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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Building of Informatics, Technology and Science Syntax: Journal of Software Engineering, Computer Science and Information Technology INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Minfo Polgan (JMP) PUBLIDIMAS ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Thanri, Yan Yang
Universitas Potensi Utama
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Sistem Pendukung Keputusan Dalam Penentuan Bonus Petugas Damkar Dengan Metode PSI (Preference Selection Index) Pada Dinas Pemadam Kebakaran Medan Petisah Berbasis Web Muhammad Arif; Yan Yang Thanri; Nandri Marsan Sitinjak
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15560

Abstract

Dinas Pemadakan Kebakaran Medan Petisah merupakan perusahaan yang bergerak di bidang pelayanan masyarakat untuk memilih petugas yang berhak menerima reward atau bonus tidaklah mudah, karena dalam proses pemilihan memiliki kriteria-kriteria tertentu yang perlu diperhatikan. Untuk itu seorang pimpinan dalam Perhitungan penilaian terhadap kriteria harus benar-benar relevan karena pada dasarnya akan berpengaruh pada hasil akhirnya. Adapun permasalahan yang ada saat ini biasanya dalam menentukan atau menyeleksi petugas yang menerima bonus dilakukan dengan cara memantau setiap kinerja petugas yang dinilai kurang efektif, selain itu belum adanya suatu sistem pendukung keputusan yang di gunakan dalam pemberian bonus pada petugas damkar. Pemilihan petugas damkar yang layak mendapatkan bonus dan banyaknya jumlah kinerja, tentunya memberikan pekerjaan yang berlebih bagi Dinas Pemadam Kebakaran untuk memilih secara manual petugas yang berhak menerima bonus dari perusahaan. Penelitian ini bertujuan untuk menghasilkan sistem pendukung keputusan yang dapat menentukan bonus ptugas berdasarkan kebutuhan perusahaan di Dinas Pemadakan Kebakaran Medan Petisah. Sistem pendukung keputuan ini menggunakan metode PSI yang diimplementasikan dengan bahasa pemrograman PHP dan database MySQL. Sistem pendukung keputusan ini akan menghasilkan output berupa proses penilaian penentuan bonus petugas damkar sampai dengan proses pembuatan laporan nilai perangkingan petugas damkar.
A Hyperparameter-Adaptive Multilayer Perceptron Framework for Revenue Prediction Based on E-Commerce User Behavior Data Safrizal; lili Tanti; Yan Yang Thanri
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.5866

Abstract

Accurate revenue prediction remains a critical challenge for e-commerce platforms due to the highly nonlinear and dynamic nature of user behavior. At the same time, many existing machine learning approaches rely on static model configurations that limit predictive robustness. Although various techniques have been proposed for e-commerce revenue prediction, a systematic, performance-driven approach to adapting Multilayer Perceptron hyperparameters remains underexplored. This study proposes a hyperparameter-adaptive Multilayer Perceptron framework for predicting e-commerce revenue based on user behavior data. Revenue prediction is formulated as a binary classification problem, where outcomes are categorized into conversion and non-conversion events. The dataset comprises 12,330 e-commerce user sessions with behavioral and contextual features, including page interactions, session duration, bounce rate, and visitor characteristics. The proposed framework employs iterative hyperparameter adaptation by evaluating multiple MLP configurations with variations in network depth, activation functions, optimization algorithms, and regularization levels. Model performance is assessed using accuracy, precision, recall, F1-score, and Area Under the Curve. Experimental results indicate that the configuration with the Adam optimizer, ReLU activation, and moderate regularizationachieves the best performance, yielding 88.93% accuracy and an AUC of 0.91. These findings confirm that hyperparameter-adaptive selection significantly enhances prediction performance compared to static model settings. The proposed framework provides a systematic approach to improving revenue prediction accuracy and offers valuable insights for data-driven decision-making and strategic planning in e-commerce environments.
PEMBUATAN APLIKASI PEMESANAN BUAH PADA TOKO BUAH SEGAR MEDAN BERBASIS ANDROID M Taufiq Yang Julian; Yan Yang Thanri
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 7, No 1 (2026): Juni 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v7i1.8835

