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Implementation of Brute Force Algorithm for Digital Land Mapping Information System: Implementasi Algoritma Brute Force untuk Sistem Informasi Pemetaan Tanah Digital
Full text linkThe Land Asset Mapping Information System of the Palu City Local Government was developed to streamline digital land record management and enhance public service delivery. However, users experience substantial delays averaging 3-5 minutes per query during manual data searches. This study aims to optimize search efficiency by implementing the Brute force string-matching algorithm, allowing users to retrieve precise land records through direct pattern input. A waterfall system development methodology was systematically applied across five phases: requirements analysis, system design, PHP/JavaScript implementation, White Box testing, and maintenance. The research team collaborated closely with 12 technical officers from the City Spatial Planning and Land Office to validate system requirements and evaluate real-world performance. The implementation of the Brute force algorithm reduced average search times by 68\% (from 185s to 59s) while maintaining 100\% accuracy in test datasets containing 5,000+ land records. Rigorous testing confirmed the algorithm\u27s reliability across various edge cases, including partial matches and special character inputs. The application of the Brute force method has transformed the system\u27s search functionality, particularly for frequent queries involving land parcel IDs and owner names. These improvements have increased daily processing capacity by 40\%, significantly benefiting urban planning and dispute resolution workflows. While demonstrating excellent performance for medium-sized datasets, the solution presents opportunities for future enhancement through hybrid approaches combining Brute force with indexing techniques for large-scale deployments beyond 50,000 records.Sistem Informasi Pemetaan Aset Tanah Pemda Kota Palu dikembangkan untuk merampingkan manajemen data digital tanah dan meningkatkan layanan publik. Namun, pengguna mengalami keterlambatan signifikan rata-rata 3-5 menit per pencarian selama proses manual. Penelitian ini bertujuan mengoptimalkan efisiensi pencarian dengan mengimplementasikan algoritma pencocokan string Brute force, memungkinkan pengguna memperoleh data tanah melalui input pola langsung. Pendekatan pengembangan sistem waterfall diterapkan secara sistematis dalam lima tahap: analisis kebutuhan, desain sistem, implementasi PHP/JavaScript, pengujian White Box, dan pemeliharaan. Tim peneliti berkolaborasi dengan 12 petugas teknis Dinas Tata Ruang dan Pertanahan untuk memvalidasi kebutuhan sistem dan mengevaluasi kinerja. Implementasi algoritma Brute force mengurangi waktu pencarian rata-rata 68\% (dari 185s ke 59s) dengan akurasi 100\% pada dataset uji berisi 5.000+ rekaman tanah. Pengujian ketat mengkonfirmasi keandalan algoritma untuk berbagai kasus kompleks termasuk pencocokan parsial dan input karakter khusus. Penerapan metode Brute force telah mentransformasi fungsionalitas pencarian sistem, khususnya untuk query frekuen seperti ID bidang tanah dan nama pemilik. Peningkatan ini menaikkan kapasitas pemrosesan harian sebesar 40\%, sangat menguntungkan workflows perencanaan kota dan penyelesaian sengketa. Meski berkinerja sangat baik untuk dataset menengah, solusi ini membuka peluang pengembangan hybrid dengan teknik pengindeksan untuk skala besar di atas 50.000 rekaman
The Role of Cognitive and Affective Post-Purchase Dissonance as Mediating Variables between Perceived Impulsiveness and Repurchase Intention
No full textThe study investigates whether cognitive and affective post-purchase dissonance mediate the relationship between perceived impulsiveness and repurchase intention. Using purposive sampling, data were collected from 220 respondents, predominantly women with bachelor’s degrees. The study applied Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. The findings indicate that cognitive and affective post-purchase dissonance do not function as mediators in the relationship between perceived impulsiveness and repurchase intention. Practical implications of the study suggest that companies, especially e-commerce platforms, should focus on minimizing post-purchase dissonance to enhance customer satisfaction and retention. Strategies such as streamlined product return policies and responsive customer service can play a vital role in achieving this. These measures can help address consumer doubts and negative emotions following impulsive purchases, fostering greater trust and loyalty. This research contributes to the understanding of consumer behavior in online retail but highlights the need for further exploration using mixed methods to better capture the emotional nuances of post-purchase dissonance. Additionally, expanding the scope to include diverse products and demographics could enrich future findings
Assessing the Influence of Green Supply Chain Initiatives on Corporate Performance Using SmartPLS
