Garuda - Garba Rujukan Digital

Designing an Android-Based Streaming Video Player Application Gatot Soepriyono
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.1088.pp2106-2118

Indonesia is one of biggest country that has a huge potential. Nowday, mobile technology is growing very rapidly. Therefore, we also have to keep following these technological developments. For example, many software of video players run on Android-based OS. The author interested in making an Android-based Video Player Streaming application using the Eclipse Kepler application. Using the Waterfall method, which is a method of sequential requirement (system analysis), system design, coding, testing, implementation and maintenance. How to install JRE, JDK, SDK, and Streaming Video Player source code, finally the author produced a video player that can be run on the Android mobile operating system, and smartphone tablets Ice Cream Sandwich and Jelly Bean. The added value of this application is able to play video live streaming. The author will continue to develop this application. So, it can be operated on the i-phone operating system, i-pod and other types according to the progress of the operating system itself.

Penerapan Data Mining Untuk Mengukur Kepuasan Mahasiswa Terhadap Pembelajaran dengan Menggunakan Algoritma Naïve Bayes Agung Triayudi; Gatot Soepriyono
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2524

Education is very important for life, with education can help in improving Human Resources (HR). Human Resources (HR) in universities are students. Increased competence in students carried out in higher education is carried out by learning. The process carried out in learning is very influential on the results obtained from learning both competencies and abilities possessed by students. Based on this, it is necessary to improve learning in higher education in order to support good results. Measuring the level of student satisfaction with learning can measure the extent to which the learning process has been carried out. The process of measuring the level of student satisfaction with learning is done first by collecting data. After collecting data, the data processing process is carried out to get the expected results. Errors in the data processing process, the results obtained are also not in accordance with the objectives carried out. Therefore, to solve the problem it is necessary to do it with the right process by using a separate method or technique where the method is data mining. Data mining is a method or technique used for data processing. The data processing process carried out in data mining is carried out on large data. The Naïve Bayes algorithm is an algorithm that is included in the classification of data mining techniques. Where the process in the nave Bayes algorithm is very dependent on the grouping process carried out on each attribute and also the target class of each object. The results of the study show that the probability value of PUAS is 0.034108116 and the probability value of NOT SATISFIED is 0. This indicates that the result of decision making is SATISFIED.

Analisa Sentimen Pengguna Transportasi Jakarta Terhadap Transjakarta Menggunakan Metode Naives Bayes dan K-Nearest Neighbor Ismia Iwandini; Agung Triayudi; Gatot Soepriyono
Journal of Information System Research (JOSH) Vol 4 No 2 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i2.2937

Social media used in communicating that is very popular in Indonesia. One of the most popular is Twitter. Twitter is a social media site where people can share information publicly. This information can be processed to make sentiment analysis. This research attempts to create a system that can detect positive or negative sentiments in public information. The method used for this sentiment classification is the comparison method of Naive Bayes Classifier and K-Nearest Neighbor Classifier using TF-IDF weighting. The input to this system is in the form of tweet data for Transjakarta, while the output of this system is in the form of visualization of positive and negative sentiment data using Streamlit which is a library from python. Based on testing the accuracy of the Naive Bayes approach for sentiment analysis of Twitter data related to the use of Transjakarta transportation is 61.1%, and the accuracy of the K-Nearest Neighbor method is 75.7%. For the two methods used in determining the level of accuracy, it can be concluded that the K-nearest-neighbor method produces better accuracy.

Sistem Informasi Pariwisata Bali Berbasis Website dengan Metode User Centered Design Irfansyah; Novi Dian Nathasia; Gatot Soepriyono
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 2 (2023): APRIL-JUNE 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i2.765

Bali is a tourist destination in Indonesia that makes the country known to the world community. Currently, tourists are facing problems with information about bali tour packages and access to accommodation. Making this application using Visual Studio Code Programming as a website page, using PHP to design a dynamic website, using CSS to control the appearance of the website, and using a MySQL database as the database. The results of this Information System study can provide complete information about Bali tour packages and accommodations. By using the User Centered Design (UCD) method, the interface displays that focus on a user's needs. The results of the design of an interface are page menus, tour package menus, hotel list menus, contact menus, and login menus. Based on the results of descriptive analysis tests assisted by the Statistical Product Service and Solutions computer program as an effort to determine the response of www.baliaset.com users involving 100 samples, it was found that the highest mean was obtained in the 8th statement item of 4.50 and the lowest mean was obtained. in the third statement item is 4.13

SISTEM INFORMASI PENJUALAN MOTOR BEKAS DENGAN MENGGUNAKAN ALGORITMA SEQUENTIAL SEARCH DAN SELECTION SORT Muhammad Hafizh Lazuardi; Nur Hayati; Gatot Soepriyono
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3952

