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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 234 Documents
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Klasifikasi Persiapan Keuangan Mahasiswa Tingkat Akhir dalam Menghadapi Dunia Kerja Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor Tiska Pattiasina; Stenly Ronaldo Titioka; Frangky Jansens Louth; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3154

Abstract

Financial preparation is an important factor for final year students in facing the world of work. This study aims to classify the financial readiness of final year students using the Naive Bayes and K-Nearest Neighbor (K-NN) algorithms based on 15 attributes related to economic conditions, financial behavior, and financial literacy. Data were obtained from 85 final year students of the Business Administration Department of Ambon State Polytechnic and processed using the Synthetic Minority Over-sampling Technique (SMOTE) technique to address class imbalance. Testing was conducted using WEKA software with a 10-fold cross-validation method. The results showed that the Naive Bayes algorithm produced an accuracy of 96.6667%, a precision of 96.7%, a recall of 96.7%, and an ROC Area of ​​0.9988. Meanwhile, the K-Nearest Neighbor (K-NN) algorithm produced an accuracy of 80.0%, a precision of 80.4%, a recall of 80.0%, and an ROC Area of ​​0.8703. These results indicate that Naive Bayes outperforms K-NN in classifying the financial readiness of final-year students. Furthermore, the application of SMOTE has been shown to improve the model's ability to recognize minority classes, resulting in a more balanced and representative classification.
Prediksi Status Pesanan Marketplace Menggunakan Algoritma Random Forest Alda Zevana Putri Widodo
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3216

Abstract

This study aims to predict order status on a marketplace platform using the Random Forest algorithm as a classification method. The dataset consists of 32,187 transaction records containing various attributes, including product price, discount, shipping cost, item weight, payment method, and product category. During the modeling process, the data were divided into 80% training data and 20% testing data to develop a model capable of classifying whether an order is likely to be successfully completed or canceled. Based on the experimental results, the Random Forest algorithm demonstrated excellent performance, achieving an accuracy of 94.78%, precision of 95.57%, recall of 99.05%, and an F1-score of 97.28%. These results indicate that the model can effectively identify transaction patterns and provide highly accurate predictions of order status. Therefore, the proposed model has the potential to serve as a decision-support tool for marketplace platforms in reducing order cancellation rates and improving transaction management efficiency.
ANALISIS KRITIS KOMPREHENSIF DAN EVALUASI METODOLOGIS PERLINDUNGAN PRIVASI DATA DALAM EKOSISTEM KECERDASAN BUATAN Julianti Juli; Yustina Fitriani; Muna Nustelu; Khalimin Khalimin; Tomi Defisa
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3219

Abstract

Perkembangan teknologi Artificial Intelligence (AI) telah memberikan berbagai manfaat di bidang pendidikan, kesehatan, keuangan, dan layanan publik. Namun, pemanfaatan AI yang bergantung pada data dalam jumlah besar juga menimbulkan risiko terhadap privasi data pribadi, seperti pengumpulan data tanpa izin, profiling pengguna, re-identifikasi data anonim, dan bias algoritma. Penelitian ini bertujuan menganalisis tantangan perlindungan privasi data dalam ekosistem AI serta strategi yang dapat diterapkan untuk mengatasinya. Metode yang digunakan adalah studi literatur sistematis dengan pendekatan kualitatif melalui analisis jurnal ilmiah, regulasi, dan laporan teknis yang relevan. Hasil penelitian menunjukkan bahwa perlindungan privasi data memerlukan pendekatan yang terintegrasi melalui penguatan regulasi, penerapan teknologi perlindungan data seperti Privacy by Design, Differential Privacy, dan Explainable AI (XAI), serta peningkatan literasi digital masyarakat. Selain itu, penelitian ini menemukan adanya tantangan implementasi regulasi dan kebutuhan akan pengaturan yang lebih spesifik terkait penggunaan AI. Oleh karena itu, kolaborasi antara pemerintah, pengembang teknologi, organisasi, dan masyarakat diperlukan untuk mewujudkan ekosistem AI yang aman, transparan, dan bertanggung jawab.
PENGEMBANGAN MODEL ARSITEKTUR TEKNOLOGI INFORMASI DESIGN THINKING UNTUK MENDUKUNG DESA CERDAS DI PABEAN UDIK Rudhy Purabaya; Anita Muliawati; Widya Cholil; Prihandoko
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3221

