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Implementation and Performance Analysis of Private Cloud Using Openstack Swift and Rclone Aditama, Candra; Priadana, Adri
Compiler Vol 8, No 1 (2019): Mei
Publisher : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1332.694 KB) | DOI: 10.28989/compiler.v8i1.428

Abstract

Many companies need a storage system that can be accessed in real time by all parts of the company. Most digital data storage methods today still use conventional methods where data is stored on an external hard disk or public cloud. Storage with external hard disk media makes accessing data difficult and has the risk of data loss when storage media is damaged. On the other hand, the storage method using public cloud requires an internet connection with large bandwidth requirements and the company still has to spend a budget on renting it. This study aims to design digital data storage methods using a private cloud that can be accessed in real time by all parts of the company without having to spend a budget on hiring a storage media and renting an internet connection with large bandwidth requirements. A private cloud was built using OpenStack Swift as an object storage service provider and Rclone as a cross-platform computer data management application. The results of this study are the creation of a private cloud that runs object storage services using Swift storage objects with relatively light and high scalability to meet the needs of storing data effectively and efficiently. A private cloud-based data storage media with relatively light and high scalability to meet data storage needs. Data storage media that can be accessed easily without having to use an internet connection with large bandwidth requirements.
Performance Analysis of Illumination Invariant Change Detection Method for Detecting Image Change in Night Vision Camera Priadana, Adri
Compiler Vol 8, No 2 (2019): November
Publisher : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1054.897 KB) | DOI: 10.28989/compiler.v8i2.514

Abstract

At present, the use of video cameras is not only limited to documenting events but is also used for surveillance systems. Changes in lighting that occur in the surveillance area is one of the problems that result in a false alarm on the surveillance system. Illumination Invariant Change Detection is a method for detecting image changes on images. This study aims to determine the performance of the Illumination Invariant Change Detection method to detect image changes in night vision surveillance cameras. The Illumination Invariant Change Detection method does not work well for detecting image changes on a night vision camera under dark lighting conditions at an average value of Lux 0 with an infrared lamp on. The accuracy of the application of the method to detect image changes on night vision cameras is 80% with the selection of the threshold value of the detection of image changes that is 75000 pixels.
Sistem Pendukung Keputusan Penerimaan Programer Software House Menggunakan Metode Simple Additive Weighting (SAW) Triharseno, Wejo; Pradnya Dhuhita, Windha Mega; Priadana, Adri
JURNAL PILAR TEKNOLOGI : Jurnal Ilmiah Ilmu Ilmu Teknik Vol 5, No 1 (2020): JURNAL PILAR TEKNOLOGI
Publisher : LPPM Universitas Merdeka Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33319/piltek.v5i1.52

Abstract

Abstract— The software house business is one of the businesses that are developing in the 4.0 industrial revolution era, both globally and locally. There are many software houses that need programmers with various qualifications to be accepted as employees both contract and permanent employees. One of the problems that arises is the frequency of employees leaving (resigning) from the software house for various reasons such as meeting deadlines, difficulty in working in teams, and also limited ability of programmers (low-skilled). many components of test results to test the ability to make programs. This study aims to build a decision support system in the selection of software house employees who can rank the results of prospective applicants' tests quickly based on the weight of predetermined criteria. Decision support systems made in this study can process test score data, prospective programmer data, and criterion data. The software house programmer acceptance decision support system created in this study successfully implemented the SAW method and was able to display the results of the ranking ranking starting from the score with the highest value to the score with the lowest value. Keywords—: decision support systems; software house programmer acceptance; simpe additive weighting; SAW.
Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter Habibi, Muhammad; Priadana, Adri; Rifqi Ma’arif, Muhammad
IJID (International Journal on Informatics for Development) Vol. 10 No. 1 (2021): IJID June
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.
Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based Priadana, Adri; Rizal, Ahmad Ashril
CAUCHY Vol 7, No 1 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i1.12488

Abstract

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.
Hashtag Analysis of Indonesian COVID-19 Tweets Using Social Network Analysis Muhammad Habibi; Adri Priadana; Muhammad Rifqi Ma'arif
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.61626

Abstract

Social media has become more critical for people to communicate about the pandemic of COVID-19. In social media, hashtags are social annotations which often used to denote message content. It serves as an intuitive and flexible tool for making huge collections of posts searchable on Twitter. Through practices of hashtagging, user representations of a given post also become connected. This study aimed to analyze the hashtag of Indonesian COVID-19 Tweets using Social Network Analysis (SNA). We used SNA techniques to visualize network models and measure some centrality to find the most influential hashtag in the network. We collected and analyzed 500.000 public tweets from Twitter based on COVID-19 keywords. Based on the centrality measurement result, the hashtag #corona is a hashtag with the most connection with other hashtags. The hashtag #COVID19 is the hashtag that is most closely related to all other hashtags. The hashtag #corona is the hashtag that most acts as a bridge that can control the flow of information related to COVID-19. The hashtag #coronavirus is the most important of hashtags based on their link. Our study also found that the hashtag #covid19 and #wabah have a substantial relationship with religious-related hashtags based on network visualization.
Instagram Hashtag Trend Monitoring Using Web Scraping Aris Wahyu Murdiyanto; Adri Priadana; Aris Wahyu Murdiyanto
Jurnal Pekommas Vol 5, No 1 (2020): April 2020
Publisher : BBPSDMP KOMINFO MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jpkm.2020.2050103

