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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi 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) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer
Muhammad Habibi
Universitas Jenderal Achmad Yani Yogyakarta
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Nursing Public Health Social Sciences Other

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Journal : Telematika : Jurnal Informatika dan Teknologi Informasi

 1 
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

Abstract

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.
Autoregressive Integrated Moving Average (ARIMA) Models For Forecasting Sales Of Jeans Products Jenny Meilila Azani Cahya Permata; Muhammad Habibi
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.7868

Abstract

Purpose: To be able to compete with other companies, it is necessary to estimate and forecast jeans products that will be ordered according to consumer demand every month, so that there is no excess inventory and product shortage. If there is a shortage of goods, the consumer will be disappointed with the seller, and vice versa if the goods are overstocked, the quality will continue to decline to the detriment of the seller and the buyer, resulting in a shortage of materials.Methodology: To overcome the problem of selling jeans products, the ARIMA method is suitable to overcome the problem of forecasting the stock of jeans sales. ARIMA model is a model that completely ignores the independent variables in making forecasts. ARIMA uses past and present values of the dependent variable to produce accurate short-term forecasting.Results: The built forecasting has a MAPE accuracy rate of 17.05% so it can be said that predicting has good results according to the criteria. Forecasting results in the following year show that sales tend to increase from the previous year.Originality: This research was conducted using sales data of jeans products at company XYZ and using the ARIMA method which previous researchers have never done.
Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine Kartikadyota Kusumaningtyas; Muhammad Habibi; Irmma Dwijayanti; Retno Sumiyarini
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.9820

Abstract

Purpose: Social media, particularly Twitter, provides a venue for individuals to share their thoughts. The public's perception of mental illnesses is often debated on Twitter. So yet, no evaluation of community tweets connected to data on mental health conditions has been performed. The purpose of this study is to examine tweets linked to mental illnesses in Indonesia in order to identify the themes of conversation and the polarity trends of these tweets.Design/methodology/approach: To address this issue, the K-Means Clustering algorithm is utilized to aggregate tweet data that is used to find themes of conversation. The emotion polarity value of each cluster result was then determined using the Support Vector Machine (SVM) approach.Findings/results: This study generated five topic clusters based on tweets about mental illness. While sentiment analysis revealed that all clusters had more negative sentiment classes than positive. Cluster 4 and Cluster 5 had the highest number of negative sentiment values. These clusters emphasize the necessity of consulting with psychiatrists and psychologists if people have mental health disorders, as well as financing for mental health disorder treatment through BPJS Kesehatan services.Originality/value/state of the art: The analysis was done in two stages: data grouping to find themes of conversation using K-Means clustering and SVM to look for positive and negative polarity values associated to twitter data about mental illness.
Co-Authors ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Fajar Syahruddin Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Kusumaningtyas, Kartikadyota M. Wahyu Indriyanto Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nurul Hikmah Pangky Putra Aziztiya Puji Winar Cahyo Ramses Caniago Rifqi Ma’arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Saputra, Andika Bayu Siti Rahmawati Sumiyarini, Retno Ulil Amri Nasron