Abstract

Smart village development has become a key strategy for accelerating digital transformation in village governance, in line with the Indramayu Regency program targeting digital villages across 309 villages. This study aims to identify information technology problems and needs and to develop a smart village information technology architecture model in Pabean Udik Village. It applies a human-centered design thinking approach through five stages—empathize, define, ideate, prototype, and test—with data collected through Focus Group Discussions (FGD), observation, and interviews. The empathize stage mapped problems across five smart village dimensions and confirmed high social readiness. The define stage concluded that the village's current information systems and technology are not yet adequate to support the smart village dimensions. The ideate stage produced a layered IT architecture model comprising eleven modules mapped to five dimensions, aligned with regional policy through a follow-up FGD with regency-level stakeholders and business/industry actors. The prototype stage realized it as a low-fidelity prototype, and the test stage validated it through expert judgment and walkthrough, yielding a "highly feasible" category with minor improvement recommendations. The study concludes that the success and sustainability of smart village development require technological readiness, policy support, and collaborative commitment across stakeholders.
ANALISIS DAN PREDIKSI TINGKAT KERENTANAN PMKS MENGGUNAKAN CHI-SQUARE FEATURE SELECTION DAN RANDOM FOREST PADA DINAS SOSIAL KOTA LUBUKLINGAU Lovhura Anaphalys Sabryna; Andri Anto Tri Susilo; Cindi Wulandari
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

People with Social Welfare Problems (PMKS) are a part of society that faces social, economic, and environmental challenges, thus needing proper support and action from local governments. However, identifying and assessing the vulnerability of PMKS at the Lubuklinggau City Social Service still encounters obstacles, such as the numerous social factors involved and the risk of bias in evaluations. Thus, this study intends to create a predictive model for PMKS vulnerability levels using a machine learning method based on Chi-Square feature selection and the Random Forest algorithm. The research starts with gathering and prepping PMKS data, which includes socioeconomic factors, family situations, and access to public services. The Chi-Square method is used to identify the most impactful features related to PMKS status. The findings show that access to public services, children’s education status, home ownership, and monthly income are the most important features, supported by the highest Chi-Square scores and very low p-values. These chosen features are then used as inputs for the Random Forest classification model. The experimental results reveal exceptional model performance, achieving accuracy, precision, recall, and F1-score values of 100% for both categories, specifically PMKS and Non-PMKS. These results suggest that combining Chi-Square feature selection and the Random Forest algorithm can yield a precise and reliable predictive model for classifying PMKS vulnerability levels. Therefore, the proposed model has significant potential as an objective and data-based support tool for the Lubuklinggau City Social Service in developing policies and ensuring better-targeted distribution of social welfare programs.
INTENSITAS PENGGUNAAN CHATGPT TERHADAP KREATIVITAS DAN PRODUKTIVITAS MAHASISWA:TINJAUAN LITERATUR SISTEMATIS Sri Muliani Sulastri; Setiawan Assegaff; Sharipuddin
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3293

Abstract

The use of ChatGPT in students' academic activities has grown rapidly, yet research findings on its influence on creativity and productivity remain scattered and have not been systematically synthesized. This study aims to map the theories, variables, methods, and findings of prior studies on ChatGPT usage intensity and its impact on students' creativity and productivity. A Systematic Literature Review (SLR) was conducted following the Kitchenham and Charters guidelines, searching the Scopus, Science Direct, and Google Scholar databases for the 2019-2025 period using the keywords ChatGPT, usage intensity, creativity, and student productivity. After a selection process based on inclusion and exclusion criteria, relevant articles were analyzed and synthesized. The review shows that Technology Acceptance Model 2 (TAM 2) and End User Computing Satisfaction (EUCS) are the most frequently used theoretical frameworks for explaining ChatGPT usage intensity, with Structural Equation Modeling-Partial Least Squares (SEM-PLS) as the dominant analytical method. This review also identifies a research gap: few studies have simultaneously integrated TAM 2 and EUCS to explain the relationship between usage intensity, creativity, and productivity among students. These findings provide a conceptual basis for developing a subsequent empirical research model.
ANALISIS QUALITY OF SERVICE JARINGAN INTERNET MENGGUNAKAN METODE MANGLE CLASS-BASED WEIGHTED FAIR QUEUEING PADA LAYANAN VIDEO CONFERENCE Darmansyah; Riska Kurniyanto Abdullah; Eduard Sinaga
Jurnal Teknologi Informasi Mura (JTI) 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.v18i1.3333

