SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
SITEKNIK JOURNAL Sistem Informasi, Teknik dan Teknologi Terapan E-ISSN 3032-3991 P-ISSN 3090-1626 Sistem Informasi, Teknik dan Teknologi Terapan publishes high-quality research bridging information systems, engineering, and applied technology. We focus on innovative solutions that provide practical impact for society and technological development. Publication Schedule JAN • APR • JUL • OCT Four issues per year Digital Eco IoT Agri Logistics Energy ● Peer-reviewed ● Open Access ● Double-blind Submit Manuscript
Pengembangan Sistem Pakar Diagnosis Tingkat Stres Menggunakan Metode Certainty Factor
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.
Copyright (c) 2026 Fairuz Destea Hafsha Permanasari, Adi Fajaryanto Cobantoro, Rifqi Rahmatika Az-Zahra (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Model Mobilenetv2 Untuk Klasifikasi Ras Anjing
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.
Copyright (c) 2026 Nurzaenab, Sul Agus Zarni, Andi Yulia Muniar, Tatik Maslihatin, Ika Yuswandari Supagi, Fitriani M. Sabir (Author) https://creativecommons.org/licenses/by/4.0
Analisis Komprehensif Teknologi Panel Surya: Prinsip, Komponen, dan Konfigurasi Sistem Pembangkit Listrik Tenaga Surya
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.
Copyright (c) 2026 Hery Sudaryanto (Author) https://creativecommons.org/licenses/by/4.0
Implementation of FSN Analysis and Periodic Review System for Inventory Management Improvement in Spare Parts Warehouse
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.
Copyright (c) 2026 Gisti Ayu Pratiwi, LEO KRISTIANA, S.T., M.T., Putu Giri Artha Kusuma, GRASS ADITYA PRINGGODANI, Theresia Jeremia Purba, Irza Farhand (Author) https://creativecommons.org/licenses/by/4.0
Dashboard-Based Distribution Requirements Planning for Reverse Logistics Scheduling Optimization in Community Waste Collection
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.
Copyright (c) 2026 Nia Novitasari, Gisti Ayu Pratiwi (Author) https://creativecommons.org/licenses/by/4.0
Information System Requirements Analysis for MSME Digitalization Using the SDLC Waterfall Approach
Micro, Small, and Medium Enterprises (MSMEs), particularly mobile vendors in the food and service sectors, face operational challenges such as manual transaction recording, limited customer reach, and unstructured order management. This study aims to analyze and design an information system that supports MSME digitalization based on real user needs. The research adopts the Software Development Life Cycle (SDLC) using the Waterfall model, limited to the analysis and design stages. Data were collected through literature review and semi-structured interviews with mobile MSME vendors. The findings highlight key issues related to transaction management, product updates, and customer interaction in dynamic environments. Based on these findings, functional requirements were formulated, including features for product management, order processing, digital transactions, and sales reporting. The system design is modeled using Unified Modeling Language (UML), including use case, activity, and class diagrams. The proposed system design provides a structured and practical solution aligned with MSME operational needs, with the potential to improve efficiency, expand market reach, and support better business decision-making.
Copyright (c) 2026 Alvan Dhan, Angelina Apriyani Pandiangan, Nadita, Ratih Echa Kurnia, Rafi Akmal Rizqullah (Author) https://creativecommons.org/licenses/by/4.0
Fine-Tuning Panoptic FPN with ResNet-50 for Maritime Obstacle Detection on the LaRS Dataset
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.
Copyright (c) 2026 Istifa Shania Putri, Sugih Ahmad Fauzan, Mega Fitri Yani, Cindy Muhdiantini (Author) https://creativecommons.org/licenses/by/4.0
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.
From Technical Security to Human Awareness: A Bibliometric Review of Cybersecurity Education Research
The rapid advancement of information technology has increased cybersecurity threats, emphasizing the importance of education in building security awareness and human-centered defense capabilities. As research on cybersecurity education continues to grow, a systematic understanding of its research trends and thematic evolution is required. This study analyzes global research trends in cybersecurity education using a bibliometric approach. A total of 189 Scopus-indexed publications from 2020 to 2026 were analyzed using the bibliometrix R package and the Biblioshiny interface. The analysis examines annual publication growth, dominant keywords, evolving research topics, and geographical research contributions. The results indicate a steady increase in scientific production, confirming cybersecurity education as an expanding research domain. While technical topics such as network and data security remain prominent, recent research shows a clear shift toward education, training, and security awareness, highlighting the growing importance of human factors in cybersecurity. In addition, the findings reveal an imbalance in global research contributions and relatively limited international collaboration. This study provides a concise overview of the development and structure of cybersecurity education research and offers insights to support future studies, curriculum development, and policy formulation.
