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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING YUME : Journal of Management Management Studies and Entrepreneurship Journal (MSEJ) Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Manajemen dan Bisnis (JIMBI) Journal of Trends Economics and Accounting Research Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Digital Business Tren Bisnis Masa Depan Innovative: Journal Of Social Science Research Priviet Social Sciences Journal TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Hoki, Leony
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Multi-Class Brain Tumor Segmentation and Classification in MRI Using a U-Net and Machine Learning Model Hendrik, Jackri; Pribadi, Octara; Hendri, Hendri; Hoki, Leony; Tarigan, Feriani Astuti; Wijaya, Edi; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5369

Abstract

Brain tumor diagnosis remains a critical challenge in medical imaging, as accurate classification and precise localization are essential for effective treatment planning. Traditional diagnostic approaches often rely on manual interpretation of MRI scans, which can be time-consuming, subjective, and prone to variability across radiologists. To address this limitation, this study proposes a two-stage framework that integrates machine learning (ML) based classifiers for tumor type recognition and a U-Net architecture for tumor segmentation. The classifier was trained to distinguish four tumor categories: glioma, meningioma, pituitary, and no tumor, while the U-Net model was employed to delineate tumor regions at the pixel level, enabling volumetric assessment. The novelty of this research lies in its dual focus that combines classification and segmentation within a single framework, which enhances clinical applicability by offering both diagnostic and spatial insights. Experimental results demonstrated that among the evaluated classifiers, XGBoost achieved the highest accuracy of 86 percent, surpassing other models such as Random Forest, SVC, and Logistic Regression, while the U-Net model delivered consistent segmentation performance across tumor types. These findings highlight the potential of hybrid ML and deep learning solutions to improve reliability, efficiency, and objectivity in brain tumor analysis. In real-world practice, the proposed framework can serve as a valuable decision-support tool, assisting radiologists in early detection, reducing diagnostic workload, and supporting personalized treatment strategies.
Analisis Pengaruh Cita Rasa, Suasana Toko dan Kualitas Pelayanan Terhadap Kepuasan Konsumen di Bambu Ungu Resto Hoki, Leony; Br Simamora, Rusdiana; Michael, Michael
Innovative: Journal Of Social Science Research Vol. 4 No. 5 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i5.14874

Abstract

Penelitian ini bertujuan untuk melihat pengaruh cita rasa, suasana toko dan kualitas pelayanan terhadap kepuasan konsumen di Bambu Ungu Resto. Metodologi penelitian yang digunakan adalah metode deskriptif kuantitatif. jenis data yang digunakan adalah jenis data kuantitatif  yaitu data  yang diperoleh dalam bentuk angka atau bilangan. Sumber data berupa data primer dan data sekunder. Data primer diperoleh dari hasil penyebaran kuesioner kepada responden, data sekunder diperoleh dari data atau studi pustaka terdahulu. Populasi yang digunakan dalam penelitian ini adalah seluruh konsumen Bambu Ungu Resto.Teknik penentuan sampel yang digunakan adalah rumus Lemeshow populasi tidak diketahui dengan toleransi 10% sehingga diperoleh 97 responden. Pengujian data dilakukan dengan uji validitas, reliabilitas, asumsi klasik, dan uji hipotesis. Hasil penelitian menunjukkan Cita Rasa berpengaruh positif dan tidak signifikan secara parsial terhadap kepuasan konsumen. Suasana Toko berpengaruh positif dan signifikan secara parsial terhadap kepuasan konsumen. Kualitas Pelayanan berpengaruh positif dan signifikan secara parsial terhadap kepuasan konsumen. Cita Rasa, Suasana Toko, dan Kualitas Pelayanan berpengaruh positif secara simultan terhadap kepuasan konsumen dengan koefisien determinasi sebesar 48.7%.
The effect of competence and discipline on employee performance at PT Lautan Belawan Jaya Hoki, Leony; Purba, Yessica Sardina; Florenza, Viona
Priviet Social Sciences Journal Vol. 4 No. 6 (2024): June 2024
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v4i6.319

Abstract

This research employs a quantitative descriptive approach. The sampling technique utilized non-probability total sampling, incorporating questionnaires, interviews, and observations with a population of 30 employees. Data analysis techniques in- clude tests for validity, reliability, multicollinearity, normality,  heteroscedasticity, t, F, multiple linear regression, and the coefficient of determination (R2). The results reveal that the coefficient of determination (Adjusted R Square) is 0.302, indicating that competence (X1) and discipline (X2) explain 30.2% of employee performance (Y), while the remaining 69.8% is influenced by other variables outside this research, such as work stress, work environment, and communication. Partial test results show that competency influences employee performance with a t-value of 3.253, which is greater than the t-table value of 1.99773, and a significance value  of 0.000, which  is less than 0.05. Discipline also influences employee performance with a t-value of 4.297, which is greater than the t-table value  of 1.99773, and a significance value  of 0.000, which is less than 0.05. The F-value of 15.259 is greater than the F-table value of 3.98, indicating that the competency (X1) and discipline (X2) variables simultaneously influence employee performance (Y) at PT Lautan Belawan Jaya.
A Comparative Study of Machine Learning and Deep Learning Models for Heart Disease Classification Simanjuntak, Martina Sances; Robet, Robet; Hoki, Leony
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11546

