Abstract:Abstract: The Academic Information System (SIAKAD) plays an important role in supporting the management of academic administration in higher education institutions, particularly for students. ABC University has implemented…
ed SIAKAD since 2018 to facilitate administrative ativities in line with its motto of a high technology campus. This study aims to measure the sucess of SIAKAD usage from the aspects of acceptance, satisfaction, suitability, and perceived benefits. The integration of the Unified Theory of Acceptance and Use of Technology (UTAUT), DeLone & McLean, and Task Technology Fit (TTF) models was carried out to obain a more comprehensive overview in assessing the success of SIAKAD. UTAUT explains the factors influencing the intention to use, DeLone & McLean emphasizes the relationship between system quality and both user satisfaction and net benefits, while TTF evaluates the fit between technology and user tasks. By combining these three models, the study addresses the limitations of each model and produces a more holistic approach in measuring acceptance, success, and the appropriateness of system use. The testing was conducted using SPSS and Structural Equation Modeling (SEM) analysis through AMOS.
Keywords: siakad; utaut; delone&mclean; penerimaan teknologi; sem
Abstract:Abstract: Posyandu cadres play an important role in supporting community health services at the village and sub-district levels. However, the selection process for the best cadres is often carried out subjectively without…
t clear and standardized criteria. This condition can lead to a decline in service quality and reduced cadre motivation. Therefore, a decision support system is needed to provide assessments that are objective, measurable, and accountable. This study aims to optimize the Posyandu cadre selection process in Lubuk Kilangan District through the development of a decision support system based on a hybrid method: Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The AHP method is applied to determine the weight of each selection criterion based on its level of importance through pairwise comparisons. Subsequently, TOPSIS is used to rank candidates according to their proximity to the ideal solution. The methodology includes a literature review, primary data collection through interviews and questionnaires with stakeholders (community health centers, cadres, and village officials), as well as the implementation and testing of the AHP–TOPSIS–based system.
Keywords: Posyandu, Cadre, AHP, TOPSIS, Decision Support System
Abstrak: Kader Posyandu memiliki peran penting dalam mendukung layanan kesehatan masyarakat di tingkat desa dan kelurahan. Namun, proses pemilihan kader terbaik masih sering dilakukan secara subjektif tanpa acuan kriteria yang jelas dan terstandarisasi. Kondisi ini dapat mengakibatkan penurunan kualitas pelayanan serta rendahnya motivasi kader. Oleh karena itu, dibutuhkan suatu sistem penunjang keputusan yang mampu memberikan hasil penilaian yang objektif, terukur, dan dapat dipertanggungjawabkan. Penelitian ini bertujuan untuk mengoptimalkan proses pemilihan kader Posyandu di Kecamatan Lubuk Kilangan melalui pengembangan sistem penunjang keputusan berbasis metode hybrid Analytical Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Metode AHP digunakan untuk menetapkan bobot masing-masing kriteria pemilihan kader berdasarkan tingkat kepentingan melalui perbandingan berpasangan. Selanjutnya, metode TOPSIS digunakan untuk melakukan pemeringkatan calon kader berdasarkan kedekatannya terhadap solusi ideal. Metode yang digunakan dalam penelitian ini mencakup studi literatur, pengumpulan data primer melalui wawancara dan kuesioner kepada stakeholder terkait (puskesmas, kader, dan perangkat desa), serta implementasi dan pengujian sistem berbasis AHP-TOPSIS.
Kata kunci: Posyandu, Kader, AHP, TOPSIS, Sistem Pendukung Keputusan
Abstract:Abstract: Land registration in Indonesia often encounters challenges in transparency, data integrity, and centralized bureaucracy. Manual and semi-digital systems remain vulnerable to manipulation and delays. The National…
l Land Agency has initiated digitalization, but several challenges remain, particularly in ensuring transparency, efficiency, and security of land ownership data. Blockchain technology offers a potential solution through its decentralized and immutable characteristics. This study adopted a design and development method consisting of system analysis, requirements identification, architecture design, implementation, and black-box testing. The developed decentralized application (DApp) integrates smart contracts, NFTs, and IPFS to manage land certificates. Core functions such as minting, transfer, splitting, and self-custody were implemented and successfully tested, with all scenarios producing expected results. The findings demonstrate that blockchain integration can enhance security, reduce duplication, and streamline land administration. The study contributes a functional prototype with practical implications for modernizing land registration in Indonesia while identifying scalability and regulatory adaptation as areas for further research.
