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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Manajemen Perhotelan dan Pariwisata Jurnal Teknik Informatika (JUTIF)
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.
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.
Comparative Evaluation Of Sparse, Dense, And Hybrid Retrieval Models On Indonesian Wikipedia Saputra, Tino; Julianto, Eric; Widjonarko, Ari; Tjahjono, Budi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5776

Abstract

This study presents a comparative evaluation of Information Retrieval (IR) models on the Indonesian Wikipedia corpus, focusing on sparse, dense, and hybrid retrieval approaches. The evaluated methods include TF-IDF and BM25 as sparse models, SBERT (MiniLM) as a dense retrieval model, and hybrid retrieval implemented through score fusion. The dataset consists of 713,044 Wikipedia articles, with experiments conducted using 1,000 test queries. Performance is measured using Precision@10 (P@10) and Mean Reciprocal Rank (MRR). The results show that BM25 achieves the highest performance, with a P@10 of 0.973 and an MRR of 0.9174, significantly outperforming TF-IDF and SBERT. Hybrid retrieval provides a slight performance improvement, where the BM25 + SBERT combination reaches a P@10 of 0.979 and an MRR of 0.9253 at higher α values. These findings indicate that lexical matching remains dominant in encyclopedic corpora, while semantic representations provide complementary improvements. However, the performance gain of hybrid retrieval is relatively marginal compared to the additional computational cost introduced by dense embedding and score fusion processes, indicating a trade-off between effectiveness and efficiency. These results highlight that, for low-resource languages such as Indonesian, lexical-based retrieval remains highly reliable, while hybrid approaches provide incremental improvements. Therefore, this study provides practical guidelines for developing efficient, scalable, and reliable Information Retrieval systems for Indonesian Wikipedia and other low-resource language corpora.
Co-Authors Ardiyansa, Safrizal Ardana Budi Tjahjono Fajri, Haidar Ahmad Maharani, Natasha Clarissa Maharani, Natasha Clarrisa Matthew, Joseph Saputra, Tino Soeprapto, Vishnuvardhana Sahishnu Syaiful Anam Widjonarko, Ari