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All Journal SISFOTENIKA Journal of Computing Theories and Applications
T. Sutojo
Universitas Dian Nuswantoro
Computer Science & IT Decision Sciences, Operations Research & Management

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Prediksi Jumlah Produksi Jenang di PT Menara Jenang Kudus Menggunakan Metode Logika Fuzzy Tsukamoto Tatak Ulul Azmi; Hanny Haryanto; T. Sutojo
SISFOTENIKA Vol 8, No 1 (2018): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.087 KB) | DOI: 10.30700/jst.v8i1.176

Abstract

Penelitian ini akan membahas tentang permasalahan yang terjadi pada PT. Menara Jenang Kudus, yaitu kesulitan dalam memprediksi jumlah produksi jenang perbulannya. Setiap bulannya penjualan jenang meningkat dan pihak perusahaan hanya mengandalkan perhitungansecara manual untuk menentukan jumlah produksi jenang pada bulan berikutnya. Dengan menggunakan perhitungan secara manual kadang terjadi kelebihan produksi yang mengakibatkan jenang menjadi basi karena jumlah produksi tidak sesuai dengan permintaan.Untuk dapat mengatasi permasalahan ini dan membantu mempermudah dalam memprediksi jumlah produksi Jenang perbulannya, maka diperlukan sistem untuk memprediksi secara otomatis menggunakan kecerdasan buatan. Logika fuzzy adalah sebuah metode dalamkecerdasan buatan yang menggunakan variabel kata-kata sebagai pengganti berhitung dengan bilangan. Dengan logika fuzzy, sistem kepakaran manusia bisa diimplementasikan ke dalam bahasa mesin secara mudah dan efisien. Logika fuzzy diaplikasikan untuk data di PT. Menara Jenang Kudus dari bulan Februari sampai September dengan hasil dari pengujian menggunakan MSE untuk Jenang Wijen sebesar 7.121, jenang spesial sebesar 15.940 dan Jenang Duren sebesar 21.168, yang menunjukkan bahwa meskipun terjadi error namun hasil prediksi masih dapat diterima.  Kata Kunci—Fuzzy Tsukamoto, jenang, kecerdasan buatan, produksi, prediksi
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Muh Galuh Surya Putra Kusuma; De Rosal Ignatius Moses Setiadi; Wise Herowati; T. Sutojo; Prajanto Wahyu Adi; Pushan Kumar Dutta; Minh T. Nguyen
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
Co-Authors De Rosal Ignatius Moses Setiadi Hanny Haryanto Minh T. Nguyen Muh Galuh Surya Putra Kusuma Prajanto Wahyu Adi Pushan Kumar Dutta Tatak Ulul Azmi Wise Herowati