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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika dan Sistem Informasi (JUISI) Engineering, Mathematics and Computer Science Journal (EMACS)
Alexander Alexander
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

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Wavelet-Based Color Histogram on Content-Based Image Retrieval Alexander Alexander; Jeklin Harefa; Yudy Purnama; Harvianto Harvianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7771

Abstract

The growth of image databases in many domains, including fashion, biometric, graphic design, architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used for finding relevant images from those huge and unannotated image databases based on low-level features of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH) on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision (0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix, and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system.
Comparison Classifier: Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) In Digital Mammogram Images Jeklin Harefa; Alexander Alexander; Mellisa Pratiwi
Jurnal Informatika dan Sistem Informasi Vol. 2 No. 2 (2016): Jurnal Informatika dan Sistem Informasi
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.26 KB)

Abstract

In order to begin the initial check on breast cancer, radiologist can use Computer Aided Diagnosis (CAD) as another option to detect breast cancer. During breast cancer check, human error is often to affecting the result. Several research before have proved that CAD is able to detect breast cancer spot more accurate. The purpose of this research is to find reliable method to classify breast cancer abnormalities. Mammography Image Analysis Society (MIAS) database is used as the sample data to the proposed system in this research. Mammograms are divided into three categorize which are normal, benign and malignant according to MIAS database. Features included in this experiment are extracted by using gray level co-occurrence matrices (GLCM) at 0º, 45º, 90º and 135º with a block size of 128x128. In classification process, this research attempt to compare k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifier in order to achieve the better accuracy. The result shows that SVM outperforms KNN in breast cancer abnormalities classification with 93.88% accuracy.
Sentiment and Topic Analysis of Public Opinion on Indonesia’s Minister of Finance Using IndoBERTweet, TF-IDF, and Latent Dirichlet Allocation Surya Sujarwo; Jeklin Harefa; Alexander Alexander
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15346

Abstract

In today’s technology-based society, people share their opinions on online social media platforms, which can be used as data for sentiment analysis. One of the most popular platforms for obtaining publicly accessible data is X. This study analyzes public views of the Ministry of Finance (MoF) by examining 9,543 tweets gathered from February to September 2025. The data collected was preprocessed through cleaning, name entities grouping, and keywords filtering, then evaluated using IndoBERTweet, and keywords were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF). For topic modelling, Latent Dirichlet Allocation (LDA) was used, and sentiment distributions were tracked over time through temporal aggregation. To obtain more specific public opinion sentiment analysis, a neutral classification was added to differentiate from the previous studies that used only positive and negative classifications. To support this approach, a pre-trained model with three sentiment classifications was used. The results show that neutral sentiment dominated the tweets followed by negative sentiment then positive sentiment, especially during the transition to the new Ministry of Finance, showing the relevance of real-world events to online public opinion on X. Based on topic trends, public opinion shows the trend change from fiscal policy and leadership to criticism and leadership change.
Co-Authors Harvianto Harvianto Jeklin Harefa Mellisa Pratiwi Purnama, Yudy Surya Sujarwo