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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

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.

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter Habibi, Muhammad; Priadana, Adri; Rifqi Maâ€™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

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.

Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform Muhammad Habibi; Puji Winar Cahyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.50574

Instagram is a social media that has the potential to be used to increase awareness of a product. Approximately 70% of users spend their time searching for a product on Instagram. Many people promote their products with a lack of attention to the target. So that not infrequently the information distributed is inaccurate information and not following user characteristics. This study aims to cluster the characteristics of Instagram users based on hashtag compatibility. The method used in this study is the K-Means Clustering method. Based on the results of the experiment, this research succeeded in clustering Instagram users based on the hashtag match on the text caption. Besides, TF-IDF can be used as a feature suitable for the K-Means Klastering method. The results of the hashtag "#kopi" analysis resulted in hashtag suggestions that can be used for the promotion of a product related to coffee, including the hashtag #coffeeshop and #coffee with total usage of 14968 captions.

Clustering followers of influencers accounts based on likes and comments on Instagram Platform Puji Winar Cahyo; Muhammad Habibi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.53028

The promotion of goods or services is now facilitated by the dissemination of information through Instagram. Dissemination of information is usually done by influencers or promotional accounts. The account used certainly has a lot of followers. Because of the large amount of follower data in that account, it can be grouped into the same characters. This is done to determine the potential for promotion using social media accounts. This study uses data from 2 popular accounts. The first account is an artist with the username ayutingting92. The second account is Infounjaya, the official promotion account from Jenderal Achmad Yani University, Yogyakarta. The results of grouping can divide follower data into two cluster groups with different interactions. The basic difference between the two groups is the number of likes and comments. The infounjaya account analysis results showed that of 4,906 followers, only 3,211 followers were actively involved in the interaction, 1,695 followers were passive followers who did not like or did not comment on the interaction. Meanwhile, the results of the ayutingting92 follower cluster show that out of 1 million sample data followers, only 13,591 followers were actively involved in the interaction of likes and comments, 986,409 were passive followers.

Entity Profiling to Identify Actor Involvement in Topics of Social Media Content Puji Winar Cahyo; Muhammad Habibi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.59869

The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.

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

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.

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

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

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.

The Development of Social Media Intelligence System for Citizen Opinion and Perception Analysis over Government Policy Muhammad Habibi; Muhammad Rifqi Ma'arif; Dayat Subekti
Telematika Vol 19, No 1 (2022): Edisi Februari 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i1.6447

In Indonesia, community involvement in development planning and public policy has generally been carried out but limitedly. Social media uploads regarding public perceptions of policy implementation in the field are valuable input for those who quickly and accurately upload existing problems.The problems that arise from this effort to use social media are 1) how to detect public conversations related to a public policy. 2) Social media data collected extensively and accelerating can be processed quickly to get real-time analysis results. 3) Making the analysis results accessible in an interactive and representative form allows government policymakers to explore appropriate data and information to formulate and formulate public policies.This research produces a social media intelligence platform that can unite public opinion regarding public perceptions of the implementation of policies issued by the government, especially local governments in Indonesia. Based on modeling the topic of Covid-19 vaccination cases, 11 topics of discussion were obtained. While the sentiment analysis results of the 11 issues resulted, topic 6 had the most negative sentiment values regarding the development of Covid-19 vaccination in Indonesia. At the same time, topics with the most positive sentiment values are topic three and topic 10. These topics discuss the vaccination process carried out by health procedures (prokes) and government policies related to COVID-19 vaccination.

INVESTIGASI DAN ANALISIS FORENSIK DIGITAL PADA PERCAKAPAN GRUP WHATSAPP MENGGUNAKAN NIST SP 800-86 dan SUPPORT VECTOR MACHINE Dedy Hariyadi; M. Wahyu Indriyanto; Muhammad Habibi
Cyber Security dan Forensik Digital Vol. 3 No. 2 (2020): Edisi November 2020
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/csecurity.2020.3.2.2193

WhatsApp merupakan platform instant messaging yang populer di Indonesia. Berdasarkan statistik dari Direktorat Tindak Pidana Siber Kepolisian Republik Indonesia pada tahun 2019 bahwa WhatsApp juga dinyatakan sebagai platform yang sering digunakan untuk mendukung tindak kejahatan. Oleh sebab itu penyidik memerlukan pemodelan untuk mempermudah dalam mengklasifikasikan konten negatif atau positif dari barang bukti digital berupa percakapan. Pemodelan dalam bentuk klasifikasi dapat membantu penyidik untuk mendeteksi kualitas percakapan pada suatu grup sehingga dapat mempercepat proses penyidikan. Dalam penelitian ini menggunakan algoritma Support Vector Machine (SVM) untuk mengklasifikasikan kualitas percakapan pada suatu grup. Pada penelitian ini berhasil mengklasifikan barang bukti digital berupa percakapan suatu grup dengan persentase kurang lebih 96,21% konten negatif. Nilai persentase tersebut dapat dijadikan suatu indikator awal dalam deteksi kualitas percakapan yang bersifat negatif. Sehingga pihak penyidik dapat mengambil tindakan penyidikan lebih intensif terkait percakapan yang bersifat negatif.

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