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All Journal Journal of Informatics Development
Silviana Widya Lestari
Institut Teknologi dan Bisnis Widya Gama Lumajang
Computer Science & IT

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Mapping Public Conversation on Indonesia's Free Nutritious Meal Program on Twitter (X) Using TF-IDF and K-Means Clustering Cahyasari Kartika Murni; Faishal Basbeth; Silviana Widya Lestari
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2081

Abstract

Public programs now generate their own online commentary, and that commentary is worth reading systematically rather than anecdotally. This paper maps the topic structure of Indonesian-language conversation about the Free Nutritious Meal program (Makan Bergizi Gratis, MBG) on Twitter (X). We scraped 3,459 tweets posted in January and February 2025, and cleaned them through case folding, filtering, tokenization, stopword removal, and stemming, which left 2,926 documents, or 84.59% of the raw set. These were weighted with Term Frequency-Inverse Document Frequency (TF-IDF) into a 2,926 x 5,000 document-term matrix and partitioned with K-Means. The Elbow Method placed the optimal solution at K = 3, with an SSE of 2,760.3617 and a Silhouette Score of 0.0092. Three topics emerged: institutional endorsement tied to food security and self-sufficiency (94 tweets, 3.21%), everyday talk about how the program is being delivered and paid for (2,012 tweets, 68.76%), and the benefits of the program for children and their health (820 tweets, 28.02%). The Silhouette Score sits close to zero. We read this as a property of the corpus rather than a failure of the procedure: every document discusses the same program, so the clusters share a large common vocabulary and their boundaries are soft. The partition should therefore be treated as a first thematic map rather than a clean separation, and contextual representations such as Word2Vec, FastText, or IndoBERT are the obvious next step.
Zero Crossing Rate (ZCR) Feature Extraction in Speech Emotion Recognition Using a Convolutional Neural Network Silviana Widya Lestari; Maysas Yafi Urrochman; Cahyasari Kartika Murni
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2083

Abstract

Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction (HCI), healthcare, and automated feedback systems (Hashem et al., 2023). However, extracting relevant acoustic features from complex speech signals remains a significant challenge (Lieskovská et al., 2021). This paper investigates the effectiveness of the Zero Crossing Rate (ZCR) feature extraction method combined with a 1D Convolutional Neural Network (CNN) architecture for speech emotion classification. By evaluating emotional speech data across benchmark datasets (RAVDESS, CREMA, SAVEE, and TESS), audio signals were preprocessed using data augmentation techniques, including noise injection, time stretching, pitch shifting, and time shifting. The ZCR feature representation was fed into a CNN model to classify seven core emotional states: anger, disgust, fear, happiness, sadness, surprise, and neutral. The proposed ZCR-CNN approach achieved a classification accuracy of 61.96% with a training loss of 0.8214 and testing loss of 0.8123. Although ZCR effectively captures high-frequency and noisiness variations indicative of specific emotional states, its performance highlights the trade-offs of relying on single-domain time features compared to spectral features (Singh & Goel, 2022).
Co-Authors Cahyasari Kartika Murni Faishal Basbeth Maysas Yafi Urrochman