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SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Published by RAM PUBLISHER
ISSN : 30901626     EISSN : 30323991     DOI : -
Core Subject : Humanities, Science, Engineering,
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan or in English the publication title Information Systems, Engineering and Applied Technology is an open access journal committed to publishing high quality research articles in the fields of Information Systems, Informatics, Digital Communication Information Technology, Tourism Technology, Transportation Technology, Agricultural Technology, Plantations, Fisheries, Marine, Environmental Technology, Artificial Intelligence, Mechanical Engineering, Electrical Engineering, Industrial Engineering and Civil Engineering. Published 4 X (Times) a year in January, April, July, and October. SITEKNIK accepts and selects quality articles and focuses on providing the best service for writers. SITEKNIK is committed to being a leading platform for researchers to share their innovative findings. We also provide a fast and transparent review process to ensure the quality and originality of each published article.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 121 Documents
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Information System Requirements Analysis for MSME Digitalization Using the SDLC Waterfall Approach Alvan Dhan; Angelina Apriyani Pandiangan; Nadita; Ratih Echa Kurnia; Rafi Akmal Rizqullah
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 2 (2026): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.19926982

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in the economy; however, they still face various challenges in adopting digital technology. This study aims to examine the requirements of an information system to support the digitalization of MSMEs using the Software Development Life Cycle (SDLC) Waterfall model. The research focuses on the analysis and design stages, which include identifying user requirements, analyzing business processes, and designing the system using Unified Modeling Language (UML). The results indicate that the proposed system should be capable of supporting integrated product management, transactions, ordering processes, and sales reporting, involving key actors such as buyers, sellers, and administrators. This study produces a system requirements model that can serve as a foundation for developing digital applications for MSMEs, thereby improving operational efficiency and enhancing business competitiveness.
Fine-Tuning Panoptic FPN with ResNet-50 for Maritime Obstacle Detection on the LaRS Dataset Istifa Shania Putri; Sugih Ahmad Fauzan; Mega Fitri Yani; Cindy Muhdiantini
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 3 (2026): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20768168

Abstract

Maritime obstacle detection is a critical challenge for Unmanned Surface Vehicles (USVs) operating in complex and dynamic environments. This study investigates the effectiveness of fine-tuning Panoptic FPN, a Mask R-CNN-based architecture augmented with Feature Pyramid Networks, for panoptic segmentation on the LaRS (Lake, River, Seas) dataset. Unlike prior work that explored model comparisons broadly, this research focuses specifically on the impact of hyperparameter tuning and backbone selection on maritime panoptic segmentation performance. Through systematic ablation studies, we demonstrate that adjusting the learning rate to 0.002 and the gamma decay factor to 0.2 yields significant improvements. Our fine-tuned Panoptic FPN with a ResNet-50 backbone achieves a Panoptic Quality (PQ) of 45.31%, surpassing the previous state-of-the-art Mask2Former Swin-B (41.7%) by 3.61 percentage points. Notably, ResNet-50 outperforms the deeper ResNet-101 backbone (36.47% PQ), suggesting that heavier architectures may overfit on domain-specific maritime datasets. Furthermore, Panoptic FPN requires only 8 hours of training compared to approximately 2 days for Mask2Former Swin-L, demonstrating superior computational efficiency. These findings highlight that targeted fine-tuning of lightweight architectures can outperform larger transformer-based models in maritime panoptic segmentation tasks.
Dashboard-Based Distribution Requirements Planning for Reverse Logistics Scheduling Optimization in Community Waste Collection Nia Novitasari; Gisti Ayu Pratiwi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 3 (2026): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.21533174

Abstract

This study aims to develop a dashboard-based waste collection scheduling system using the Distribution Requirements Planning (DRP) approach to improve the effectiveness of sorted gallon waste collection at Bank Sampah Bersinar (BSB), Bojongsoang. BSB manages six recycle points with a total weekly collection demand of 4,050 kg, while the available fleet consists of only one pick-up vehicle with a capacity of 1,000 kg. The absence of a structured collection schedule previously caused simultaneous collection activities and potential over-dimension and over-load problems. This study applies DRP to allocate collection activities based on demand volume, fleet capacity, operational time, and BSB’s three-day collection policy. The result shows that all weekly waste volume can be collected within three operational days by visiting two recycle points per day, with two trips conducted each day. In addition, a monitoring dashboard was developed to support daily trip tracking, collection volume monitoring, and recycle point performance evaluation. The proposed system provides a practical solution for improving reverse logistics operations, reducing fleet overload risk, and supporting data-based decision-making in community waste management.
Implementation of FSN Analysis and Periodic Review System for Inventory Management Improvement in Spare Parts Warehouse Leo Rama Kristiana; Gisti Ayu Pratiwi; Putu Giri Artha Kusuma; Grass Aditya Pringgodani; Irza Farhand; Theresia Jeremia Purba
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 3 (2026): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.21533846

