengenalan Pola Angka Menggunakan Pendekatan Optimisasi Sistem Kekebalan Buatan (Artificial Immune System) Prahartiningsyah, Anggari Ayu; Kurniawan, Tri Basuki
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.2997

Abstract

The general election in Indonesia itself still experiences technical and non-technical problems where the technical problems occur in the recapitulation of votes from sheet C1 which are still incorrectly inputted and done manually. The problem occurred with the difference in the uploaded C1 data and the data in the KPU Situng and the C1 sheet uploaded was blurry, unclear, sheet C1 which was crossed out or folded in the KPU Situng. The purpose of this research is to reduce errors in data input and change the work that is done manually to the system, create a number pattern recognition system using an Artificial Immune System optimization approach, test and analyze the work of the system by taking into account the level of accuracy, preciseness and speed in recognize number patterns. The system created to applies an artificial immune system optimization approach with the Artificial Immune System using the Randomized Real-Valued Negative Selection Algorithm algorithm.
Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes Qisthiano, M Riski; Kurniawan, Tri Basuki; Negara, Edi Surya; Akbar, Muhammad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3030

Abstract

Many parameters affect the timeliness of student graduation, starting from the student's interest in certain majors, the type of class chosen, to the grades for each semester obtained. This is a determining factor in how students can graduate on time or not at the end of their education. So a model is needed to predict student graduation rates on time, using alumni data whose data is obtained from several universities in Palembang City. The model used is a Naïve Bayes algorithm which serves as a model for classification. The dataset used is alumni data that has been collected from several universities, while the attributes used are the Department, College, Class Type, Temporary IP Value from semester 1 to 4, graduation year, and college generation. Then from the attributes and models used, the researcher used the Python 3 programming language and the Jupyter Notebook tools to process the prepared dataset. Furthermore, the distribution of the dataset is divided by 70% for training data and 30% for testing data. To test the algorithmic process used by researchers using K-Fold Validation. The results of this study are the accuracy of the prediction model carried out, where the accuracy results obtained from the Python 3 programming language and the Naïve Bayes algorithm are 0.8103.
Pengujian Validitas dan Reliabilitas Model UTAUT 2 dan EUCS Pada Sistem Informasi Akademik Aprilisa, Shinta; Samsuryadi, Samsuryadi; Sukemi, Sukemi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3074

Abstract

The research instrument is used to collect data or measure the object of a study. The purpose of this study was to determine which instruments were declared valid and reliable and to find out the variables with the highest validity and reliability values. Validity testing is carried out to determine the effectiveness of an instrument, while reliability testing is carried out to show the level of reliability of the indicators used. Testing the validity and reliability using software SmartPLS version 3.3.2 with a measurement scale that is Likert scale. Validity testing is done by looking at the average variance extracted (AVE) value and the comparison of the latent variable correlations values, while reliability testing is done by looking at the composite reliability value. The population in this study were active students at the State Islamic University (UIN) Raden Fatah Palembang, totaling 19,260 students with the determination of the sample using the Slovin formula with a level of significance = 5%. Data collection in this study was carried out by distributing online questionnaires. The questionnaire was made based on indicators on the model used, where the models used were UTAUT 2 and EUCS. The UTAUT 2 model can be used to measure the level of user acceptance of the system consisting of the variables of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, perceived value, habit, and behavioral intention. Furthermore, the EUCS model is used to measure the level of user satisfaction which consists of content, accuracy, format, ease of use, timeliness, and user satisfaction variables. The results of the validity and reliability testing state that all indicators are valid and reliable with an AVE value > 0.50 in the validity test and the composite reliability value > 0.70 in the reliability test. The validity test with the highest value is found in the ease of use variable with an AVE value of 0.826 and reliability testing with the highest value is found in the performance expectancy variable with a composite reliability value of 0.924. With this research, it is expected to obtain variables in the model to evaluate user acceptance and satisfaction with academic information systems
Penerapan Metode Simple Additive Weighting (SAW) pada Sistem Informasi Pemilihan Asisten Praktikum Gustalika, Muhamad Azrino; Rakhmadani, Diovianto Putra; Segara, Alon Jala Tirta
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3065

Abstract

Each campus has technology that is used to communicate and exchange information using technology in the form of a website. The use of the website itself has penetrated into the world of education, one of which is the laboratory on campus. Laboratory on campus cannot be separated from the existence of a practicum assistant. Every semester the faculty of informatics opens registration for practicum assistants, but there are obstacles that candidates who register still use the manual method in their selection. So they need the Simple Additive Weighting (SAW) method. This study uses the Simple Additive Learning method which will increase the highest score level of 1.39 with the weight indicator used in the selection of practicum assistants and get an average score of 4.9 out of 5.0 so that it is very effective for admins (laborers) to manage and lecturers, to see recommendations for prospective practicum assistants, the best are web-based
Penerapan Algoritma Boyer Moore Dalam Pencarian Barang Hilang pada Aplikasi FindIt Berbasis Android Setiawan, Muhammad Afif; Andryana, Septi; Gunaryati, Aris
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3093

Abstract

Losing is an experience that every human has experienced during his life span, and this is a natural thing. Loss of goods is an event that can make a person panic, especially if the lost item is valuable to him. Therefore, with the development of technology, the author tries to design an application that can be a medium for information on lost items for the community, this application is designed based on Android and is named FindIt. This FindIt app uses Firebase Authentication and Firebase Storage as the main database. This application can be run in real-time because it uses Realtime Firebase. Algorithm testing was carried out between Knuth Morris Pratt and Boyer Moore, and the result is Boyer Moore is faster in string matching. The test results using this algorithm are 100% accurate. Application testing is done using the Blackbox method, where the functions and features of the application run as expected. With this application, it is hoped that the community can help each other to find goods and post their findings on this application
Optimasi Hyperparameter TensorFlow dengan Menggunakan Optuna di Python: Study Kasus Klasifikasi Dokumen Abstrak Skripsi Mujilahwati, Siti; Sholihin, Miftahus; Wardhani, Retno
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3090

Abstract

In today's rapidly growing digital era, the role of computing in artificial intelligence is needed to be able to help business people. Both in the fields of economy, health, and education. The use of machine learning will help related parties in viewing, analyzing, and making decisions. With machine learning, all problems related to data can be solved quickly and precisely. The problem is that the thesis document will increase every year, it will become a useless document if the data processing is not carried out. Past thesis data can be used for analysis and decision-making in the next thesis era. Python is one of the most popular programming languages used for machine learning. One reason is that there are many python-based libraries. Keras is a python-based machine learning library. TensorFlow can be used when dealing with large amounts of data processing, including thesis abstract data. Thus, this study classified 140 thesis abstract documents using hard-TensorFlow with the aim that based on the abstract content it would be classified into 6 classes, namely Android Applications, Data Mining, RPL, SPK, Digital Image Processing, and Expert Systems. The results of the classification with training data as many as 82 documents with model setting batch size = 12 and epoch = 2 with an Accuracy value of 89.04%. While the test loss test data has a higher value than the Accuracy value obtained by 66.66%. By utilizing maximizing TensorFlow performance by adding a parameter that Scikit Learn has, namely Optuna. The test data was optimized with a trial value of 500, the Accuracy increased to 76.19%