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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Manajemen Perhotelan dan Pariwisata
Julianto, Eric
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analisis Pengaruh Store Atmosphere Terhadap Keputusan Pembelian Di Groen Kopi: indonesia Matthew, Joseph; Soeprapto, Vishnuvardhana Sahishnu; Julianto, Eric
Jurnal Manajemen Perhotelan dan Pariwisata Vol. 6 No. 2 (2023)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jmpp.v6i2.61975

Abstract

Di Indonesia saat ini tengah mengalami kenaikan jumlah kedai kopi yang drastis. Dalam hal ini pemilik usaha kedai kopi dituntut untuk dapat menciptakan strategi yang tepat untuk dapat meningkatkan keputusan pembelian konsumen. Dalam pembahasan kali ini ialah terkait pengaplikasian strategi store atmosphere dari suatu kedai kopi. Store atmosphere merupakan sebuah ciri khas yang wajib dimiliki sebuah toko, yang dimana hal tersebut mampu membangun citra di benak pelanggan. Adapun tujuan dari dilaksanakannya penelitian ini untuk mengetahui pengaruh store atmosphere dari masing-masing dimensi secara parsial dan simultan. Target populasi pada penelitian ini merupakan sejumlah pengunjung yang pernah berkunjung dan melakukan pembelian di Groen Kopi. Dalam pengambilan sampel peneliti menggunakan purposive sampling dengan total sampel sebanyak 106 orang responden. Berdasarkan hasil uji t didapati hasil bahwa store exterior, general interior, dan interior display memiliki pengaruh terhadap keputusan pembelian, sedangkan store layout tidak berpengaruh terhadap keputusan pembelian.
OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE Ardiyansa, Safrizal Ardana; Maharani, Natasha Clarissa; Anam, Syaiful; Julianto, Eric
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1053-1066

Abstract

Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.
An Explainable Deep Learning Approach for Brain Tumor Detection Using MobileNet and Grad-CAM Visualization Gaib, Amalan Fadil; Ardiyansa, Safrizal Ardana; Wijaya, Anggito Karta; Julianto, Eric; Mahayudha, I Gusti Ngurah Bagus Ferry; Royan, Ando Zamhariro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35901

Abstract

Brain tumor detection remains a significant challenge due to the complex variations in tumor appearance. Although deep learning models have demonstrated high accuracy, their limited interpretability hinders clinical adoption. To address this issue, this study integrates Gradient-weighted Class Activation Mapping (Grad-CAM) into Convolutional Neural Networks (CNNs) to enhance the visual interpretability of predictions. Grad-CAM extends Class Activation Mapping (CAM) and is applicable to a wide range of deep learning architectures. The primary contribution of this work is the demonstration that combining Grad-CAM with MobileNet architectures yields an interpretable and efficient framework for diagnosis of brain tumor, effectively balancing accuracy, computational efficiency, and clinical transparency. Using a Brain Tumor MRI dataset, MobileNetV4 achieved an accuracy of 98.29% with the shortest training time (1738.82 seconds) and an ROC accuracy of 99.96%. MobileNetV3 achieved 99.62% accuracy with an ROC accuracy of 99.92%. Grad-CAM effectively highlighted tumor regions while showing uniform attention in non-tumor cases, thereby reducing false positives. These results demonstrate that lightweight models can achieve a strong balance between predictive performance, training efficiency, and interpretability. The proposed framework thus supports the development of explainable and efficient diagnostic tools for clinical practice.
IMPROVING SUPPORT VECTOR MACHINE PERFORMANCE WITH BINARY GAUSSIAN IMPROVED WHALE OPTIMIZATION ALGORITHM: A CASE STUDY ON DIABETES DATA Fajri, Haidar Ahmad; Ardiyansa, Safrizal Ardana; Anam, Syaiful; Maharani, Natasha Clarrisa; Julianto, Eric
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2531-2542

Abstract

Diabetes mellitus is a chronic condition with high blood sugar that can cause severe organ damage, affecting all ages globally. Early diagnosis is crucial for improving patients' quality of life, and machine learning offers a promising approach. The Support Vector Machine (SVM) is effective for classification, but feature selection is essential to enhance the relevance of features. The Whale Optimization Algorithm (WOA) is an optimal method for global feature selection, but it has a drawback-premature convergence, which can lead to suboptimal results. This issue should be addressed by modifying mutation operations, convergence factors, and population initialization, resulting in Binary Gaussian IWOA (BGIWOA). This research focuses on feature selection using BGIWOA, comparing it with Variance Inflation Factor (VIF) using SVM. The result show that BGIWOA is better than VIF and the best configuration BGIWOA’s parameter is with linear kernel. This configuration produces the best accuracy of 95.00%. BGIWOA-SVM demonstrates better accuracy with stable consistency compared to VIF-SVM. The best SVM model achieves average accuracy of 95.62% for training data and 95.58% for validation data, with an accuracy of 93.85% for the test data. This model also yields an average precision of 94.00%, a recall of 91.00%, and an -score of 92.00%. The model was also better than SVM without optimization, which only achieved a training accuracy of 84.25% and a testing accuracy of 81.30%. This model can assist in diagnosing diabetes with accurate and consistent predictions for new data. The results are specific to the diabetes dataset used in this research, so further testing on other binary datasets is necessary to confirm the model's effectiveness and generalizability across different domains and types of data.
Co-Authors Ardiyansa, Safrizal Ardana Fajri, Haidar Ahmad Gaib, Amalan Fadil Maharani, Natasha Clarissa Maharani, Natasha Clarrisa Mahayudha, I Gusti Ngurah Bagus Ferry Matthew, Joseph Royan, Ando Zamhariro Soeprapto, Vishnuvardhana Sahishnu Syaiful Anam Wijaya, Anggito Karta