Copyright (c) 2026 Cindy Muhdiantini, Mega Fitri Yani, Istifa Shania Putri (Author) https://creativecommons.org/licenses/by/4.0
A Bibliometric Analysis of Research Trends in AI Integration within Cloud Computing
The integration of Artificial Intelligence (AI) and cloud computing has emerged as a rapidly expanding research area, driven by the need for scalable, elastic, and cost-efficient intelligent systems. Cloud infrastructures enable dynamic resource allocation and pay-as-you-go models, making them ideal environments for AI model training and deployment. Despite the growing volume of publications, a structured mapping of the intellectual landscape of AI–cloud integration remains necessary. This study aims to analyze the research landscape of AI integration in cloud computing using a bibliometric approach. Data were collected from the Scopus database for the period 2021-2026 using the query “Artificial Intelligence” AND “Cloud Computing”, focusing on English-language articles. The analysis was conducted using the Bibliometrix package in R to examine Annual Scientific Production, Countries Collaboration World Map, Most Relevant Affiliations, Co-occurrence Network, Thematic Map, Most Relevant Words, Trend Topics. The findings reveal a significant increase in publications after 2021, indicating accelerating academic interest in AI–cloud convergence. International collaboration is dominated by countries such as India, China, Saudi Arabia, the United States, and the United Kingdom. Thematic analysis shows that artificial intelligence and cloud computing function as foundational themes, with machine learning acting as a key driving force. Emerging topics such as edge computing and real-time systems suggest a shift toward intelligent, distributed, and data-intensive cloud environments.
Copyright (c) 2026 Mega Fitri Yani, Istifa Shania Putri, Cindy Muhdiantini, Farid Munadhil (Author) https://creativecommons.org/licenses/by/4.0
Analisis Tata Kelola Sistem Informasi Dengan Framework COBIT 5 : Studi Kasus Pada PT. Bintang Mutiara Cemerlang
Perkembangan teknologi informasi saat ini memberikan dampak yang begitu besar bagi sebuah perusahaan untuk mencapai tujuannya. PT. Bintang Mutiara Cemerlang adalah perusahaan yang bergerak di bidang distribusi makanan dan minuman yang dipasok ke setiap kota di Indonesia. Melalui wawancara yang kami lakukan, diketahui beberapa hal yang menjadi sumber permasalahan saat ini yang perlu mendapat perhatian antara lain pengiriman yang lambat, kualitas produk yang tidak sesuai dengan kesepakatan/buruk, tidak memberikan solusi ketika terjadi masalah. terjadi, untuk memberikan harga yang tidak masuk akal. Karena sesuai dengan fungsi teknologi informasi yang memiliki peran penting, maka diperlukan evaluasi tata kelola untuk mengetahui kapabilitas yang dimiliki oleh suatu teknologi informasi. Pada penelitian ini framework yang digunakan adalah framework COBIT 5 dengan tahapan Assessment Process Activities. Domain yang digunakan dalam penelitian ini adalah EDM02 (Ensure Benefits Delivery), BAI06 (Manage Changes), DSS01 (Manage Operations). Agar PT. Bintang Mutiara Cemerlang disarankan untuk melengkapi kekurangan yang dibutuhkan dan mengoptimalkan kekuatan perusahaan agar tidak terjadi kesalahan yang fatal. Hal ini juga bertujuan untuk memaksimalkan dan meningkatkan tata kelola teknologi informasi di dalam perusahaan.
