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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 249 Documents
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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 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.
PERANCANGAN UI/UX APLIKASI ABSENSI BERBASIS MOBILE MENGGUNAKAN METODE DESIGN THINKING Rangga Gading Satria; Anis Lelitasari; Reza Ilyasa; Yudha Wibowo; Mohammad Roffi Suhendry; Rizky Sabana
Jurnal Teknologi Informasi Mura Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura MARET
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.3191

Abstract

Perkembangan teknologi mobile mendorong kebutuhan akan sistem absensi yang lebih efektif dan efisien dibandingkan metode manual yang masih banyak digunakan. Proses absensi konvensional sering menimbulkan berbagai permasalahan, seperti ketidakakuratan data, potensi kecurangan, serta keterbatasan dalam pengolahan dan pelaporan data. Oleh karena itu, penelitian ini bertujuan untuk merancang user interface aplikasi absensi berbasis mobile yang mampu meningkatkan kemudahan penggunaan dan pengalaman pengguna. Metode yang digunakan dalam penelitian ini adalah Design Thinking yang terdiri dari tahapan empathize, define, ideate, prototype, dan testing. Melalui pendekatan ini, kebutuhan pengguna dianalisis secara mendalam untuk menghasilkan desain antarmuka yang intuitif, sederhana, dan sesuai dengan karakteristik pengguna. Hasil penelitian menunjukkan bahwa rancangan user interface yang dihasilkan mampu meningkatkan kemudahan navigasi, mempercepat proses absensi, serta memberikan pengalaman pengguna yang lebih baik. Dengan demikian, perancangan user interface berbasis metode Design Thinking memiliki peran penting dalam meningkatkan efektivitas penggunaan aplikasi absensi mobile.
PENDEKATAN COMPUTER VISION BERBASIS FUSION CNN DAN GRAD-CAM UNTUK IDENTIFIKASI PENYAKIT DAUN CABAI Andri Anto Tri Susilo; Lukman Sunardi; Budi Santoso
Jurnal Teknologi Informasi Mura 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.v18i2.3033

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has created new opportunities for modernizing the agricultural sector, particularly in the early detection and classification of plant diseases based on digital images. A Computer Vision-based approach has emerged as an effective solution, as it enables the automation of visual analysis that was previously reliant on manual observation. In this study, a method based on Fusion Convolutional Neural Networks (CNN) is proposed, combining the strengths of ResNet and DenseNet architectures to produce more robust and discriminative feature representations. In addition, this research integrates an Explainable Artificial Intelligence (XAI) technique using Grad-CAM to provide visual interpretations of the model’s decisions, thereby enhancing user trust in the developed system. The dataset used consists of three main classes of chili leaf conditions: Bacterial Spot, Curl Virus, and Healthy. Experimental results demonstrate that the proposed model achieves excellent performance, with an accuracy of 98%. Further analysis through the classification report indicates that the Healthy class attains perfect performance, with precision, recall, and f1-score all reaching 1.00. Meanwhile, the Bacterial Spot class achieves a recall of 1.00 and an f1-score of 0.97, indicating the model’s capability to correctly identify all samples in this class. The Curl Virus class also shows strong performance, with a precision of 1.00, recall of 0.95, and f1-score of 0.97. Overall, the macro average and weighted average f1-scores both reach 0.98, reflecting the model’s stability and consistency across all classes. Furthermore, the implementation of Grad-CAM is able to highlight specific regions on chili leaves that contribute to the model’s predictions, providing deeper insight into the disease patterns recognized by the model. This not only enhances interpretability but also supports visual validation by users. Therefore, this study demonstrates that the combination of Fusion CNN and Grad-CAM is not only effective in improving classification accuracy but also ensures transparency in the decision-making process, making it highly suitable for intelligent decision-support systems in precision agriculture
DIGITALISASI PENGELOLAAN SURAT PADA PT PUPUK SRIWIDJAJA MENGGUNAKAN METODE FAST Nurul Adha Oktarini Saputri; R.M. Nasrul Halim
Jurnal Teknologi Informasi Mura 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.v18i2.3193

