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Website security is an important aspect of designing a website and managing web systems. However, many developers still pay little attention to security aspects from the early stages of development. In fact, the website that has been built will be the target of attacks by hackers at any time. Therefore, this research aims to analyze the vulnerability of the SMAN 1 Banjar Agung website based on the OWASP Top 10 standard. The research method was conducted through vulnerability assessment using OWASP ZAP tools with the stages of spidering, passive scanning, and active scanning. This test allows identification of vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), and security configuration weaknesses. The scan results showed eight vulnerabilities, consisting of two medium, three low, and three informational vulnerabilities. Although the risk level is low, the website still requires mitigation through the application of security headers, dependency updates, and removal of sensitive information to make the system more secure and stable.

This study aims to develop a predictive model using linear regression to identify potential arrhythmias in the elderly based on electrocardiogram (ECG) data. Data were collected through observations at healthcare facilities from elderly patients with indications of arrhythmia, then preprocessed such as cleaning, normalization, feature selection, and outlier checking were carried out. The features used include PR interval, QRS duration, QT interval, and heart rate. The dataset was divided into training data (80%) and test data (20%) to build and evaluate the model. The training results showed that the model was able to predict the risk of arrhythmia with a Mean Squared Error (MSE) value of 0.15 and a coefficient of determination (R²) close to 1. Evaluation using a confusion matrix showed an accuracy of 76.19%, precision of 82.80%, recall of 76.19%, and F1 score of 72.70%. These results prove that linear regression can be used as an initial approach in the early detection of arrhythmias non-invasively in the elderly. This study provides a foundation for the development of ECG data-based clinical decision support systems and suggests future exploration of more complex models and integration with real-time monitoring technologies.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Selecting the right supplier is a critical aspect of supply chain management, especially in a retail business like Parfume Corner, which relies on product quality, availability, and on-time delivery. This study aims to implement the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method as a multi-criteria decision-making approach to determine the best perfume supplier. The VIKOR method was chosen because of its ability to handle conflicts between criteria and produce optimal compromise solutions. The evaluation criteria used include product quality, price, on-time delivery, after-sales service, and flexibility in negotiations. Data were collected from five potential suppliers through observation, interviews, and historical transaction documents. The analysis results showed that one supplier obtained the lowest VIKOR index score, thus being determined as the best compromise solution. The implementation of the VIKOR method proved effective in providing objective and transparent recommendations, which can support Parfume Corner's strategic decisions in building long-term partnerships with reliable suppliers. This approach can also be adapted by similar businesses to improve procurement efficiency and quality. The test results obtained were that in the expert test a Good value was obtained, namely 80%, while in the system test a Very Good conclusion was obtained, namely 100%.

Web security is an important aspect in maintaining data integrity and confidentiality in the digital age, where cyber threats are increasingly complex and difficult to detect. This research was conducted because there are still many web systems that are vulnerable to attacks due to weak early detection of security gaps. For this reason, this study implements a combination of Nmap and Metasploit Framework as the main tools in proactively detecting and testing system vulnerabilities. The research method was carried out in three stages, namely data collection by scanning the network using Nmap to identify open ports and services, selecting the appropriate testing tools, and controlled exploitation using Metasploit on the Metasploitable2 test system. The results of the study show that Nmap is capable of mapping the attack surface in detail, while Metasploit can validate the scan results through exploitation of vulnerable services such as vsftpd 2.3.4, which successfully provided root access to the target system. The combination of these two tools has proven to be effective in conducting systematic, fast, and accurate early detection of attacks, so that it can be used as a preventive measure to improve web security from potential cyber threats.

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

CV KIA is a shop engaged in the field of computer sales and services, coming directly to make transactions and provide computer service information services. So there are obstacles that are quite time consuming and transportation costs for customers to find out information on goods, stock of goods, and the process of purchasing goods because customers have to come directly to the store. Product information on CV Kia cannot be updated in real time so that there is a delay in calculating stock of goods. The method used in this study is the prototype development method and is designed using UML. This system uses two programming languages, namely PHP. Implementation using the Xampp application, and MySQL. The results of this study are the design and creation of a web-based sales information system. The system that is built will also later facilitate the sales transaction process which can later reduce the level of competition with the outside market. This system displays information about product sales, and can carry out the sales transaction process so that customers do not need to come to the store to get information and make product purchases. The results of testing that has been carried out involving 10 Respondents that the conclusion of the quality of the feasibility of the software produced has a percentage of success with an average total of 100%.  Keywords: Sales, Prototype, PHP, Information Systems, Sublime Text

