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OPTIMASI DISPOSISI SURAT DIGITAL MELALUI SISTEM INFORMASI BERBASIS WEB DI SMPN 1 BOJONEGARA Muhamad Shiraj Al-Ayyubi; Sigit Auliana; Mochammad Darip
INFOTECH journal Vol. 11 No. 2 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i2.15262

Abstract

Mail management at SMPN 1 Bojonegara is still done manually, causing delays in disposition, difficulty in searching for archives, and the risk of document loss. This study aims to design a web-based mail disposition information system to simplify and accelerate the administrative process. The system was developed using the Waterfall method through the stages of needs analysis, design, implementation, and testing. Analysis was carried out through observation and interviews, followed by design using UML, development with PHP (Laravel), and a MySQL database. The system was tested using the black-box method and showed that all features functioned as intended. As a result, letter recording, disposition, and archive searching can be done digitally and efficiently. This system also allows disposition to be carried out without the physical presence of the principal. Compared to previous research, this system is specifically designed for school needs and supports the recording of outgoing mail. Thus, this system supports a faster, neater, and safer correspondence process.
GAME EDUKASI ADMINISTRASI ARSIP SURAT BERBASIS CONSTRUCT 2 DI BIRO UMUM SEKRETARIAT DAERAH Mochammad Darip; Ali Rochman; Wahyu Amaldi
INFOTECH journal Vol. 12 No. 1 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v12i1.15275

Abstract

Pengelolaan arsip surat yang efektif merupakan elemen vital dalam menunjang efisiensi birokrasi dan akuntabilitas kerja di lingkungan pemerintahan. Namun, masih banyak pegawai yang memiliki pemahaman terbatas mengenai prosedur kearsipan, akibat kurangnya media pembelajaran yang menarik dan kontekstual. Penelitian ini bertujuan untuk mengembangkan aplikasi game edukasi sebagai media pembelajaran interaktif dalam memahami prosedur pengelolaan arsip surat di biro umum sekretariat daerah. Metode pengembangan yang digunakan adalah model ADDIE. Aplikasi dirancang berbasis Construct 2 dengan fitur utama berupa materi pengelolaan surat, simulasi permainan, serta pengenalan kode jenis surat. Hasil pengujian menunjukkan bahwa aplikasi dinilai sangat layak oleh pengguna, dengan mayoritas responden menyatakan sangat setuju terhadap manfaat, tampilan, dan kemudahan aplikasi. Temuan ini menunjukkan bahwa game edukasi dapat menjadi solusi inovatif dalam meningkatkan pemahaman kearsipan secara digital, khususnya bagi pegawai yang memiliki latar belakang non-arsiparis. Aplikasi ini juga berpotensi diterapkan sebagai media pelatihan berbasis kompetensi dalam sistem birokrasi pemerintahan.
Implementasi Support Vector Machine (SVM) Untuk Deteksi Serangan Jaringan Pada Sistem Keamanan Jaringan Kampus Darip, Mochammad; Sapaatullah, Asep; Rahmat, Rahmat
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2602

Abstract

Network security in campus environments faces increasingly complex challenges due to the rapid growth of internet usage, digital academic systems, and the large number of devices connected to the network. One of the main problems is the limitation of conventional security systems in detecting new or anomalous network attacks. Traditional systems generally rely on predefined attack signatures, making them ineffective against previously unknown attacks. Therefore, this study proposes a solution by implementing the Support Vector Machine (SVM) method for automatic network attack detection. The research method includes the collection of campus network traffic data, data preprocessing stages such as data cleaning, normalization, and feature selection, SVM model training, and performance evaluation using confusion matrix and ROC curve. The results show that the SVM model is able to classify normal traffic and attack traffic with very high accuracy. These findings indicate that SVM is an effective method for intrusion detection and can significantly enhance campus network security in an adaptive and efficient manner.
Analisis Performa Support Vector Machine untuk Klasifikasi Risiko Kredit Nasabah pada Perbankan Daerah Sapaatullah, Asep; Rahmat, Rahmat; Darip, Mochammad
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2603

Abstract

Credit risk assessment is a crucial component of the banking system because it directly relates to a financial institution's ability to manage potential losses due to non-performing loans. Banks often face difficulties in accurately classifying customer credit risk levels, especially when the data being analyzed is complex, nonlinear, and contains interacting variables. Conventional methods such as regression analysis often fail to capture hidden patterns in such data. Therefore, this study aims to apply the Support Vector Machine (SVM) algorithm as a solution to classify bank customers' credit risk levels based on attributes such as income, loan amount, length of employment, payment history, debt-to-income ratio, and asset ownership status. The research process begins with data collection and pre-processing, including data cleaning and normalization to ensure a uniform distribution of values. The data is then divided into training and test data with specific proportions. An SVM model is then applied using several kernel types, such as linear, polynomial, and radial basis function (RBF), to determine the best-performing kernel. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics to measure classification performance. Test results show that the SVM model with the RBF kernel provided the best results, achieving an accuracy rate of over 90% and minimizing classification errors in the high-risk category. In conclusion, the application of the SVM algorithm has proven effective in classifying customer credit risk levels with high accuracy and stability, making it a reliable tool for banks in the creditworthiness analysis process and more accurate, data-driven strategic decision-making