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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Jurnal CoreIT Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology Jurnal Abdi Masyarakat Indonesia
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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AN EVALUATION OF THE SUCCESSFUL IMPLEMENTATION OF THE INFORMATION SYSTEM PLATFORM MERDEKA MENGAJAR USING HUMAN ORGANIZATION TECHNOLOGY FIT MODEL APPROACH Abidin, Uun; Hariguna, Taqwa; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4282

Abstract

The implementation of technology in education has great potential to improve the quality of learning that supports the implementation of the Merdeka curriculum. The Merdeka Mengajar platform (MMP) is designed to help educators by providing various features including self-development, inspiration and teaching. Uneven ICT infrastructure and teachers' personal abilities are problems in the implementation of the MMP, so it is necessary to analyze the success of the implementation of the MMP. The purpose of this study is to analyze the success of the implementation of the information system for the Merdeka Mengajar Platform by adopting the Hot Fit Model by expanding the Technology component with the ICT Infrastructure variable, expanding the Human component with the personal competence variable, expanding the organizational component with the organizational culture variable and the training & learning variable which can affect the successful implementation of the MMP. The data obtained were 328 respondents who were analyzed using SmartPLS 3.2.9. The analysis results obtained the proposed conceptual model has an accuracy of 58.6%. Net benefits are influenced by system use, user satisfaction, personal competence, structure, environment, organizational culture, and training & learning. Service quality, system quality, information quality, and ICT infrastructure have a positive impact on system use and user satisfaction.
Enhancing Digital Marketing Strategies with Machine Learning for Analyzing Key Drivers of Online Advertising Performance Berlilana, Berlilana; Hariguna, Taqwa; El Emary, Ibrahiem M. M.
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.658

Abstract

The rapid growth of digital advertising has underscored the need for data-driven strategies to optimize campaign performance. This study applies machine learning techniques to analyze online advertising data, aiming to identify key performance drivers and provide actionable insights for optimizing marketing strategies. The dataset includes metrics such as clicks, displays, costs, and revenue, which were preprocessed, analyzed, and modeled using ensemble methods, including Random Forest and Gradient Boosting. These ensemble methods were chosen for their ability to handle high-dimensional data, mitigate overfitting, and capture complex, nonlinear relationships between variables. Random Forest, with its bagging approach, enhances generalization by reducing variance, while Gradient Boosting incrementally corrects errors by focusing on hard-to-predict instances, improving overall predictive performance. Descriptive analysis revealed significant variability in campaign outcomes, with cost and user engagement emerging as primary predictors of revenue. Machine learning models demonstrated strong predictive accuracy, with Random Forest achieving 92% accuracy and an F1-score of 89%. Visualizations such as feature importance charts, correlation heatmaps, and learning curves validated the robustness of the models and highlighted key insights, including inefficiencies in cost allocation and the limited impact of certain categorical features like placement. The study emphasizes the potential of machine learning to optimize digital marketing strategies by identifying critical factors that influence campaign success. The findings provide a scalable framework for resource allocation, audience targeting, and strategic decision-making in online advertising. Future research could further enhance predictions by incorporating additional features, such as audience demographics and temporal trends, to provide deeper insights into campaign dynamics.
Optimization of Recommender Systems for Image-Based Website Themes Using Transfer Learning Wahid, Arif Mu'amar; Hariguna, Taqwa; Karyono, Giat
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.671

