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All Journal Jurnal Teknologi Terapan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Syntax: Journal of Software Engineering, Computer Science and Information Technology 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 Mechanical 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.
ANALISIS BRAND LAYANAN AKADEMIK PERGURUAN TINGGI INDONESIA MENGGUNAKAN KLASIFIKASI TEKS DI MEDIA SOSIAL Hashri Hayati; Muhammad Riza Alifi
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 1 (2025): Juni 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i1.6924

Abstract

 Penelitian ini bertujuan untuk menganalisis persepsi komunitas eksternal terhadap brand akademik perguruan tinggi di Indonesia melalui media sosial, khususnya Twitter/X. Seiring dengan tingginya jumlah perguruan tinggi dan angka partisipasi kasar (APK), kompetisi antar institusi pendidikan tinggi semakin kuat, mendorong perlunya diferensiasi brand yang disampaikan ke publik. Dalam studi ini, dikumpulkan post dari 30 akun resmi X perguruan tinggi di Indonesia yang kemudian diklasifikasikan ke dalam lima kategori brand akademik: Innovative, Global Impact, Student Engagement, Career Focused, dan Research Excellent. Proses klasifikasi dilakukan dengan membangun model pembelajaran menggunakan algoritma Naïve Bayes, yang diimplementasikan melalui pustaka pemrosesan bahasa alami di lingkungan Node.js. Untuk mengevaluasi kinerja model, dilakukan pengujian terhadap dataset uji terpisah, dan dihitung metrik evaluasi berupa precision, recall, dan accuracy berdasarkan nilai True Positive, False Positive, dan False Negative yang diperoleh melalui confusion matrix untuk setiap kelas. Hasil evaluasi menunjukkan bahwa model yang dikembangkan memiliki performa nilai rata-rata precision sebesar 80,8%, recall sebesar 78,8%, dan accuracy sebesar 80%, sehingga dapat diandalkan sebagai alat bantu untuk memahami kesesuaian antara brand yang dikomunikasikan dan persepsi publik secara daring. Kata Kunci— brand akademik, brand perguruan tinggi, klasifikasi teks, naïve bayes, media sosial. ABSTRACTThis study aims to analyze the perceptions of external communities regarding the academic branding of Indonesian universities through social media, particularly Twitter/X. With the growing number of higher education institutions and rising gross enrollment rates, competition among universities has intensified—prompting the need for more distinct and strategic public brand positioning. In this study, posts were collected from 30 official university X accounts in Indonesia and categorized into five academic brand themes: Innovative, Global Impact, Student Engagement, Career Focused, and Research Excellent. The classification process involved building a supervised machine learning model using the Naïve Bayes algorithm, implemented with a natural language processing library in the Node.js environment. To evaluate the model's performance, a separate test dataset was used, and evaluation metrics—namely precision, recall, and accuracy—were calculated for each class based on values of True Positive, False Positive, and False Negative derived from a confusion matrix. The results indicate that the developed model performs well, achieving average scores of 80,8% for precision, 78,8% for recall, and 80% for accuracy, making it a reliable tool for assessing the alignment between institutional brand communication and public perception in online discourse. Keywords—academic brand, university brand, text classification, naïve bayes, social media.  
Co-Authors Cholid Fauzi Djoko Cahyo Utomo Lieharyani Muhammad Riza Alifi Transmissia Semiawan