Abstract

Effective inventory management is essential for maintaining spare parts availability and ensuring high service levels in the automotive industry. PT IAMI, particularly the PSM Dept. and PC Dept. Bekasi, identified inventory management issues at its Medan Depot as a pilot project for inventory improvement. The depot initially achieved a fill rate of only 60%, below the company’s target of 70%, due to the absence of standardized spare parts classification, review period policies, replenishment planning, and an integrated monitoring system. To address these issues, a community service program was conducted by Telkom University through technical assistance in developing an inventory management framework. The program integrated Fast-Slow-Non Moving (FSN) Analysis, the Periodic Review System (R,s), and a dashboard-based inventory monitoring system using Microsoft Excel. The implementation stages included field observation, focus group discussion, operational data analysis, spare parts classification, inventory parameter calculation, dashboard development, and validation with the partner. The results showed that the proposed inventory policy improved the fill rate from 60% to 99%, exceeding the company target. In addition, the dashboard improved inventory visibility by displaying stock status, fill rate, safety stock, reorder warnings, and other operational indicators.
Analisis Komprehensif Teknologi Panel Surya: Prinsip, Komponen, dan Konfigurasi Sistem Pembangkit Listrik Tenaga Surya Hery Sudaryanto
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 3 (2026): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.21322583

Abstract

Energi surya memainkan peran penting dalam transisi global menuju energi terbarukan, dengan teknologi fotovoltaik (PV) memimpin pasar. Artikel ini memberikan analisis komprehensif tentang teknologi panel surya, meliputi prinsip kerja fundamentalnya, komponen sistem penting, dan konfigurasi struktural di pembangkit listrik tenaga surya. Artikel ini mengevaluasi sel surya berbasis silikon tradisional bersama dengan teknologi generasi berikutnya yang sedang berkembang. Analisis kuantitatif menunjukkan bahwa panel silikon monokristalin konvensional mencapai efisiensi komersial tertinggi sebesar 15% hingga 22%, sementara teknologi polikristalin dan film tipis menawarkan efisiensi yang lebih rendah, masing-masing sebesar 13% hingga 18% dan 10% hingga 12%. Untuk mengatasi batasan efisiensi teoritis silikon, sel surya perovskit yang sedang berkembang telah menunjukkan efisiensi laboratorium melebihi 25%, dan struktur tandem/multi-junction mendorong batas kinerja melewati 30% dengan mengoptimalkan penyerapan spektral. Di luar fisika tingkat sel, studi ini meneliti komponen balance of system (BOS) yang penting, termasuk inverter, pengontrol pengisian daya, dan infrastruktur penyimpanan baterai (seperti timbal-asam dan litium-ion). Selain itu, studi ini merinci dinamika rekayasa dan operasional dari konfigurasi pembangkit listrik tenaga surya on-grid, off-grid, dan hibrida. Dengan mengintegrasikan rekayasa struktur, ilmu material, dan desain sistem, ulasan ini berfungsi sebagai referensi teknis strategis untuk mengoptimalkan penyebaran, efisiensi, dan keandalan instalasi pembangkit listrik tenaga surya modern.
Pengembangan Sistem Pakar Diagnosis Tingkat Stres Menggunakan Metode Certainty Factor Fairuz Destea Hafsha Permanasari; Adi Fajaryanto Cobantoro; Rifqi Rahmatika Az-Zahra
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 3 (2026): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.21724197

Abstract

Proses penyusunan skripsi sering menimbulkan stres pada mahasiswa akibat tuntutan akademik, keterbatasan waktu, dan berbagai kendala selama penelitian. Kondisi tersebut dapat memengaruhi motivasi dan konsentrasi sehingga diperlukan sistem untuk mendeteksi kondisi psikologis mahasiswa secara dini. Penelitian ini bertujuan merancang dan membangun sistem pakar diagnosis tingkat stres mahasiswa menggunakan metode Certainty Factor (CF) di Institut Agama Islam Riyadlotul Mujahidin (IAIRM) Ngabar. Sistem dikembangkan berbasis web menggunakan framework Laravel, PHP, dan MySQL dengan basis pengetahuan yang mengacu pada instrumen Depression Anxiety Stress Scales (DASS-42) yang terdiri atas 42 gejala. Pengujian dilakukan menggunakan white box testing untuk memastikan fungsi dan alur logika sistem berjalan sesuai rancangan. Hasil penelitian menunjukkan bahwa sistem mampu menghitung nilai Certainty Factor berdasarkan nilai CF User dan CF Pakar serta menghasilkan diagnosis secara otomatis. Pada pengujian, kategori P01 (Stres Ringan) memperoleh nilai CF tertinggi sebesar 82,4%, sedangkan P02 (Kecemasan) sebesar 28% dan P03 (Depresi) sebesar 0%, sehingga sistem menetapkan diagnosis Stres Ringan dengan tingkat keyakinan 82,4%. Sistem yang dikembangkan dapat dimanfaatkan sebagai alat bantu deteksi dini tingkat stres mahasiswa selama proses penyusunan skripsi.
Implementasi Model Mobilenetv2 Untuk  Klasifikasi Ras Anjing Nurzaenab; Sul Agus Zarni; Andi Yulia Muniar; Tatik Maslihatin; Ika Yuswandari Supagi Supagi; Fitriani M. Sabir
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 3 (2026): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.21579001