Copyright (c) 2026 Naufal Fathoni Putra, Adnan Buyung Nasution, Maulidya Putri Kinanti Ritonga (Author) https://creativecommons.org/licenses/by/4.0
Prediksi Kelulusan Mahasiswa Pada Program Studi Akuntansi Fakultas Ekonomi Dan Bisnis Universitas Khairun Dengan Algoritma C4.5
Tingkat kelulusan tepat waktu mahasiswa pada Program Studi Akuntansi Fakultas Ekonomi dan Bisnis Universitas Khairun masih relatif rendah. Berdasarkan data lima tahun terakhir, rerata kelulusan tepat waktu tercatat mencapai 17,61%. Kondisi tersebut mendorong perlunya upaya untuk mengidentifikasi dan memprediksi faktor-faktor yang memengaruhi kelulusan mahasiswa sebagai dasar intervensi akademik yang lebih dini. Penelitian ini mengembangkan aplikasi prediksi kelulusan mahasiswa menggunakan metode decision tree dengan algoritma C4.5. Variabel yang digunakan meliputi asal sekolah, penghasilan orang tua, jalur masuk, IPK, indeks prestasi semester 1 hingga semester 6, serta status beasiswa. Dataset penelitian terdiri dari 83 mahasiswa angkatan 2020 sebagai data pelatihan dan 137 mahasiswa angkatan 2021 sebagai data pengujian. Kinerja model dievaluasi menggunakan teknik K-Fold Cross Validation dengan nilai K=5. Hasil pengukuran dan pengujian menunjukkan bahwa model C4.5 menghasilkan akurasi 69,67%, presisi 24,49%, recall 70,59%, dan F1-score 36,36%. Faktor yang paling dominan dalam menentukan kelulusan adalah indeks prestasi semester 6 pada kategori A. Model yang dihasilkan berpotensi dimanfaatkan sebagai alat pendukung evaluasi akademik dan pengambilan keputusan pada institusi pendidikan.
Copyright (c) 2026 Usman Sadik, Santosa, Salim Albaar (Author) https://creativecommons.org/licenses/by/4.0
Analisis Strategis Dampak Transformasi Digital Indonesia: Studi Literatur pada Sektor Publik, Ekonomi, dan Pendidikan
Transformasi digital merupakan instrumen strategis untuk meningkatkan efisiensi operasional dan kualitas pelayanan di berbagai sektor. Namun, implementasinya di Indonesia masih menunjukkan hasil yang beragam dan ketimpangan dampak antar wilayah. Penelitian ini menggunakan metode Systematic Literature Review (SLR) terhadap 20 jurnal ilmiah bertema transformasi digital di Indonesia pada sektor publik, UMKM, dan pendidikan dalam rentang tahun 2020–2025. Data dianalisis secara kualitatif melalui teknik analisis tematik untuk memetakan pola keberhasilan dan hambatan sistemik. Hasil penelitian menunjukkan bahwa transformasi digital di Indonesia masih didominasi oleh perubahan administratif (digitization) dan belum sepenuhnya mencapai transformasi substansial. Keberhasilan transformasi bergantung pada variabel multiplikatif antara kepemimpinan digital dan kesiapan operasional SDM. Adanya kesenjangan digital (digital divide) yang signifikan, di mana wilayah metropolitan (Jabodetabek) mendapatkan dampak ekonomi positif yang nyata, sementara wilayah daerah (Pariaman dan Bima) masih menghadapi hambatan literasi dan infrastruktur. Hambatan utama yang teridentifikasi fragmentasi data (silo data), rendahnya kompetensi digital, dan isu keamanan siber.
Copyright (c) 2026 Dewi Setiowati, Diah Indriani, Inayah Wisartika, Selvi Alvinda Fitriyani (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Adoption and Implementation In Indonesia: Policy Frameworks, Sectoral Applications, and Future Prospects
Indonesia stands at a pivotal moment in its digital transformation journey, with Artificial Intelligence (AI) emerging as a strategic catalyst for economic growth and social development. This paper presents a comprehensive analysis of AI adoption and implementation in Indonesia, examining the national policy framework, sectoral applications, technological infrastructure, and institutional mechanisms established to accelerate AI development. The research reveals that Indonesia's National AI Strategy (Stranas KA) 2020-2045, complemented by institutional structures such as the AI Innovation Center (PIKA) and the Artificial Intelligence Industry Research and Innovation Collaboration (KORIKA), has created a comprehensive ecosystem for AI advancement. Current implementations span critical sectors including healthcare, agriculture, finance, manufacturing, and government services. However, significant challenges persist, particularly in digital infrastructure development, cybersecurity readiness, talent acquisition and retention, and ethical AI governance. Analysis of 43 recent studies from accredited journals indicates that quantitative research methodologies dominate AI investigations in Indonesia, with healthcare and education emerging as primary research foci. This paper concludes that while Indonesia possesses considerable potential to leverage AI for competitive advantage—with projected economic contributions reaching USD 366 billion over the next decade—successful realization requires sustained investment in infrastructure, comprehensive talent development programs, robust ethical frameworks, and enhanced cross-sector collaboration. The findings underscore the necessity of bridging the gap between policy formulation and operational implementation to ensure Indonesia emerges as a regional AI leader in Southeast Asia.
