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This study aims to identify and analyze the logical fallacies that arise in public conversations about environmental issues and natural resource management on Indonesian social media using a descriptive qualitative approach. Data were collected through scraping techniques using the Python programming language and subsequently analyzed using an informal logic framework to identify various forms of fallacious reasoning. The findings reveal the presence of multiple fallacies that substantially influence public understanding of causal relationships within environmental dynamics. These fallacies contribute to oversimplification, shifts in attention away from scientific evidence, and the emergence of distorted perceptions regarding structural factors in spatial planning and natural resource governance. This study contributes to environmental communication scholarship by emphasizing the importance of critical thinking literacy in improving the quality of public deliberation and supporting the formulation of evidence-based environmental policies.

The rapid advancement of information and communication technology has increased the need for data security and confidentiality in digital information exchange. Various threats, such as eavesdropping, data theft, and information manipulation, require effective security methods capable of protecting message content without attracting unauthorized attention. One of the techniques used to address this issue is steganography, which conceals secret information within digital media so that the existence of the message remains undetectable. This study aims to implement the Least Significant Bit (LSB) method on digital facial images as a medium for embedding secret messages and to evaluate the quality of the resulting stego images. The system was developed using the Python programming language with a Tkinter-based graphical user interface to facilitate the processes of message embedding and extraction. Prior to embedding, the secret message was secured using an XOR encryption operation with a specific key. The embedding process was performed by modifying the least significant bits of image pixels, resulting in minimal visual distortion. Experimental results indicate that the LSB method successfully conceals secret messages while maintaining the visual quality of the cover image. The evaluation produced low Mean Squared Error (MSE) values and Peak Signal-to-Noise Ratio (PSNR) values exceeding 85 dB, indicating a very high similarity between the original and stego images. Furthermore, the hidden message could be accurately extracted using the correct key, provided that the image had not undergone compression or manipulation. Therefore, the LSB-based steganography approach on digital facial images is proven to be an effective method for enhancing information security and confidentiality.

Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.

The increase in activity and security threats on Ubuntu server causes the volume of system logs to become very large and difficult to analyze manually. This condition potentially leads administrators to experience delays in detecting abnormal activities, such as repeated login attempts and web access patterns related to online gambling promotions. Therefore, this research aims to develop a machine learning-based Early Warning System capable of automatically detecting anomalous activities. The system is developed using the Python programming language and runs on an Ubuntu server by utilizing authentication logs and web access logs as the main data sources. The anomaly detection model is trained using normal activity data collected directly from the Ubuntu server logs to learn standard system behavior patterns. During the operational phase, the system reads server logs in real-time, extracts activity features, and analyzes them using the Isolation Forest algorithm. Activities detected as anomalies trigger alert notifications via Telegram to the administrator without performing automatic blocking. The results show that the system is able to provide early warnings for suspicious activities, thereby helping to improve server security more effectively.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

 The rapid adoption of smartphones among Indonesian digital natives has increased reliance on biometric authentication systems. However, empirical evidence regarding the relationship between user satisfaction and security risk awareness remains limited, particularly in developing-country contexts. This study investigates the behavioral dynamics of biometric security perception among 266 respondents, consisting of 221 high school students and 45 university students in Indonesia. A Python-based computational pipeline incorporating Akaike Information Criterion (AIC) validation and 1,000-iteration stochastic bootstrapping was employed to evaluate nonlinear behavioral patterns using Polynomial Regression and Ordinary Least Squares (OLS) multivariate analysis. The results confirm the existence of a nonlinear Security Paradox. While the overall population demonstrates a positive quadratic trajectory, the university student group exhibits a concave-down parabolic relationship (a=−0.0460), indicating a decline in perceived utility beyond a specific security threshold. The identified behavioral breaking point occurs at X≈5.45 (95% CI: 2.99–20.77), suggesting that excessive security hardening may reduce perceived usability and increase cognitive friction. Furthermore, the ablation analysis reveals that security risk awareness (p<0.001) is the strongest predictor of user satisfaction, exceeding the influence of daily usage intensity. Segment-level analysis further demonstrates behavioral divergence between respondent groups. High school students exhibit relatively uniform satisfaction toward biometric systems, whereas university students display greater variability and more critical perceptions regarding authentication friction. These findings indicate that highly rigid security configurations may become less effective for users with higher digital literacy and risk awareness. This study contributes a computationally validated behavioral framework for understanding security–utility trade-offs and provides a conceptual foundation for developing adaptive, user-centric, and friction-aware biometric authentication systems.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

