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Jurnal Teknologi Informasi MURA
Published by Universitas Bina Insan Lubuklinggau
ISSN : 20856156     EISSN : 26148722     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Other
JTI (Jurnal Teknologi Informasi MURA) publish articles on Information System from various perspectives, covering both literary and fieldwork studies.
Arjuna Subject : Teknik (semua kategori) - Teknologi Media
Articles 316 Documents
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PENGEMBANGAN SISTEM INFORMASI MANAJEMEN MASJID TERINTEGRASI "MASJIDMU" BERBASIS WEB MENGGUNAKAN METODE PROTOTYPE bakti, ahmad mutatkin; Effendy, Irman; Azir, Muhammad
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2900

Abstract

Manajemen masjid yang profesional, transparan, dan akuntabel merupakan kebutuhan mendesak di era digital. Penelitian ini dilatarbelakangi oleh permasalahan di Cabang Muhammadiyah Ilir Barat Satu Palembang, di mana pengelolaan data administrasi, kegiatan dakwah, dan keuangan masih dilakukan secara manual dan terfragmentasi (data silo). Hal ini menyebabkan kesulitan pemantauan oleh pimpinan cabang, risiko kesalahan informasi dakwah karena ketiadaan moderasi, serta kurangnya transparansi laporan keuangan kepada jamaah. Penelitian ini bertujuan mengembangkan sistem informasi terintegrasi "MasjidMU" berbasis web. Metode pengembangan sistem yang digunakan adalah Prototype, yang memungkinkan interaksi intensif antara pengembang dan pengguna dalam merancang antarmuka sebelum implementasi. Sistem dibangun menggunakan Framework Laravel 12 dan Tailwind CSS 4.0. Hasil penelitian ini adalah sebuah sistem informasi yang mampu mengintegrasikan data dari tingkat Masjid, Ranting, hingga Pusat (Cabang). Fitur unggulan meliputi dashboard multi-level, mekanisme moderasi konten berjenjang, dashboard mandiri untuk ustadz, dan laporan keuangan publik yang real-time. Pengujian Black Box menunjukkan bahwa seluruh fitur fungsional berjalan valid. Implementasi sistem ini diharapkan dapat meningkatkan efisiensi administrasi dan kepercayaan jamaah melalui transparansi data.
EVALUASI KINERJA ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK ANALISIS SENTIMEN KOMENTAR PUBLIK TERHADAP PEMERINTAHAN PRESIDEN PRABOWO ARPAN, ATIKA; Sylvia, Sylvia; Permata, Rizka
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2864

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen komentar publik terhadap pemerintahan Presiden Prabowo menggunakan algoritma Support Vector Machine (SVM). Data komentar diambil dari platform media sosial dan melalui tahapan preprocessing seperti case folding, tokenization, stopword removal, stemming, dan ekstraksi fitur menggunakan TF-IDF. Model SVM diterapkan untuk mengklasifikasikan komentar ke dalam kategori positif, negatif, dan netral. Berdasarkan hasil evaluasi, algoritma SVM menunjukkan tingkat akurasi sebesar 92%, yang menandakan efektivitasnya dalam mengidentifikasi sentimen publik. Penelitian ini diharapkan dapat berkontribusi pada pengembangan metode analisis sentimen serta meningkatkan pemahaman terhadap persepsi masyarakat mengenai kinerja pemerintahan.
PERBANDINGAN KINERJA ALGORITMA SVM DAN NAIVE BAYES PADA KLASIFIKASI PRESTASI AKADEMIK SISWA: STUDI KASUS SMAS BPD TOBELO SELATAN Pattiasina, Tiska; Fredriksz, Grace; Luturmas, Join Rachel; Salhuteru, Andrie CH; Matuankotta, Febiola; Nunumete, Laura S; Jupriyanto, Jupriyanto
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2915