This research examines the application of the Elliptic Curve Digital Signature Algorithm (ECDSA) in a blockchain system as a security solution for digital payment systems in Indonesia. Using a descriptive-qualitative approach based on literature review and conceptual simulations using Python, this study discusses the working principles of ECDSA, its advantages over other digital signature algorithms, and the challenges of its adoption in Indonesia. The results show that ECDSA provides high cryptographic efficiency, maintains transaction authenticity and integrity, and supports a transparent decentralized system. The academic simulations include not only KYC processes, top-ups, transactions, validation by validators, and block recording, but also demonstrates the formation of an interconnected multi-level blockchain and tests scenarios for rejecting manipulated or invalid transactions. The contribution of this research lies not only in the theoretical review but also in the implementation illustrations that can be used as a basis for education and the initial development of blockchain-based digital payment systems. The research results show that ECDSA is capable of providing a high level of efficiency in the encryption and transaction verification process, maintaining data integrity and authenticity, and supporting a decentralized and transparent system. The academic simulations included the KYC process, wallet creation using ECDSA keys, balance top-ups through bank integration, transaction creation and validation, and block recording in the blockchain. Specifically, the simulations successfully demonstrated how new blocks are added to the chain by referencing the previous block's hash, as well as how transactions with corrupted signatures, manipulated amounts, or insufficient balances are automatically rejected by the validator consensus mechanism, maintaining system integrity. This research contributes not only theoretically, but also through conceptual representations that can be used as an educational foundation and for the initial development of blockchain-based digital payment systems in Indonesia.

This study aims to analyze and compare the performance of the Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree algorithms in predicting the purchase intention of e-commerce visitors using the Online Shoppers Purchasing Intention Dataset, which consists of 12,330 records and 18 variables, with the Revenue variable serving as the classification target. The preprocessing stage involved transforming categorical and boolean variables into numerical form, standardizing features using StandardScaler, and splitting the dataset into 80 percent training data and 20 percent testing data. Model evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics, and was further strengthened by 10-fold cross-validation to obtain more stable results. The findings indicate that KNN achieved the highest accuracy of 0.866180, while Naive Bayes produced the highest recall value of 0.690998 and the highest ROC-AUC value of 0.821696. Meanwhile, Decision Tree demonstrated relatively balanced performance with an accuracy of 0.857259 and an F1-score of 0.571776, whereas the cross-validation results identified KNN as the model with the highest average accuracy of 0.8770. These findings suggest that the selection of a classification model for purchase intention prediction cannot rely solely on a single evaluation metric, as each algorithm possesses different strengths. Therefore, a comparative approach among algorithms can help determine the most suitable model for supporting consumer behavior analysis on e-commerce platforms.

ABSTRACT Information services on campus are often delayed due to reliance on staff, resulting in long queues and inefficient waiting times. This study aims to design and develop a robotic interaction system based on speech recognition and Natural Language Processing (NLP), equipped with a virtual button as an alternative activation method. The system allows users to interact with the robot using voice, while the virtual button provides an additional option for users who are more comfortable with touch-based interaction. The research method employed is prototype development, which includes the design, implementation, and evaluation of the system. Testing was conducted to assess the effectiveness of the system in delivering information services quickly and accurately. The results show that the developed system can enhance service efficiency, reduce dependence on staff, and facilitate faster and more practical interactions between users and the robot. This study is expected to contribute to the development of technology-based public service systems, especially in the campus environment. Keywords: robotic interaction, speech recognition, NLP, virtual button, public service

