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All Journal JTT (Jurnal Teknologi Terapan) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pengabdian Masyarakat IPTEK Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Hashri Hayati
Politeknik Negeri Bandung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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ANALISIS SENTIMEN PADA TWEET TERKAIT VAKSIN COVID-19 MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Hashri Hayati; Muhammad Riza Alifi
Jurnal Teknologi Terapan Vol 7, No 2 (2021): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v7i2.349

Abstract

Covid-19 is a disease that has been declared a global pandemic since March 2020. One of the challenges in dealing with the current Covid-19 pandemic is the widespread doubts about the use of vaccines, even though vaccination is one of the most successful ways to deal with infectious disease outbreaks. Vaccine hesitancy can be observed, among others, from public sentiment or perception on social media, one of them is Twitter. The existence of social media can affect the absorption of information received by a person, in this case social media is also a medium for anti-vaccine propaganda which can result in a decrease in public confidence in the Covid-19 vaccine. This study aims to develop a classification model using the Support Vector Machine (SVM) method for sentiment analysis of Tweet related to the Covid-19 vaccine. Several previous studies have conducted sentiment analysis related to Covid-19, but this research specifically conducts sentiment analysis on the topic of the Covid-19 vaccine so that data preparation and model configuration will be different. This study also uses the Design Science Research Methodology (DSRM) for research as a whole before focusing on the use of the SVM method. The results of the study consist of an algorithm for creating data sets and a classification model for sentiment analysis that can be used to determine public perceptions of the issue of Covid-19 vaccination. This study also compares the use of unigram and bigram tokenization. Based on the results obtained, the average value of each aspect of the evaluation measurement is higher when the bigram tokenization is used. Although higher, the value obtained has an insignificant difference in the range of 0.6% - 0.7%. In the evaluation results using unigram and bigram tokenization, the highest scores for all aspects of measurement, namely accuracy, recall, f-measure, and precision were 84%.
Penerapan Algoritma Regresi Linier pada Prediksi Tarif Influencer Media Sosial Muhammad Riza Alifi; Hashri Hayati; Cholid Fauzi
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (764.283 KB) | DOI: 10.47065/josh.v4i1.2361

Abstract

The influencer industry has emerged as a result of social media disruption, and its members can affect audience interest in the goods and services being advertised. Because advertising performance on social media is more quantifiable than it is with traditional media, using influencer services is thought to be preferable. Influencer rates are often dependent on reach, engagement, and follower count. However, since there is no reference standard used in determining the prices, it could harm one of the parties. In order to reduce the impact of losses for both influencers in giving rates and clients in accepting rate offers, this study intends to propose a solution in the form of a machine learning-based influencer rate prediction model that can be used as a reference. The stages of this study are literature review, data gathering, pre-processing of the data, linear regression model development, and model evaluation. Five different models were produced as a result of this investigation. One of the best models has an MAE of 145401.484375, an MSE of 7.222241e+10, and an RMSE of 268742.250. These findings are affected by the hyperparameter learning rate of 0.001 and the epoch of 1,000. Most of the test data have not been completely represented by the model. The little number of datasets utilized for training, only 161 rows with 4 positively correlated attributes, is one of the reasons why the model is not really optimal. Nevertheless, from the standpoint of using a relatively small dataset, the model developed in this study is quite successful because several of the prediction results are fairly near to the real value, one of which is the prediction value with an error difference of −347.69.
Pemodelan Data Relasional pada NoSQL Berorientasi Dokumen Muhammad Riza Alifi; Transmissia Semiawan; Djoko C.U. Lieharyani; Hashri Hayati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 3: Agustus 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v11i3.3704

Abstract

Data management technology that continues to develop and boost the popularity of document-based not only structured query language (NoSQL) has become the most-used data model. Behind its popularity, data management technology offers an intriguing advantage, namely flexible data storage, whether in terms of data forms and sizes or structured and unstructured data. However, this data modeling flexibility has its challenge due to its impact on more complex scheme creations, without being accompanied by any need-based design patterns. This study aims to model relational data on the document-based NoSQL at its conceptual, logical, and physical levels. The conceptual design was developed based on processes, rules, and business requirements. The logical and physical designs were developed based on the extended references and computed design patterns determined from the operating workload. The relational data model design on the document-based NoSQL was successfully formed using the entity relationship diagram (ERD) with Chen notation for the conceptual, and collection relationship diagram (CRD) for both logical and physical levels. The conceptual design focused on the representation of entities, attributes, and relationships. Unlike the conceptual design which tends to be abstract, the focus of the logical design is on the collection schema (embedded and reference) representation, including design patterns influenced by the formation of relationships. Furthermore, the focus of physical level design is to represent the schema in a more concrete form. The physical design is almost the same as the logical one, the difference lies only in the detail addition for data types and structures. The evaluation of data model designs was also carried out for each level. This study contributes to designing a data model with the advantage of read-intensive capability since a joint operation among collections is not required and the computation process recurrence for derivative attributes is not necessary.
Pelatihan Pembelajaran Computational Thinking Untuk Guru SMP 1 Negeri Baleendah Aprianti Nanda Sari; Trisna Gelar; Hashri Hayati; Lukmannul Hakim Firdaus; Ade Hodijah; Muhammad Riza Alifi
Jurnal Pengabdian Masyarakat IPTEK Vol. 4 No. 1 (2024): Edisi Januari 2024
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v4i1.9570

Abstract

Salah satu misi dari SMP Negeri 1 Baleendah adalah melaksanakan proses belajar dan bimbingan secara efektif yang dapat menggali seluruh potensi yang dimiliki siswa sehingga dapat menghasilkan siswa yang berprestasi. Peningkatan prestasi siswa dapat diraih dengan berbagai cara, salah satunya dengan peningkatan kompetensi Computational Thinking (CT). Aktifitas CT dengan format permainan dan multidisiplin dapat meningkatakan kreativitas dari siswa. Pemberian pelatihan aktifitas CT Unlugged seperti Lego-Clone dan Educational Robot dan Plugged dengan pengembangan games, animasi, dan video dengan media Scratch dapat meningkatan kompetensi guru dalam membuat bahan ajar dan media pembelajaran yang kreatif dan menarik. Tahapan pengabdian terdiri dari analisa situasi dan kebutuhan, perancangan bahan ajar pelatihan, pelaksanaan pelatihan, pendampingan peserta pelatihan, evaluasi dan capstone project. Dari hasil evaluasi, kemampuan CT guru yang mengikuti pelatihan meningkat. Selain itu, guru-guru yang mengajar mata Pelajaran berbeda berhasil berkolaboarsi mengembangkan bahan ajar sederhana berbasis CT yang multidisiplin menggunakan Scratch. Selain melakukan pelatihan, Guru berhasil menyelesaikan Capstone Project yang berupa Implementasi CT untuk bahan ajar mulai dari inisiasi ide, pembuatan bahan ajar dan implementasi pada kegiatan belajar mengajar pada masing-masing kelas.
Co-Authors Ade Hodijah Cholid Fauzi Djoko Cahyo Utomo Lieharyani Gelar, Trisna Lukmannul Hakim Firdaus Muhammad Riza Alifi Sari, Aprianti Nanda Transmissia Semiawan