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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Indonesian Journal of Electrical Engineering and Computer Science
Lathifah Alfat
Universitas Pembangunan Jaya
Computer Science & IT Control & Systems Engineering

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Web mining and sentiment analysis of COVID-19 discourse in online forum communities Masurah Mohamad; Suraya Masrom; Khairulliza Ahmad Salleh; Lathifah Alfat; Muhammad Nasucha; Nur Uddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1280-1287

Abstract

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
PENGEMBANGAN APLIKASI KLASIFIKASI INDIVIDU DENGAN GANGGUAN SPEKTRUM AUTISME BERDASARKAN DSM-5 MENGGUNAKAN PENDEKATAN NAIVE BAYES Ihsan Ihsan; Lathifah Alfat; Riny Nurhajati
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/mnx97052

Abstract

The neurological disorder known as autism spectrum disorder (ASD) has an impact on a person's behavior, social communication, and interest patterns. Both repeated habits and communicative skills are lacking in this developmental condition. The World Health Organization (WHO) estimates that one out of every 100 youngsters worldwide has ASD. The Indonesian Ministry of Health in 2021 showed data on the increasing number of children with autism, which reached around 2.4 million with cases reaching 500 children every year. The use of machine learning can help classify and predict ASD based on health parameters. Using the Naive Bayes algorithm and Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) data, this study attempts to create a classification application for people with ASD and assess how the model performs in grouping people with ASD. The results showed that the classification model developed produced optimal performance with an accuracy value reaching 95% while the highest precision, recall and F1-score values reached 100%. Evaluation using the macro average metric resulted in a precision value of 94%, recall of 87%, and f1-score of 90%. The weighted average metric produces positive precision, recall, and F1-score values of 95%. The developed model is integrated into a web-based application that features real time early screening and storage of user prediction results. The development of this application is expected to facilitate early screening so as to help determine effective interventions for individuals with autism spectrum disorders, thus making a positive contribution to the treatment of this disorder in daily practice.
APLIKASI PROFILING KEBUTUHAN PELAJARAN TAMBAHAN SISWA SMA MENGGUNAKAN ALGORITMA RANDOM FOREST Nabiel Fauzan Ramadhan; Lathifah Alfat
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4094

Abstract

This study presents EduTrack, a profiling application that uses a Random Forest classifier to identify Indonesian high-school students’ needs for additional Math tutoring. The dataset consists of students’ chapter-wise Math scores, processed with Pandas and Scikit-learn and stored via SQLAlchemy. The backend is implemented in Flask, while the frontend employs Bootstrap with Chart.js for charts and DataTables for tabular display. Dummy evaluation yields model performance around 90% accuracy, with precision 88%, recall 91%, and F1-score 89% (Table 1, Figure 2). Evaluation metric formulas (precision = TP/(TP+FP), recall = TP/(TP+FN), F1 = 2 * precision * recall / (precision + recall)) are included for clarity. EduTrack is designed not only as a predictive tool, but also as a practical decision-support system for teachers. By visualizing student performance at the chapter level, the application enables educators to identify learning gaps more intuitively and implement timely interventions. This helps shift teaching strategies from reactive to proactive, ultimately supporting personalized learning and improving academic outcomes.
Co-Authors Ihsan Ihsan Khairulliza Ahmad Salleh Masurah Mohamad Muhammad Nasucha Nabiel Fauzan Ramadhan Nur Uddin Riny Nurhajati Suraya Masrom