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All Journal Jurnal Riset Informatika Jurnal Teknologi dan Manajemen Industri Terapan
R. Mohamad Atok
Sepuluh Nopember Institute of Technology
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Active Learning Query by Committee Labeling Method to Increase Accuracy and Efficiency of Sentiment Analysis Classification Dipa Anasta Iskandar; R. Mohamad Atok
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1427.54 KB) | DOI: 10.34288/jri.v7i4.386

Abstract

This study proposes the Query by Committee (QBC) labeling method to improve the accuracy of classification models—specifically XLM-RoBERTa—and to increase labeling efficiency compared to manual, supervised labeling, which generally requires more time and resources. The dataset consists of unannotated healthcare-industry application reviews scraped from Google Play. Six distinct labeling strategies were applied as input for fine-tuning XLM-RoBERTa models under identical hyperparameter settings. The six labeling approaches were evaluated namely Rating-based labeling, Lexicon-based labeling, QBC for Rating-Vader labeling, QBC for Rating-Pseudo labeling, QBC for Vader-Pseudo labeling, and QBC triplet for Rating-Pseudo-Vader labeling. Each labeled dataset was split using stratified random sampling, and class weights were set to “auto” during training to address label imbalance. All models were subsequently tested on the IndoNLU SmSA test dataset, with performance compared in terms of accuracy, precision, recall, and F1-score. Results indicate that the triplet QBC approach (combining Rating, VADER, and Pseudo labeling) outperformed all other methods, achieving an accuracy of 91.4%, a precision of 91.28%, a recall of 91.4%, and an F1-score of 91.21%. These findings demonstrate that the QBC labeling method can serve as an effective and efficient alternative to manual annotation for similar classification tasks
Decision Support Framework Berbasis Machine Learning untuk Strategi Perdagangan Karbon pada PLTU Batubara Mahsun Ismail; R. Mohamad Atok
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 5 No. 3 (2026): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v5i3.1888

Abstract

PLTU batubara di Indonesia ditetapkan sebagai compliance entity fase pertama dalam skema Nilai Ekonomi Karbon (NEK) dengan kewajiban mengelola emisi CO2 pada mekanisme perdagangan karbon. Praktik pelaporan emisi pembangkit pada umumnya bersifat reaktif dan retrospektif, sehingga sulit menjadi dasar keputusan perdagangan karbon yang bersifat forward-looking. Penelitian ini mengembangkan decision support framework empat tahap berbasis machine learning yang menerjemahkan prediksi emisi tingkat pembangkit menjadi pertimbangan strategi perdagangan karbon, meliputi (1) prediksi emisi CO2 menggunakan model machine learning terpilih dengan dataset 14.549 catatan operasional dari satu unit PLTU batubara 660 MW (net) selama tahun 2025; (2) proyeksi Business-as-Usual (BAU) dengan koreksi realization rate tiga tahun; (3) optimasi berbasis tiga skenario konservatisme (Pesimis, Moderat, Optimis); dan (4) evaluasi posisi emisi menjadi estimasi nilai transaksi dengan harga referensi IDXCarbon. Pada kondisi BAU, unit menempati posisi surplus 136.205 t-CO2 terhadap estimasi kuota emisi sebesar 4.599.623 t-CO2 dengan estimasi pendapatan Rp 8,01 miliar. Pada skenario Moderat, surplus meningkat menjadi 254.093 t-CO2 dengan tambahan manfaat ekonomi Rp 6,93 miliar. Kerangka ini menempatkan analitika prediktif sebagai bahan pertimbangan pada aspek manajerial dan aspek finansial dalam perdagangan karbon.
Co-Authors Dipa Anasta Iskandar Mahsun Ismail