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Menampilkan 1–10 dari 29 artikel
Application of the K-Means Method for Grouping Product Data Based on Sales Level
Vol 5
, No 1
(2026)
Ritel modern di Indonesia tumbuh pesat dengan keragaman produk yang makin kompleks, sehingga pengelolaan data penjualan menjadi penting bagi pengambilan keputusan manajerial. Penelitian ini bertujuan mengelompokkan produk di Indomaret Kotaraja berdasarkan perilaku penjualan untuk mendukung keputusan terkait persediaan, penataan rak, dan promosi. Metode yang digunakan adalah klastering K-Means dengan implementasi di RapidMiner. Dataset mencakup penjualan bulanan selama satu tahun untuk produk mak...
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Application of the K-Means Method for Grouping Product Data Based on Sales Level
Matuan, Helson
; Dude, Esau
; Mallo, Atius
; Yowey, Herlina
; Patey, Yusuf Selius
; Sutejo, Heru
; Matuan, Helson
; Dude, Esau
; Mallo, Atius
; Yowey, Herlina
; Patey, Yusuf Selius
; Sutejo, Heru
JUISI : Jurnal Ilmiah Sistem Informasi
Vol 5
, No 1
(2026)
Ritel modern di Indonesia tumbuh pesat dengan keragaman produk yang makin kompleks, sehingga pengelolaan data penjualan menjadi penting bagi pengambilan keputusan manajerial. Penelitian ini bertujuan mengelompokkan produk di Indomaret Kotaraja berdasarkan perilaku penjualan untuk mendukung keputusan terkait persediaan, penataan rak, dan promosi. Metode yang digunakan adalah klastering K-Means dengan implementasi di RapidMiner. Dataset mencakup penjualan bulanan selama satu tahun untuk produk mak...
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Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction
Kusuma, Muh Galuh Surya Putra
; Setiadi, De Rosal Ignatius Moses
; Herowati, Wise
; Sutojo, T.
; Adi, Prajanto Wahyu
; Dutta, Pushan Kumar
; Nguyen, Minh T.
Journal of Computing Theories and Applications
Vol 3
, No 2
(2025)
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, dee...
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Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection
Setiadi, De Rosal Ignatius Moses
; Ojugo, Arnold Adimabua
; Pribadi, Octara
; Kartikadarma , Etika
; Setyoko, Bimo Haryo
; Widiono, Suyud
; Robet, Robet
; Aghaunor, Tabitha Chukwudi
; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications
Vol 2
, No 4
(2025)
Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To addres...
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Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow
Setiadi, De Rosal Ignatius Moses
; Warto, Warto
; Muslikh, Ahmad Rofiqul
; Nugroho, Kristiawan
; Safriandono, Achmad Nuruddin
Journal of Computing Theories and Applications
Vol 2
, No 4
(2025)
Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset wit...
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Feature Fusion with Albumentation for Enhancing Monkeypox Detection Using Deep Learning Models
Pratama, Nizar Rafi
; Setiadi, De Rosal Ignatius Moses
; Harkespan, Imanuel
; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications
Vol 2
, No 3
(2025)
Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Giv...
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A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification
Journal of Computing Theories and Applications
Vol 2
, No 3
(2025)
This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in pe...
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Sentiment Analysis for Political Debates on YouTube Comments using BERT Labeling, Random Oversampling, and Multinomial Naïve Bayes
Journal of Computing Theories and Applications
Vol 2
, No 3
(2025)
The 2024 Indonesian Presidential Election marked the fifth general election in the country, aimed at electing a new President and Vice President for the 2024–2029 term. Candidates competed to succeed the outgoing president, who had served two constitutional terms. A key aspect of this election was the candidate debates, where each candidate presented their vision, allowing the public to assess their policies. These debates were broadcast on platforms like YouTube, giving the public a space to co...
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ANALYSIS AND DESIGN OF WEB-BASED GROCERY SALES INFORMATION SYSTEMS AT TRI KARYA STORES
JURNAL ILMIAH KOMPUTER GRAFIS
Vol 17
, No 2
(2024)
important for various groups of people, especially in supporting appropriate decision making through the use of information technology. In the business world, an efficient system can provide real-time information, thus simplifying operational processes. However, at Toko Tri Karya, the process of selling groceries is still done manually, starting from inputting sales data to making reports. This causes various obstacles, such as delays in data processing and the possibility of errors. Therefore,...
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Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction
Setiadi, De Rosal Ignatius Moses
; Muslikh, Ahmad Rofiqul
; Iriananda, Syahroni Wahyu
; Warto, Warto
; Gondohanindijo, Jutono
; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications
Vol 2
, No 2
(2024)
Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or densit...
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