The bodywork industry in Indonesia is under high competitive pressure, requiring companies to be more adaptive in understanding customer behavior in order to maintain business continuity. PT. Bengawan Karya Sakti as one of the national bodywork companies, has not optimally utilized historical transaction data to assess customer loyalty. This study aims to identify customer loyalty segmentation through the application of the RFM (Recency, Frequency, Monetary) method, which is used to analyze sales transaction data in 2022 and 2023. The study uses the CRISP-DM approach which includes the stages of business understanding, data exploration, data cleaning and processing, modeling, evaluation, and implementation of results. The transaction data analyzed includes attributes of transaction date, customer, number of transactions, and transaction value, which are then processed into RFM scores based on the transaction year and classified into categories such as Very Loyal, Loyal, At Risk, and others. The segmentation results show an increase in the number of very loyal customers from 2022 to 2023, as well as a significant decrease in inactive and at-risk customers. The chi-square statistical test shows that the difference in customer distribution between years is statistically significant (p-value <0.05), indicating a real influence from the company's strategy or external factors. The main conclusion of this study is that the RFM method is effective in the bodywork industry to support data-based marketing decision making and more targeted customer retention strategies.

Maraknya kasus kecurangan atau manipulasi dokumen sertifikat atau piagam penghargaan pada proses Penerimaan Peserta Didik Baru (PPDB) di berbagai wilayah di Indonesia, menjadi permasalahan yang serius untuk diselesaikan. Tindakan ini dapat merugikan para siswa yang telah berprestasi secara jujur, merusak kepercayaan masyarakat terhadap integritas pendidikan dan kredibilitas sistem PPDB itu sendiri, sehingga berdampak pada kepercayaan masyarakat kepada institusi atau lembaga pendidikan dan pemerintah. Metode penelitian yang dipergunakan adalah case studies research. Peneliti menggunakan metode Rapid Application Development (RAD) untuk pengembangan sistem dan blackbox testing berbasis equivalent partition untuk metode pengujian sistem. Berdasarkan hasil blackbox testing berbasis equivalent partition, bahwa sistem informasi verifikasi online yang dibangun, secara keseluruhan fungsionalitasnya sudah berjalan dengan baik. Sistem yang dibangun juga sudah sesuai dengan kebutuhan pengguna dalam mengakomodir permasalahan terkait dengan verifikasi sertifikat secara online, sehingga dapat meminimalisir praktik kecurangan atau manipulasi dokumen sertifikat atau piagam penghargaan.

Penelitian ini bertujuan untuk mengembangkan sistem pemilihan supplier yang efektif dengan menggunakan metode TOPSIS dan pendekatan Rapid Application Development (RAD). CV. Berjaya Jaya Abadi, sebuah perusahaan kontraktor yang bergerak di bidang penyediaan berbagai jenis cat, menghadapi tantangan dalam memilih supplier terbaik di antara beberapa alternatif. Metode TOPSIS digunakan karena kemampuannya untuk memberikan hasil yang lebih objektif berdasarkan kriteria yang telah ditetapkan, dibandingkan dengan metode subjektif yang sebelumnya digunakan oleh perusahaan., mengalami kesulitan dalam menentukan supplier terbaik di antara banyak pilihan yang tersedia. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem pemilihan supplier menggunakan metode TOPSIS yang diimplementasikan melalui pendekatan Rapid Application Development (RAD). Sistem ini dirancang untuk mempermudah pengambilan keputusan dalam memilih supplier dengan mempertimbangkan enam kriteria utama, yaitu Pengiriman, Sejarah Kinerja, Harga, Kualitas, Loyalitas, dan Pelayanan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan dapat memberikan peringkat supplier berdasarkan kriteria yang telah ditentukan, sehingga dapat membantu perusahaan dalam memilih supplier yang paling sesuai dengan kebutuhan. Metode RAD yang digunakan dalam pengembangan sistem juga terbukti efektif dalam mempercepat proses perancangan dan implementasi sistem. Dengan pengujian Black Box Testing, sistem yang dibangun menunjukkan hasil yang memuaskan dalam mendukung pengambilan keputusan di perusahaan.

Pusat data menjadi tempat sekaligus aset penting organisasi. Demikian pula rencana pengelolaan keberlanjutan (kontinuitas) sangat penting untuk menjaga organisasi tetap dapat menjalankan proses bisnisnya ketika terjadi bencana pada pusat data atau infrastruktur teknologinya. Evaluasi tata kelola menjadi suatu hal yang penting dan harus dilakukan untuk mengetahui sejauh mana pengelolaan pusat data dan kesiapan rencana pengelolaan keberlanjutan (kontinuitas) suatu organisasi. Metode penelitian yang digunakan meliputi menentukan area fokus evaluasi, menyusun instrumen evaluasi (interview, kuesioner dan test sistem), pelaksanaan evaluasi, pengolahan hasil evaluasi, analisa gap dan penyusunan strategi perbaikan. Tingkat kematangan saat ini berada pada level 3. Tingkat kematangan yang diharapkan berada pada level 5, kecuali untuk komponen informasi dan komponen budaya, etika dan perilaku berada pada level 4. Strategi perbaikan dirumuskan bertahap untuk strategi perbaikan dari level 3 ke level 4 dan strategi perbaikan dari level 4 ke level 5. Perumusan strategi perbaikan disusun berdasarkan tujuh komponen sistem tata kelola dengan urutan prioritas berdasarkan hasil analisa gap.

Berdasarkan hasil penelitian yang telah dilakukan CV Marecayasa Media diperoleh data bahwa yang menjadi masalah yaitu dalam proses melakukan presensi yang masih menggunakan cara manual seperti karyawan harus mengisi google form yang di buat terlebih dahulu menyebabkan seringnya terjadi manipulasi data seolah olah karyawan tersebut masuk dikarenakan sulit untuk mengenali karyawan hanya dalam bentuk namanya saja pada saat melakukan presensi.   Dari permasalahan tersebut di atas maka penulis membangun Aplikasi Presensi Berbasis Mobile Dengan Face Recognition dengan tujuan Aplikasi ini agar memudahkan karyawan dalam melakukan presensi masuk, presensi keluar, izin sakit dan izin cuti, serta membantu meminimalisir manipulasi data.   Metode penelitian yang digunakan adalah metode pengembangan perangkat lunak (Scrum) yaitu dari Analisis Kebutuhan, Product Backlog, Sprint Planning, Sprint Backlog, Sprint, Sprint Review  dan Pengujian (Testing). Untuk peracangannya pada aplikasi presensi berbasis mobile dengan face recognition  ini di bangun menggunakan Bahasa pemrorgraman Dart, dengan framework Flutter, serta untuk databasenya menggunakan Postgresql dan Backend API menggunakan baha pemrograman Golang dengan framework GO-Fiber.   Berdasarkan uji coba yang telah di lakukan menggunakan pengujian system black box rancang bangun aplikasi presensi berbasis mobile dengan face recognition berhasil di bangun dan menggunakan uat testing (User Acceptance Testing) mendapatkan presentase sekitar 95% dari aplikasi tersebut.

SD Andir Mukti merupakan salah satu instansi pendidikan swasta di Kota Bandung yang berada dalam naungan Yayasan Andir Mukti sangat membutuhkan teknologi informasi untuk memanajemen data inventory barang di SD Andir Mukti, yang mana pada saat ini masih menggunakan pengelolaan data inventory manual. Pengelolaan data yang bersifat manual tersebut dinilai kurang efektif bagi keberlangsungan kegiatan pengelolaan data karena sering terjadinya ketidaksesuaian dalam pengelolaan barang. Perancangan dalam penelitian ini menggunakan bahasa pemrograman PHP ( Hypertext Prepocessor ), HTML ( Hyper Text Markup Language ), CSS, Javascript, Bootstrap yang didukung oleh database MySQL serta dibantu Framework Codeigniter untuk mempermudah dan mempercepat proses pengembangan aplikasi. Hasil dari keseluruhan pengujian dengan menggunakan blackbox menunjukan bahwa aplikasi ini berjalan dengan baik secara fungsional dan non-fungsional, pengujian blackbox yang dilakukan secara non-fungsional yaitu dalam setiap pergantian halaman membutuhkan respon atau waktu kurang lebih 10 detik, dari hasil pengujian whitebox menunjukan status valid pada tahap pengujian unit dan pada pengujian UAT ( User Acceptance Testing ) dengan menggunakan variabel perceived usefulness menunjukkan hasil yang cukup signifikan yaitu mencapai 85% dengan status memuaskan.

Penilaian kinerja karyawan menggunakan metode Balanced Scorecard (BSC) sangat efektif dalam mengukur penilaian kinerja karyawan dari berbagai perspektif, seperti finansial, pelanggan, proses internal dan pertumbuhan dan pembelajaran. Dengan demikian, penelitian ini bertujuan untuk merancang dan mengimplementasikan aplikasi penilaian kinerja karyawan dengan menggunakan metode Balanced Scorecard (BSC) pada perusahaan PT. ForIT Asta Solusindo. Pada penelitian ini penulis merancang dan mengimplementasikan aplikasi penilaian kinerja karyawan menggunakan metode Balanced Scorecard (BSC) dengan empat perspektif utama, yaitu finansial yang diterapkan pada divisi akuntan, pelanggan yang diterapkan pada divisi pemasaran; proses internal dan pertumbuhan dan pembelajaran pada berbagai divisi. Untuk masing-masing perspektif memiliki nilai bobot yang berbeda namun dengan jumlah keseluruhan bobot yang sama dengan nilai persentase yang mencapai 100%. Aplikasi ini berbasis web dengan menggunakan metode pengembangan perangkat lunak Agile dengan model Scrum untuk memastikan adaptabilitas dan kualitas produk. Aplikasi ini juga dibangun menggunakan framework Laravel dan menggunakan basis data MySQL sebagai tempat penyimpanan data. Hasil dari keseluruhan pengujian unit yang dilakukan dengan pengujian whitebox pada kode internal menggunakan PHPUnit. Hasil dari pengujian blackbox sesuai dengan kebutuhan fungsional dan non-fungsional aplikasi. Hasil dari skor Likert pada pengujian User acceptance test (UAT) menunjukkan dari faktor kepuasan pengguna mencapai 78% dan faktor kemudahan pengguna mencapai 96%. Kata Kunci: Balanced Scorecard (BSC), Key Performance Indicators (KPI), Penilaian kinerja, Perspektif, Agile Scrum.  

Pengolahan data hasil survei kepuasan mahasiswa terhadap layanan akademik yang dilakukan LPM saat ini dirasa masih memiliki banyak keterbatasan dan melewati beberapa proses Panjang, dalam  mengumpulkan data survei mahasiswa, masih dilakukan sendiri sendiri dengan menggunakan framework yang berbeda beda. Tujuan penelitian yaitu pembuatan sistem informasi yang mengimplemenatasikan metode servqual untuk penilaian kepuasan­ mahasiswa unisnu terhadap pelayanan akademik. Sumber data dari hasil kuesioner mahasiswa TA.2020 berjumlah 4.433 dan hasil implementasi menggunakan perhitungan slovin 100 sampel data. Metode Service Quality diperoleh rata-rata dari nilai 5 aspek yaitu keandalan, daya tanggap, jaminan, empati, dan nyata, dengan predikat PUAS dan hasil pengujian perangkat lunak (black box testing) dan ISO 9126 sangat layak untuk digunakan. Hasil sistem yang telah dibangun mempercepat admin mengolah data kuisioner penilaian sesuai dengan kriteria dengan banyak kemudahan yang di hasilkan oleh aplikasi ini.

Pengembangan sistem informasi akademik bertujuan untuk memberikan sarana dasar kepada perguruan tinggi. Sistem informasi akademik diperlukan untuk mengelola seluruh kegiatan akademik diantaranya mengelola kartu rencana studi, mengelola kartu hasil studi, mengelola data dosen dan tenaga kependidikan, mengelola data mahasiswa, mengelola kelas, mengelola pertemuan dan presensi. Memiliki sistem informasi akademik akan mengurangi resiko keamanan data serta mendukung kemandirian pengelolaan teknologi informasi. Pengembangan sistem informasi ini menggunakan metode penghimpunan data menggunakan metode tanya jawab (interview) dan pengamatan lapangan. Performance, Information, Economics, Control, Efficiency dan Service adalah metode PIECES yang akan digunakan dalam menganalisa sistem. Unified Modelling Language (UML) adalah metode perancangan sistem yang digunakan dalam penelitian ini. System engineering, Requirement analysis, Design, Coding, Testing dan Maintenance adalah metode pengembangan sistem Waterfall yang peneliti gunakan. Sistem informasi akademik dikembangkan sesuai kebutuhan institusi saat ini tetapi seiring perkembangan peraturan dan kebutuhan institusi maka perlu dilakukan evaluasi kinerja secara periodik.