Copyright (c) 2026 Saskiya Farannisa (Author) https://creativecommons.org/licenses/by/4.0
Perancangan Enterprise Architecture Pada UPT Perpustakaan Pondok Pesantren Salafiyah Syafi’iyah Sukorejo Situbondo Berbasis TOGAF ADM
UPT Perpustakaan Pondok Pesantren Salafiyah Syafi’iyah Sukorejo merupakan pusat literasi yang berperan penting dalam mendukung kegiatan pendidikan santri dan masyarakat. Namun, proses pelayanan, integrasi data, dan pengelolaan sistem informasi masih menghadapi beberapa kendala, seperti pencatatan manual yang tersisa, hilangnya kartu santri, keterbatasan waktu layanan, serta persoalan teknis jaringan. Penelitian ini bertujuan merancang Enterprise Architecture menggunakan kerangka kerja TOGAF ADM sebagai panduan pengembangan sistem informasi perpustakaan. Hasil penelitian berupa model arsitektur bisnis, data, aplikasi, dan teknologi yang dapat dijadikan blueprint pengembangan sistem informasi terpadu guna meningkatkan efisiensi layanan dan mendukung proses digitalisasi perpustakaan.
Copyright (c) 2026 Akhlis Munazilin, Arif Ferdiansyah, Bagus Maulana Zulkarnain (Author) https://creativecommons.org/licenses/by/4.0
Rancangan Arsitektur Sistem Analisis Sentimen Kinerja POLRI Berbasis Cloud PaaS dan IndoBERT
Pada era ini, kepercayaan publik terhadap institusi penegak hukum seperti POLRI sangat dipengaruhi oleh opini yang berkembang di media sosial. Namun, analisis terhadap data masif (Big Data) ini menghadapi dua tantangan utama yaitu keterbatasan metode klasik dalam memahami konteks bahasa Indonesia (seperti sarkasme dan bahasa gaul) serta tingginya kebutuhan sumber daya komputasi untuk menjalankan model Deep Learning. Penelitian ini bertujuan untuk merancang sebuah kerangka kerja sistem analisis sentimen terintegrasi yang tidak hanya akurat, tetapi juga efisien secara infrastruktur dan strategis dalam pengambilan keputusan. Metodologi penelitian ini menggabungkan model IndoBERT untuk klasifikasi teks kontekstual, metode Analytic Hierarchy Process (AHP) untuk pembobotan prioritas kinerja, dan arsitektur Cloud Platform as a Service (PaaS) sebagai lingkungan implementasi. Hasil penelitian ini berupa rancangan arsitektur sistem yang memanfaatkan layanan serverless dan GPU berbasis cloud untuk efisiensi biaya dan skalabilitas otomatis. Simulasi sistem menunjukkan bahwa integrasi IndoBERT mampu mendeteksi sentimen negatif terselubung, sementara AHP berhasil mentransformasi data sentimen menjadi daftar prioritas perbaikan yang dapat ditindaklanjuti (actionable insights). Penelitian ini menyimpulkan bahwa adopsi arsitektur berbasis Cloud PaaS adalah solusi paling layak (feasible) untuk mengimplementasikan model NLP mutakhir di lingkungan pemerintahan tanpa investasi perangkat keras yang masif.
Copyright (c) 2026 Novantri Prasetya Putra (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Literature Review on AI Architecture Frameworkfor Product Analysis & Recommendation System in Electronic Service
The rapid growth of electronic services has created significant opportunities for personalized product recommendations through artificial intelligence (AI) systems. However, existing recommendation algorithms face critical challenges, including scalability, cold-start issues, and performance degradation in big data environments. This research performs a systematic review of 73 studies published from 2022 until 2024 to examine AI architecture frameworks applied to product analysis and recommendation systems in electronic service. The review identifies dominant frameworks such as CNN, RNN/LSTM, TensorFlow, Spark, and emerging technologies like GNN, alongside distributed infrastructures such as Hadoop for large-scale data processing. Research methods observed include experiments, benchmarks, simulations, surveys, and case studies. Key findings emphasize performance and efficiency improvements, accuracy, and scalability concerns. Based on these insights, this paper proposes a multi-layered AI architecture framework integrating data ingestion, distributed storage, model development, MLOps orchestration, privacy-preserving learning, and adaptive feedback loops. The proposed framework addresses scalability and sustainability challenges while ensuring high-performance recommendation capabilities. This study contributes a comprehensive blueprint for organizations seeking to deploy robust, scalable, and privacy-aware AI systems in dynamic e-service environments.