Lagu Jawa modern merupakan perkembangan kontemporer dari musik tradisional Jawa yang semakin banyak dinikmati generasi muda melalui platform digital. Namun, meningkatnya jumlah lagu baru membuat pengguna sulit menemukan lagu yang sesuai dengan preferensi musikal jika pencarian hanya dilakukan berdasarkan judul atau nama artis. Penelitian ini mengembangkan pendekatan rekomendasi berbasis konten melalui klasterisasi fitur audio terhadap 100 lagu Jawa modern yang dikumpulkan dari YouTube. Fitur audio diekstraksi menggunakan Librosa pada Python, meliputi tempo BPM, danceability, RMS energy, zero-crossing rate, MFCC means, spectral contrast means, dan chroma features means, sehingga setiap lagu menghasilkan 36 fitur numerik. Algoritma K-Means digunakan untuk membentuk tiga klaster, PCA digunakan untuk memvisualisasikan distribusi klaster, dan Silhouette Score digunakan untuk mengevaluasi kualitas pengelompokan. Hasil penelitian mengidentifikasi tiga gaya musikal yang dapat diinterpretasikan, yaitu Jawa modern enerjik, lagu tradisional atau akustik, serta Jawa eksperimental atau ambient, dengan Silhouette Score sebesar 0,114. Temuan ini menunjukkan bahwa fitur audio mampu merepresentasikan karakter lagu secara objektif dan mendukung sistem rekomendasi adaptif untuk pelestarian musik daerah. Pendekatan ini juga membantu memperluas akses pendengar terhadap kekayaan musik lokal di era digital secara berkelanjutan.

This study aims to design and develop a web-based New Student Admission System (PPDB) Website at MIS Chairul Bariyyah Medan Krio to overcome the limitations of manual registration systems. The method used is Agile, as it supports iterative, flexible, and adaptive system development according to user needs. Data collection techniques include observation, interviews, literature study, and documentation. The system is developed using the Python programming language with the Django framework and MySQL database. The results show that the developed system improves the efficiency of the registration process, minimizes data recording errors, and facilitates real-time management of applicant data. In addition, the Website also serves as an information and promotional medium for the school that can be accessed anytime. Based on Blackbox testing and User Acceptance Test (UAT), the system is proven to run well and is easy to use. Therefore, this web-based PPDB system is effective in improving the quality of Administrative services at MIS Chairul Bariyyah.

Lagu Jawa modern merupakan perkembangan kontemporer dari musik tradisional Jawa yang semakin banyak dinikmati generasi muda melalui platform digital. Namun, meningkatnya jumlah lagu baru membuat pengguna sulit menemukan lagu yang sesuai dengan preferensi musikal jika pencarian hanya dilakukan berdasarkan judul atau nama artis. Penelitian ini mengembangkan pendekatan rekomendasi berbasis konten melalui klasterisasi fitur audio terhadap 100 lagu Jawa modern yang dikumpulkan dari YouTube. Fitur audio diekstraksi menggunakan Librosa pada Python, meliputi tempo BPM, danceability, RMS energy, zero-crossing rate, MFCC means, spectral contrast means, dan chroma features means, sehingga setiap lagu menghasilkan 36 fitur numerik. Algoritma K-Means digunakan untuk membentuk tiga klaster, PCA digunakan untuk memvisualisasikan distribusi klaster, dan Silhouette Score digunakan untuk mengevaluasi kualitas pengelompokan. Hasil penelitian mengidentifikasi tiga gaya musikal yang dapat diinterpretasikan, yaitu Jawa modern enerjik, lagu tradisional atau akustik, serta Jawa eksperimental atau ambient, dengan Silhouette Score sebesar 0,114. Temuan ini menunjukkan bahwa fitur audio mampu merepresentasikan karakter lagu secara objektif dan mendukung sistem rekomendasi adaptif untuk pelestarian musik daerah. Pendekatan ini juga membantu memperluas akses pendengar terhadap kekayaan musik lokal di era digital secara berkelanjutan.

