SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan E ISSN : 3032-3991 | P ISSN : 3090-1626 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.
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
Perancangan Sistem Pemesanan pada Usaha Mikro, Kecil, dan Menengah (UMKM) LA Group Kabupaten Jombang Berbasis Website
UMKM LA Group, usaha mikro di Jombang yang menyediakan jasa sewa makeup, hairdo, dekorasi lamaran, serta baju pengantin dan adat, menghadapi kendala serius dengan proses pemesanan manual. Metode ini, yang mengandalkan media sosial dan aplikasi pesan instan, menyebabkan pencatatan tidak akurat, konfirmasi lambat, dan manajemen data transaksi yang tidak efisien. Untuk mengatasi masalah ini, sebuah sistem pemesanan berbasis web telah dirancang dan diimplementasikan menggunakan PHP dan MySQL. Tujuannya adalah mempermudah pelanggan dalam melakukan pemesanan sekaligus membantu UMKM mengelola data pesanan secara lebih sistematis, terintegrasi, dan efisien. Pengembangan sistem ini mengikuti model Waterfall, meliputi analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Pendekatan ini dipilih karena strukturnya yang sistematis, menjamin pengembangan yang terorganisir dan terdokumentasi dengan baik. Hasil pengujian black box menunjukkan bahwa semua fungsi sistem berjalan sesuai spesifikasi, tanpa ditemukan kesalahan pada proses input, pengolahan, maupun output data. Sistem ini terbukti meningkatkan efisiensi pemrosesan pesanan, mengurangi kesalahan pencatatan, dan mempercepat pelayanan pelanggan. Dengan adanya sistem ini, UMKM LA Group kini dapat mengelola pesanan secara lebih profesional, terstruktur, dan terdokumentasi.
Copyright (c) 2025 Muhammad Arif Zikir Risky Arif (Author) https://creativecommons.org/licenses/by/4.0
Business Model for Effectiveness of Human-AI Collaboration Patterns in Digital Fiction Storytelling: A Systematic Literature Review
The unavoidable pace of Artificial Intelligence (AI) is changing its role from a simple tool to a potential thought partner in various creative fields, including digital storytelling. This research discusses the effectiveness of human-AI collaboration patterns specifically in the aspect of fictional storytelling, for which creativity, narrative structure, and imagination are crucial. By a Systematic Literature Review (SLR), this research synthesizes results from published article research from 2020 to 2025, with the focus on how AI supports and co-creates with human authors throughout the storytelling process. This research also discovered and identified several benefits, which include increased creativity, idea generation, and efficiency, and also discussed challenges such as authority for storytelling, author originality, and ethical considerations in the ownership of a story. This research also discusses implications for future development and proposes a collaboration model that involves two types of human actors, which are writers and editors, and then collaborating with an AI system by a human-centered business design approach. Those results offer a framework business model for implementing practical storytelling systems by results of literature review and suggest that fictional storytelling may be an ideal and potential field for learning about AI's creative potential. The contribution of this research is the aspect of human-centered AI and provides guidelines for designing collaborative storytelling platforms that include both human and AI roles
Copyright (c) 2025 Daffa Shidqi Thamrin (Author) https://creativecommons.org/licenses/by/4.0
AI Web-based Computer Service Management System at PUSCOM
This research aims to develop a web-based computer service management system with artificial intelligence (AI) integration at PUSCOM to address challenges in manual service management, such as customer data recording, service status tracking, and report generation. The problems faced by PUSCOM include potential data errors, loss of physical documents, and delays in performance evaluation due to manual processes. The research method used is the Agile SDLC approach, covering problem identification, data collection through interviews and documentation, functional and non-functional requirements analysis, system modeling using UML, NoSQL Firebase database design, interface design, implementation using Next.js and Javascript, and AI chatbot integration using Vercel AI SDK with the Google Gemini model. The research results demonstrate the successful development of a system capable of automating data recording, facilitating online service registration, managing products, and providing an AI chatbot to assist admins in report generation and real-time damage analysis. This system is proven to enhance operational efficiency, reduce manual errors, and support strategic decision-making at PUSCOM, contributing to improved service quality and customer satisfaction.
