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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika dan Rekayasa Komputer PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING
Marrylinteri Istoningtyas
Universitas Dinamika Bangsa
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences

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Implementasi Algoritma Naive Bayes Untuk Memprediksi zona dan Tingkat Penyebaran Covid-19 Di Provinsi Jambi Jopi Mariyanto; Sandi Pramadi; Kholil Ikhsan; Imelda Yose; Errissya Rasywir; Marrylinteri Istoningtyas; Yovi Pratama
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 2 No 2 (2022): JAKAKOM Vol 2 No 2 September 2022
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (546.835 KB) | DOI: 10.33998/jakakom.2022.2.2.115

Abstract

Abstrak - Pada masa pandemi Virus Corona (Covid-19), Informasi merupakan hal yang sangat penting bagi masyakarat. Salah satu sumber informasi yang digunakan melalui internet yaitu website berita seperti kompas.com, detik.com, tribunnews.com. Namun, artikel berita terkait topik pembahasan kematian pasien, pasien yang sembuh dari Virus Corona dan zona pada suatu tempat belum dapat dikelompokan. Hal ini dikarenakan angka pasien yang meninggal dan angka pasien yang sembuh akibat virus corona terus bertambah. Oleh karena itu, dibutuhkannya sistem yang dapat mengelompokan berita berdasarkan data yang telah ada sebelumnya. Hal ini dapat diatasi menggunakan text mining, salah satunya metode klasifikasi pada text mining dapat mengelompokan data pada suatu objek yang belum diketahui sebelumnya. Kata Kunci : covid-19, klasifikasi, algoritma naïve bayes Abstract - During the Corona Virus (Covid-19) pandemic, information is very important for the community. One source of information used through the internet is news websites such as kompas.com, detik.com, tribunnews.com. However, news articles related to the topic of discussing patient deaths, patients recovering from the Corona Virus and zones in one place cannot be grouped. This is because the number of patients who died and the number of patients who recovered from the corona virus continued to increase. Therefore, we need a system that can classify news based on pre-existing data. This can be overcome using text mining, one of which classification methods in text mining can group data on an object that has not been previously known. Keywords : covid-19, classification, nave bayes algorithm
ANALYSIS OF THE EFFECT OF ALFAGIFT'S ELECTRONIC SERVICE QUALITY BASED ON OBJECTIVES USING THE E-SERVQUAL METHOD Rafa Rizana Eka Putri; Marrylinteri Istoningtyas; Maria Rosario Boerroek
International Conference on Business Management and Accounting Vol 1 No 1 (2022): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v1i1.2856

Abstract

The times and technology are becoming retail business opportunities to compete online. This research was conducted with the aim to determine the effect of Alfagift service quality and the magnitude of the influence of e-servqual on user satisfaction Alfagift application. The study population was all people who had used the Alfagift application and the number was not known with certainty. So that the sample was taken using the non-probability sampling method type Purposive Sampling with the number of respondents taken as many as 130 respondents in the city of Jambi. The research data were processed using multiple linear regression methods through Statistical Product and Service Solution (SPSS) version 25. The results of data analysis showed the e-service quality hypothesis used in the study had a positive and significant effect on user satisfaction (0.878; t 10.991; sig 0.00).
ANALISIS KUALITAS WEBSITE SAMSAT JAMBI MENGGUNAKAN METODE DELONE AND MCLEAN Maria Rosario B; Marrylinteri Istoningtyas; Fitria Febrianti
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 6 No 2 (2021): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v6i2.1743

Abstract

The SAMSAT website is an application of information technology that is used to facilitate motorized vehicle users to obtain information related to services provided by the SAMSAT office. Based on the results of the initial questionnaire that the researcher has distributed to 50 respondents, 82% of the respondents stated that the SAMSAT Jambi website has weaknesses, including the website is less attractive in terms of interface and there is an error menu on the website. The purpose of this study is to analyze the level of success of the website SAMSAT Jambi using the Delone and Mclean method and to determine the effect of independent variables (system quality, information quality, service quality) on the dependent variable (use, user satisfaction, net benefits). Data analysis using SEM and SmartPLS software. Based on the results of the data processing of the Jambi community questionnaire, it was found that of the 9 hypotheses proposed in this study, only 7 hypotheses were acceptable including system quality on usage, system quality on user satisfaction, quality of information on usage, quality of information on user satisfaction, use of user satisfaction, use of net benefits, and user satisfaction with net benefits
ANALISIS KOMPARATIF KINERJA LOGISTIC REGRESSION DAN RANDOM FOREST BERBASIS TF-IDF UNTUK KLASIFIKASI SENTIMEN KOMENTAR TIKTOK TERHADAP PROGRAM MAKAN BERGIZI GRATIS Febriza Evan Nugraha; Ibni Faiq Athallah; Marrylinteri Istoningtyas
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2515

Abstract

Sentiment analysis of public policy on social media has become increasingly important as public participation in digital spaces continues to grow. This study compares the performance of Logistic Regression and Random Forest algorithms based on TF-IDF for classifying the sentiment of TikTok comments regarding the Free Nutritious Meal Program into three classes: positive, neutral, and negative. The dataset consists of 4,811 public comments collected from six TikTok videos between January and June 2026. After preprocessing and manual labeling, 3,884 valid comments were obtained with a sentiment distribution of 39.73% negative, 37.97% positive, and 22.30% neutral. Class imbalance was addressed using SMOTE on the training data, and the dataset was split using an 80:20 stratified split. Evaluation results show that Logistic Regression outperformed Random Forest across all metrics, achieving an accuracy of 0.76 and a macro F1-score of 0.74 compared to Random Forest's accuracy of 0.73 and macro F1-score of 0.71. In both models, the neutral class consistently showed the lowest performance, indicating semantic ambiguity that cannot be optimally captured by frequency-based feature representations. This study provides empirical evidence that Logistic Regression is more suitable for Indonesian social media text sentiment classification with TF-IDF representation, and recommends exploring context-based models such as IndoBERT for future research.
Co-Authors Errissya Rasywir Febriza Evan Nugraha Fitria Febrianti Ibni Faiq Athallah Imelda Yose Jopi Mariyanto Kholil Ikhsan Maria Rosario B Maria Rosario Boerroek Rafa Rizana Eka Putri Sandi Pramadi Yovi Pratama