Studi ini dirancang untuk menginvestigasi pengaruh Suhu Permukaan Laut (SST) dan konsentrasi klorofil-a terhadap variabilitas hasil tangkapan ikan di kawasan Pantai Selatan melalui pendekatan model regresi linear berganda dengan estimasi Ordinary Least Squares (OLS). Pada tahap diagnostik awal, terdeteksi adanya indikasi autokorelasi positif pada residual model (statistik Durbin–Watson = 0,993), suatu kondisi yang berpotensi menginduksi bias pada estimasi standard error sehingga dapat mengkompromikan validitas inferensi statistik. Guna mengantisipasi permasalahan tersebut, dilakukan koreksi menggunakan metode Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors yang diimplementasikan melalui komputasi Python. Hasil estimasi pasca-koreksi mengungkapkan bahwa secara simultan, variabel SST dan klorofil-a memberikan pengaruh yang signifikan terhadap keragaman hasil tangkapan ikan, dengan nilai koefisien determinasi (R²) sebesar 23,7%. Berdasarkan uji parsial (uji t terkoreksi), SST menunjukkan pengaruh negatif yang signifikan secara statistik terhadap hasil tangkapan, sementara klorofil-a tidak memperlihatkan pengaruh yang signifikan. Temuan tersebut mengimplikasikan bahwa variabel termal (SST) memiliki kontribusi yang lebih dominan dibandingkan indikator produktivitas primer (klorofil-a) dalam memengaruhi dinamika hasil tangkapan ikan pada wilayah kajian.

Ketidakseimbangan antara tingkat persediaan dan permintaan pasar dapat mengakibatkan kelebihan stok, meningkatnya biaya penyimpanan, dan meningkatnya risiko kerusakan produk. PT. Tridaya Sakti Medima, sebuah perusahaan distribusi farmasi yang berlokasi di Palembang, menghadapi tantangan serupa dalam mengelola persediaannya. Penelitian ini bertujuan untuk menganalisis pola pembelian dengan menerapkan teknik penambangan data menggunakan algoritma Apriori dan untuk merancang sistem rekomendasi produk yang mendukung pengambilan keputusan dalam pengendalian persediaan. Studi ini mengadopsi metodologi CRISP-DM (Cross Industry Standard Process for Data Mining), yang terdiri dari tahap pemahaman bisnis, persiapan data, pemodelan, evaluasi, dan penerapan. Data transaksional yang dianalisis terdiri dari catatan penjualan historis dari Januari hingga Desember 2024, dengan total 5.410 entri setelah proses pembersihan data. Analisis dilakukan menggunakan Python, sedangkan Streamlit digunakan untuk mengembangkan dasbor interaktif untuk visualisasi. Temuan menunjukkan bahwa algoritma Apriori berhasil mengidentifikasi aturan asosiasi antarproduk berdasarkan metrik dukungan dan kepercayaan. Nilai kepercayaan dan peningkatan tertinggi ditemukan pada kombinasi produk (ITR) PECTORIN SYRUP 120 ML @ 24 dan (ITR) ITRABAT SYR 100 ML @ 24 dengan rasio lift 16,43. Hasil ini disajikan melalui dasbor yang dirancang untuk membantu PT. Tridaya Sakti Medima dalam meningkatkan manajemen persediaan dan mengatasi masalah ketidakseimbangan stok.