Abstract

Mail management is an important part of the company's administrative process, especially in terms of archiving, recording, and efficient distribution of documents. PT Pupuk Sriwidjaja Palembang as a large company requires a system that is able to handle letter management effectively and in a structured manner. So far, the letter management process is still mostly done manually, potentially causing problems such as delays, lost documents, and difficulties in searching for archives. This study aims to design and develop a web-based letter management system to support the digitalization process of administration at PT. Pupuk Sriwidjaja Palembang. The system built is a web-based application and uses the FAST (Framework for the Application of Systems Thinking) method, which is a structured approach in information system development. This study implements all FAST stages from needs analysis to system testing and evaluates the level of user acceptance using the System Usability Scale (SUS) method. The result of this study is a digital letter management application that supports the process of incoming and outgoing letters, recording, and searching for archives quickly and in a structured manner. The results of the system evaluation using the SUS method obtained a score of 84.0 which is in the Excellent category with an Acceptable user acceptance level. The implementation of the system is able to accelerate the document search process, improve the letter archiving process, and reduce the use of physical documents in the administrative process so that it can support the digitalization of administration at PT Pupuk Sriwidjaja Palembang.
PENERAPAN MACHINE LEARNING DALAM DETEKSI SERANGAN MALWARE PADA CIC IIOT DATASET 2025 Muhammad Fikri Akbar; Novi Lestari; Deni Nurdiansyah; Armanto Armanto
Jurnal Teknologi Informasi Mura 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.v18i2.3300

Abstract

The development of the Industrial Internet of Things (IIoT) has increased the efficiency and productivity of industrial systems, but has simultaneously increased vulnerability to cyber threats, particularly malware attacks that can disrupt computer systems, networks, and IoT devices. This study aims to build and evaluate a machine learning model for classifying malware threats in IIoT infrastructure. The dataset used is the CIC IIoT Dataset 2025, containing normal (benign) network traffic and two types of malware attacks, namely Mirai SYN Flood and Mirai UDP Flood. The main problem in the dataset is class imbalance, so the SMOTE technique is applied to balance the data distribution, alongside the challenge of similar characteristics between attack classes. Three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest, were used to build multi-class and binary classification models. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. The multi-class results show that Random Forest achieved the best performance with 87% accuracy and a weighted F1-score of 0.87, followed by SVM (86%; 0.86) and KNN (85%; 0.86), while in the binary classification scenario Random Forest reached 98.42% accuracy. These results are expected to provide insight into the most effective algorithm for detecting and classifying malware attacks in IIoT networks, thereby improving the security and reliability of IIoT-based industrial systems.
COMPARING GAUSSIAN AND DISCRETIZED NAIVE BAYES FOR FOREX TRADING WITH ONNX-INTEGRATED MT5 EXECUTION Abdillah Baradja; Bayu Mukti; Muhamad Fadel Amrullah
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3367

Abstract

Predicting non-stationary, non-normal hourly foreign exchange rates using probability-based classifiers is challenging, and the standard Gaussian assumption for Naive Bayes can be fragile. This study evaluated and compared three Naive Bayes representations for hourly foreign exchange rate prediction to examine the impact of feature discretization. The representations included Gaussian Naive Bayes on continuous indicators, equal-width binned Bernoulli Naive Bayes, and equal-frequency binned Bernoulli Naive Bayes. Relative Strength Index, Average True Range, and Moving Average Convergence Divergence indicators were computed from hourly historical bars for three major currency pairs. The models were trained in Python, exported via the Open Neural Network Exchange, and integrated into MetaTrader 5 for backtesting under a standardized execution gate. Backtests revealed that the quantile-binned equal-frequency model achieved consistent profitability across all three currency pairs, whereas the Gaussian and uniform-binned models demonstrated unstable performance and significant drawdowns. The findings suggested that quantile-based discretization mitigated the impact of outliers and improved classifier robustness in non-stationary market environments.
EVALUASI KINERJA DECISION TREE DAN LOGISTIC REGRESSION PADA ANALISIS SENTIMEN ULASAN GOJEK Resti Julia Amayani Yenni; Dodo Zeinal Abidin; Jasmir
Jurnal Teknologi Informasi Mura 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.v18i2.3292

Abstract

Sentiment analysis of Gojek reviews has been widely conducted; however, comparisons of Decision Tree and Logistic Regression with class imbalance handling using SMOTE and ADASYN remain limited. This study aims to compare the performance of both algorithms under baseline, SMOTE, and ADASYN scenarios using 225,002 Gojek reviews represented by TF-IDF. Hyperparameter tuning was performed using Grid Search and 5-Fold Cross Validation, while differences between models were evaluated using the McNemar test. The novelty of this study lies in the comparative evaluation of six configurations, consisting of two classification algorithms across three class imbalance handling scenarios, systematically analyzed through hyperparameter tuning, 5-Fold Cross Validation, and the McNemar test. The results show that Logistic Regression with SMOTE achieved the highest cross-validation F1-score of 0.8946, while Decision Tree achieved its highest score of 0.8100 under the baseline scenario. These findings indicate that the effectiveness of oversampling depends on the algorithm used. This study contributes empirical evidence for selecting sentiment classification strategies and supports the automation of user review evaluation.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS STRATEGI DIGITALISASI UMKM DI KOTA LUBUKLINGGAU MENGGUNAKAN KOMBINASI METODE AHP DAN SAW Desi Hartati; Wisnumurti Wisnumurti; Sri Tita Faulina; Endang Rahmayanti; Wenny Septafiana Aulia
Jurnal Teknologi Informasi Mura Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura MARET
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.3396