Abstract

Recommender systems play a crucial role in personalizing user experiences in e-commerce, digital media, and web design. However, traditional methods such as Collaborative Filtering and Content-Based Filtering struggle to account for visual preferences, limiting their effectiveness in domains were aesthetics influence decision-making, such as website theme recommendations. These systems face challenges such as data sparsity, cold-start problems, and an inability to capture intricate visual features. To address these limitations, this study integrates Convolutional Neural Networks (CNNs) with advanced recommendation models, including Inception V3, DeepStyle, and Visual Neural Personalized Ranking (VNPR), to enhance the accuracy and personalization of visually-aware recommender systems. A quantitative research approach was employed, using controlled experiments to evaluate different combinations of feature extractors and recommendation models. Data was sourced from ThemeForest, a widely used platform for website themes, and underwent preprocessing to ensure consistency. The models were evaluated using precision, recall, F1 score, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG) to measure recommendation quality. The results indicate that Inception V3 + VNPR outperforms other model combinations, achieving the highest accuracy in personalized theme recommendations. The integration of transfer learning further improved feature extraction and performance, even with limited training data. These findings underscore the importance of combining deep learning-based feature extraction with recommendation models to improve visually-driven recommendations. This study provides a comparative analysis of CNN-based recommender systems and contributes insights for optimizing recommendations in visually complex domains. Despite improvements, challenges such as dataset diversity remain a limitation, affecting generalizability. Future research could explore alternative CNN architectures, such as ResNet and DenseNet, and incorporate user feedback mechanisms to further enhance recommendation accuracy and adaptability.
The Success Evaluation of Platform Merdeka Mengajar (PMM) Implementation in Purbalingga Regency Using HOT-Fit Model Abidin, Uun; Hariguna, Taqwa; Barkah, Azhari Shouni
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.33441

Abstract

The industrial 4.0 revolution increasingly develops. It also affects the education field. This issue can be used to improve the quality of education by improving teachers quality. The Merdeka Mengajar platform (PMM) is used to improve the quality of teachers in implementing the Merdeka curriculum (Independent Platform). When it is implemented, teachers have difficulty to adapt the independent teaching platform and not all teachers understand technology. The purpose of this research is to analyze the factors that influence the successful implementation of the Merdeka Mengajar Platform (PMM) using the Hot fit method which assesses system success from the aspects of human, organization and technology. The research sample was 220 vocational high school teachers in Purbalingga Regency. The results of this research can be concluded that all variables contained in the Hot Fit model. They are Service Quality, System Quality and Information Quality, System Use and User Satisfaction. Structure and Environment have a positive and significant effect on the successful implementation of the Merdeka Mengajar Platform (PMM) used by vocational teachers in Purbalingga Regency. The Research Model in this study has a level of feasibility and accuracy of 71.6%, while the rest is influenced by other variables which is not included in this study. By this research, we can find out the factors that influence the successful implementation of the Merdeka Mengajar Platform (PMM) in Purbalingga Regency.
Assessing the Acceptance and Trust in Student Information Systems Through a Modified TAM Perspective Hidayat, Muhammad Taufik Nur; Hariguna, Taqwa; Saputra, Dhanar Intan Surya
Applied Information System and Management (AISM) Vol 8, No 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.42324

Abstract

The rapid development of information technology has motivated universities to implement technology-based student information systems to enhance the efficiency and effectiveness of student data management. This research seeks to evaluate acceptance and trust in student information systems at universities using a modified version of the Technology Acceptance Model, incorporating perceived trust as an additional variable. The study involved a sample of 200 active university students, with data analyzed using the structural equation modeling approach. Findings from the analysis show that both perceived usefulness and perceived ease of use significantly impact students’ intention to adopt the system, which in turn influences actual system usage. Additionally, perceived trust emerged as a critical factor in reinforcing both the intention to use and the subsequent actual use of the student information system. The results indicate that the intention to use the system acts as an essential mediator in the relationships between students’ perceptions of usefulness, ease of use, trust, and their actual usage behavior. These results have significant implications for universities aiming to improve the adoption of student information systems. Enhancing user experience, building system trust, and ensuring robust security should be prioritized in the development and refinement of such systems. By focusing on these aspects, institutions can foster higher acceptance and sustained usage, leading to more effective student data management and a better overall educational experience.
Analisis Validitas dan Reliabilitas Kuesioner dengan Metode Partial Least Squares Structural Equation Modeling pada Aplikasi SMARTPLS Yarsasi, Sri; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.885