Pasar sepeda motor bekas hingga saat ini masih menjadi pilihan bagi sebagian konsumen. Pada toko Jaya Motor belum memanfaatkan teknologi komputerisasi dalam proses sirkulasi jual beli sepeda motor, masih menggunakan proses pencatatan manual menggunakan media pembukuan. Oleh karena itu, dibutuhkan adanya suatu pengembangan sistem informasi menggunakan metode waterfall, algoritma sequential searching untuk klasifikasi pencarian data motor bekas berdasarkan merek, kategori, harga dan selection sort untuk mengurutkan data dari ascending atau descending. Penelitian ini menghasilkan dalam pencarian menggunakan proses memori maksimum 357 MB dengan hasil output sesuai keinginan pengguna dengan respons times kurang dari 1 detik. Pengujian sistem menggunakan blackbox sudah sesuai dan pengujian System Usability Scale mempunyai rata-rata skor 79,08 grade B+ yang berarti Good.

Rancang Bangun Website Pengamanan Database E-Voting dengan Menerapkan Algoritma Rivest Shamir Adleman (RSA) Danis Setiawan; Andrianingsih Andrianingsih; Gatot Soepriyono
Jurnal Teknologi Informatika dan Komputer Vol 9, No 2 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i2.1687

Algoritma Rivest Shamir Adleman (RSA) dapat diterapkan pada sistem pemungutan suara elektronik untuk mengenkripsi informasi pemilih dan hasil pemilu sehingga hanya orang yang berwenang yang dapat mengaksesnya. Ini dapat membantu menjaga kerahasiaan informasi pemilih dan mencegah manipulasi data pemilu. Metode Agile baik untuk proyek yang membutuhkan fleksibilitas dan kemampuan menyesuaikan diri dengan perubahan. Untuk menggunakan kriptografi algoritma kunci asimetris RSA, ada tiga langkah utama yang dilakukan, yaitu pembangkitan kunci dan operasi enkripsi dan dekripsi. Karena algoritma ini termasuk algoritma asimetris yang menggunakan kunci enkripsi dan kunci dekripsi, data yang dikirim antara pengguna dan server dienkripsi dengan kunci publik dan kunci privat, sehingga hanya dapat didekripsi dengan kunci yang disimpan oleh pengguna sendiri di server. Salah satu metode pengujian keamanan yang dikenal sebagai pengujian brute force bertujuan untuk mencoba semua kombinasi password atau kunci enkripsi yang mungkin untuk mendapatkan akses yang tidak sah. Pengujian yang melibatkan alat seperti OWASP ZAP dan BurpSuite menemukan kerentanan keamanan seperti SQL injection dan Cross-Site Scripting (XSS). Penelitian ini berhasil mengembangkan situs web E-Voting yang melindungi data dengan menggunakan algoritma RSA. Sistem ini memungkinkan pemilih untuk melakukan pemilihan elektronik dengan aman dan efektif. Website evoting harus dipantau secara teratur untuk mencegah serangan keamanan yang dapat merusak kerahasiaan dan integritas data pemilih.

Implementasi Data Mining dengan Algoritma Apriori dalam Menentukan Pola Pembelian Aksesoris Laptop Gatot Soepriyono; Agung Triayudi
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.v7i4.6555

Consumer purchasing patterns are an important factor in the business world, which influences marketing strategies, stock management and company profits. In the context of the laptop accessories business, a deep understanding of consumer purchasing patterns is very necessary to increase operational efficiency and customer satisfaction. Data mining, as a powerful data analysis method, has become an effective tool in uncovering these patterns. One of the data mining algorithms that is often used to analyze association patterns is the Apriori algorithm. This research applies the Apriori algorithm to identify and analyze purchasing patterns for laptop accessories from transaction data obtained from a retail store. By analyzing this data, we can identify items that are frequently purchased together and purchasing patterns that may not be immediately apparent to humans. The results of this analysis provide valuable insight into consumer preferences, helping retail stores to design more effective marketing strategies. The results of this research can also be used to manage stock more efficiently. By knowing deeper purchasing patterns, retail stores can predict stock needs more accurately, reduce the risk of excess inventory, and optimize operational expenses. Thus, this research can help increase company profits and satisfy customers by providing accessories that suit their preferences. In the increasingly developing information era, the use of data mining and algorithms such as Apriori is becoming increasingly important. This research is an example of how data analysis can be used in the real world to support smarter and more efficient decision making in the laptop accessories business. As a result, a better understanding of consumer behavior and purchasing patterns can provide a strong foundation for developing successful business strategies.