Copyright (c) 2025 Muhammad Irvan Shandika, Kusnawi, S.Kom, M.Eng (Author) https://creativecommons.org/licenses/by/4.0
Perencanaan Strategi PT. Paragon Technology and Innovation di Era Society 5.0
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
Copyright (c) 2025 Ika Putri Puspitsari (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Study of Lean Startup and Design Thinking to Accelerate Startup Market Adaptation
This study aims to compare two popular innovation approaches, namely Lean Startup and Design Thinking, in the context of speed of responding to market needs. Lean Startup is known for its focused scientific method that encourages hypothesis testing and product validation through the Build-Measure-Learn cycle, while Design Thinking emphasizes deep understanding of users and the creation of innovative solutions through the process of research, problem definition, ideation, prototyping, and evaluation. Through literature analysis and comparison of the two approaches, this study found that although both have the same goal of creating relevant products, they have different methods and focuses. Lean Startup is more efficient in a dynamic and competitive environment, while Design Thinking produces solutions that are more in line with user needs. This study recommends that companies and innovators consider the specific context of the innovation project being implemented and combine elements of both approaches to achieve optimal results. These findings provide valuable insights into the application of Lean Startup and Design Thinking in product development and innovation.
Copyright (c) 2025 Diki Wahyudi Diki (Author) https://creativecommons.org/licenses/by/4.0
Increasing the Turnover of MSMEs in Healthy Beverages Through Digital Marketing Strategies: A Case Study of Shuguci and WHF MSMEs
This study aims to analyze the effectiveness of applying Digital Marketing strategies in increasing the revenue and brand awareness of the Healthy Beverage MSMEs, Shuguci and WHF. The method used is qualitative with a case study approach, where data were collected through observation, in-depth interviews, and documentation of the MSMEs' digital marketing activities.The results show that after the mentoring program, the MSMEs' revenue increased by 50%, accompanied by improved understanding among owners and employees regarding digital marketing strategies. They are now more adept at using hashtags, determining optimal posting times, segmenting markets, and utilizing trending music in digital content. Additionally, customer interaction significantly improved, as indicated by the growing number of customers reaching out via direct messages during promotions and actively participating in live sessions on social media. The referral program offering discounts to customers who recommended the products also proved effective in increasing the number of new customers. It can be concluded that implementing Digital Marketing strategies can be an effective solution for MSMEs to increase their revenue and brand awarene
Copyright (c) 2025 Dr. Mochamad Teguh Kurniawan, ST., MT, Samia Fadilah (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Digital Governance Framework Implementation to Enhance Digital Transformation
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.
Copyright (c) 2025 Farhana Zahra (Author) https://creativecommons.org/licenses/by/4.0
Random Search Optimization Using Random Forest Algorithm For Liver Disease Prediction
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.
Copyright (c) 2025 RIYAN BAYU SATRIYA, Kusnawi Kusnawi (Author) https://creativecommons.org/licenses/by/4.0
Customer Relationship Management (CRM) Strategy of PT ASDP Indonesia Ferry (Persero): A Customer Satisfaction and Digital Transformation Approach
This study evaluates the CRM strategy of PT ASDP Indonesia Ferry through a mixed-methods approach combining survey data from 2,000 customers and in-depth interviews with company directors. The 2023 Customer Satisfaction Index score of 5.34 reveals improved but uneven performance, with walk-on passengers (5.39) significantly more satisfied than vehicle users (5.31). While digital initiatives like the Ferizy platform and service standardization programs show promise, key challenges emerge in three areas: persistent offline transaction preferences (80% among vehicle users), inconsistent service quality across branches (CSI range 5.13-5.41), and growing competitive pressures. The analysis identifies successful CRM pillars including digital transformation, service excellence programs, and targeted engagement strategies, but highlights the need for more segmented approaches to address distinct customer needs. Strategic recommendations emphasize enhanced digital integration with real-time tracking capabilities, operational improvements in fleet management, and continuous performance monitoring systems. These findings contribute practical insights for transportation service providers operating in archipelagic environments, demonstrating how CRM implementation must balance technological innovation with operational realities to achieve sustainable customer satisfaction improvements.
Copyright (c) 2025 Hasan Abdullah Muhammad, Fitri Adini Firdaus, Ni Ketut Mega Diana Putri (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Random Forest Algorithm Using Random Search for Alzheimer's Disease Detection
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.