Abstract

Validitas dan reliabilitas instrumen merupakan aspek krusial dalam penelitian kuantitatif, karena kualitas pengukuran yang rendah dapat menghasilkan data yang tidak akurat dan mengarah pada kesimpulan yang menyesatkan. Penelitian ini bertujuan untuk mengevaluasi kualitas pengukuran instrumen kuesioner menggunakan pendekatan Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan bantuan perangkat lunak SmartPLS. Metode ini dipilih karena mampu menangani model yang kompleks dan data yang tidak memenuhi asumsi normalitas. Hasil analisis menunjukkan bahwa sebagian besar indikator memiliki nilai outer loading di atas 0,70, nilai Composite Reliability (CR) berada dalam rentang 0,817 hingga 0,914, dan nilai Average Variance Extracted (AVE) melebihi 0,50, yang menunjukkan bahwa instrumen memiliki konsistensi internal dan validitas konvergen yang baik. Namun demikian, terdapat dua indikator dengan nilai outer loading di bawah ambang batas, yaitu X3.2 sebesar 0,612 dan Y2.4 sebesar 0,588, yang perlu dievaluasi ulang. Temuan ini menegaskan bahwa pendekatan PLS-SEM efektif untuk memvalidasi instrumen, terutama dalam penelitian dengan sampel terbatas dan desain eksploratori. Studi ini memberikan kontribusi metodologis terhadap pengembangan instrumen penelitian yang lebih akurat dan adaptif, serta menunjukkan urgensi penggunaan pendekatan statistik modern dalam evaluasi instrumen di berbagai bidang keilmuan.
Deep Reinforcement Learning-Based Control Architectures for Autonomous Maritime Renewable Energy Platforms Sabah, Sura; Hussain, Refat Taleb; Mohammed, Ismail Abdulaziz; Jawad, Haider Mahmood; Abbas, Intesar; Hariguna, Taqwa
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1209

Abstract

Autonomous vessels driven by renewable energy are increasingly envisioned as vital for sustainable ocean?operations such as environmental monitoring, offshore power generation, and long-haul unmanned surface vehicles. Implementing fine-scale control of these systems has proven challenging however,?due to time-varying sea-state dynamics, sporadic energy inputs, the possibility of failure at the component level, and the requirement for coordination between multiple agents. In the article, an end-to-end deep reinforcement learning-based hierarchical control solution with real-time navigation and?its synthesis for energy optimization is proposed. It combines high-level energy regulation with low-level actuator scheduling so as to react to the variations of?the environment and internal perturbations. Simulations using actual wave realizations, sensor failures, actuator outages, and network communication variation were used?to demonstrate the performance of the control system in the following 5 performance aspects: energy saving, navigation accuracy, communication reliability, fault tolerant and multi-agent coordination. Results indicate that the architecture sustained over 80% of the performance and achieved energy efficiencies up to 54.5% in the?best case under failure scenarios. Performance-measures demonstrated reasonable scalability?up to 5–7 agents without significant communication overhead. The findings support the applicability of deep reinforcement learning for real-time maritime control under uncertainty, offering a viable alternative to conventional rule-based or predictive control strategies. The framework’s modular design allows for future integration with federated learning, hybrid control models, or autonomous deployment. The article contributes to the growing field of intelligent marine systems by providing a robust and adaptable control strategy for sustainable and scalable operations in autonomous maritime environments.
Comparison of Accuracy and Computation Time for Predicting Earthquake Magnitude in Java Island Yuniarto, Abdul Hakim Prima; Hariguna, Taqwa; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5044

Abstract

Java Island has numerous active faults, making earthquake magnitude prediction a crucial component of disaster mitigation efforts. This study conducted a rigorous comparative analysis of four machine learning algorithms—Random Forest, Neural Network, Linear Regression, and Support Vector Machine—to determine their effectiveness in this specific task. The methodology employed involved systematic hyperparameter optimization for each model to ensure a fair and robust evaluation, with performance measured by Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and training time. The results showed that all three nonlinear models significantly outperformed Linear Regression. Random Forest achieved the highest accuracy (RMSE 0.5445), but Support Vector Machine and Neural Network demonstrated very competitive and nearly equal performance. The study concluded that while Random Forest has a slight advantage, several state-of-the-art models are highly capable of addressing this problem after appropriate optimization. This underscores the critical role of methodical tuning and implies that model selection in practical applications depends on a trade-off between modest improvements in accuracy and computational efficiency.
Enhancing Accessibility in Local Government Data Portals via Retrieval- Augmented Generation: A Case Study on Satu Data Indonesia in Banyumas Regency Hadie, Agus Nur; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5153

