Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Best : Journal of Applied Electrical, Science and Technology Jurnal Mahasiswa Entrepreneur
Lutfi, Moch
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics Physics Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PENGAMANAN RUANG DENGAN PENGENALAN POLA WAJAH SECARA REALTIME MENGGUNAKAN METODE VIOLA JONES Hidayat, Taufiq; Lutfi, Moch
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol 1, No 2 (2019): Vol 1, No 2 (2019): Desember 2019
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v1i2.3446

Abstract

Deteksi wajah banyak diperlukan di berbagai cabang keamanan, seperti dalam aspek pengawasan, keselamatan, verifikasi dan identifikasi. Dalam bidang keamanan, pengenalan wajah banyak diperlukan untuk otentifikasi, misalkan otentifikasi pelaku kriminal, seperti deteksi terhadap pelaku teror, pencegahan terhadap terpidana korupsi yang hendak melarikan diri ke luar negeri dll. Dalam penelitian computer vision banyak metode yang bisa digunakan untuk deteksi wajah, salah satunya adalah dengan menggunakan metode Viola Jones. Metode ini digunakan untuk mendeteksi wajah dengan menggunakan klasifikasi berdasarkan pendekatan algoritma AdaBoost dan Haar Cascade. Pada algoritma Viola Jones ini, metode Adaboost digunakan untuk menjadi penentu nilai ambang batas, sedangkan Haar Cascade digunakan untuk klasifikasi area sub windows.  Tujuan penelitian ini yaitu penerapan metode Viola Jones dalam mendeteksi wajah seseorang, yang dapat dijadikan sistem keamanan tambahan dalam  suatu ruangan. Hasil pengujian untuk mendeteksi wajah dengan Viola Jones adalah akurasi ketika wajah dalam posisi frontal dengan webcam sebesar 100% dengan waktu deteksi kurang dari 1 detik. Sedangkan batas kemiringan maksimum ±70 dan jaraknya 20 – 120 cm. Pengujian sistem menggunakan metode Eigenface diperoleh nilai akurasi sebesar 90%. DOI : https://doi.org/10.26905/jasiek.v1i2.3446
PENGARUH GAYA KEPEMIMPINAN TRANSFORMASIONAL, TRANSAKSIONAL DAN DISIPLIN KERJA TERHADAP KINERJA KARYAWAN DINAS PERTANIAN KABUPATEN BONDOWOSO Lutfi, Moch; Azhad, Muhammad Naely; Saidah, Nur
Jurnal Mahasiswa Entrepreneurship (JME) Vol 2 No 1 (2023): JANUARI 2023
Publisher : Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (539.311 KB) | DOI: 10.36841/jme.v2i1.2650

Abstract

This study aims to examine and analyze the influence of transformational, transactional and work discipline leadership styles on the performance of Bondowoso Agricultural Service employees. This type of research. causality research, namely research that explains the relationship that influences each other between the independent variable and the dependent variable. The population in this study were Bondowoso Agriculture Office employees, the sample used was 100 respondents. The analysis tool uses multiple linear analysis. The results of the study prove that transformational, transactional and work discipline leadership styles have a significant effect on the performance of Bondowoso Agricultural Service employees.
Handling Imbalanced Fraudulent Transaction Data Using SMOTE-Tomek and Random Forest: A Classification Approach Ilham, Mohamad; Winarno, Adi; Lutfi, Moch; Indrasetianingsih, Artanti
BEST Vol 7 No 1 (2025): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/best.vol7.no1.10335

Abstract

This research aims to address the class imbalance problem in fraud detection using hybrid resampling techniques, specifically SMOTE-Tomek, combined with Random Forest classifiers. Imbalanced data in fraud detection tasks can severely hinder model performance, resulting in poor detection of minority (fraud) cases. By employing SMOTE to oversample minority class instances and Tomek links to clean the borderline majority class samples, this study evaluates the effectiveness of this hybrid method in improving classification metrics. Using a benchmark credit card fraud dataset, we compare the performance of Random Forest models with and without the hybrid sampling approach. The experimental results show that SMOTE-Tomek significantly enhances recall and F1-score without sacrificing accuracy. This finding underscores the importance of using appropriate resampling strategies for improving model robustness in fraud detection.
Optimization of the Naive Bayes Algorithm with SMOTETomek Combination for Imbalance Class Fraud Detection Arsanto, Arief Tri; Faizin, Arif; lutfi, Moch; Saadah, Zulfatun Nikmatus
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4719

Abstract

The use of credit cards in the modern era is increasing. Therefore, it is necessary to prevent it with the use of technology such as address verification systems (AVS), card verification methods (CVM), and personal identification Numbers (PIN). Dataset analysis needs to be carried out to analyze the history of transactions that have been carried out. In the fraud detection dataset, it can be seen that there are attributes that cause data imbalance. Class imbalance in a dataset is a significant problem in machine learning that can affect overall model performance. The number of majority samples is more significant in one class than the number of minority classes. This research used an oversampling approach using a combination of smote and tomek-link. The focus of this research is card fraud classification. Detection of imbalanced datasets or imbalanced classes is carried out using the Naive Bayes method as a classification algorithm. In addition, a combination of resampling techniques is also applied to overcome imbalanced classes in this dataset through the SMOTETomek approach. SMOTETomek is a method that reduces the number of samples by considering two adjacent data from the minority and majority classes. Meanwhile, from the problems above, the results of the performance of Naïve Bayes, which experienced issues with data imbalance in this study, a resampling method was proposed in the hope of improving the performance of the Naïve Bayes algorithm and in the results of the AUC ROC curve, the SMOTETomek method could improve the performance of the Naïve Bayes algorithm. The higher the ROC score. -AUC, the better the model performance in terms of its ability to differentiate between two classes, but the accuracy results do not experience a significant change.
Co-Authors Adi Winarno Arsanto, Arief Tri Azhad, Muhammad Naely Faizin, Arif Indrasetianingsih, Artanti Mohamad Ilham Saadah, Zulfatun Nikmatus Saidah, Nur Wiwik Handayani