No full textThis study investigates the influence of Green Supply Chain Initiatives (GSCI) on Corporate Performance (CP), emphasizing the mediating role of Operational Efficiency (OE). Using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS, the research analyzes survey data collected from SESINDO organizations. GSCI, encompassing practices such as green procurement, eco-design, and waste management, are examined for their impact on financial, operational, and environmental performance. The findings reveal a significant positive relationship between GSCI and CP, highlighting that sustainable practices enhance financial outcomes, operational efficiencies, and environmental impact reduction. Furthermore, OE is identified as a partial mediator, amplifying the benefits of GSCI on CP. This underscores the importance of optimizing resource utilization and streamlining processes to maximize the impact of sustainability initiatives. The study contributes to theory by integrating the Resource-Based View (RBV) and Institutional Theory, offering a comprehensive understanding of GSCI adoption and outcomes. Practically, it provides actionable insights for business leaders and policymakers to promote sustainability while achieving competitive advantage. Despite its contributions, the study acknowledges limitations, such as its cross-sectional design and regional focus, and suggests future research in diverse contexts. This research underscores the transformative potential of GSCI in driving sustainable corporate success
13th Floor Family Hub Strategy to Boost Weekend Occupancy and Attendance
No full textThe rising demand for family-friendly entertainment highlights a gap in the market for innovative, interactive recreational spaces. Many current hubs focus on traditional arcade games, overlooking immersive and diverse experiences for all age groups. Transforming the 13th floor into a family entertainment hub aims to address this gap. The main objective of the project is to repurpose the space into an interactive hub that boosts weekend occupancy, attracts local families and staycation guests, and creates a signature brand experience through unique, engaging features. This project employs market research, customer insights, and financial analysis to design the layout and entertainment zones. The strat- egy includes branding, interactive design, and diverse activity areas tailored to both children and adults. Development stages cover concept design to full-scale renovation, blending modern technology with traditional games to appeal to a wide audience. The expected outcomes include a 25% increase in weekend occupancy and a 30% rise in staycation bookings. The unique ”TIME-VERSE” branding is projected to generate 50% of brand awareness, while the variety of activities ensures appeal to families with different interests. Financial forecasts predict profitability with a break-even point within six months of launch. Ulti- mately, this transformation will fill a key gap in the local market by offering an engaging, memorable experience for families. With its strong financial potential and distinctive branding, the 13th-floor hub is positioned to become a premier destination for both local residents and tourists, enhancing the area’s reputation as a weekend getaway spot
Analisis Pengaruh Curve Number terhadap Debit BanjirMenggunakan Metode Pemodelan Hidrologi di DAS Juana: Analysis Curve Number Influence on Flood Discharge using Hydrological Modeling in Juana Watershed
Full text linkThis research aims to investigate the effect of Curve Number (CN) on flood discharge in the Juana watershed. Using a case study approach, analysis was carried out to understand the relationship between CN values and hydrological characteristics and flood risk in the region. The research method involves hydrological modeling which takes into account the CN value as a key parameter. Spatial and temporal data on land cover, soil type and vegetation are used to determine CN values in various land change scenarios. The study results indicate a trend of increasing Curve Number (CN) values in 5 Subwatersheds, a decrease in 2 Subwatersheds, and no change in 1 Subwatershed. The highest increase in CN value was observed in the Juana 2 Subwatershed, with an increase of (+) 0.935, while the largest decrease was found in the Gembong Subwatershed, with a decrease of (-) 0.349. Both the increase and decrease in CN values are directly proportional to the resulting flood discharge. These findings have important implications for disaster risk management, regional planning, and sustainable policy development in the Juana River Basin and similar regions worldwide. By understanding more deeply the influence of CN on flood discharge, more effective mitigation measures can be designed to protect communities and ecosystems that are vulnerable to flood threats.Penelitian ini bertujuan untuk menyelidiki pengaruh Curve Number (CN) terhadap debit banjir di DAS Juana. Dengan menggunakan pendekatan studi kasus, analisis dilakukan untuk memahami hubungan antara nilai CN dengan karakteristik hidrologis dan risiko banjir di wilayah tersebut. Metode