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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


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


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.
The Relational Data Model on The University Website with Search Engine Optimization Muhammad Riza Alifi; Hashri Hayati; Muhammad Galih Wonoseto
IJID (International Journal on Informatics for Development) Vol. 10 No. 2 (2021): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.3223


The visibility of a university’s website on the search engine becomes an essential factor to reach a wider audience. One way to improve the visibility of a website is through Search Engine Optimization (SEO). University’s website development with SEO is inseparable from the data model because SEO supporting factors are parts of the consideration in the components and structure of the data model. This study aims to build a data model for a university website accompanied by SEO. The relational data model is used in this study based on the performance and maturity in defining schema-based design. This study was conducted through four sequential stages: literature review, planning, implementation, and evaluation. The resulting relational data model is one that has accommodated four supporting factors for SEO, namely Meta description, Meta keywords, URL structure, and image description. This study has succeeded in building a relational data model at the abstraction level of conceptual and logical.  In the conceptual data model, one entity and 11 attributes are formed. The logical data model was implemented in independent work environments using RelaX and operational requirements can be fulfilled by representing each table or relationship in the schema using relational algebra.
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


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.
RANCANG BANGUN WEBSITE INFORMASI SEKOLAH DI MTS FATAHILLAH CIMAHI Asri Maspupah Asri Maspupah; Fitri Diani; Ade Chandra Nugraha; Muhammad Riza Alifi; Hashri Hayati; Djoko Cahyo Utomo Lieharyani
Jurnal Difusi Vol 5 No 2 (2022): Jurnal Difusi
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M) Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/difusi.v5i2.2488


Website merupakan salah satu media online untuk memberikan informasi pada pihak internal maupun eksternal. Salah satu upaya untuk memperluas jangkauan area promosi adalah melalui media online. Salah satunya dengan pembuatan profil sekolah melalui website. Website dapat digunakan sebagai sarana penyampaian informasi dari pihak internal kepada eksternal. Saat ini MTs fatahillah sudah memiliki website, namun masih terdaftar dalam blogspot dan belum memiliki nama domain sendiri. Tampilan website saat ini masih belum menunjukkan profil sekolah. Dengan demikian pengabdian ini mengusulkan tentang rancang bangun website informasi sekolah. Metode pengembangan website menggunakan metode software development life cycle yaitu identifikasi masalah, pengumpulan data, analisis kebutuhan, perancangan website dan implementasi dan testing. Hasil dari rancang bangun website memperlihat 5 perbedaan antara website saat ini dengan website yang sudah dikembangkan. Hasil uji coba kepada mitra menunjukkan bahwa website telah sesuai harapan dan dapat dioperasikan dengan baik oleh pihak sekolah, yaitu dengan nilai skor rata-rata diatas 3,22 sebagai feedback dari mitra.