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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 50 Documents
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Water Quality Analysis and Consumption Feasibility Using Support Vector Machine and CatBoosting with Hyperparameter Tuning Rahayu, Christa Putri; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17342085

Abstract

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.
Integrating Augmented Reality (AR) in Education in the Era of Society 5.0: A Systematic Literature Review Asyam, Muhammad Rizq Dzaki
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17386571

Abstract

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
Chili Leaf Disease Classification Using Transfer Learning with VGG16 and MobileNetV2 Combined with Random Search Hyperparameter Tuning Aryawijaya; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17383224

Abstract

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.
Performance Study of 13.56 Mhz Full-Bridge Inverter on Wireless Power Transfer System for Electric Vehicle Charging MT, Meky Taba Orlando; Hery Sudaryanto; Iqbal Ahmad Dahlan; Aam Muharam
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 4 (2025): October
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17448266

Abstract

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
Perencanaan Strategi PT. Paragon Technology and Innovation di Era Society 5.0 Putri Puspitsari, Ika
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.16555419

Abstract

Perkembangan teknologi digital yang dicakup dalam konsep Society 5.0 memiliki dampak besar ke sejumlah sektor industri. Society 5.0 yang menggunakan teknologi canggih berupa Artificial Intelligence , Internet of Things dan Big Data lebih berfokus pada peningkatan efisiensi dan keberlangsungan sistem dalam melakukan kegiatan industri. Penelitian ini menganalisis dampak Society 5.0 terhadap bisnis industri kosmetik dengan kasus PT. Paragon Technology and Innovation yang merupakan perusahaan kosmetik terbesar di kawasan ASEAN. Penelitian ini bertujuan mengetahui strategi apa yang dapat diterapkan PT. Paragon dalam menghadapi perubahan teknologi digital dan mengidentifikasi pengembangan strategi transformasi digital yang efektif mengacu pada prinsip Integrated Technology Management Planning. Data hasil analisis menunjukkan bahwa dengan penerapan teknologi digital seperti Big Data, AI dan IoT, efisiensi kerja dari PT. Paragon meningkat yang kemudian dapat meningkatkan daya saing perusahaan di pasaran global serta menjaga keberlangsungan perusahaan dan patuh terhadap bisnis hijau. PT. Paragon dapat merancang strategi transformasi berdasarkan ITMP secara integratif dan adaptif sesuai era Society 5.0
Rancangan Arsitektur Sistem Analisis Sentimen Kinerja POLRI Berbasis Cloud PaaS dan IndoBERT Novantri Prasetya Putra
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 1 (2026): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18366548

Abstract

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.
Perancangan Enterprise Architecture Pada UPT Perpustakaan Pondok Pesantren Salafiyah Syafi’iyah Sukorejo Situbondo Berbasis TOGAF ADM Akhlis Munazilin; Arif Ferdiansyah; Bagus Maulana Zulkarnain
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 1 (2026): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18334065

Abstract

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.
Analisis Strategis Dampak Transformasi Digital Indonesia: Studi Literatur pada Sektor Publik, Ekonomi, dan Pendidikan Dewi Setiowati; Diah Indriani; Inayah Wisartika; Selvi Alvinda Fitriyani
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 1 (2026): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18395894

Abstract

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. 
A Systematic Literature Review on AI Architecture Frameworkfor Product Analysis & Recommendation System in Electronic Service Ahmadi, Irfan Fahmi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 1 (2026): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18431166

Abstract

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.
ARTIFICIAL INTELLIGENCE ADOPTION AND IMPLEMENTATION IN INDONESIA: POLICY FRAMEWORKS, SECTORAL APPLICATIONS, AND FUTURE PROSPECTS Saskiya Farannisa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 3 No. 1 (2026): January
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18431719

Abstract

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.

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