Copyright (c) 2025 Hasyim Sri Wahyudi, Ferian Fauzi Abdulloh (Author) https://creativecommons.org/licenses/by/4.0
Trends and Innovations in CRM for Patient Management: A Literature Review
Customer Relationship Management (CRM) in healthcare services has evolved rapidly with advancements in information technology. CRM not only functions as a patient relationship management tool but also as a solution to enhance data-driven care, service personalization, and operational efficiency. Current trends in CRM involve the utilization of Artificial Intelligence (AI) and Machine Learning (ML), integration with wearable devices for real-time health monitoring, the use of Big Data for analyzing population health trends, as well as the adoption of telemedicine and mobile health applications connected to CRM. This study aims to review the latest developments in CRM for patient management and its impact on healthcare systems.
Copyright (c) 2025 Cindy Muhdiantini, Mega Fitri Yani, Ibnu Zulkarnain (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of SVM and Random Forest Algorithms in the Case of the Influence of Music on Mental Health
Mental health disorders are conditions that impress a person's behavior, mindset, and emotions. According to WHO data, the rate of mental disorders in Asia has increased significantly in the past two decades, with about one-fifth of the world's adolescent population experiencing stress each year. Music has long been known to have a positive influence on mental health, and music therapy is used as one approach to assist individuals in improving social, mental, and physical conditions. In this study, the authors used data mining techniques to identify relevant patterns regarding the influence of music on mental health. Two classification algorithms, namely the Support Vector Machine (SVM) and Random Forest, is used to analyze and characterize the data. SVM is known to excel at managing high-dimensional data, while Random Forest is effective at handling data with missing outliers and features. This study purpose to oppose the performance of the two algorithms in classifying the influence of music on mental health to identify the superior algorithm in this context. The Random Forest algorithm gets 93% accuracy and SVM gets 95% accuracy, the hyperparameter tuning on the SVM algorithm has a better performance than Random Forest with an accuracy score of 97% for SVM, while for Random Forest it gets an accuracy score of 94%. The results of the study are expected to provide insight into the use of music as a mental health therapy tool.
Copyright (c) 2025 Karisma Septa Kresna, Kusnawi, S.Kom., M.Eng. (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Stress Classification Among Students Using Random Forest Algorithm
Stress is a condition of physical and psychological discomfort experienced by students due to academic pressure, demands from parents and teachers, and schoolwork. This stress can lead to physical tension, behavioral changes, and mental health problems if not handled properly. Random Forest is a promising approach to analyze and classify student stress. The aim of this study is to classify stress among students to enable the development of targeted interventions to support student well-being and academic success. The dataset used was sourced from Kaggle and included 1100 datasets with 21 columns. The research stages included data preprocessing, exploratory data analysis, modeling, Decision tree classification and evaluation of the confusion matrix model and Deployment as a measure of stress level. Classification results were evaluated by calculating accuracy, precision, recall and f1-score for stress classes (low, medium and high). The results of this study resulted in an accuracy value before tuning of 87.27% and after tuning of 88.64%. This research can provide insights for schools, parents, and government to develop more effective strategies in addressing the problem of stress among students. The use of Random Forest algorithm is proven to be effective in analyzing and classifying stress, so that it can help in decision making and appropriate welfare interventions to tackle before stress reaches critical levels.
