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All Journal TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Inovasi Bisnis Manajemen dan Akuntansi
Muhammad Maulana
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Other

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Aplikasi Segmentasi Jenis Makanan Berdasarkan Kandungan Gizi Menggunakan Algoritma K-Means Valentina Gracia Mardianti; Hadist Hadist; Kristian Charles; Muhammad Maulana; Weisky Steven Dharmawan
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp35-41

Abstract

Public understanding of food nutrition remains limited due to the lack of accessible educational tools. This study aims to develop a web-based food segmentation system capable of clustering food items based on their nutritional characteristics using the K-Means algorithm. The research employs the Food Composition Dataset, focusing on key attributes including nitrogen factor, fat factor, and specific gravity. The methodology consists of data preprocessing, determining the optimal number of clusters using the Elbow Method, and visualizing the clustering outcomes through Principal Component Analysis (PCA). The results indicate that the optimal number of clusters is K = 3, with clear separation demonstrated by PCA, which shows an explained variance of 82%. The resulting clusters represent groups of foods with similar nutritional profiles, such as high-protein, high-fat, or high-density items. The clustering results were implemented into an interactive Streamlit-based web application, allowing users to explore and interpret the segmentation results easily. The study concludes that the K-Means algorithm is effective for grouping foods based on nutritional attributes, and the developed system can serve as a practical tool for nutritional education and balanced diet analysis.
Analisa Komparasi Kinerja Algoritma K-Nearest Neighbor (K-NN) dan Decision Tree dalam Klasifikasi Situs Web Phising: Penelitian Fajar Dwi Prasetyo; Muhammad Maulana; Faris Ramadhan; Ananda Lutfi Setiabudi; Imam Budiawan; Desmulyati Desmulyati
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4965

Abstract

Phishing attacks represent a significant cybersecurity threat aimed at stealing sensitive user information through psychological manipulation using fake websites. Conventional detection methods relying on blacklists are considered ineffective in recognizing zero-day attacks or newly published phishing sites. This study aims to develop an automated detection model using a Machine Learning approach by comparing the performance of two Supervised Learning algorithms: K-Nearest Neighbor (K-NN) and Decision Tree. The dataset used is sourced from the UCI Machine Learning Repository, consisting of 11,055 records with 30 URL characteristic features. Performance evaluation was conducted using Accuracy metrics and Confusion Matrix analysis. Experimental results indicate that the Decision Tree algorithm significantly outperforms K-NN with an accuracy of 95.21%, while K-NN achieved an accuracy of only 60.11%. Furthermore, Decision Tree demonstrated a very low False Negative rate, making it a more recommended model for real-time cybersecurity system implementation.
Analisis Dampak Beban Kerja dan Lingkungan Kerja Terhadap Kinerja Karyawan Pada PT. Festival Suara Indonesia Jakarta Selatan Muhammad Maulana; Lukman Hakim; Iwan Supriyanto
Jurnal Inovasi Bisnis Manajemen dan Akuntansi Vol. 4 No. 1 (2026): JIBMA : Jurnal Inovasi Bisnis Manajemen dan Akuntansi
Publisher : PT. Karya Inovatif Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65255/jibma.v4i1.246

Abstract

In today’s highly competitive business environment, employee performance is a crucial factor in achieving organizational goals. This study aims to analyze the effect of workload and work environment on employee performance at PT. Festival Suara Indonesia Jakarta Selatan. The research applied a quantitative approach using a survey method. Data were collected from 34 employees through questionnaires and analyzed using SPSS version 27. The analysis techniques included validity and reliability tests, classical assumption tests, and multiple linear regression. The results show that both workload (X1) and work environment (X2) partially have a significant impact on employee performance (Y), as indicated by the t-value of workload 2.999 > t-table 2.040 with a significance of 0.005 < 0.05, and the t-value of work environment 2.356 > t-table 2.040 with a significance of 0.025 < 0.05. Simultaneously, both variables significantly influence employee performance, proven by the F-value of 27.030 > F-table 3.30 and a significance of 0.001 < 0.05. The coefficient of determination (R²) of 0.636 indicates that 63.6% of the variation in employee performance is explained by workload and work environment.
Co-Authors Ananda Lutfi Setiabudi Desmulyati, Desmulyati Fajar Dwi Prasetyo Faris Ramadhan Hadist Hadist Imam Budiawan Iwan Supriyanto Kristian Charles Lukman Hakim Valentina Gracia Mardianti Weisky Steven Dharmawan