Copyright (c) 2026 Irfan Fahmi Ahmadi (Author) https://creativecommons.org/licenses/by/4.0
Performance Study of 13.56 Mhz Full-Bridge Inverter on Wireless Power Transfer System for Electric Vehicle Charging
This research developed a series of GaN MOSFET-based full bridge inverters (TP90H050) with UCC27524 drivers for Wireless Power Transfer (WPT) applications of military vehicles, which are targeted to operate at the ISM frequency of 13.56 MHz. The LTspice simulation showed a potential for near-sinusoidal waves (THD<1%) and a power efficiency of ≈2.13 kW at 50Ω. However, the PCB prototype was only capable of stable operation up to 666.66 kHz with a clean box wave output, and separate tests on fourth-order LC–Butterworth filters achieved a sinusoidal signal with an efficiency of ≈75%. Failure analysis attributed MOSFET damage to switching path length, parasitic effects, and protection limitations. Significant differences were found between the simulation and the implementation at 666.66 kHz, where the hardware RMS voltage was only ≈33% of the simulation. Improvements going forward include the use of precision oscillators/DDSs, drivers with protective features (UVLO and active Miller-clamp), calibrated snubber, and closed controls
Copyright (c) 2025 Meky Taba Orlando MT, Hery Sudaryanto, Iqbal Ahmad Dahlan, Aam Muharam (Author) https://creativecommons.org/licenses/by/4.0
Perancangan Sistem Arsip Digital menggunakan Fitur QR Code di BPJS Ketenagakerjaan Kabupaten Banyuwangi
Archive management at the BPJS Ketenagakerjaan (Employment Social security Agency) ini Banyuwangi Regency still faces challenges ini searching and tracking document. Manually stored archives often lead to delays ini service delivery and the risk of data loss. Therefore, a digital archive system ini needed to facilitate the process of recording, tracking, and reporting archive better. This study aims to create a digital archive system utilizing QR Codes using the SSADM method. The research steps include needs analysis, data model design, prosess flow design, and system interface design. The results show that the system can facilitate in managing archive searches using QR Codes, and improve efficiency and accuracy ini administrative processes.
Copyright (c) 2025 Elsa Ayu Wardani, Ahmad Baijuri, Citra Ayu Pratiwi (Author) https://creativecommons.org/licenses/by/4.0
Perancangan Dashboard Digital Untuk Monitoring Kinerja Organisasi Serta Informasi Untuk Pelanggan Pada PT PLN (Persero) UP3 Banyuwangi
Perkembangan teknologi mendorong PT PLN (Persero) UP3 Banyuwangi untuk berinovasi dalam mengelola informasi. Organisasi ini membutuhkan sistem terintegrasi untuk memantau kinerja dan melayani pelanggan, tetapi belum memiliki dashboard digital yang memadai. Hambatan ini memengaruhi pemantauan internal dan penyampaian informasi eksternal. Penelitian ini bertujuan merancang Dashboard Digital untuk memvisualisasikan data kinerja dan layanan pelanggan secara informatif. Keunikannya adalah menggabungkan pemantauan kinerja internal dengan informasi eksternal pelanggan dalam satu sistem. Metodologi yang digunakan adalah model Waterfall, didukung dengan pengumpulan data melalui observasi, wawancara, dan studi literatur. Perancangan visualnya menggunakan Figma, sementara pemodelan sistemnya menggunakan UML (use case, activity, dan class diagram). Hasil penelitian menunjukkan bahwa dashboard yang dirancang berhasil meningkatkan efisiensi pemantauan, transparansi layanan, dan mendukung pengambilan keputusan manajerial. Sistem ini memberikan kontribusi signifikan terhadap efisiensi organisasi dan kepuasan pelanggan.