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are a vital pillar of the Indonesian economy, including in Lubuklinggau City. However, the digital transformation of MSMEs still faces various obstacles, such as low digital literacy among human resources, uneven information and communication technology (ICT) infrastructure, weak digital data security, and limited capital for digitalization investment. Various policy strategy alternatives have been proposed, yet local government requires an objective tool to determine which strategy should be prioritized given limited budget and time. This study aims to build a Decision Support System (DSS) model to determine the priority of MSME digitalization strategies in Lubuklinggau City using the Simple Additive Weighting (SAW) method. The study employs five evaluation criteria—impact on MSME performance, urgency, ease of implementation, implementation cost, and existing regulatory support—along with five policy strategy alternatives adapted from an MSME digitalization policy brief for Lubuklinggau City. The SAW calculation results show that the strategy of improving human resource capacity and digital literacy obtained the highest preference value (Vi = 0.970), followed by the integration of services through the Silampari Smart City platform (Vi = 0.753), capital assistance and digital access subsidies (Vi = 0.718), digital data security education (Vi = 0.690), and equitable distribution of ICT infrastructure (Vi = 0.665). These results indicate that the SAW method can serve as a systematic, transparent, and accountable decision-support tool for policymakers in formulating MSME digitalization program priorities.
DEVELOPMENT OF A WEB-BASED POSYANDU INFORMATION SYSTEM WITH RANDOM FOREST FOR TODDLER GROWTH-STATUS CLASSIFICATION: A SECONDARY-DATA REPLICATION Dika Pratama; Brestina Gultom
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i3.3484

Abstract

Background: Community-based child-growth monitoring requires reliable record management and transparent classification. Objective: This study documents a web-based Posyandu prototype for Mekar Jaya Village and replicates a four-class anthropometric classification experiment using an identifiable public dataset. Methods: The prototype used Laravel 10 and MySQL. A Kaggle dataset (version 2; 120,999 records) was analyzed using age, sex, and height. Stratified group-based splitting yielded 96,909 training and 24,090 untouched test records; identical predictor vectors were grouped, and random oversampling was restricted to training data. Random Forest (200 trees) and Gaussian Naive Bayes were compared on the same test set. Results: Random Forest achieved 98.89% accuracy (95% bootstrap CI: 98.76–99.02%), macro-F1 98.49%, and macro-recall 98.42%; Gaussian Naive Bayes achieved 49.63%, 41.42%, and 43.36%, respectively. Among 2,755 stunted test records, 40 were classified as Normal; none of the 3,961 severely stunted records was classified as Normal. Conclusion: The replication provides auditable evidence for reproducing dataset labels, while the web prototype demonstrates an application interface. The new results are not a verification of previously reported accuracies or evidence of local clinical validity; label provenance, local validation, and software acceptance testing remain necessary.
ANALYSIS OF STUDENT SATISFACTION WITH MENTAL HEALTH APPLICATIONS IN DKI JAKARTA USING EUCS METHOD Annisa Amelia; Rumadi Hartawan; Anton Zulkarnain Sianipar
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i3.3249

Abstract

Mental health applications have become important digital services for students because they provide flexible access to information, consultation, and psychological support. However, application use does not always mean that users are satisfied with the information content, accuracy, interface, ease of use, and service timeliness. This study aims to analyze the level of user satisfaction with mental health applications among students in DKI Jakarta using the End User Computing Satisfaction (EUCS) method. This study used a quantitative approach with a Likert-scale questionnaire distributed to 100 students who had used Halodoc or Riliv. The data were analyzed through validity testing, reliability testing, and descriptive statistics using SPSS 27. The results show that all questionnaire items are valid, with correlation values greater than the r table value of 0.1966, and all variables are reliable, with Cronbach's Alpha values above 0.6. The EUCS evaluation scores fall into the very satisfied category, namely Content 4.48, Accuracy 4.45, Format 4.44, Ease of Use 4.49, and Timeliness 4.47. The Ease of Use dimension obtains the highest score. These results indicate that mental health applications have met users' main needs, especially in terms of operational ease. Keywords: EUCS, user satisfaction, mental health application, students, DKI Jakarta

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