This study analyzes the performance of the Dual Modulus RSA algorithm in securing text data using Python. The rapid growth of digital technology has increased the risk of data security threats, making efficient and secure encryption essential. Dual Modulus RSA is a modification of the classic RSA algorithm that uses two different moduli in the encryption and decryption process, thus increasing security levels because attackers must factorize two moduli simultaneously. This research uses an experimental quantitative approach by measuring the execution time of encryption and decryption processes with variations in plaintext length (5, 10, and 15 characters). Implementation was carried out using Python 3 with the time.perf_counter() function for microsecond-precision measurement. The results show that the Dual Modulus RSA algorithm successfully encrypts and decrypts all test plaintexts correctly. Encryption time ranged from 0.0212 ms to 0.0823 ms, while decryption time ranged from 0.0422 ms to 0.0955 ms. There is a positive linear relationship between plaintext length and processing time. Decryption is consistently slower than encryption due to the larger private key exponent (d1=2753, d2=3533) compared to the public exponent (e=17). The main factors affecting performance are exponent size, dual modulus overhead, CPU caching effects, and Python interpretation overhead. This study recommends using Dual Modulus RSA with hybrid encryption for practical implementation to balance security and performance.

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

PT. XYZ is one of the largest retail companies in Indonesia. PT. XYZ has a website-based application that functions to help employees who work in IT Support to handle operational problems. However, this website has not implemented a session management system which can affect the level of security. Therefore, a system design was created for real-time session management using Firestore, Python, and Javascript. Firestore is used as a database to store sessions and Firestore has a feature called the Firestore snapshot listener which functions to detect changes in real-time. The research method includes identifying needs, initial prototype design, prototype creation, prototype evaluation, prototype refinement, and system implementation. Black Box Testing is used as a method for testing this system, where the results are in accordance with the needs and interviews are conducted with the admin of this website to test the session management system and find out the admin's response regarding the designed session management system. The results obtained are satisfactory because security can be further improved, user experience is improved, and the company's needs can be achieved.

This study aims to compare the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) in predicting student performance based on academic scores. Student performance is defined as the average of math scores, Reading Scores, and writing scores. This study uses a quantitative approach with a comparative design based on predictive modeling. The data used is secondary data from the Student Prediction dataset obtained through the Kaggle platform, which was processed using the Python programming language through the Google Colab platform. The analysis stages included the formation of performance variables, the separation of training and test data with a ratio of 80:20, model training, and evaluation using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The results show that the Multiple Linear Regression model produced an MSE value of 2.74 × 10⁻²⁸, an MAE of 1.51 × 10⁻¹⁴, and an R² of 1.000. Meanwhile, Random Forest Regression produced an MSE of 0.296, an MAE of 0.375, and an R² of 0.998. These findings indicate that both models have a very high level of accuracy, but Multiple Linear Regression provides the best performance. This is due to the strong linear relationship between the input variables and the target variables formed directly from the combination of academic values. Thus, the linear regression model is proven to be more suitable for use in data structures that have simple linear relationships compared to ensemble-based models.

Manual management of sports field bookings is still widely practiced and often leads to scheduling conflicts, data recording errors, and low service efficiency. This study aimed to design and develop a desktop-based sports field booking application that automates the booking process and manages schedules in a structured manner. The research employed a system design and development method using an object-oriented programming (OOP) approach. Data were collected through direct observation of the booking process, interviews with field managers, and documentation of system requirements. The application was developed using the Python programming language with the PyQt5 framework for the graphical user interface and MySQL as the database management system. The results showed that the developed application is capable of managing field data, schedules, bookings, and user information in an integrated manner while reducing recording errors and minimizing scheduling conflicts. The application of OOP resulted in a modular, well-organized, and maintainable system structure. This application is expected to improve the efficiency and accuracy of sports field booking management and provide a practical solution for implementing a computerized booking system.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.