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Jurnal Teknologi Informasi Mura
Published by Universitas Bina Insan Lubuklinggau
ISSN : 20856156     EISSN : 26148722     DOI : https://doi.org/10.32767/jti.v15i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Focus and Scope Manajemen TI dan Tata Kelola TI e-Government e-Kesehatan, e-Learning, e-Manufaktur, e-Commerce ERP dan Manajemen Rantai Pasokan Manajemen Proses Bisnis Sistem Cerdas Kota Pintar Teknologi Awan Cerdas Peralatan Cerdas & Perangkat Komputasi yang Dapat Dipakai Sistem Robot Jaringan Sensor Cerdas Infrastruktur Informasi untuk Smart Living Spaces Sistem Transportasi Cerdas Pemodelan Konseptual, Bahasa dan desain Rekayasa Perangkat Lunak Jaringan yang berpusat pada informasi Interaksi Komputer Manusia Media, Game, dan Teknologi Seluler Penambangan Data Pengambilan Informasi Informasi keamanan Pemrosesan Bahasa Alami
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 218 Documents
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ANALISIS SENTIMEN THREADS KULINER YOGYAKARTA MENGGUNAKAN FINE-TUNED SENTENCE TRANSFORMER Kadir, Nurul Tiara; Rukhmanti Udhayana Hr, Khadijah Febriana; Wahyu Setyaningsih, Putry; Muthia, Muthia
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.v17i2.2894

Abstract

Media sosial menjadi sumber penting dalam memahami persepsi wisatawan terhadap destinasi kuliner. Salah satu platform yang relatif baru dan belum banyak dieksplorasi dalam penelitian akademik adalah Threads, yang menyediakan percakapan berbasis teks. Penelitian ini bertujuan menganalisis sentimen pengguna Threads terhadap wisata kuliner Yogyakarta serta mengevaluasi potensi Threads sebagai sumber data alternatif dalam pemetaan persepsi pengguna. Data dikumpulkan melalui proses scraping dan mendapatkan 1.002 postingan Threads dengan kata kunci Kuliner Jogja pada periode awal Oktober 2024 hingga awal Desember 2025. Metode penelitian mencakup tahapan text mining, pra-pemrosesan teks, serta penerapan model kecerdasan buatan fine-tuned Sentence Transformer untuk klasifikasi sentimen ke dalam kategori positif, netral, dan negatif. Selain itu, dilakukan ekstraksi entitas untuk mengidentifikasi objek kuliner yang paling sering dibicarakan. Hasil analisis menunjukkan bahwa persepsi terhadap kuliner Yogyakarta di Threads didominasi sentimen positif sebesar 57.8%, diikuti sentimen netral sebesar 38.3%, sementara sentimen negatif relatif kecil yaitu 3.9%. Dominasi istilah seperti warung, soto, dan ayam mengindikasikan kuatnya daya tarik kuliner tradisional dalam persepsi wisata kuliner Yogyakarta. Penelitian ini menunjukkan efektivitas fine-tuned Sentence Transformer dengan data latih terbatas pada domain kuliner berbahasa Indonesia.
IMPLEMENTASI DEEP LEARNING ALEXNET UNTUK DETEKSI DAN KLASIFIKASI TANDA TANGAN Nurdiansyah, Deni; Sobri, Ahmad; Sunardi, Lukman; Rusdiyanto, Rusdiyanto
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.v17i2.2897

Abstract

The problem in this research is that the manual signature verification process is still widely used. However, this method is prone to human error and is highly subjective, so its accuracy in distinguishing genuine and fake signatures is not optimal. The pattern recognition extraction process in signatures uses the Alexnet algorithm. This study uses a digital signature image dataset consisting of two classes, with 90 images per class. Furthermore, the signature pattern recognition extraction process based on digital images can be performed using the Alexnet model. The purpose of this paper is to help classify signature types, which can facilitate the medical treatment process. The analysis uses deep learning with Python tools. Explicitly, the total sample size in Figure "Distribution of Classes in Training, Validation, and Testing Data" (image_98f1fc.png) shows that the number of samples for the 'full_forg' class is fewer than for the 'full_org' class. Although the model performs very well on the minority class, the presence of perfect recall for the 'full_org' class will be interesting to observe.
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.

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