Abstract

In recent years, Instagram has become one of the fastest growing social media platforms. Searching images on Instagram can be done by using a particular keyword or often known as the hashtag. The hashtag is one of the parameters that can use to find out the topics that are being talked about on social media. There are many advantages for knowing a hot topic on social media to support decision making. This study aims to monitor trends of hashtags on the Instagram platform using web scraping techniques. This research has succeeded in extracting and analyzing post data on Instagram to provide trend information from a #MerryChrismas hashtag. The results of this study are the visible trend in the #MerryChrismas hashtag experienced an increase in the last two days, namely on 24 and 25 December 2019. In addition, this research also succeeded in displaying posts with the most number of likes and comments from a hashtag at a certain time period.
Klasterisasi udang berdasarkan ukuran berbasis pemrosesan citra digital menggunakan metode CCA dan DBSCAN Adri Priadana; Aris Wahyu Murdiyanto
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.2.2020.106-112

Abstract

The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.
Analisis Waktu Terbaik untuk Menerbitkan Konten di Instagram untuk Menjangkau Audiens Aris Wahyu Murdiyanto; Adri Priadana; Aris Wahyu Murdiyanto
Jurnal Penelitian Pers dan Komunikasi Pembangunan Vol 24 No 1 (2020): Jurnal Penelitian Pers dan Komunikasi Pembangunan
Publisher : Balai Pengembangan SDM dan Penelitian Komunikasi dan Informatika (BPSDMP Kominfo) Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (453.019 KB) | DOI: 10.46426/jp2kp.v24i1.118

Abstract

Instagram is a social media that allows us to easily promote products where one of them is done by publishing promotional content. However, posting promotional material at the right time to get an optimal response from the audience is a complex problem. This study aims to analyze the best publishing time to publish promotional content from 10 open trip service provider accounts on the Instagram platform. Researchers use the web scraping method to extract data from Instagram accounts and the aggregation, ordering, and selecting methods to analyze the best time. The basis used to determine the best time is the number of likes and comments on all posts. This research has succeeded in extracting Instagram's web data and analyzing post data from several Instagram accounts of open trip service providers. The results of this study indicate that each account has a different best time to publish content. For example, the best time to post content from an Instagram @hvtrip account is Friday between 20:00 and 20.59. The study can be used as a recommendation for Instagram account holders of open trip service providers regarding the best time to publish promotional content on Instagram to reach an optimal audience. Of course, this is not limited to Instagram accounts open service providers only. Keywords: social media analytics, Instagram data, marketing, open trip services, the best time ABSTRAK Instagram merupakan salah satu media sosial yang memungkinkan kita untuk mempromosikan produk dengan mudah dimana salah satunya dilakukan dengan cara menerbitkan konten promosi. Akan tetapi, penerbitan konten prosmosi pada waktu yang tepat untuk mendapatkan tanggapan dari audiens secara optimal merupakan masalah yang kompleks. Penelitian ini bertujuan menganalisis waktu penerbitan terbaik untuk menerbitkan konten promosi dari 10 akun penyedia jasa open trip pada platform Instagram. Peneliti menggunakan metode web scraping untuk mengekstrak data dari akun Instagram dan metode aggregation, ordering, dan selecting untuk menganalisis waktu terbaik. Dasar yang digunakan untuk menentukan waktu terbaik adalah jumlah suka dan komentar pada semua post. Penelitian ini telah berhasil mengekstraksi data web Instagram dan melakukan analisis data post dari beberapa akun Instagram penyedia jasa open trip. Hasil penelitian ini menunjukkan bahwa setiap akun memiliki waktu terbaik yang berbeda-beda untuk menerbitkan konten. Sebagai contoh, waktu terbaik untuk menerbitkan konten dari akun Instagram @hvtrip adalah hari Jumat antara jam 20.00 sampai jam 20.59. Hasil dari penelitian ini dapat dijadikan sebagai sebuah rekomendasi bagi pemilik akun Instagram penyedia jasa open trip mengenai waktu terbaik untuk menerbitkan konten promosi pada Instagram untuk menjangkau audiens secara optimal. Tentunya, hal ini tidak terbatas pada akun Instagram penyedia jasa open trip saja. Kata kunci: analisis media sosial, data Instagram, pemasaran, jasa open trip, waktu terbaik
Analysis of web scraping techniques to get keywords suggestion and allintitle automatically from Google Search Engines Aris Wahyu Murdiyanto; Aris Wahyu Murdiyanto; Adri Priadana
Compiler Vol 10, No 2 (2021): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.619 KB) | DOI: 10.28989/compiler.v10i2.1064

Abstract

Keyword research is one of the essential activities in Search Engine Optimization (SEO). One of the techniques in doing keyword research is to find out how many articles titles on a website indexed by the Google search engine contain a particular keyword or so-called "allintitle". Moreover, search engines are also able to provide keywords suggestion. Getting keywords suggestions and allintitle will not be effective, efficient, and economical if done manually for relatively extensive keyword research. It will take a long time to decide whether a keyword is needed to be optimized. Based on these problems, this study aimed to analyze the implementation of the web scraping technique to get relevant keyword suggestions from the Google search engine and the number of "allintitle" that are owned automatically. The data used as an experiment in this test consists of ten keywords, which each keyword would generate a maximum of ten keywords suggestion. Therefore, from ten keywords, it will produce at most 100 keywords suggestions and the number of allintitles. Based on the evaluation result, we got an accuracy of 100%. It indicated that the technique could be applied to get keywords suggestions and allintitle from Google search engines with outstanding accuracy values.