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All Journal Jurnal TIMES JOURNAL OF APPLIED INFORMATICS AND COMPUTING YUME : Journal of Management Management Studies and Entrepreneurship Journal (MSEJ) Jurnal Teknik Informatika (JUTIF) 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
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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.
Co-Authors Agusman, Veirry 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 Surianty Yessica Sardina Purba