Abstract

Identifikasi ras anjing secara visual merupakan tantangan signifikan bagi masyarakat awam karena kemiripan fitur morfologi yang sangat tinggi antar spesies. Kesalahan identifikasi berdampak serius pada ketidaktepatan pola perawatan harian dan penanganan medis spesifik. Penelitian ini mengimplementasikan teknologi visi komputer melalui model Deep Learning berbasis arsitektur MobileNetV2 untuk klasifikasi otomatis ras Basenji, Chow, Dingo, dan Lhasa. Permasalahan utama yang diangkat adalah tingginya variasi intra-kelas dan kemiripan ekstrem antar-kelas pada citra hewan. Metodologi penelitian menggunakan 600 citra berkualitas tinggi dengan tahap preprocessing ketat dan augmentasi data intensif. Teknik transfer learning diterapkan melalui fine-tuning pada 50 lapisan terakhir MobileNetV2. Untuk meningkatkan stabilitas, model dimodifikasi dengan menambahkan lapisan Dense 32 unit, Dropout 0.6, serta Regularizer L2 sebesar 0.003 guna menekan risiko overfitting. Hasil pengujian menunjukkan performa impresif dengan akurasi pelatihan 96% dan akurasi pengujian 93%. Waktu pelatihan sangat efisien, yakni hanya 3 menit untuk 10 epoch. Analisis confusion matrix menunjukkan keunggulan model pada ras Basenji, meskipun terdapat tantangan tekstur bulu pada Lhasa dan Chow. Konfigurasi ini terbukti efektif, ringan, dan siap untuk implementasi real-time pada perangkat seluler.
Optimization of Random Forest Algorithm Using Random Search for Alzheimer's Disease Detection Hasyim Sri Wahyudi; Ferian Fauzi Abdulloh
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16554889

Abstract

Alzheimer's disease is a type of neurodegenerative disorder that causes a decline in cognitive function. Early detection is crucial to enable more effective interventions and slow the progression of the disease. However, the diagnosis of Alzheimer's disease often faces challenges, particularly in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the Random Forest algorithm using the Random Search method for detecting Alzheimer's disease. The Random Forest algorithm was applied as the primary model in this research, while hyperparameter optimization was performed using the Random Search method to improve model performance. The results showed that the Random Forest model without optimization achieved an accuracy of 96%. After performing hyperparameter optimization, the model's accuracy increased to 97%. In conclusion, the application of hyperparameter optimization using the Random Search method successfully enhanced the performance of the Random Forest model. The resulting model provides more accurate predictions, making it a reliable tool for the early detection of Alzheimer's disease.
Random Search Optimization Using Random Forest Algorithm For Liver Disease Prediction RIYAN BAYU SATRIYA; Kusnawi Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15468679

Abstract

The liver is a vital human organ with complex and diverse functions. One of the diseases that affect the liver is hepatitis or liver disease. Early detection is crucial to enable more effective intervention and slow the progression of the disease. However, diagnosing liver disease often faces challenges, especially in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the Random Forest algorithm using the Random Search method for liver disease detection. The Random Forest algorithm is applied as the primary model in this research, while hyperparameter optimization is performed using the Random Search method to enhance model performance. The results show that the Random Forest model without optimization achieves an accuracy of 93%. After hyperparameter optimization, the model's accuracy increases to 94%. In conclusion, applying hyperparameter optimization using the Random Search method successfully improves the performance of the Random Forest model. The resulting model provides more accurate predictions.
Analysis of Digital Governance Framework Implementation to Enhance Digital Transformation Farhana Zahra
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15400843

Abstract

Digital transformation (DT) is increasingly crucial for public and private organizations seeking agility and efficiency. However, many entities pursue DT initiatives without first establishing a structured digital governance framework, resulting in fragmented implementation and strategic misalignment. This study aims to analyze how the absence of governance mechanisms affects transformation effectiveness and identify foundational elements necessary to initiate governance in such contexts. The novelty of this research lies in its analytical focus on organizations lacking pre-existing governance structures. Unlike prior studies that assume the presence of governance, this research offers insights for institutions starting from zero. Using a qualitative descriptive method through literature review and document analysis, this study investigates key governance gaps, risks, and challenges in low-governance environments. The findings reveal that the absence of digital governance leads to inefficiencies, redundant systems, poor risk management, and low accountability. By synthesizing best practices and proposing phased implementation strategies, the research provides practical guidance to build governance capabilities from the ground up. This study offers both theoretical contributions and actionable recommendations for sustainable digital transformation.

Page 11 of 13 | Total Record : 121

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