Keywords: blockchain; decentralized application; land registration; NFT; smart contract.
Abstract:Abstract: In the era of sensitive health data and frequent cyberattacks, securing electronic medical records (EMR) has become a critical challenge. This study proposes a hybrid encryption framework combining Affine and AES…
ES algorithms with an AI-based key management module to enhance EMR security while maintaining efficiency. A dataset of 1,000 simulated records was evaluated using five cryptographic configurations: Affine-only, AES-only, RSA-only, Affine–AES, and Affine–AES with AI. Performance was measured through encryption/decryption latency and ciphertext size, while security was assessed under brute-force, SQL injection, and phishing simulations. The AI decision tree for key generation was evaluated using accuracy, precision, recall, F1-score, and entropy metrics. Results show that the AI-enhanced hybrid method eliminates brute-force success, introduces only minor latency overhead, and generates high-entropy keys with reliability above 98%. These findings indicate that integrating AI-based dynamic key regeneration into hybrid encryption can improve EMR security while remaining practical for clinical and cloud-based healthcare systems. Future work should involve real clinical datasets and explore post-quantum cryptographic extensions.
Keywords: AI key management; attack resistance; encryption performance; electronic medical records; hybrid encryption
Abstrak: Di era meningkatnya sensitivitas data kesehatan dan maraknya serangan siber, perlindungan Rekam Medis Elektronik (RME) menjadi tantangan penting. Penelitian ini mengusulkan kerangka enkripsi hibrida yang menggabungkan algoritma Affine dan AES dengan modul manajemen kunci berbasis AI untuk meningkatkan keamanan RME tanpa mengorbankan efisiensi. Dataset simulasi berisi 1.000 entri diuji menggunakan lima konfigurasi kriptografi: Affine-only, AES-only, RSA-only, Affine–AES, serta Affine–AES dengan AI. Performa diukur melalui latensi enkripsi/dekripsi dan ukuran ciphertext, sedangkan keamanan dievaluasi melalui simulasi serangan brute force, SQL injection, dan phishing. Model decision tree untuk manajemen kunci dinilai menggunakan metrik akurasi, presisi, recall, F1-score, dan entropi. Hasil menunjukkan bahwa metode hibrida dengan AI menghilangkan keberhasilan brute force, menambah overhead latensi yang minimal, serta menghasilkan kunci berentropi tinggi dengan reliabilitas di atas 98%. Temuan ini menunjukkan bahwa regenerasi kunci dinamis berbasis AI dalam skema enkripsi hibrida dapat meningkatkan keamanan RME sekaligus tetap praktis untuk sistem klinis dan layanan kesehatan berbasis cloud. Penelitian selanjutnya disarankan menggunakan dataset klinis nyata dan mengeksplorasi kriptografi pascakuantum.
Kata kunci: enkripsi hibrida; ketahanan serangan; kinerja enkripsi; manajemen kunci berbasis AI; rekam medis elektronik
Abstract:Abstract: Ineffective drug demand management can lead to problems such as imbalanced drug distribution, excess stock, or shortages in community health centers. To address this, data mining can be utilized to support the…
planning and control process of drug inventory. Clustering techniques were chosen because they are able to group drug data based on certain characteristics, thus identifying stable and unstable drug supply patterns. This study aims to group drug data at Simpang Kawat Community Health Center in Jambi City, which can be used as a reference in planning drug needs in the next period. Data grouping is divided into three categories: slow-moving, medium-moving, and fast-moving. The research data includes attributes of drug name, initial stock, receipt, inventory, usage, and final stock, with a total of 1758 data sets, which were processed using the CRISP-DM framework through the RapidMiner application. Cluster quality evaluation was carried out using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm obtained a DBI value of 0.175, smaller than K-Medoids which obtained a value of 0.354. Because a smaller DBI value indicates better cluster quality, K-Means provides more optimal clustering results than K-Medoids. Through these clustering results, community health centers can utilize drug cluster information to support more efficient drug procurement planning, as well as reduce the risk of excess or shortage of stock.