Copyright (c) 2025 Nida Nida Lutfiyah, A. Hamdani, Cahyo Prabowo (Author) https://creativecommons.org/licenses/by/4.0
Studi Pengaruh Kesadaran Keamanan Siber Terhadap Kepercayaan Pengguna dalam Menggunakan SIAKAD Esa Unggul dengan Metode Analisis Deskriptif Kuantitatif
Penelitian ini menyelami dinamika interaksi antara kesadaran keamanan siber dengan kepercayaan pengguna terkait sistem informasi akademik SIAKAD di Universitas Esa Unggul. Seiring berkembangnya lanskap digital, ancaman siber pun turut berevolusi, menjadikan kewaspadaan pengguna sebagai hal yang sangat penting. Riset ini mengadopsi pendekatan deskriptif kuantitatif, menyebarkan kuesioner Google Formulir yang dirancang cermat untuk menggali perspektif dari para mahasiswa pengguna aktif SIAKAD. Tujuan utamanya adalah memotret tingkat ‘melek’ keamanan siber mereka saat ini dan memetakannya dengan keyakinan mereka terhadap kapabilitas proteksi sistem. Analisis awal mengindikasikan adanya keterkaitan yang dapat diamati antara pemahaman pengguna akan ancaman siber dan praktik aman berinternet, dengan kesediaan mereka mempercayakan data akademik sensitif kepada SIAKAD. Temuan riset menyoroti area-area spesifik di mana inisiatif peningkatan kesadaran dapat secara signifikan memperkukuh kepercayaan pengguna, sekaligus memperkuat postur keamanan infrastruktur digital universitas secara keseluruhan. Tulisan ini diakhiri dengan usulan intervensi edukatif yang terarah guna meningkatkan kesadaran keamanan siber, demi terciptanya lingkungan digital yang lebih aman dan terpercaya bagi sivitas akademika Esa Unggul.
Copyright (c) 2025 Samuel Mahesa Sinulingga, Jesen Candra, Johta Sansanda Suntana, Gifaren Permana, Farrel Reyhan Putra, Dewi Setiowati (Author) https://creativecommons.org/licenses/by/4.0
Water Quality Analysis and Consumption Feasibility Using Support Vector Machine and CatBoosting with Hyperparameter Tuning
Water quality analysis plays an important role in determining the suitability of water for human consumption. This study aims to build a machine learning model that is able to classify water quality based on several parameters such as pH, hardness, solids content, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The dataset used comes from Kaggle with a total of 3,276 sample data. The two main algorithms applied in this study are Support Vector Machine (SVM) and CatBoost. The research process includes data preprocessing, data balancing using SMOTE, modeling, and model performance evaluation. Hyperparameter tuning is applied to both algorithms to improve performance. The results show that CatBoost has the best performance with an accuracy of 95.8% after hyperparameter tuning, compared to SVM which achieved an accuracy of 77.9%. In addition, CatBoost excels in all evaluation metrics, including precision, recall, and F1-score.
Copyright (c) 2025 Christa Putri Rahayu, Kusnawi S.Kom, M.Eng (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Teknologi Mediapipe Menggunakan Metode CNN Berbasis Website Untuk Pengamanan VVIP Dalam Mobil
Penelitian ini bertujuan mengembangkan sistem deteksi gestur tangan berbasis MediaPipe dan Convolutional Neural Network (CNN) guna meningkatkan efektivitas pengamanan VVIP. Mengingat ancaman modern yang semakin kompleks, sistem ini dirancang untuk mendeteksi gestur darurat secara real-time dan memungkinkan respons cepat. Metodologi yang digunakan meliputi pengumpulan dataset gestur tangan, anotasi data menggunakan MediaPipe, dan pelatihan model CNN di Google Colab. Kinerja model dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Pengujian juga dilakukan dalam berbagai kondisi, seperti pencahayaan rendah dan gerakan cepat, untuk menilai ketangguhan sistem di dunia nyata. Hasilnya, sistem ini berhasil mendeteksi gestur tangan darurat dengan akurasi tinggi dan kecepatan kurang dari satu detik. Kinerja optimal, dengan akurasi mendekati 100%, tercapai pada kondisi pencahayaan yang baik. Meskipun akurasi sedikit menurun pada kondisi ekstrem, integrasi sistem pada platform website memungkinkan pengawasan dan pengambilan keputusan cepat di pusat komando. Penelitian ini membuktikan bahwa kombinasi MediaPipe dan CNN adalah solusi inovatif, namun optimasi lebih lanjut tetap dibutuhkan.
Copyright (c) 2025 M. Ilham AlFatrah, Hery Sudaryanto, H. A. Danang Rimbawa (Author) https://creativecommons.org/licenses/by/4.0
Comparison Of Efficientnet And Yolov8 Algorithms In Motor Vehicle Classification
The YOLOv8 accuracy curve highlights clear overfitting. As shown in the graph, the model reaches 100% training accuracy from the first epoch and remains flat, indicating it memorized the training data. However, validation accuracy lags behind, fluctuating between 90% and 92% without significant improvement. This discrepancy between training and validation performance suggests that YOLOv8 struggles to generalize to unseen data. The issue likely stems from its architecture, which is optimized for object detection tasks that prioritize object localization over feature extraction for classification. When repurposed for classification, YOLOv8 may not extract the nuanced visual patterns needed to differentiate similar classes, such as trucks and buses. Consequently, although YOLOv8 performs well on the training set, its classification accuracy in real-world scenarios is limited. Addressing this may require architectural adjustments, stronger regularization, or more diverse training data to enhance the model’s generalization for pure classification tasks.