penelitian melibatkan pemodelan hidrologi yang memperhitungkan nilai CN sebagai parameter kunci. Data spasial dan temporal tentang tutupan lahan, jenis tanah, dan vegetasi digunakan untuk menentukan nilai CN dalam berbagai skenario perubahan lahan. Hasil kajian yang diperoleh adalah adanya tren peningkatan nilai Curve Number pada 5 Sub DAS, penurunan nilai Curve Number pada 2 Sub DAS dan nilai Curve Number yang tetap pada 1 Sub DAS. Peningkatan nilai Curve Number terbesar terdapat pada Sub DAS Juana 2 sebesar (+) 0.935, sedangkan penurunan terbesar terdapat pada Sub DAS Gembong sebesar (-) 0.349. Peningkatan maupun penurunan nilai CN berbanding lurus dengan nilai Debit Banjir yang dihasilkan. Temuan ini memiliki implikasi penting untuk manajemen risiko bencana, perencanaan wilayah, dan pengembangan kebijakan yang berkelanjutan di DAS Juana dan wilayah serupa di seluruh dunia. Dengan memahami lebih dalam pengaruh CN terhadap debit banjir, langkah-langkah mitigasi yang lebih efektif dapat dirancang untuk melindungi masyarakat dan ekosistem yang rentan terhadap ancaman banjir
AI Adoption Barriers in SMEs Analyzing Through the Technology Organization Environment TOE Framework
No full textArtificial Intelligence (AI) has become a key driver of innovation, improving efficiency, decision-making, and competitiveness in businesses worldwide. However, the adoption of AI tools among Small and Medium Enterprises (SMEs), especially in developing countries like Indonesia, remains limited. This study aims to explore the barriers hindering AI adoption in SMEs in Indonesia using the Technology Organization Environment (TOE) framework. A survey was conducted among Indonesian SMEs across various sectors to capture their perceptions regarding AI implementation. The survey focused on technological, organizational, and environmental factors that influence AI adoption. The findings reveal that technological barriers, such as high implementation costs and system complexity, are significant challenges for SMEs. Organizational barriers, including limited digital literacy, a lack of skilled workforce, and resistance to change, also hinder AI adoption. Furthermore, environmental barriers like insufficient government support, regulatory uncertainty, and low market pressure constrain SMEs\u27 adoption readiness. This study extends the TOE framework to the context of AI adoption in SMEs in developing economies. Addressing the identified barriers is essential for accelerating digital transformation and enabling SMEs to leverage AI for sustainable growth in the digital economy
Organizational Readiness and Barriers to Digital Transformation in Indonesian SMEs
No full textDigital transformation (DT) is essential for enhancing competitiveness, ef- ficiency, and innovation in the digital era. However, Small and Medium En- terprises (SMEs) in Indonesia face significant barriers to adopting digital tech- nologies due to limited resources, lack of digital skills, and environmental con- straints. This study aims to assess the organizational readiness and identifybarriers to DT adoption among Indonesian SMEs using the Technology Organization Environment (TOE) framework. A survey of 210 SMEs from various sectors was conducted to evaluate their readiness levels and perceived barriers. The results indicate that while SMEs show moderate technological readiness, organizational readiness remains low, primarily due to inadequate human re- source competencies, resistance to change, and limited financial capacity. Addi- tionally, environmental factors such as regulatory uncertainty and weak institu- tional support further impede progress. This study contributes to the existing literature by highlighting the specific challenges faced by SMEs in emerging economies, particularly Indonesia. Based on the findings, the study offers prac- tical recommendations for policymakers, industry associations, and SME own- ers to enhance organizational readiness and mitigate barriers, accelerating the digital transformation process for SMEs in Indonesia
Integration of Business Intelligence and Predictive Analytics for Student Success Based on Blockchain: Integrasi Business Intelligence dan Analitik Prediktif untuk Keberhasilan Mahasiswa Berbasis Blockchain
Full text linkIn the digital era, education is undergoing a significant transformation, with predictive analytics becoming an important approach to increasing student success. Rapid advancements in technology have enabled institutions to collect and analyze diverse datasets, yet challenges remain in ensuring data accuracy, transparency, and reliability. This research explores the integration of blockchain technology to address data integrity challenges, with a focus on its application in predictive analytics. The objective is to enhance the reliability of student-related data while improving the effectiveness of academic performance predictions. Specifically, this research examines the relationship between Academic Performance Metrics (APM), Student Engagement Data (SED), Socioeconomic Factors (SEF), Blockchain-Enabled Data Integrity (BDI), and Predictive Algorithm Efficiency (PAE). Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, data were collected through structured surveys and institutional records involving higher education students. The constructs were validated through measurement model testing before proceeding to structural path analysis. The results show the significant influence of socio-economic factors and blockchain-based data integrity on academic outcomes, while student engagement and predictive algorithm efficiency also demonstrate moderate effects. The study also identifies areas that require improvement in predictive models, particularly regarding the alignment of input variables with algorithm design. These findings emphasize the importance of leveraging technology to develop more equitable and effective educational strategies, while underscoring the need for continued improvements in construct design to increase the reliability and validity of models. This research contributes to the growing field of educational data science by offering a blockchain-enhanced framework for predictive analytics in education.Di era digital, dunia pendidikan mengalami transformasi signifikan, di mana analitik prediktif menjadi pendekatan penting untuk meningkatkan keberhasilan mahasiswa. Kemajuan teknologi yang pesat memungkinkan institusi untuk mengumpulkan dan menganalisis berbagai jenis data, namun tantangan dalam menjamin akurasi, transparansi, dan keandalan data masih tetap ada. Penelitian ini mengeksplorasi integrasi teknologi blockchain untuk mengatasi tantangan integritas data, dengan fokus pada penerapannya dalam analitik prediktif. Tujuannya adalah meningkatkan keandalan data terkait mahasiswa serta efektivitas prediksi terhadap kinerja akademik. Secara khusus, penelitian ini mengkaji hubungan antara Academic Performance Metrics (APM), Student Engagement Data (SED), Socioeconomic Factors (SEF), Blockchain-Enabled Data Integrity (BDI), dan Predictive Algorithm Efficiency (PAE). Dengan menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM), data dikumpulkan melalui survei terstruktur dan catatan institusi yang melibatkan mahasiswa perguruan tinggi. Validasi konstruk dilakukan melalui pengujian model pengukuran sebelum analisis jalur struktural. Hasil menunjukkan pengaruh signifikan dari faktor sosial ekonomi dan integritas data berbasis blockchain terhadap hasil akademik, sementara keterlibatan mahasiswa dan efisiensi algoritma prediktif juga menunjukkan pengaruh sedang. Studi ini juga mengidentifikasi area yang perlu ditingkatkan dalam model prediktif, khususnya dalam penyelarasan variabel input dengan desain algoritma. Temuan ini menegaskan pentingnya pemanfaatan teknologi untuk mengembangkan strategi pendidikan yang lebih adil dan efektif, serta perlunya peningkatan desain konstruk untuk meningkatkan keandalan dan validitas model. Penelitian ini berkontribusi dalam pengembangan ilmu data pendidikan melalui kerangka kerja analitik prediktif berbasis blockchain
Digital Transformation in Library Recommendation System Using k-NN Collaborative Filtering: Transformasi Digital dalam Sistem Rekomendasi Buku Perpustakaan Menggunakan k-NN
Full text linkLibraries, as essential information centers, play a crucial role in providing diverse resources to meet the information needs of visitors. In the digital age, libraries face challenges in efficiently managing their vast collections while offering personalized services that cater to the varying needs of users. The primary goal of this research is to improve the management of library resources by developing a personalized book recommendation system. This system aims to provide relevant book suggestions based on individual preferences, specifically tailored to the academic needs and interests of university students. To achieve this, the research applies a combination of User-Based Collaborative Filtering (UBCF) and k-Nearest Neighbors (k-NN) algorithms, which are powerful techniques in the field of data mining. These methods are used to analyze the academic performance (measured by the students\u27 Indeks Prestasi Semester (IPS) scores) and book preferences to create a personalized recommendation system. The study demonstrates that the integration of UBCF and k-NN significantly enhances the accuracy and relevance of book recommendations, providing students with more tailored suggestions based on their academic achievements and preferences. The results indicate that such a recommendation system not only improves the user experience but also contributes to the enhancement of students\u27 academic performance by offering them books that align with their learning needs, ultimately supporting the academic goals of higher education institutions.Perpustakaan, sebagai pusat informasi, memegang peranan penting dalam menyediakan berbagai sumber daya untuk memenuhi kebutuhan informasi pengunjung. Di era digital ini, perpustakaan menghadapi tantangan dalam mengelola koleksi yang sangat besar dan menawarkan layanan personalisasi yang sesuai dengan kebutuhan penggunanya. Tujuan utama dari penelitian ini adalah untuk meningkatkan