Abstract

Students’ academic achievement is an important indicator of the success of the educational process; however, its assessment is often subjective and not yet fully data-driven. Therefore, a systematic analytical approach is required to classify students’ academic achievement objectively and accurately. This study aims to compare the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying the academic achievement of grade III students at SMAS BPD Tobelo Selatan. A data mining approach using classification techniques was applied, involving 17 attributes as predictor variables and two target classes of academic achievement, namely Very Good and Good. Data processing and model evaluation were conducted using the WEKA software, with performance measured through accuracy, precision, recall, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results indicate that the SVM algorithm achieves the best performance in terms of accuracy, precision, and recall, each reaching 97.78%, while the Naive Bayes algorithm obtains the highest AUC-ROC value of 98.08%. These findings demonstrate that SVM is superior in prediction accuracy, whereas Naive Bayes shows excellent capability in class discrimination. This study is expected to support data-driven academic decision-making in school environments.
SISTEM INFORMASI GEOGRAFIS PARIWISATA BERBASIS WEB MENGGUNAKAN REACT JS DI KABUPATEN SUBANG Ahmad, Hermansyah Nur; Jupriyanto, Jupriyanto
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2917

Abstract

Subang Regency, West Java has significant natural and cultural tourism potential; however, tourism location information is not yet optimally integrated. Scattered information makes it difficult for tourists to obtain accurate location data, descriptions, and access to tourist destinations. This study aims to develop a web-based Geographic Information System (GIS) for tourism using React JS with a full-stack architecture. The system development applies the Agile method, enabling iterative and adaptive development based on user requirements. The system utilizes interactive digital maps based on Leaflet, search and category filtering features, multiple map display modes, and a review and rating feature without user login. The results show that the system provides accurate tourism location information, is easy to access, and offers real-time visit statistics. This system is expected to support regional tourism promotion and assist tourism management in data-driven decision-making.
PENERAPAN CONVOLUTIONAL NEURAL NETWORK DALAM KLASIFIKASI SERANGAN DDOS (DISTRIBUTED DENIAL OF SERVICE) PADA DATASET CICIOT 2023 Armanto, Armanto; Alamsyah, Muhammad Nur
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2919

Abstract

Abstract Distributed Denial of Service (DDoS) attacks are a serious threat to network security, aiming to disrupt services by overwhelming system resources with malicious traffic. The increasing complexity and variety of DDoS attack patterns demand the implementation of adaptive and accurate detection methods. This study examines the application of a Convolutional Neural Network (CNN) to classify DDoS attacks using the CICIoT 2023 dataset, which realistically represents Internet of Things (IoT) network traffic. The dataset underwent preprocessing stages including data cleaning, normalization, and splitting training and test data. A CNN model was designed to automatically extract features from network traffic data and classify between normal traffic and DDoS attacks. Test results show that the CNN model is capable of providing high levels of accuracy, precision, and recall, making it effective in detecting DDoS attacks in IoT environments. Thus, the CNN approach can be a reliable solution for enhancing deep learning-based intrusion detection systems in the face of dynamic DDoS threats. Keywords : Convolutional Neural Network, DDoS, Network Security, CICIoT 2023,Attack Classification.
ANALISIS SISTEM KEAMANAN DATA UNIVERSITAS BINA INSAN DARI KEJAHATAN SIBER MENGGUNAKAN PENDEKATAN METODE OWASP Susanto, Erwin
Jurnal Teknologi Informasi Mura Vol 18 No 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2925

Abstract

This study aims to analyze the data security system at Universitas Bina Insan against cybercrime threats using the Open Web Application Security Project (OWASP) Top 10 approach. Data security is an important aspect because higher education institutions store sensitive information such as academic data, user identities, and administrative records. This research uses a qualitative method with data collection techniques through interviews, observations, documentation studies, as well as web application security testing using OWASP ZAP. The results show that the system has implemented basic security measures; however, several vulnerabilities that may pose risks were still found, such as injection, cross-site scripting (XSS), weaknesses in authentication and access control, and suboptimal security configurations with risk levels ranging from low to high. Questionnaire findings support the technical results and indicate that OWASP Top 10 is effective as a framework for identifying, evaluating, and mitigating vulnerabilities. This study provides comprehensive prevention strategy recommendations to improve data security continuously.

Page 32 of 32 | Total Record : 316

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