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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan International Journal of New Media Technology Compiler JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Informatika : Jurnal Informatika, Manajemen dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Cyber Security dan Forensik Digital (CSFD) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer
Muhammad Habibi
Universitas Jenderal Achmad Yani Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Journal : Compiler

 1 
ANALYSIS OF MARKETPLACE CONVERSATION TRENDS ON TWITTER PLATFORM USING K-MEANS Nasron, Ulil Amri; Habibi, Muhammad
Compiler Vol 9, No 1 (2020): Mei
Publisher : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.936 KB) | DOI: 10.28989/compiler.v9i1.579

Abstract

Businesses began to shift from the marketing process that used to use conventional media to switch to using the internet and social media. This is because the cost of marketing using the internet and social media is cheaper than using conventional media. The problem that is often faced by businesspeople when marketing on social media is that they rarely see a marketplace that is becoming a trend and is being discussed by consumers on social media, so the marketing process is carried out less than the maximum. This study aims to analyze conversation trends related to the marketplace on the Twitter platform. The method used in this study is the K-Means Clustering method. Based on the results of the study found that the application of the K-Means Clustering method can produce sufficient information as a basis for consideration of businesspeople in choosing a marketplace. Marketplace trend analysis results show that Shopee, Lazada, and Tokopedia are highly discussed marketplaces on Twitter.
ANALYSIS OF MARKETPLACE CONVERSATION TRENDS ON TWITTER PLATFORM USING K-MEANS Ulil Amri Nasron; Muhammad Habibi
Compiler Vol 9, No 1 (2020): Mei
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (683.942 KB) | DOI: 10.28989/compiler.v9i1.579

Abstract

Businesses began to shift from the marketing process that used to use conventional media to switch to using the internet and social media. This is because the cost of marketing using the internet and social media is cheaper than using conventional media. The problem that is often faced by businesspeople when marketing on social media is that they rarely see a marketplace that is becoming a trend and is being discussed by consumers on social media, so the marketing process is carried out less than the maximum. This study aims to analyze conversation trends related to the marketplace on the Twitter platform. The method used in this study is the K-Means Clustering method. Based on the results of the study found that the application of the K-Means Clustering method can produce sufficient information as a basis for consideration of businesspeople in choosing a marketplace. Marketplace trend analysis results show that Shopee, Lazada, and Tokopedia are highly discussed marketplaces on Twitter.
A social network analysis: identifying influencers in the COVID-19 vaccination discussion on twitter Muhammad Habibi; Puji Winar Cahyo
Compiler Vol 10, No 2 (2021): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (705.467 KB) | DOI: 10.28989/compiler.v10i2.1074

Abstract

Social media analytics, especially Twitter, has experienced significant growth over the last few years. The data generated by Twitter provides valuable information to many stakeholders regarding user behavior, preferences, tastes, and characteristics. The presence of influencers on social media can invite interaction with other users. An influencer can affect the speed of spreading information on social media. This study looks at the influence of influencers and information dissemination channels on Twitter data related to COVID-19 vaccination in Indonesia as one of the hot Twitter discussion trends. This study applies Social Network Analysis (SNA) as a theoretical and methodological framework to show that interactions between users have differences on the network when the analyzed tweets are divided into mention and retweet networks. This study found that the key accounts in disseminating information related to Covid-19 vaccination were dominated by official accounts of government organizations and online news portals. The official Twitter account of government organizations turns out to have an essential role in disseminating information related to COVID-19 vaccination, namely the @KemenkesRI account belonging to the Ministry of Health of the Republic of Indonesia and the @Puspen_TNI account belonging to the TNI Information Center.
Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text Aris Wahyu Murdiyanto; Muhammad Habibi
Compiler Vol 11, No 2 (2022): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (794.192 KB) | DOI: 10.28989/compiler.v11i2.1327

Abstract

The volume of digital documents available online is growing exponentially due to the increasing use of the internet. Categorization of information obtained online is needed to make it easier for recipients of information to determine and filter which information is needed. Classification of web pages can be based on titles and descriptions, which are text data that can be done by utilizing deep learning technology for text classification. This study aimed to conduct data training and analysis experiments to determine the accuracy of the proposed deep learning architecture in classifying web page titles and descriptions. In this research, we proposed a Convolution Neural Network (CNN) architecture that generates few parameters. The training and evaluation set was conducted on the web page dataset provided by DMOZ. As a result, the proposed CNN architecture with the number of N (Dropout + 1D Convolution + ReLU activation) equal to 1 achieves the best validation accuracy. It achieves 79.51% with only generates 825,061 parameters. The proposed CNN architecture achieved outperformed performance on the accuracy of the five other technologies in the state-of-the-art.
Co-Authors A. Sinay, Stani ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfirna Rizqi Lahitani Alfun Roehatul Jannah Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Elvika Fit Ari Shanti, Elvika Fit Ari Endah Puji Astuti, Endah Puji Fajar Syahruddin Fresia Nandela, Emilia I Gede Iwan Sudipa Ika Arfiani Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Jullev Atmadji, Ery Setiyawan Kharisma Kristi, Ria Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi M. Wahyu Indriyanto Muhammad Erwan Syah Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nur Azizah Firdausa Oktaviana, Dela Pangky Putra Aziztiya Puji Winar Cahyo Purnamaningsih, Nur’Aini Purwatiningsih, Agustina Ramses Caniago Rifanny Br Girsang, Ribka Rifqi Ma’arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Rizky Fauzi Akbar Rizqi Lahitani, Alfirna Rukmi, Dwi Kartika Saputra, Andika Bayu Sujono Riyadi Sumiyarini, Retno Suwarno Tegar Agustian Tri Sunarsih Ulil Amri Nasron Wahyuningsiam, Astr