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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Ardytha Luthfiarta
Universitas Dian Nuswantoro, Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pengaruh Oversampling dan Cross Validation Pada Model Machine Learning Untuk Sentimen Analisis Kebijakan Luaran Kelulusan Mahasiswa Mufida Rahayu; Ardytha Luthfiarta; Lailatul Cahyaningrum; Alya Nurfaiza Azzahra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7012

Abstract

The Minister of Education, Culture, Research and Technology issued a new policy on graduation standards for undergraduate and postgraduate students. This policy was delivered on August 29, 2023, on live streaming YouTube Kemendikbudristek at the Merdeka Belajar seminar episode 26: Transformation of National Standards and Higher Education Accreditation. The policy has caused various kinds of positive and negative responses in the community. Based on this problem, this research analyzes the sentiment of how the attitude and response of the community regarding this matter, so that it can be useful for the community in the future. This research uses two algorithms Nave Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) with data collection done through YouTube video comments getting a total dataset of 1085 data. After that, enter the data pre-processing which is then labeled using the Lexicon-based method with the stemming Sastrawi method. Datasets are grouped into positive sentiment and negative sentiment where the labeling results show unbalanced label data. Then the oversampling method Synthetic Minority Over-sampling Technique (SMOTE) is performed so that the data can be balanced and produce good accuracy. The test results after the SMOTE technique show that the NBC algorithm has the highest accuracy compared to KNN. The accuracy results are 74%, precision 74.6%, recall 74% and f1-score 73.9%. While KNN produces an accuracy of 50.2%, precision of 75.2%, recall of 50.2%, and f1-score of 34.5%.
Ensemble Klasifikasi Penyakit Tuberculosis Pada Hasil Pengobatan Menggunakan Metode Hybrid K-Nearest Neighbor (K-NN), Decision Tree dan Support Vector Machine (SVM) Alya Nurfaiza Azzahra; Junta Zeniarja; Ardytha Luthfiarta; Mufida Rahayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7021

Abstract

Tuberculosis (TB) is an infectious disease with the highest cause of death in the world. This disease can be transmitted through the air and attacks the pulmonary respiratory system. The increase in TB cases from year to year is due to little information about the treatment of this disease. This requires the process of diagnosing and treating TB requiring accurate data analysis. From these problems, classification of tuberculosis disease is needed to improve better treatment results. In this study, experiments were used with the Hybrid model classification algorithm with a method that combines three approaches, namely K-Nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM) to classify treatment results using the Ensemble classification method and aims to combine each method in order to create a stronger Ensemble model and increase accuracy in treatment results, using data from the Semarang City Health Service or what is called Tuberculosis Information System (SITB) data in 2020-2023 with 80% training data and test data 20%. Based on the results of testing and analysis using the confusion matrix, the highest accuracy value was obtained at 78.55% using K-Fold Cross validation, namely k equals 7 and the Ensemble model obtained high results for treatment outcomes.
Klasifikasi Pesan Penipuan pada Platform WhatsApp Menggunakan Metode Naïve Bayes Berbasis TF-IDF, N-Gram, dan Chi-Square Hardika Nur Saputra; Ardytha Luthfiarta
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9941

Abstract

The rapid development of digital communication has led to an increase in message exchanges across various platforms, accompanied by the widespread spread of fraudulent messages (scams). This situation demands an automated system capable of identifying and classifying messages quickly and accurately. This study aims to develop a text-based message classification system on the WhatsApp platform using the Naïve Bayes algorithm. The research stages include text preprocessing consisting of case folding, cleaning, normalization, stopword removal, and stemming to improve data quality. Next, feature extraction is carried out using Term Frequency-Inverse Document Frequency (TF-IDF) combined with the N-Gram (unigram) approach to represent each word in the text, and Chi-Square feature selection is applied to obtain the most relevant features in the classification process. The dataset used consists of three categories of WhatsApp messages: normal, promotional, and fraudulent. In addition, this study also applies a data balancing method using Random Oversampling to increase the number of minority class samples in the training data for optimal model performance. The main contribution of this research is the application of a combination of TF-IDF unigram, Chi-Square feature selection, and Random Oversampling in the Naïve Bayes algorithm to improve the classification performance of Indonesian WhatsApp messages, especially in conditions of unbalanced class distribution. Model evaluation is carried out using a Confusion Matrix with accuracy, precision, recall, and F1-score metrics. The test results show that the model built is able to achieve an accuracy level of 95.63%, so the method used is proven to be effective in classifying WhatsApp messages accurately and consistently.
Co-Authors Alya Nurfaiza Azzahra Hardika Nur Saputra Junta Zeniarja Lailatul Cahyaningrum Mufida Rahayu