Perbandingan Kinerja Algoritma Clustering Data Mining Untuk Prediksi Harga Saham Pada Reksadana dengan Davies Bouldin Index Gatot Soepriyono; Agung Triayudi
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.v7i4.6623

Mutual funds are a container that can be used to accommodate funds from the public which will later be distributed to the owners of the company. The ease of investing in share prices cannot be separated from the ease of obtaining information. The share price that is very popular with the public is the share price for banks, whether privately owned or government owned. However, even though banks are very close and popular with capital market players, this does not rule out the possibility of a decline in share prices. This problem is not a problem that can be considered trivial and ignored, if you continuously experience losses from the capital market it will certainly give rise to distrust or a lack of interest in the public to participate in investing in companies. Predictions for stock prices must be done well and correctly and get accurate results, therefore it is necessary to use a special technique or method to help carry out the prediction process until results are obtained with a good level of accuracy. The expected prediction process is in line with the concept of data mining. The process of applying clustering for predictions is also considered very suitable, this is because in stock prices there is no target class for each data. The K-Means algorithm and K-Medoids algorithm are part of cluster data mining to be used to make predictions based on cluster formation. The purpose of the comparison is to get more reliable results, where these results can be seen from better algorithm performance. The performance measurement process for the K-Means and K-Medoids algorithms will later be assessed based on the Davies Bouldin Index (DBI). The results of the research show that the performance results of the K-Means algorithm are better than the K-Medoids algorithm. This is proven by the DBI value obtained from the K-Means algorithm being no more than 0.6, while in the K-Medoids algorithm the DBI value obtained is up to 5.822. Overall, each stock data has an optimal cluster based on the clustering process with the K-Means algorithm. The optimal cluster results in BMRI stock data, the optimal cluster is at K=4 with a DBI value of 0.501. In the BBNI stock data, the optimal cluster is at K=4 with a DBI value of 0.500. In the BBCA stock data, the optimal cluster is at K=3 with a DBI value of 0.441. In the BNGA stock data, the optimal cluster is at K=2 with a DBI value of 0.263. In the BDMN stock data the optimal cluster is at K=2 with a DBI value of 0.028 and in the MEGA stock data the optimal cluster is at K=4 with a DBI value of 0.353.

Komparasi Metode Weighted Product (WP) Dan Simple Additive Weighting (SAW) Pada Sistem Pendukung Keputusan Dalam Menentukan Pembangunan Infrastruktur Kelurahan Flipo Hariski; Agung Triayudi; Gatot Soepriyono
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3520

The goverment gives urban villages more power includes urban village development. In accordance with local village goverment policies, there are specific criteria for allocating funds for village infrastructure development. The aim is to make the development of urban village infrastructure more equitable and targeted. Priorities for urban village infrastructure development must be decided. Decisions on urban infrastructure development are still made by voting and voting and often more significant developments have to be postponed due to losing votes. To prioritizes urban infrastructure development, urban village officials can use decision support system. Prioritization of urban village infrastructure development is determined using Simple Additive Weight (SAW) and Product weightn(WP) methodologies. Each proposal will be assessed according to the criteria chosen by the Kelurahan to determine development priorities. It is expected that the decision support system will be easier, more accurate, and faster in determining development priorities in Rangkapan jaya urban village. Comparison of SAW and WP methods using 10 alternative data, shows that both methods get accurate data and are suitable when applied as ranking of infrastructure development.

Penerapan Market Basket Analysis Data Mining Pada Penjualan Batik dengan Menerapkan Algoritma Apriori Gatot Soepriyono
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5198

Batik is a cloth that is depicted by applying wax to the cloth and processing it in a certain way. In certain batik companies, there are products that are characteristic of that company. As time goes by, problems arise with the sale of batik due to lack of proper stock availability in shops, making it difficult for customers to order batik. This problem that occurs is certainly a problem that must be resolved. If the available stock of goods does not match the customer's wishes, the goods will be piled up in the shop's stock. Apart from that, if the customer does not find a batik model that suits his wishes, it will cause the customer to switch to another shop. The sales results are reported in the form of a ledger or entered into a computer. The report produced is a sales transaction data report. Data mining itself is a process of processing quite large amounts of data. In the future, the data recorded in the ledger can be used as information in determining business strategies for batik sales. Market Basket Analysis aims to manage customer data or sales data. The a priori algorithm is an association part of mining data. A priori algorithms can help in forming candidate item combinations. From the results of the research carried out, there is 1 combination of items that meets the support value of 30%, namely items T09 and T12, where the support value obtained is 30.76% and with a confidence value of 100%.

Title

Found 10 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 10 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 10 Documents
Search