Abstract

Heart disease remains one of the leading causes of mortality worldwide, necessitating accurate early detection. This study aims to compare the performance of several Machine Learning (ML) and Deep Learning (DL) algorithms in heart disease classification using the Heart Disease dataset with 918 samples. The methods tested included Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), and Deep Neural Network (DNN). Preprocessing included feature normalization, data splitting (80:20), and simple hyperparameter tuning for parameter-sensitive models. Evaluations were conducted using accuracy, precision, recall, F1-score, AUC, and confusion matrix analysis to identify error patterns. The results showed that SVM and DNN achieved the highest accuracies of 91.3% and 92.1%, respectively. However, DNN has higher computational costs and risks of overfitting on small datasets. These findings confirm that traditional ML models such as SVM remain highly competitive on tabular medical data.
Comparative Analysis of XGBoost, KNN, and SVM Algorithms for Heart Disease Prediction Using SMOTE-Tomek Balancing Yuliana, Yuliana; Robet, Robet; Hoki, Leony
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15469

Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection crucial for improving patient outcomes. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting heart disease using the 2015 BRFSS dataset, which includes responses from 253,680 individuals. The three algorithms examined are Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data preprocessing steps involved feature encoding, class imbalance handling using the Synthetic Minority Over-sampling Technique combined with Tomek Links (SMOTE-Tomek), and hyperparameter tuning through RandomizedSearchCV. The models were assessed on a hold-out validation set using several metrics, including accuracy, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), F1-score, precision, and recall. The results demonstrated that XGBoost achieved the highest performance, with an accuracy of 94%, a ROC-AUC score of 0.98, and an F1-score of 0.94. In comparison, KNN achieved an accuracy of 87% (ROC-AUC 0.95), while SVM attained an accuracy of 79% (ROC-AUC 0.86). These findings suggest that XGBoost is a robust model for large-scale heart disease classification and holds potential for implementation in clinical decision support systems.
Pengaruh Fear Of Missing Out, Word Of Mouth, Dan Social Enviroment Terhadap Keputusan Pembelian Iphone (Studi Kasus Pada Mahasiswa STMB Multismart) Sofyan, Silvia; Hoki, Leony; Ali, Habib Akbar
Jurnal Ilmiah Manajemen dan Bisnis (JIMBI) Vol 6, No 2 (2025): Jurnal Ilmiah Manajemen dan Bisnis (JIMBI) - Desember
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jimbi.v6i2.6473

Abstract

The iPhone has become a social status symbol, especially among college students. This study analyzes the influence of Fear of Missing Out (FOMO), Word of Mouth (WOM), and the Social Environment on iPhone purchasing decisions among 76 STMB Multismart students. The research method used a quantitative approach with a questionnaire and multiple regression analysis using SPSS 25. The results show that FOMO, WOM, and the Social Environment have a positive and significant influence on purchasing decisions. Simultaneously, they explain 69.5% of the variation in iPhone purchasing decisions. These findings have implications for marketers in designing strategies based on FOMO, WOM, and social influence, as well as the importance of consumer education on campus.
Dampak Harga dan Kualitas Produk Terhadap Tingkat Kepuasan Pelanggan dengan Value Perception Sebagai Mediasi Hoki, Leony; Sinaga, Jack
Journal of Trends Economics and Accounting Research Vol 6 No 3 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jtear.v6i3.2627

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh harga produk dan kualitas produk terhadap kepuasan pelanggan sate Pak Etek yang dimediasi oleh value perception atau persepsi nilai dari benak pelanggan. Harga produk merujuk pada jumlah biaya yang harus dibayarkan pelanggan untuk mendapatkan produk, sedangkan kualitas produk mencakup aspek kandungan bahan baku, cita rasa dan tekstur daging yang dirasakan oleh pelanggan. Persepsi nilai atau value perception mencerminkan penilaian subjektif terhadap biaya yang dikeluarkan dan manfaat produk yang didapatkan. Penelitian ini menggunakan metode kuantitatif dengan pengumpulan data melalui kuesioner kepada total 100 responden. Data dianalisis menggunakan uji validitas, reliabilitas, asumsi klasik dan regresi linier berganda. Hasil analisis menunjukkan bahwa harga dan kualitas produk berpengaruh terhadap kepuasan pelanggan melalui value perception dengan nilai signifikansi 0.041 dan 0.024 yang lebih kecil dari 0.05. Maka variabel mediasi value perception mampu memediasi pengaruh dari harga dan kualitas produk terhadap kepuasan pelanggan. Berdasarkan penelitian ini, didapatkan kesimpulan bahwa faktor harga, kualitas produk dan juga value perception dari pelanggan menjadi aspek yang penting dan harus diperhatikan oleh Sate Gerobak Pak Etek dalam strategi pemasaran untuk tetap mempertahankan kepuasan pelanggan.
Co-Authors Agusman, Veirry Ali, Habib Akbar Ali, Rabei Raad Andy Br Simamora, Rusdiana Chandra David David Didik Aryanto Edi Wijaya Feriani Astuti Tarigan Ferry Hidayat Florenza, Viona Fransisca Fransisca Hendri Hendri Hendri Hendri Jackri Hendrik Johan, Ricky Michael Michael Muhammad Iqbal Octara Pribadi Robet Robet Robin Robin, Robin Simanjuntak, Martina Sances Sinaga, Ira Sinaga, Jack Sofyan, Silvia Surianty Yessica Sardina Purba Yuliana Yuliana