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All Journal INTECOMS: Journal of Information Technology and Computer Science Journal Innovations Computer Science
Hutauruk, Lucas Namora
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Computer Science & IT

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Bisnis Intelegen Status Gizi Balita Posyandu Pada RW07 Kelurahan Jatinegara Jakarta Timur Dengan Tableau Public Fauzan, Ahmad; NurZahra, Febi; Hutauruk, Lucas Namora; Wahyudi, Tri
INTECOMS: Journal of Information Technology and Computer Science Vol 8 No 2 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i2.14534

Abstract

Pengolahan data di posyandu masih menghadapi sejumlah tantangan seperti kurangnya akurasi data, keterlambatan pelaporan serta minimnya pemanfaatan data utuk pengambilan keputusan. Dengan memanfaatkan data yang berkualitas, kelompok balita yang berisiko tinggi mengalami gizi buruk atau stunting dapat diidentifikasi dan diberikan intervensi secara lebih cepat dan efektif. Oleh karena itu, diperlukan upaya untuk meningkatkan kualitas pengelolaan data di Posyandu agar lebih akurat, efisien, dan relevan dalam mendukung pengambilan keputusan. Pada kesempatan ini kami memberikan kontribusi dalam pengembangan kebijakan yang lebih efektif terkait pengelolaan data Posyandu di wilayah Rw07, Kelurahan Jatinegara, Kecamatan Cakung, Jakarta Timur. Dengan menggunakan metode kualitatif yang kita lakukan dengan cara turun secara lansgung ke objek penelitian guna untuk mengamati proses serta orang-orang yang terlibat di dalam system. Peneliti turun langsung dalam kegiatan Posyandu dan mengobservasi untuk memperoleh data rill sebagai bahan penulisan laporan. Fokus utama dalam bisnis intelegen ini yaitu melakukan visualisasi data menggunakan Tableau untuk menganalisis tren pertumbuhan gizi balita berdasarkan data yang telah dikumpulkan dan memanfaatkan fitur-fitur visualisasi Tableau dalam menyajikan informasi mengenai pola pertumbuhan gizi balita secara komprehensif dan mudah dipahami oleh berbagai pihak yang berkepentingan.
Support Vector Machine-Based Sentiment Analysis of Customer Reviews for Android Smartphone Products on Shopee Marketplace Hutauruk, Lucas Namora; Lestari, Sri; Aula, Raisah Fajri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.321

Abstract

The rapid expansion of e-commerce in Indonesia has resulted in a surge of unstructured online reviews, especially on platforms such as Shopee. These reviews offer valuable insights into customer satisfaction, product complaints, and purchasing behavior but remain largely underutilized due to their volume and informal language style. This study applies Support Vector Machine (SVM) with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to classify reviews of Android smartphones into positive, negative, and neutral categories. Using a dataset of 300 manually annotated reviews from Samsung, Xiaomi, and Oppo official stores, the model achieved an accuracy of 76.67% and demonstrated stable results through 5-fold cross-validation. The negative class showed the highest performance (F1 = 0.86), while the neutral class performed weakest (F1 = 0.62), reflecting challenges posed by mixed opinions and underrepresented samples. Compared with Naïve Bayes and Logistic Regression, the SVM model consistently outperformed both baselines, confirming its suitability for high-dimensional text data and informal Indonesian expressions. The findings highlight SVM’s potential to support automated sentiment monitoring in e-commerce, enabling businesses to identify emerging issues, improve customer service strategies, and leverage positive reviews for marketing. Future research should consider larger and more balanced datasets, techniques for handling imbalanced classes, and integration with deep learning models such as LSTM or BERT to improve performance and generalization.
Support Vector Machine-Based Sentiment Analysis of Customer Reviews for Android Smartphone Products on Shopee Marketplace Hutauruk, Lucas Namora; Lestari, Sri; Aula, Raisah Fajri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.321

Abstract

The rapid expansion of e-commerce in Indonesia has resulted in a surge of unstructured online reviews, especially on platforms such as Shopee. These reviews offer valuable insights into customer satisfaction, product complaints, and purchasing behavior but remain largely underutilized due to their volume and informal language style. This study applies Support Vector Machine (SVM) with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to classify reviews of Android smartphones into positive, negative, and neutral categories. Using a dataset of 300 manually annotated reviews from Samsung, Xiaomi, and Oppo official stores, the model achieved an accuracy of 76.67% and demonstrated stable results through 5-fold cross-validation. The negative class showed the highest performance (F1 = 0.86), while the neutral class performed weakest (F1 = 0.62), reflecting challenges posed by mixed opinions and underrepresented samples. Compared with Naïve Bayes and Logistic Regression, the SVM model consistently outperformed both baselines, confirming its suitability for high-dimensional text data and informal Indonesian expressions. The findings highlight SVM’s potential to support automated sentiment monitoring in e-commerce, enabling businesses to identify emerging issues, improve customer service strategies, and leverage positive reviews for marketing. Future research should consider larger and more balanced datasets, techniques for handling imbalanced classes, and integration with deep learning models such as LSTM or BERT to improve performance and generalization.
Co-Authors Ahmad Fauzan Aula, Raisah Fajri NurZahra, Febi Sri Lestari Tri Wahyudi