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All Journal Proceeding International Conference on Information Technology and Business JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma
Lamasitudju, Chairunnisa
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Computer Science & IT Economics, Econometrics & Finance Education

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PUBLIC SENTIMENT ANALYSIS OF 'DIRTY VOTE' DOCUMENTARY FILM ON TWITTER USING NAÏVE BAYES WITH GRID SEARCH OPTIMIZATION Bagaskara, Febrian Chrissma; Syahrullah, Syahrullah; Hendra, Andi; Lamasitudju, Chairunnisa; Rinianty, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2682

Abstract

The film "Dirty Vote" provides a realistic depiction of alleged fraud issues within Indonesia's democratic system, released ahead of the 2024 elections. This has sparked various public opinions, both in favor of and against the film, potentially affecting the stability of Indonesia’s democratic system. The aim of this research is to analyze the public's reaction to the "Dirty Vote" documentary, which could serve as a consideration for assessing public awareness in rationally responding to a film and improving the quality of democracy in Indonesia. This research will test the accuracy of data used in classification using the Naive Bayes Classifier based on collected Twitter data. The evaluation results of the Naive Bayes model for sentiment classification showed an accuracy of 86%, with a precision of 84% and a recall of 91%. When compared to the implementation of hyperparameter tuning using grid search with a stratified k-fold combination and parameter configurations for alpha: [0,1], binarize: [0.0], and fit prior: [true, false], better results were obtained with an accuracy of 90%, a precision of 87%, and a recall of 94%. This demonstrates that using parameter optimization methods from grid search can help improve the accuracy of a classification model. It is hoped that this research will contribute significantly to the development of Indonesia’s democratic system, particularly in raising public awareness to think more rationally and critically when evaluating and analyzing a film.
Implementation of QR Code in A Student Attendance Information Based On WhatsApp Gateway Karnita Sumbaluwu, Harlin Feby; Angreni, Dwi Shinta; Pusadan, Mohammad Yazdi; Lamasitudju, Chairunnisa; Lapatta, Nouval Trezandy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6308

Abstract

The attendance information system at Senior High School 7 Sigi, still uses a manual attendance system, namely writing on paper sheets. The problem that often occurs is the loss of student attendance books which causes the school to have difficulty in recapitulating attendance and also reporting attendance to parents. Another problem that occurs due to manual attendance is that parents cannot directly monitor their children's attendance at school which causes some students to skip school. The recommended solution is to use an attendance information system by utilizing QR Code technology so that student attendance is more practical and also the data storage is much safer. WhatsApp Gateway is used as a monitoring medium for parents because this system will send notifications via the WhatsApp application every time the lesson starts, effectively and in real-time. This attendance system uses the Waterfall method which starts from the planning, analysis, design and implementation stages
Automatic Identification of Herbal Medicines Based on Medicinal Plant Leaf Images Using the Scale Invariant Feature Transform (SIFT) Features Kasim, Anita Ahmad; Bakri, Muhammad; Lamasitudju, Chairunnisa; Fachrozi, Ahmad
Prosiding International conference on Information Technology and Business (ICITB) 2023: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 9
Publisher : Proceeding International Conference on Information Technology and Business

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Background: A few people prefer to consume medicinal plants compared to modern medicine. This is because modern medicine contains chemicals which over time can have a bad impact on the kidneys, and medicinal plants are also considered cheap treatments. Meanwhile, in our current environment, there are plants that grow and have certain benefits, but some people don't know whether these plants are herbal medicinal plants or not. By utilizing technology, people can find out about herbal medicinal plants based on the leaves by photographing them on an Android smartphone. Method: The method used to extract features from the leaf image is Scale Invariant Feature Transform (SIFT). Aim: This research aims to recognize leaves whose images have been photographed or uploaded. The system will identify herbal medicinal plants using the leaf image of the plant using the Scale Invariant Features Transform (SIFT) method. Result: Feature Extraction and Support Vector Machine (SVM). With this system, it is hoped that users will be able to identify herbal medicinal plants that may grow in the surrounding environment. Based on the description in the background above, the problem formulation in this research is how to identify herbal medicinal plants using leaf images using Android-based SIFT feature extraction. Conclusion: The results of the confusion matrix test explain that this system has an average accuracy of 77%, which means that this system is quite good at identifying leaf images, even though the error rate is quite high at 23%.Keywords—Medicinal Plant Leafs, SVM, SIFT
Analisis Sentimen Terhadap Kinerja Awal Pemerintahan Menggunakan IndoBERT Dan SMOTE Pada Media Sosial X Ihalauw, Sahron Angelina; Trezandy Lappata, Nouval; Wiria Nugraha, Denny; Wirdayanti; Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2957

Abstract

Social media platform X has become a key channel for expressing public opinion on political issues, including evaluating the early performance of the government. The first 100 days of an administration are a strategic period to assess policy direction and public perception. This study aims to apply and evaluate the IndoBERT model for sentiment analysis of Indonesian-language tweets discussing the 100-day performance of the Prabowo–Gibran administration, as well as to assess the impact of using the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. A total of 15,027 tweets were collected through API crawling and processed through several stages: preprocessing, labeling using the InSet Lexicon, data splitting, and fine-tuning IndoBERT. Two scenarios were tested — without SMOTE and with SMOTE oversampling. The results show that both models achieved the same overall accuracy of 87%, but performance varied across sentiment classes. The model without SMOTE performed better in the positive class with 93% precision, whereas the SMOTE-applied model improved performance in the neutral class (F1-score increased from 70% to 71%; recall from 69% to 71%) and in the negative class (precision increased from 88% to 90%). Considering the balance across classes, the SMOTE-based model was selected as the final model and implemented into a Streamlit application for interactive sentiment analysis. This study expands the application of IndoBERT in the Indonesian political domain by combining the lexical InSet approach with SMOTE oversampling — a combination rarely applied in Indonesian political sentiment analysis. The findings highlight the importance of data balancing strategies in improving transformer-based model performance on imbalanced datasets. Future research is encouraged to explore alternative balancing methods, expand training data, and test other transformer variants to enhance accuracy and generalization.
Co-Authors Amriana Andi Hendra Anita Ahmad Kasim Bagaskara, Febrian Chrissma Dwi Shinta Angreni Fachrozi, Ahmad Ihalauw, Sahron Angelina Karnita Sumbaluwu, Harlin Feby Mohammad Yazdi Pusadan MUHAMMAD BAKRI Nouval Trezandy Lapatta Rinianty, Rinianty Rizka Ardiansyah Syahrullah Syahrullah Wirdayanti Wiria Nugraha, Denny Yazdi Pusadan, Mohammad Yulianti, Indira