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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal Computer Science (ijcs)
Natasyah, Mei Intan
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Computer Science & IT

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Klasifikasi Opini Tidak Informatif Pada Program Makan Bergizi Gratis (MBG) Menggunakan Random Forest Syabilla, Lailla Syal; Natasyah, Mei Intan; Fathoni, Fathoni; Siahaan, Jeremiah Alwin
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12509

Abstract

Pemerintahan Prabowo-Gibran meluncurkan kebijakan strategis Program Makan Bergizi Gratis (MBG) untuk menjamin pemenuhan hak dasar anak atas pangan yang aman, sehat, dan bergizi. Urgensi program ini didasarkan pada angka stunting di Indonesia tahun 2024 yang mencapai 14%, sehingga peluncuran kebijakan Program Makan Bergizi Gratis (MBG) memicu diskusi publik yang masif di platform media sosial X dengan jumlah pengguna mencapai 24,7 juta orang. Namun, volume data yang besar tersebut menghadirkan masalah "Data Sampah" (Noise) berupa spam, promosi jualan, hingga akun bot yang berpotensi menyebabkan bias pada analisis opini publik terhadap program Program Makan Bergizi Gratis (MBG). Penelitian ini bertujuan membangun model klasifikasi opini tidak informatif dengan mengimplementasikan algoritma Random Forest berbasis Knowledge Discovery in Database (KDD) sebagai tahap pra-pemrosesan sebelum analisis sentimen lanjutan. Data yang digunakan berjumlah 10.000 tweet bersumber dari Kaggle, diproses melalui lima tahapan KDD meliputi Data Selection, Data Preprocessing, Data Transformation, Data Mining, dan Data Evaluation dengan menggunakan RapidMiner. Representasi fitur dilakukan dengan pembobotan TF-IDF dan validasi model menggunakan k-fold Cross Validation dengan k=10. Hasil evaluasi menunjukkan model mencapai akurasi 82,03%, precision 93,78%, recall 68,61%, dan F-Measure 79,24%. Hasil ini membuktikan bahwa pendekatan KDD berbasis Random Forest efektif digunakan sebagai pipeline filter noise yang terstruktur untuk teks media sosial berbahasa Indonesia, khususnya pada domain opini kebijakan pemerintah.
Hybrid Machine Learning for Knowledge Discovery in E-Commerce Reviews Siahaan, Jeremiah Alwin; Syabilla, Lailla Syal; Fadli, M. Thoriqul; Natasyah, Mei Intan; Meiriza, Allsela; Tania, Ken Ditha; Rifai, Ahmad
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12635

Abstract

The rapid growth of e-commerce platforms like Tokopedia has triggered a massive accumulation of over 65,000 customer reviews, yet it is often accompanied by information pollution in the form of non-informative reviews that hinder consumer decision-making processes. This research aims to extract new knowledge regarding these review characteristics through the implementation of the Knowledge Discovery in Database (KDD) framework, integrating a hybrid K-Means Clustering and Random Forest algorithm. Diverging from conventional classification approaches, this study utilizes K-Means as an exploratory instrument to naturally map six latent topic patterns of reviews based on their textual structure. Experiments were conducted on 35,000 data samples using TF-IDF features enriched by cluster labels as structural predictors. The results indicate that the hybrid model achieves 94.41% accuracy with an F1-score of 0.90 for the non-informative class, showing high stability via 5-Fold Cross-Validation (94.56% ± 0.19%) . The most crucial knowledge discovery is evidenced through SHAP analysis, where the cluster feature ranks 7th out of 1,001 predictor features, confirming that semantic grouping provides a richer structural context than pure lexical features . Furthermore, error analysis reveals specific linguistic challenges such as sarcasm and semantic ambiguity as constraints in automated review detection . This research provides a managerial contribution to e-commerce platforms in enhancing information quality and mitigating information overload issues.
Co-Authors Ahmad Rifai Allsela Meiriza, Allsela Fadli, M. Thoriqul Ken Ditha Tania Siahaan, Jeremiah Alwin Syabilla, Lailla Syal