Abstract

Peningkatan kebutuhan internet di lingkungan akademis untuk pembelajaran daring, penelitian, dan komunikasi menimbulkan tantangan baru dalam manajemen bandwidth, khususnya untuk aplikasi sensitif jaringan seperti video conference. Penelitian ini menganalisis Quality of Service (QoS) pada jaringan internet pada Insantsi XYZ dengan menerapkan metode Mangle Class-Based Weighted Fair Queuing (CBWFQ). Simulasi jaringan dilakukan menggunakan GNS3 dengan platform video conference Zoom, dan pengukuran QoS menggunakan Wireshark beserta Zoom Network Diagnostics. Parameter QoS diukur meliputi throughput, packet loss, delay, dan jitter pada enam router (Gedung A, Gedung B, Gedung E, Gedung F, Gedung G, dan Lab). Hasil penelitian menunjukkan bahwa implementasi metode CBWFQ memberikan peningkatan kinerja jaringan yang signifikan dibandingkan kondisi normal. Throughput meningkat 39,86% (Wireshark) dan 35,33% (Zoom Network Diagnostic). Packet loss berkurang 70,19% (Wireshark) dan 82,87% (Zoom Network Diagnostic). Delay menurun 11,90% (Wireshark) dan 60,56% (Zoom Network Diagnostic). Jitter berkurang 11,05% (Wireshark) dan 57,05% (Zoom Network Diagnostic). Metode CBWFQ terbukti efektif dalam meningkatkan kualitas layanan video conference dengan memberikan prioritas bandwidth yang lebih optimal.
Pengembangan Model Hybrid DenseNet-SVM Untuk Klasifikasi Penyakit Buah Jambu Berdasarkan Citra Digital Reza Novriansah; Asep Toyib Hidayat; Harma Oktavia LW3
Jurnal Teknologi Informasi Mura (JTI) 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.v18i1.2958

Abstract

Abstract Diseases in guava fruit such as Anthracnose and fruit fly attacks can drastically reduce crop quality. Manual identification is often subjective and slow. This research proposes a hybrid model combining Deep Learning architecture DenseNet121 as a feature extractor and Support Vector Machine (SVM) as a classifier. The dataset used is the "Guava Disease Dataset" which has been augmented into 3,784 images. The results showed that the hybrid DenseNet-SVM model with a linear kernel achieved the highest testing accuracy of 99.62%. This proves that combining deep feature extraction with an optimal margin classifier is highly effective for plant disease detection. Keywords— guava, disease classification, DenseNet121, SVM, digital image
PENERAPAN RETRIEVAL-AUGMENTED GENERATION(RAG) DAN LARGE LANGUAGE MODEL(LLM) PADA CHATBOT PELAYANAN PUBLIK DINAS ADMINISTRASI DAN DAN PENCATATAN SIPIL KABUPATEN MUSI RAWAS UATARA Ari Bauceng; Adri Anto Tri Susilo; Harma Oktavia Lingga Wijaya; Asep Toyib Hidayat
Jurnal Teknologi Informasi Mura (JTI) 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.v18i1.2964

Abstract

The development of information technology encourages government agencies to improve the quality of public services through digital services. The Population and Civil Registration Office (Disdukcapil) of North Musi Rawas Regency is required to provide fast and easily accessible population administration services. This study designed and implemented a WhatsApp-based chatbot as a population information service medium. The methods used included needs analysis, system design, implementation, and functionality testing. The chatbot provides information related to document requirements, service schedules, administrative flows, and officer contact features. The implementation results show that the system is able to improve service efficiency, facilitate information access, and reduce the burden of manual service. This chatbot has the potential to become an adaptive and sustainable digital public service solution.
PENERAPAN METODE ARAS DALAM PEMILIHAN LOKASI REHABILITASI TAMBANG TIMAH DI KEPULAUAN BANGKA BELITUNG Nurhaeka Tou; Putri Mentari Endraswari; Iski Zaliman; A. Taqwa Martadinata; Marhasi Putri
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.3116

Abstract

Determining the priority for the rehabilitation of former tin-mining land is still done manually, so the assessment process is time-consuming and can introduce subjectivity into decision-making. Differences in land conditions based on several criteria make the process of determining rehabilitation priorities more complex, so a system capable of providing objective and structured recommendations is needed. This study aims to develop a Decision Support System (DSS) using the ARAS (Additive Ratio Assessment) method to determine rehabilitation priorities for former tin mining land. The ARAS method is used through the stages of determining criteria and weights, normalizing the decision matrix, calculating utility values, and ranking alternative land areas. The results show that the system is able to generate rehabilitation priority recommendations in accordance with the results of manual calculations, and helps the decision-making process become more effective and efficient. Testing using the Mean Absolute Percentage Error (MAPE) method yielded an error of 13.3%, which falls within the good category. These results indicate that the system has a fairly high level of accuracy in supporting the determination of rehabilitation priorities for former tin mining land.

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