Abstract

Public access to local government data in Indonesia, such as that in the Satu Data Indonesia portal for Banyumas Regency, is severely hampered by outdated search interfaces and the technical complexity of handling heterogeneous data formats like PDF, Excel, and CSV. This research directly addresses this accessibility gap by designing, developing, and evaluating an intelligent question-answering system. We introduce a novel application of a Retrieval- Augmented Generation (RAG) architecture tailored for Indonesian local government data. The core novelty lies in our methodology for handling heterogeneous data formats (PDF, Excel, CSV) by integrating a low-code orchestrator (n8n) with a high-performance vector database (pgvector), a practical solution for a common public sector challenge. The system utilizes the text-embedding-3-large model for semantic understanding and gpt-4.1 for generating grounded, factual answers. The system's effectiveness was rigorously validated, achieving a perfect 100% score across accuracy, precision, recall, and F1-score on defined test cases. Crucially, usability testing with end-users confirmed the system is perceived as significantly more efficient and user-friendly than manual data searching. The primary impact of this work is a validated, replicable blueprint for local governments to democratize public information. By transforming complex data retrieval into an intuitive conversation, this research offers a practical AI solution to enhance governmental transparency and citizen engagement.
Penguatan Keterampilan Menulis Ilmiah Dosen Universitas Amikom Purwokerto pada Bidang Data Science untuk Publikasi Internasional Hariguna, Taqwa; Sarmini, Sarmini; Wahid, Arif Mu'amar; Pratama, Satrya Fajri; Yi, Ding
Jurnal Abdi Masyarakat Indonesia Vol 5 No 5 (2025): JAMSI - September 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2082

Abstract

Kemampuan menulis ilmiah merupakan keterampilan esensial bagi dosen di bidang data science untuk meningkatkan produktivitas dan kualitas publikasi di jurnal internasional bereputasi.. Permasalahan utama yang dihadapi oleh dosen di Fakultas Ilmu Komputer Universitas Amikom Purwokerto adalah kurangnya keterampilan dalam menulis artikel ilmiah yang memenuhi standar jurnal internasional, yang berdampak pada terbatasnya publikasi mereka. Sebagai solusi, kegiatan pengabdian ini menyelenggarakan pelatihan dan pendampingan penulisan artikel ilmiah dengan tujuan untuk meningkatkan kapasitas dosen dalam menulis secara sistematis dan sesuai standar jurnal internasional. Workshop interaktif yang diadakan diikuti oleh 25 dosen dari empat program studi, dengan sesi teori, praktik langsung, dan peer review. Hasil evaluasi menunjukkan peningkatan signifikan pada kemampuan peserta: aspek struktur penulisan meningkat dari 60% menjadi 80%, bahasa ilmiah dari 58% menjadi 82%, serta pemahaman standar jurnal dari 52% menjadi 76%. Selain itu, 8 peserta berhasil menghasilkan draf artikel yang siap disubmit ke jurnal internasional. Kegiatan ini juga berhasil mendorong terbentuknya komunitas penulis ilmiah yang menjadi langkah awal dalam membangun budaya akademik kolaboratif secara berkelanjutan di Fakultas Ilmu Komputer.
Co-Authors Abbas, Intesar Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Anton Satria Prabuwono Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Berlilana Berlilana Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Hussain, Refat Taleb Imam Tahyudin Jawad, Haider Mahmood Johan Reimon Batmetan Kim, Jong Il Mohammed, Ismail Abdulaziz Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nurwanti, Aulia Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Sabah, Sura Salsabila, Salsabila Sarmini Sarmini Subhan Mahendrasyah, Muhammad Susanti, Nindi Nofi Tikaningsih, Ades Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding