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Penelitian ini bertujuan untuk mengevaluasi Sistem Surat Keterangan Pendamping Ijazah (SKPI) di Universitas Semarang melalui audit dengan menggunakan kerangka kerja COBIT 2019, dengan fokus pada domain Deliver, Service and Support (DSS) dan Monitor, Evaluate and Assess (MEA). SKPI berfungsi sebagai dokumen resmi yang memberikan informasi tambahan mengenai kompetensi lulusan di luar nilai akademik (ijazah), sehingga penting untuk memastikan kualitas dan relevansinya dengan kebutuhan industri. Metodologi yang digunakan dalam penelitian ini meliputi pengumpulan data primer melalui observasi, wawancara, dan kuesioner, serta data sekunder dari literatur terkait. Hasil penelitian menunjukkan bahwa tingkat kapabilitas pada sub-domain DSS dan MEA berada pada level 4 yang dilabeli sebagai terkelola, dengan nilai rata-rata masing-masing 3,73 untuk DSS dan 3,85 untuk MEA. Meskipun demikian, terdapat sejumlah rekomendasi untuk meningkatkan nilai Maturity Level sistem, dengan GAP masing-masing sebesar 1,07 untuk DSS dan 1,04 untuk MEA. Rekomendasi yang disampaikan meliputi peningkatan kompetensi petugas teknis, pengembangan aplikasi mobile, dan sosialisasi prosedur penyajian SKPI secara digital. Dengan adanya rekomendasi tersebut, diharapkan dapat memberikan masukan positif dalam pengelolaan SKPI di Universitas Semarang dan meningkatkan daya saing lulusan di pasar kerja.Kata Kunci: Audit Sistem, Maturity Level, Rekomendasi, Deliver, Service and Support (DSS), Monitor, Evaluate and Assess (MEA)

Business is an economic activity carried out by individuals or organizations to produce and sell goods or services with the aim of making a profit. The NSH Group Store is a business that sells carpets, pillows, bolsters, and dolls located in the Sei Dadap I/II Plantation, Sei Dadap District, Asahan Regency, North Sumatra 21225. The NSH Group Store was established in 2016 and is owned by Mrs. Siti Komariah Siregar. Among the challenges faced by the NSH Group Store owner are irregular stock procurement. Sales transaction processes still use conventional methods, reducing efficiency and time effectiveness, and potentially leading to data errors. Supply Chain Management is a series of approaches used to efficiently integrate suppliers so that goods can be distributed in the right quantities, locations, and at the right time, with the aim of minimizing overall system costs. A bolster pillow is a pillow that can function as both a pillow and a bolster. Bolster pillows are oval and long, so they can be hugged while sleeping. The benefits of a bolster pillow include maintaining a proper sleeping position, reducing pressure on joints, helping reduce aches, improving sleep quality, and improving overall health. Therefore, by implementing Supply Chain Management (SCM), data processing will be faster and more accurate.

Demam berdarah dengue merupakan tantangan kesehatan masyarakat yang terus berulang di wilayah tropis, termasuk Indonesia. Penelitian ini bertujuan untuk memprediksi jumlah kasus tahunan dengan memanfaatkan lima algoritma pembelajaran mesin, yaitu Regresi Linier, Decision Tree, Random Forest, Support Vector Machine (SVM), dan Neural Network. Data historis tahun 2017–2024 diolah menggunakan teknik windowing deret waktu untuk menghasilkan fitur lag yang sesuai bagi pembelajaran terawasi. Evaluasi kinerja dilakukan melalui metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), serta koefisien determinasi (R²). Model Decision Tree menunjukkan performa paling unggul pada sebagian besar indikator. Prediksi untuk tahun 2025 mengindikasikan adanya peningkatan moderat jumlah kasus. Namun, rendahnya nilai R² pada seluruh model mengisyaratkan perlunya pendekatan multivariat yang lebih kompleks dengan mempertimbangkan faktor iklim, lingkungan, dan demografi. Hasil penelitian ini menegaskan pentingnya kualitas data dan pemilihan fitur yang tepat dalam peramalan epidemiologis guna mendukung perencanaan kesehatan yang lebih efektif.

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.

Business ventures are activities carried out by individuals or organizations involving the production, sale, purchase, or exchange of goods and services, with the aim of generating profit. A basic necessities store (commonly known as a “sembako” store in Indonesia) sells daily staple needs, especially the nine essential commodities (sembako), which include items such as rice, sugar, cooking oil, eggs, salt, and other key food ingredients. UD. Putri 2, located in Dusun 1A, Sumber Harapan Village (21261), Tinggi Raja Subdistrict, Asahan Regency, was established in 2018 and has since become an essential part of the local community. This has required UD. Putri 2 to constantly monitor their stock inventory. However, the company still faces inefficiencies in managing sales data processing, which often leads to inventory shortages. When the supply of goods is insufficient to meet customer demand, customers may turn to other stores. If this occurs repeatedly, the store risks losing profit due to the unavailability of goods. Supply Chain Management (SCM) refers to the integrated processes and production activities starting from the acquisition of raw materials from suppliers, the value-adding processes that turn raw materials into finished products, the inventory storage process, and the distribution of finished goods to retailers and consumers. The implementation of SCM can optimize inventory management of staple goods, minimize inventory costs, and improve supply chain efficiency at UD. Putri 2.

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.

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

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%.

This study compares two service management models to evaluate the governance of the Wi-Fi network in Dusun Gita Nagari Baru. The main objective is to measure user satisfaction and service quality following the implementation of the COBIT 2019 framework, particularly the DSS02 domain (Manage Service Requests and Incidents). The research employed a mixed methods approach, using historical-comparative document analysis and Likert scale questionnaires distributed to 21 active users. The data were analysed through gap analysis, capability level mapping, and descriptive statistical analysis to identify performance differences between two periods. The results indicate that most indicators in the COBIT 2019 capability model are at Level 4 (Predictable), one indicator reaches Level 5 (Optimising), and another indicator is at Level 3. Indicators directly related to the DSS02 domain, such as ease of reporting, response speed, schedule accuracy, and repair time, demonstrate the most significant improvements. These findings support the hypothesis that implementing COBIT 2019-based governance for DSS02 can enhance user satisfaction and the quality of Wi-Fi network services in rural areas. This study also provides practical recommendations for the sustainable management of digital infrastructure in areas with limited access.

Social media has become a strategic tool for the government to disseminate public information quickly, interactively, and efficiently in the digital era. The Lampung Provincial Government utilizes various social media platforms such as Facebook, Instagram, and TikTok to support public communication activities. This study aims to analyze the effectiveness of public communication by measuring the level of activity of Regional Apparatus Organization (OPD) social media accounts using the logistic regression method. Data were collected through web scraping techniques on the official OPD social media accounts and then processed using a quantitative approach. The results show that the level of social media activity influences the effectiveness of public communication and the transparency of government information. These findings are expected to serve as a basis for local governments in designing public communication strategies that are more optimal and adaptive to developments in digital technology.

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.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

This study aims to evaluate and compare the performance of two neural network architectures under the Recurrent Neural Network (RNN) category, namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), in predicting earthquake magnitude in Indonesia. The dataset used consists of daily earthquake magnitude records from 2008 to 2023, preprocessed into time series format and normalized using the MinMax method. The training process was conducted using various combinations of batch size and epoch, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and relative prediction accuracy. The evaluation results show that LSTM with a batch size of 32 and 50 epochs provides the best prediction performance, achieving a MAE of 0.2227 and 93.65% accuracy. Meanwhile, GRU performed optimally at a batch size of 64 and 50 epochs, with a MAE of 0.2229 and 93.66% accuracy. The prediction visualization shows that LSTM offers greater stability and precision in tracking actual data patterns. These findings indicate that LSTM holds stronger potential for supporting earthquake prediction systems based on time series data.

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication

Deteksi nyeri secara objektif merupakan tantangan penting dalam dunia medis, terutama bagi pasien yang tidak mampu menyampaikan rasa sakitnya secara verbal, seperti bayi, lansia, atau penderita gangguan komunikasi. Teknologi non- invasif berbasis sensor menjadi solusi potensial untuk mengatasi keterbatasan metode subjektif. Penelitian ini bertujuan meninjau secara sistematis literatur terkini mengenai penerapan Radar MIMO dan algoritma kecerdasan buatan dalam deteksi nyeri non-invasif. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan pedoman PRISMA 2020, melalui penelusuran basis data IEEE Xplore, ScienceDirect, PubMed, Google Scholar, dan SpringerLink untuk periode 2021– 2025. Dari hasil seleksi diperoleh 17 artikel inklusi yang mencakup penggunaan Radar MIMO, UNBC-McMaster, BioVid, Medical Imaging (CT/MRI), Radar SISO, serta studi review, survey, bibliometrik, dan teoretis. Dari sisi algoritma, CNN dan SVM menjadi pendekatan paling dominan, diikuti Neural Network dan metode lain, dengan tren yang mengarah pada penggunaan multimodal untuk meningkatkan akurasi. Hasil penilaian kualitas dengan GRADE menunjukkan mayoritas studi berkualitas sedang, dengan keterbatasan utama pada ukuran sampel kecil, pelabelan nyeri yang belum konsisten, bias populasi, serta kurangnya validasi klinis nyata. Kesimpulannya, Radar MIMO dan algoritma deep learning memiliki potensi besar untuk deteksi nyeri non-invasif. Namun, penelitian lanjutan perlu difokuskan pada pembangunan dataset yang lebih inklusif, standarisasi pelabelan nyeri, serta pengujian dalam konteks klinis, dengan memperhatikan aspek etika dan privasi agar teknologi ini dapat diimplementasikan secara luas dalam layanan kesehatan.

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

Penelitian ini dilakukan untuk menganalisis dan membandingkan sentimen pelanggan terhadap lima restoran Sunda di Kota Bogor menggunakan metode Aspect-Based Sentiment Analysis (ABSA) berbasis Fine-Tuning IndoBERT. Ulasan pelanggan di platform digital seperti Google Review berpengaruh besar terhadap citra dan keputusan konsumen, sementara jumlah ulasan yang besar sulit dijelaskan secara manual. Data penelitian diperoleh dari 3.232 ulasan Google Review dan diproses menjadi 3.010 data yang dikelompokkan berdasarkan lima aspek utama, yaitu makanan, pelayanan, harga, suasana, dan fasilitas. Metode Fine-Tuning IndoBERT digunakan untuk mengklasifikasikan sentimen positif, netral, dan negatif, dengan evaluasi melalui metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa model memiliki performa sangat baik dengan akurasi tertinggi sebesar 97,51% pada aspek pelayanan dan terendah 92,52% pada aspek makanan, serta nilai F1-score makro di atas 0,91. Analisis menunjukkan bahwa Bumi Aki unggul pada aspek makanan dan fasilitas, Saung Abah pada pelayanan, Saung Kuring pada harga, dan Gumati pada suasana. Hasil penelitian ini menunjukkan bahwa Fine-Tuning IndoBERT efektif dalam memahami opini pelanggan berbahasa Indonesia dan dapat menjadi acuan bagi pelaku usaha kuliner dalam meningkatkan kualitas layanan.

Meningkatnya ketergantungan pada sistem komunikasi digital telah memperkuat kebutuhan akan metode yang andal untuk melindungi data sensitif dari akses tidak sah. Studi ini memperkenalkan mekanisme keamanan terintegrasi yang menggabungkan enkripsi ChaCha20 dengan steganografi citra Least Significant Bit (LSB), yang menargetkan perlindungan data berbasis citra digital. ChaCha20, sebuah cipher aliran modern yang dikenal akan kecepatan dan keamanannya, digunakan untuk mengenkripsi pesan teks biasa (plaintext), menghasilkan ciphertext yang sangat aman. Data terenkripsi kemudian disematkan ke dalam citra sampul — khususnya, logo universitas — menggunakan teknik LSB, yang mengubah bit paling tidak signifikan dari nilai piksel untuk menyembunyikan informasi tanpa memengaruhi kualitas citra secara signifikan. Pendekatan dua lapis ini memastikan kerahasiaan dan penyembunyian informasi sensitif. Sistem ini dievaluasi menggunakan metrik objektif seperti Rasio Sinyal terhadap Derau Puncak (PSNR) dan Indeks Kesamaan Struktural (SSIM) untuk menilai fidelitas citra setelah penyisipan data. Hasil menunjukkan bahwa metode ini mempertahankan integritas visual (PSNR > 50 dB) sekaligus memungkinkan ekstraksi data yang akurat. Integrasi ChaCha20 dan steganografi LSB menawarkan solusi yang ringan, aman, dan efektif untuk perlindungan informasi digital, khususnya cocok untuk komunikasi akademis atau kelembagaan di mana gambar logo berfungsi sebagai pembawa konten terenkripsi yang tersembunyi.

Penelitian ini bertujuan untuk mengimplementasikan sistem Supply Chain Management (SCM) dalam pengelolaan persediaan sembako di Toko Grosir Rohman guna meningkatkan efisiensi operasional dan mengatasi permasalahan kehabisan atau kelebihan stok. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan studi kasus, melalui observasi, wawancara, dan dokumentasi. Sistem SCM yang dirancang berbasis website menggunakan PHP dan MySQL, dengan pemodelan UML untuk menganalisis kebutuhan dan perancangan sistem. Hasil dari implementasi sistem menunjukkan peningkatan akurasi data stok, efisiensi dalam proses pemesanan, serta tersedianya informasi real-time yang membantu pengambilan keputusan. Dengan sistem ini, Toko Grosir Rohman mampu mengelola persediaan sembako secara lebih optimal, meningkatkan kepuasan pelanggan, dan memperkuat daya saing usaha.