Abstract

Perkembangan teknologi informasi mendorong perubahan perilaku konsumen dalam berbelanja, termasuk dalam pemenuhan kebutuhan buah segar secara daring. Toko Buah Segar Medan menghadapi tantangan operasional yang signifikan karena sistem pemesanan yang berjalan saat ini masih menggunakan media sosial WhatsApp. Ketika terjadi lonjakan pesanan, tingginya volume percakapan yang masuk sering kali menyebabkan aplikasi WhatsApp mengalami gangguan (crash atau error). Selain itu, proses pelayanan informasi ketersediaan produk, pencatatan pesanan, hingga perhitungan total harga yang masih dilakukan secara manual memicu antrean panjang bagi pelanggan, meningkatkan risiko kesalahan pencatatan, serta menurunkan efisiensi layanan. Penelitian ini bertujuan untuk membangun aplikasi pemesanan buah berbasis Android guna mengotomatisasi proses pemesanan, menyajikan informasi katalog produk secara real-time, meminimalkan antrean, dan memperbaiki manajemen inventaris toko. Metode pengembangan yang digunakan adalah metode Waterfall, meliputi analisis kebutuhan, desain sistem menggunakan UML, implementasi dengan Android Studio berbasis bahasa Java, serta pengujian sistem menggunakan black box testing. Aplikasi yang dirancang telah dilengkapi dengan fitur katalog produk, keranjang belanja (troli), manajemen stok otomatis oleh admin, dan validasi unggah bukti pembayaran.. Aplikasi ini mampu memberikan kemudahan akses bagi konsumen di wilayah Kota Medan sekaligus menjadi solusi digital yang efektif dalam meningkatkan efisiensi operasional dan daya saing Toko Buah Segar Medan di era digital. Kata Kunci— Android, Pemesanan Online, Toko Buah, Waterfall, Java, WhatsApp Crash. ABSTRACT The rapid advancement of information technology has transformed consumer shopping behavior, particularly in purchasing fresh fruits online. Toko Buah Segar Medan faces significant operational challenges due to its reliance on WhatsApp for handling customer orders. During peak ordering periods, the high volume of incoming chats frequently causes the WhatsApp application to crash or error. Furthermore, manual processes for updating product availability, recording order details, and calculating total prices lead to long customer queues, higher risks of data entry errors, and reduced service efficiency. This study aims to develop an Android-based fruit ordering application to automate transactions, provide real-time catalog updates, eliminate communication bottlenecks, and improve inventory management. The development follows the Waterfall method, which includes requirements analysis, system design using UML, implementation via Android Studio using Java, and evaluation through black-box testing. The application features a product catalog, a shopping cart, automated stock control for the admin, and payment validation through receipt uploads. This application simplifies access for customers in the Medan City area while delivering an effective digital solution to enhance operational efficiency and the competitive edge of Toko Buah Segar Medan in the digital era.Keywords— Android, Online Ordering, Fruit Store, Waterfall, Java, WhatsApp Crash. 
Optimasi Model Evaluasi Kinerja Karyawan Berbasis Rekam Jejak Digital Menggunakan PCA dan Algoritma Machine Learning Yan Yang Thanri; Juli Iriani; Angel Gowasa; Luthfi Zaidi
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.8402

Abstract

In the era of digital transformation, organizations face challenges in evaluating employee performance objectively and based on data. Traditional performance appraisal systems often contain subjectivity and limitations in data integration, making them less effective in dynamic work environments. This study aims to develop a performance evaluation model based on digital footprints using machine learning and multivariate analysis. Digital footprints include work activity data (daily working hours, screen time, meetings, and emails), wearable data (physical steps, sleep duration, and stress levels), satisfaction (work-life balance, organizational support), capability (tech skills score, job level, and training), and organizational data (salary, incentives, and overtime). Principal Component Analysis (PCA) is used to reduce data dimensions and identify key performance indicators. Three machine learning algorithms—Decision Tree, Random Forest, and Gradient Boosting—are applied to classify employee performance into Low, Average, Good, and Excellent categories. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics. The results show that the Gradient Boosting model combined with PCA delivers the best performance with an accuracy of 0.887 and an F1-score of 0.884. The application of PCA significantly improved classification model performance by reducing noise and multicollinearity in high-dimensional data. These findings highlight the great potential of leveraging employees' digital behavioral data to build a transparent and adaptive performance evaluation system. This study contributes to the development of intelligent HR management and supports data-driven decision-making in modern organizations.
Performance Comparison of Decision Tree, KNN, and Naive Bayes for Air Quality Classification Yan Yang Thanri; Juli Iriani Iriani; Lili Tanti Tanti; Luthfi Zaidi Zaidi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5121

Abstract

Air quality degradation has become a critical environmental and public health issue, necessitating accurateand reliable classification models to support effective monitoring systems. This study aims toconduct a comparative analysis of four machine learning algorithms-Decision Tree, k-Nearest Neighbor (kNN), Naive Bayes, and Stochastic Gradient Descent (SGD)-for classifying air quality using environmental parameters, including particulate matter ≤ 2.5 μm (PM2.5), carbon monoxide (CO), temperature, humidity, nitrogen dioxide (NO2), and sulfur dioxide (SO2). The methodology employssupervised learning, where each model is trained and evaluated using classification accuracy, area under the receiver operating characteristic curve (AUC), F1-Score, precision, recall, and Matthews Correlation Coefficient (MCC), supported by ROC curve and confusion matrix analyses. The results show that the Decision Tree algorithm achieves the best overall performance, attaining a classification accuracy of 93.8% with a balanced precision, recall, and F1-Score, indicating strong and consistent predictive capability. The kNN and Naive Bayes models record the highest AUC values (0.980 and 0.982, respectively), demonstrating excellent class separability, although their accuracy and F1-Score are lower than those of the Decision Tree. In addition, the SGD model, implemented with a modified Huber loss function and L2 regularization, provides interpretable feature-weight analysis, identifyingPM2.5 and CO as dominant indicators of the Hazardous air quality class, while temperature and humidity significantly influence the Fair and Good classes. Based on the comprehensive evaluation, the Decision Tree algorithm is recommended as the most reliable model for accurate air quality classification, whereas the SGD model is particularly suitable for feature contribution analysis to enhance interpretability. These findings offer practical insights for selecting appropriate machine learning models in air quality monitoring and decision-support systems.
Co-Authors Andra Alfira Andrian Syahputra Angel Gowasa Bob Subhan Riza Bob Subhan Riza Bob Subhan Riza, Bob Subhan Faisal Tanjung Fitri Pranita Nasution Juli Iriani Juli Iriani Juli Iriani Juli Iriani Iriani Juli Iriani, Juli Lahmudin Sipahutar Lili Tanti lili Tanti Luthfi Zaidi Luthfi Zaidi Zaidi M Taufiq Yang Julian Muhammad Arif Nandri Marsan Sitinjak Nety Juwita Lubis Riza, Bob Subahan Rizki Maulida Safrizal Safrizal Safrizal Situmeang, Desmon Julisten Subhan, Zhafira Nur Windy, Micky Alviansyah Zaidi, Luthfi _, Safrizal