Keywords: data mining; clustering; k-means; k-medoids; davies-bouldin index
Abstract:Abstract: In the era of the Internet of Things (IoT), cyber threats are increasingly complex and dynamic, thus demanding an adaptive and intelligent network security system. This study proposes a Convolutional Neural Network…
work (CNN)-based Intrusion Detection System (IDS) implemented through a Federated Learning (FL) approach in a Non-Independent and Identically Distributed (Non-IID) data environment. This approach allows the model to be trained in a distributed manner across multiple IoT devices without having to collect sensitive data to a central server, thereby maintaining data privacy while increasing the efficiency of the training process. The experiment used the CIC IoT 2023 dataset, which represents various modern IoT network traffic patterns. The results show that the proposed CNN–FL model achieves an overall accuracy of 0.99, with excellent performance in detecting various types of network traffic. The model obtains a perfect recall value (1.00) for normal traffic (Benign), as well as a very high F1-score for DDoS (0.99) and DoS (0.99) attacks. Stable and consistent performance across all five federation rounds demonstrates that this approach is a reliable, efficient, and accurate solution for detecting threats in distributed and privacy-preserving IoT networks.
Keywords: cnn; federated_learning; ids; non-iid; ciciot2023
Abstrak: Dalam era Internet of Things (IoT), ancaman siber semakin kompleks dan dinamis, sehingga menuntut sistem keamanan jaringan yang adaptif dan cerdas. Penelitian ini mengusulkan Intrusion Detection System (IDS) berbasis Convolutional Neural Network (CNN) yang diterapkan melalui pendekatan Federated Learning (FL) pada lingkungan data yang bersifat Non-Independent and Identically Distributed (Non-IID). Pendekatan ini memungkinkan model dilatih secara terdistribusi di berbagai perangkat IoT tanpa harus mengumpulkan data sensitif ke server pusat, sehingga mampu menjaga privasi data sekaligus meningkatkan efisiensi proses pelatihan. Eksperimen menggunakan dataset CIC IoT 2023, yang merepresentasikan berbagai pola lalu lintas jaringan IoT modern. Hasil penelitian menunjukkan bahwa model CNN–FL yang diusulkan mencapai akurasi keseluruhan sebesar 0.99, dengan performa yang sangat baik dalam mendeteksi berbagai jenis lalu lintas jaringan. Model memperoleh nilai recall sempurna (1.00) untuk lalu lintas normal (Benign), serta nilai F1-score yang sangat tinggi untuk serangan DDoS (0.99) dan DoS (0.99). Kinerja yang stabil dan konsisten di seluruh lima putaran federasi membuktikan bahwa pendekatan ini merupakan solusi yang andal, efisien, dan akurat untuk mendeteksi ancaman pada jaringan IoT yang bersifat terdistribusi dan menjaga privasi (privacy-preserving).
Kata kunci: cnn; federated_learning; ids; non-iid; ciciot2023
Abstract: Abstract: The Ombudsman of the Republic of Indonesia is an institution tasked with supervising the administration of public services and handling community complaint reports related to allegations of maladministration.…
on. The purpose of this research is to create a decision support system using the Analytic Hierarchy Process (AHP) method, which facilitates the determination of priority handling of community complaint reports at the Ombudsman of the Republic of Indonesia Bengkulu Representation. This decision support system is built on a web-based platform using PHP programming language with a MySQL database that can be accessed offline by the admin of the Ombudsman. With the existence of this priority recommendation, it is expected that work will become more effective and efficient, as resources can be focused on reports that most need attention. Based on the test data used, which consists of 12 Community Complaint Reports from July 2024, it was found that the priority handling recommendations for community complaint reports were derived from 3 reports with the highest final AHP values. The recommended priority handling reports are registration number 0021/LM/VII/2024/BKL with a final AHP value of 2.074, registration number 0020/LM/VII/2024/BKL with a final AHP value of 1.964, and registration number 0018/LM/VII/2024/BKL with a final AHP value of 1.866.