Copyright (c) 2025 Ferian Fauzi Abdulloh, Favian Afrheza Fattah, Devi Wulandari, Ali Mustopa (Author) https://creativecommons.org/licenses/by/4.0
Peran Mahasiswa IT dalam Meningkatkan Kesadaran Privasi di Media Sosial pada Era Digital
Di era digital yang semakin berkembang, media sosial telah menjadi bagian yang tidak terpisahkan dari kehidupan sehari-hari, terutama di kalangan generasi muda. Namun, maraknya penggunaan media sosial tidak dibarengi dengan kesadaran yang memadai akan pentingnya perlindungan data dan privasi pengguna. Penelitian ini bertujuan untuk mengkaji peran mahasiswa Teknologi Informasi (TI) dalam meningkatkan kesadaran privasi di media sosial melalui pendidikan digital, kampanye keamanan siber, dan pengembangan teknologi perlindungan data. Metode yang digunakan adalah pendekatan kuantitatif dengan teknik analisis deskriptif, melibatkan pengisian kuesioner oleh mahasiswa aktif di bidang TI dan analisis konten kampanye yang telah dilakukan. Hasil penelitian menunjukkan bahwa mahasiswa TI memiliki kontribusi yang signifikan dalam menyebarkan literasi digital melalui media sosial, seminar, dan proyek pengembangan aplikasi keamanan data. Selain itu, pemahaman teknis yang mereka miliki menjadikan mereka sebagai agen perubahan yang potensial dalam mendorong masyarakat untuk lebih peduli terhadap isu privasi daring. Penelitian ini merekomendasikan sinergi antara mahasiswa, lembaga pendidikan, dan masyarakat luas untuk memperkuat budaya kesadaran privasi dalam menghadapi tantangan era digital yang terus berkembang.
Copyright (c) 2025 Fima Yardhaka Kabira, Bagus Saputro, Dewi Setiowati (Author) https://creativecommons.org/licenses/by/4.0
Strategi Terbaik Transfer Pengetahuan dalam K3: Integrasi Teknologi dan Manajemen Pengetahuan
Penelitian ini mengeksplorasi peran Manajemen Pengetahuan (Knowledge Management/KM) dalam meningkatkan sistem Keselamatan dan Kesehatan Kerja (K3) di dalam organisasi. Dengan menganalisis komponen utama seperti siklus hidup pengetahuan, faktor-faktor pendukung, dan area penerapan, studi ini menyajikan model konseptual yang mengintegrasikan proses KM dengan teknologi dan metodologi untuk mendukung pelatihan, pengambilan keputusan, dan praktik keselamatan. Temuan menunjukkan bahwa perolehan, berbagi, dan pemanfaatan pengetahuan merupakan inti dari sistem pengetahuan K3 yang efektif, serta bahwa elemen pendukung seperti budaya organisasi, kepemimpinan, dan ketersediaan sumber daya sangat penting untuk memastikan aliran pengetahuan yang efisien di seluruh organisasi. Selain itu, adopsi alat digital seperti IoT, AI, ontologi, dan grafik pengetahuan secara signifikan meningkatkan pengumpulan data real-time, strukturisasi pengetahuan, dan analisis risiko keselamatan. Model ini menawarkan kerangka kerja holistik dan sistematis untuk menerapkan KM dalam K3 guna mendorong budaya keselamatan yang proaktif, berbasis data, dan berorientasi pembelajaran.
Copyright (c) 2025 Annisa Humairo, Akbar Habib Buana Wibawa Putra, Laily Indaryani, Muharman Lubis (Author) https://creativecommons.org/licenses/by/4.0
Chili Leaf Disease Classification Using Transfer Learning with VGG16 and MobileNetV2 Combined with Random Search Hyperparameter Tuning
Chili is one of the main food commodities in Indonesia with considerable economic value. Frequent climate changes have made chili plants more vulnerable to pest and disease attacks. Early identification of these diseases is crucial, as delays can lead to crop failure. However, this process presents its own challenges, as it requires specific expertise and considerable time. This study employs the transfer learning method using the VGG16 and MobileNetV2 architectures to build a model capable of classifying diseases in chili plants based on leaf images, along with the use of Random Search hyperparameter tuning to improve model accuracy. The results show that the use of transfer learning for disease classification achieved high accuracy, with MobileNetV2 reaching an accuracy score of 88% without tuning. Meanwhile, the application of Random Search hyperparameter tuning proved effective in improving model accuracy, particularly with the VGG16 architecture, which saw a significant accuracy increase from 51% to 89%. It can be concluded that the transfer learning method is well-suited for identifying diseases in chili plants based on leaf images with high accuracy, and that the application of Random Search hyperparameter tuning successfully enhanced the model’s performance.
Copyright (c) 2025 Aryawijaya, Kusnawi (Author) https://creativecommons.org/licenses/by/4.0
Peran Manajemen Pengetahuan dalam Pengembangan Kompetisi dan Kinerja Pegawai di Perguruan Tinggi : Pendekatan Study Literature Review
Dalam era transformasi digital, perguruan tinggi dituntut untuk meningkatkan kinerja institusional melalui optimalisasi pengelolaan pengetahuan dan sumber daya manusia. Studi ini bertujuan untuk mengevaluasi kontribusi integratif antara manajemen pengetahuan dan praktik manajemen sumber daya manusia terhadap peningkatan kinerja pegawai di lingkungan perguruan tinggi. Melalui pendekatan Systematic Literatur Review (SLR), penelitian ini menganalisis 26 artikel dari database scopus yang diterbitkan dalam rentang waktu 2019 – 2025. Hasil sintesis menunjukkan bahwa penerapan strategi Knowledge Management (KM) seperti knowledge sharing, knowledge contribution dan knowledge transfer, serta praktik Human Resource Management (HRM) seperti training development, performance evaluation dan talent management berkontribusi positif terhadap pengembangan kompetensi pegawai dan pencapaian performansi kinerja. Studi ini juga mengusulkan model konseptual yang mengintegrasikan proses KM dan HRM sebagai fondasi dalam membangun budaya organisasi yang kolaboratif inovatif dan berkelanjutan. Implikasi praktis dari temuan ini memberikan arahan strategis bagi pengelola perguruan tinggi dalam merancang kebijakan pengembangan pegawai berbasis pengetahuan untuk mendukung keunggulan institusi
Copyright (c) 2025 Mercy Kristina Possumah, Yulio Ferdinand, Neca Aqila, Muharman Lubis (Author) https://creativecommons.org/licenses/by/4.0
Organizational Impact of Knowledge Management on Innovation Intellectual Capital Research and Creativity: A Systematic Literature Review
Rapid changes in global competitiveness require organizations to manage knowledge as a strategic resource to improve innovation, intellectual capital, and creativity. This study explores the impact of Knowledge Management (KM) on organizational innovation by analyzing its relationship with intellectual capital and digital governance. The objective of the research is to examine how KM processes influence innovation performance, and how intellectual capital acts as a mediating factor in this relationship. The novelty of this study lies in its synthesis of KM, innovation, and intellectual capital through the lens of digital governance. The research uses a systematic literature review method by analyzing ten peer-reviewed articles published between 2020 and 2025, selected through the PRISMA 2020 protocol from databases such as Scopus, ScienceDirect, and Google Scholar. The findings show that KM enhances innovation when supported by strong human, structural, and relational capital, along with well-structured digital systems. This study contributes to the development of adaptive strategies for innovation-driven organizations based on integrated knowledge practices
Copyright (c) 2025 Farhana Zahra, Dzakiyyah Al Kaazhim, Putri Luthfiah Harmaya, Muharman Lubis (Author) https://creativecommons.org/licenses/by/4.0
Integrating Augmented Reality (AR) in Education in the Era of Society 5.0: A Systematic Literature Review
The advent of the term Society 5.0 brings in a new concept of human-centered technology integration, education included. Augmented Reality (AR) provides an interactive and immersive learning experience. The objective of this research is to systematically examine the trends, advantages, challenges, and relevance of the use of AR in education within the context of Society 5.0. Through the application of the Systematic Literature Review (SLR) method, 46 research articles published on ScienceDirect between 2020 and 2025 were filtered through inclusion and exclusion criteria. The findings indicate that AR adoption is on the rise, specifically in higher education and in medical and engineering disciplines. Most studies cite the function of AR in aiding enhanced learning outcomes, student motivation, and interactive simulations. However, the use of AR must also navigate infrastructural limitations, teacher preparedness, and budget constraints. This study charts the deployment of AR in a human-centered education system and offers direction to subsequent research and development
Copyright (c) 2025 Muhammad Rizq Dzaki Asyam (Author) https://creativecommons.org/licenses/by/4.0