pengelolaan sumber daya perpustakaan dengan mengembangkan sistem rekomendasi buku yang dipersonalisasi. Sistem ini bertujuan untuk memberikan saran buku yang relevan berdasarkan preferensi individu, khususnya yang disesuaikan dengan kebutuhan akademik dan minat mahasiswa. Untuk mencapai hal ini, penelitian ini menggunakan kombinasi metode User-Based Collaborative Filtering (UBCF) dan k-Nearest Neighbors (k-NN), yang merupakan teknik yang kuat dalam bidang penambangan data. Metode-metode ini digunakan untuk menganalisis kinerja akademik (diukur dengan Indeks Prestasi Semester (IPS) mahasiswa) dan preferensi buku untuk menciptakan sistem rekomendasi yang dipersonalisasi. Penelitian ini menunjukkan bahwa integrasi UBCF dan k-NN secara signifikan meningkatkan akurasi dan relevansi rekomendasi buku, memberikan saran yang lebih sesuai kepada mahasiswa berdasarkan pencapaian akademik dan preferensi mereka. Hasil penelitian menunjukkan bahwa sistem rekomendasi ini tidak hanya meningkatkan pengalaman pengguna, tetapi juga berkontribusi pada peningkatan kinerja akademik mahasiswa dengan menawarkan buku yang sesuai dengan kebutuhan belajar mereka, yang pada akhirnya mendukung tujuan akademik institusi pendidikan tinggi
Sustainable Practices in Learning Factories: Technology for SDG4: Penerapan Teknologi Berkelanjutan dalam Learning Factories untuk Mendukung SDG 4
Full text linkLearning factories serve as innovative platforms to promote sustainable education by integrating advanced technologies, contributing directly to achieving Sustainable Development Goal 4 (SDG4), which focuses on ensuring inclusive and equitable quality education and lifelong learning opportunities for all. This study investigates the application of sustainable practices in learning factories, aiming to identify technological innovations that enhance education while promoting resource efficiency and accessibility. The objective of this research is to explore how these technologies can address educational challenges and contribute to sustainable development. Employing a mixed-methods approach, this research combines in-depth qualitative case studies of learning factories with quantitative data from surveys and interviews conducted with educators, students, and industry stakeholders. The findingsreveal that implementing technologies such as automation, virtual and augmented reality, and digital twins improves learning outcomes by providing immersive, hands-on experiences while minimizing environmental impacts. Additionally, the results highlight the importance of fostering partnerships between academic institutions and industries to create a collaborative ecosystem for sustainable innovation. The conclusion emphasizes the transformative role of learning factories in advancing sustainable education through technology, suggesting that broader adoption of these practices can accelerate progress toward SDG4. This study offers practical recommendations for educators, policymakers, and industry leaders to integrate technology-driven sustainability in educational environments, thus supporting global efforts toward equitable and quality education.Pabrik pembelajaran berfungsi sebagai platform inovatif untuk mempromosikan pendidikan berkelanjutan dengan mengintegrasikan teknologi canggih, yang berkontribusi langsung dalam mencapai Tujuan Pembangunan Berkelanjutan 4 (SDG4), yang berfokus pada memastikan pendidikan yang inklusif dan berkualitas serta peluang pembelajaran sepanjang hayat untuk semua. Penelitian ini menyelidiki penerapan praktik berkelanjutan di pabrik pembelajaran, dengan tujuan untuk mengidentifikasi inovasi teknologi yang dapat meningkatkan pendidikan sekaligus mempromosikan efisiensi sumber daya dan aksesibilitas. Tujuan dari penelitian ini adalah untuk mengeksplorasi bagaimana teknologi-teknologi ini dapatmengatasi tantangan pendidikan dan berkontribusi pada pembangunan berkelanjutan. Dengan menggunakan pendekatan metode campuran, penelitian ini menggabungkan studi kasus kualitatif mendalam tentang pabrik pembelajaran dengan data kuantitatif dari survei dan wawancara yang dilakukan dengan pendidik, siswa, dan pemangku kepentingan industri. Hasil penelitian menunjukkan bahwa penerapan teknologi seperti otomatisasi, realitas virtual dan tertambah, serta kembaran digital dapat meningkatkan hasil pembelajaran dengan menyediakan pengalaman langsung yang imersif, sekaligus meminimalkan dampak lingkungan. Selain itu, hasil penelitian menyoroti pentingnya membina kemitraan antara lembaga akademik dan industri untuk menciptakan ekosistem kolaboratif bagi inovasi berkelanjutan. Kesimpulannya, pabrik pembelajaran memiliki peran transformasional dalam memajukan pendidikan berkelanjutan melalui teknologi, yang menunjukkan bahwa adopsi yang lebih luas dari praktik-praktik ini dapat mempercepat kemajuan menuju SDG4. Penelitian inimenawarkan rekomendasi praktis bagi pendidik, pembuat kebijakan, dan pemimpin industri untuk mengintegrasikan keberlanjutan berbasis teknologi dalam lingkungan pendidikan, dengan demikian mendukung upaya global menuju pendidikan yang adil dan berkualitas