Keywords: decision support system; priority recommendation; public complaint report; AHP Method (analytic hierarchy process method)
Abstract:Abstrack: This research aims to improve battery performance and safety on the ECGO2 electric motorcycle by re-assembling the battery system using 18650 lithium cells, Daly BMS 13S/7A battery management system, and XH-M604…
4 module. The configuration used is 13S5P (65 cells), resulting in a total voltage of 48.1 V and a capacity of 14 Ah, or equivalent to 673.4 Wh of energy. Compared to the ECGO2 built-in battery that requires 4-7 hours of charging time, this system is able to speed up charging to ±1.6 hours using a 7 A current charger. Test results using an oscilloscope show that the voltage of the assembled battery is more stable under load than that of a single battery, with minimal ripple. The estimated operating time of an 800 W electric motor using a 673.4 Wh battery is about 50 minutes. To achieve 2 hours of operation, the 13S10P configuration or energy-saving mode (400-500 W) can be used. The system is also more cost-effective at Rp2,678 per Wh compared to the manufacturer's version of Rp4,464 per Wh, as well as improved safety against leakage and overheating.
Keywords: 18650 lithium battery; daly bms; electric motorcycle; fast charging.
Abstrak: Penelitian ini bertujuan untuk meningkatkan performa dan keamanan baterai pada sepeda motor listrik ECGO2 dengan merakit ulang sistem baterai menggunakan sel lithium 18650, sistem manajemen baterai Daly BMS 13S/7A, dan modul XH-M604. Konfigurasi yang digunakan adalah 13S5P (65 sel), menghasilkan tegangan total 48,1 V dan kapasitas 14 Ah, atau setara dengan energi 673,4 Wh. Dibandingkan baterai bawaan ECGO2 yang memerlukan waktu pengisian 4–7 jam, sistem ini mampu mempercepat pengisian menjadi ±1,6 jam menggunakan charger arus 7 A. Hasil pengujian menggunakan osiloskop menunjukkan bahwa tegangan baterai rakitan lebih stabil di bawah beban dibandingkan baterai tunggal, dengan ripple minimal. Estimasi lama pengoperasian motor listrik 800 W menggunakan baterai 673,4 Wh adalah sekitar 50 menit. Untuk mencapai 2 jam pengoperasian, dapat digunakan konfigurasi 13S10P atau mode hemat energi (400–500 W). Sistem ini juga lebih hemat biaya dengan efisiensi harga Rp2.678 per Wh dibandingkan Rp4.464 per Wh versi pabrikan, serta meningkatkan keamanan terhadap kebocoran dan panas berlebih.
Kata kunci: baterai lithium 18650; daly bms; sepeda motor listrik; pengisian daya cepat.
Abstract:Abstract: INET Computer Palembang, as a computer training institution, faces difficulties in understanding participant characteristics due to variations in age, educational background, and chosen course packages. This study…
udy aims to analyze participant criteria and group them based on similarities using the K-Means Clustering algorithm. The data used were historical records of course participants from 2022 to 2025. The research process followed the CRISP-DM stages, starting from data cleaning and transformation, determining the optimal number of clusters using the Elbow Method, to evaluating cluster quality with the Davies-Bouldin Index. The implementation was carried out using Python and the scikit-learn library. The results show that the optimal number of clusters is k=5 with a Sum of Squared Errors (SSE) value of 1064.66 and a Davies-Bouldin Index (DBI) score of 0.820, indicating good cluster quality. The resulting clustering provides a structured profile of participants and demonstrates that K-Means is effective in segmenting course participants. These findings are expected to assist the institution in designing more targeted training programs.
Keywords: clustering; data mining; elbow method; k-means; computer course
Abstract:The development of digital technology encourages universities to improve effectiveness and efficiency in data management, particularly in recording and reporting faculty performance. Some lecturers still face difficulties…
s in reporting their performance in the SISTER application due to challenges in locating documents scattered across various archives, which often leads to issues such as delays in reporting, low information accuracy, and lack of transparency of faculty performance documents for institutional needs. This study aims to optimize the digitalization of faculty performance documents based on cloud computing using the Agile Unified Process (AUP) approach, which is implemented in the development of a cloud-based system by utilizing Google Drive as the storage medium for digital faculty performance documents. The AUP methodology was chosen for its ability to combine flexible iterative and incremental principles, allowing the system to adapt quickly and continuously to user needs. Testing using Equivalence Partitioning, based on the functional and non-functional requirements of the system, has shown results in accordance with expectations.