Copyright (c) 2025 Raffa Nur Listiawan Dhito Eka Santoso, Kusnawi (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of Support Vector Machine and Gradient Boosting Machine Algorithms for Heart Disease Prediction
Cardiovascular disease ranks among the primary causes of mortality globally, with death rates rising each year. Assessing heart disease risk is crucial for enhancing the efficiency of prevention and treatment strategies. This study seeks to evaluate the effectiveness of two machine learning techniques, namely Support Vector Machine and Gradient Boosting Machine, in forecasting heart disease using a dataset obtained from Kaggle. The research process starts with gathering data, followed by exploratory analysis, preprocessing through label encoding, handling class imbalance with SMOTE, and normalizing data using Standard Scaler. Features were selected using the Correlation Thresholding method. Subsequently, the dataset was divided into training and testing sets to develop predictive models. The model performance was assessed using evaluation metrics, including accuracy, precision, recall, and F1-Score. The findings indicate that the Gradient Boosting Machine outperformed the Support Vector Machine, achieving an accuracy of 98% compared to SVM's accuracy of 93%. This research is expected to contribute to healthcare practices by enabling early detection of heart disease risks. Future research is recommended to explore other algorithms or employ more diverse datasets to achieve better results
Copyright (c) 2025 Tegar Wirawan, Kusnawi (Author) https://creativecommons.org/licenses/by/4.0
Pengembangan Aplikasi Laundry Berbasis Android di Wilayah Kota Pekanbaru
Fokus penelitian ini adalah membuat aplikasi penjemputan dan pengantaran cucian bernama Clean Clothes yang dibuat khusus untuk wilayah Kota Pekanbaru. Aplikasi ini berbasis Android dan menggunakan Google Maps API dan Firebase sebagai Backend as a Service (BaaS). Arsitektur layanan mikro digunakan pada aplikasi ini agar dapat berjalan secara mandiri dan memungkinkan pengembangan fitur baru sesuai kebutuhan bisnis dengan cepat tanpa mengorbankan kinerja fungsionalitas aplikasi yang sudah ada. Fokus penelitian ini adalah untuk membantu mengembangkan aplikasi berbasis arsitektur layanan mikro yang dapat digunakan oleh penduduk Kota Pekanbaru untuk penjemputan dan pengantaran cucian yang direalisasikan pada Firebase. Untuk mengembangkan aplikasi ini, model waterfall dengan pendekatan penelitian kualitatif digunakan. Diharapkan aplikasi ini akan meningkatkan kualitas layanan penjemputan dan pengantaran cucian di seluruh Kota Pekanbaru.
Copyright (c) 2025 Muhammad Dwi Hary Sandy (Author) https://creativecommons.org/licenses/by/4.0
Risk Management in Financial Technology: A Systematic Literature Reviewto Support Sustainability and Security of Digital Financial Services
The rapid growth of the financial technology (FinTech) sector has revolutionized financial services by enhancing convenience, speed, and efficiency. However, this expansion also introduces significant risks, necessitating robust information technology (IT) risk management strategies. This study systematically reviews the existing literature on FinTech risk management, focusing on frameworks, algorithms, policies, and technical aspects that influence risk management practices. By integrating advanced algorithms such as Artificial Intelligence and Deep Forest with established frameworks like ISO 31000:2018 and NIST, the research provides a comprehensive perspective on managing risks in FinTech, bridging gaps not extensively covered in previous studies. A systematic literature review methodology identified and analyzed 17 key studies from an initial pool of 134 documents sourced from databases such as Scopus and Google Scholar. Findings highlight the critical role of advanced technologies and established frameworks in mitigating risks and underscore the need for continuous adaptation to evolving challenges. This research offers valuable insights for financial institutions and policymakers, promoting sustainable and secure digital financial services.
Copyright (c) 2025 Mega Fitri Yani, Cindy Muhdiantini, Syifa Nur Aini (Author) https://creativecommons.org/licenses/by/4.0
Data Mining Clustering and Correlation Analysis of Marine Potential Insights from Capture Fisheries Coral Reef Quantity and Plankton Abundance
Indonesia, sebagai negara kepulauan dengan wilayah laut yang luas, memiliki potensi besar dalam memanfaatkan sumber daya laut, seperti terumbu karang, mangrove, budidaya, dan penangkapan ikan laut. Pemanfaatan yang berkelanjutan tidak hanya berdampak positif pada ekosistem laut tetapi juga meningkatkan kesejahteraan masyarakat. Untuk mendukung pengelolaan berkelanjutan, diperlukan analisis data mendalam guna mengidentifikasi pola dan hubungan yang relevan. Data mining menjadi alat efektif untuk menggali pola yang tersembunyi, terutama melalui teknik clustering. Analisis clustering dilakukan terhadap data perikanan tangkap, kuantitas terumbu karang, dan kelimpahan plankton guna menemukan kelompok homogen dalam dataset. Proses ini diawali dengan pemilihan data sesuai kriteria, dilanjutkan dengan preprocessing untuk menyaring data redundan. Hasilnya, terdapat tiga cluster utama: cluster 0 berfokus pada terumbu karang, cluster 1 pada jumlah ikan tangkap, dan cluster 2 pada kelimpahan plankton.
Copyright (c) 2025 Cindy Muhdiantini, Mega Fitri Yani, Ilham Auliya Rahman, Ati Maryati (Author) https://creativecommons.org/licenses/by/4.0