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All Journal KOLONI Jurnal Elektronika dan Teknik Informatika Terapan
Mus Ali Alsabah
Politeknik Negeri Bengkalis
Civil Engineering, Building, Construction & Architecture Computer Science & IT Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Implementasi Media Promosi Neon Box Dalam Mendukung Identitas Bisnis Polbeans Nur Faizi Faizi; Mus Ali Alsabah; Shylvester Simanjuntak; D. Sartika Silaban
KOLONI Vol. 5 No. 3 (2026): SEPTEMBER 2026
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/koloni.v5i3.1458

Abstract

Visual identity is an important aspect in building the image, attractiveness, and competitiveness of a business. POLBEANS requires a promotional medium that can increase business visibility while strengthening brand identity so that it can be easily recognized by the public. This study aims to design, develop, and implement a neon box as a promotional medium to support the strengthening of POLBEANS' business identity and to evaluate its effectiveness in influencing customer perceptions. The research employed a Research and Development (R&D) method consisting of several stages, including needs identification, design development, material selection, product fabrication, functional testing, implementation, and evaluation. The neon box design integrates the POLBEANS logo, brand colors, and business information to create a consistent visual identity. The fabrication process included the assembly of an aluminum frame, installation of acrylic panels, LED lighting modules, and power supply, followed by functional and structural testing. The effectiveness of the implementation was evaluated through observation and questionnaires distributed to customers based on indicators of visual attractiveness, visibility, ease of recognizing the business location, and strengthening of business identity. The implementation results indicate that the neon box functions effectively as an informative and attractive outdoor promotional medium while improving the visibility and ease of recognizing the POLBEANS business location. The consistent use of visual identity elements also contributes to a more professional and recognizable business image. Therefore, the neon box can serve as an effective branding medium for strengthening the business identity of POLBEANS and has potential for application in similar businesses.
Konfigurasi Backpropagation Neural Network Ringan untuk Klasifikasi Kesuburan Tanah Berbasis Nilai NPK: Analisis Hyperparameter dan Uji Derau Simulasi Marzuarman Marzuarman; Agustiawan Agustiawan; Mus Ali Alsabah; Azizul Azizul; M. Afridon; Jefri Lianda; Fandy Octafiandri; Cindy Witra Nuansa
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 4 No. 3 (2026): September: Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i3.1622

Abstract

Soil fertility classification from nitrogen (N), phosphorus (P), and potassium (K) values can be embedded in low-cost devices, but the architecture and hyperparameters of a Backpropagation Neural Network (BPNN) are often chosen by trial and error, and the effect of sensor uncertainty is rarely examined. This study aims to find a lightweight BPNN configuration and evaluate it under simulated sensor noise. A dataset of 200 soil samples from Bengkalis Island and Sungai Pakning, labeled into three fertility classes, was used. Hidden-layer size, learning rate, and number of epochs were explored by grid search with 5-fold stratified cross-validation repeated three times, and Gaussian noise of 0–20% of each feature's range was added to the test data. Networks with at least two hidden neurons reached 100.00% accuracy without noise, whereas one hidden neuron reached 94.50%. Within 25 epochs, accuracy rose from 32.00% at a learning rate of 0.0001 to 100.00% at 0.1. The lightweight 3–2–3 configuration (17 parameters) matched the 3–12–8–3 reference (179 parameters) at every noise level, with 95.87% versus 95.57% accuracy at 10% noise, and trained about 30 times faster. A two-neuron BPNN is therefore sufficient for this dataset. The findings are limited to simulated noise and 200 samples, so validation with real RS485 sensor readings is required.  
Co-Authors Afridon, M Agustiawan, Agustiawan Azizul Azizul Cindy Witra Nuansa D. Sartika Silaban Fandy Octafiandri Lianda, Jefri Marzuarman, Marzuarman Nur Faizi Faizi Shylvester Simanjuntak