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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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EFFECTIVENESS OF UI/UX DESIGN OF DIGITAL POSYANDU RECORDING APPLICATION IN RW 2 JABUNGAN USING DESIGN THINKING Diah Pertiwi; Dody Indra Sumantiawan
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.v18i3.3261

Abstract

 Posyandu, which stand for integrated service post, plays a crucial role in Indonesia’s public health system, but most still rely on ineffciet manual recording methods. This research aims to design the user interface (UI) and user experience (UX) of a digital posyandu recording application in RW 2 Jabungan. The research approach used is qualitative eith the Design Thinking method which includes five stages, namely empathize (observation and interviews in RW 2 Jabungan), define (identification of data access and monitoring problems), ideate (application concept with NIK search feature, data management, and growth charts), prototype (high-fidelity prototype using figma) and testing (Usability testing with System Usability Scale questionnaire. The research results show an average SUS score of 78,85 in the “Good” category. Todler mothers users gave a score of 86,67 in the “Excellent” category, posyandu cadre users gave a score of 70,83 in the “Good” category. The Design Thinking approach proved effective in creating user-centered solutions that exceed expectations. The application is ready to be implemented and has potential to become a posyandu digitalization model.
PENGEMBANGAN CHATBOT BERBASIS NATURAL LANGUAGE PROCESSING “GoLotim” UNTUK LAYANAN INFORMASI WISATA KABUPATEN LOMBOK TIMUR Dwinita Arwidiyarti; Lalu M. Takdir Darmawan Bakti
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.v18i3.3276

Abstract

East Lombok Regency has diverse tourism destinations, but information is fragmented across various platforms. This study aims to develop "GoLotim," a custom-built Natural Language Processing (NLP) chatbot integrated into a web-based tourism information system. Unlike pre-built frameworks (e.g., Dialogflow, Rasa), this approach ensures data sovereignty, lightweight deployment, and full control over localized domain terminology. The system was developed using the Waterfall SDLC with ReactJS, Flask, and MongoDB. The knowledge base comprises a manually compiled JSON dataset consisting of 6 distinct intents (tags) and 42 training question patterns. User queries undergo tokenization, case folding, and stemming, followed by text representation via a 128-dimensional Bag of Words (BoW). The feature vector is classified using a Neural Network with a 64-neuron hidden layer (ReLU) and a Softmax output layer. Evaluation was conducted via Black Box Testing and 30 predefined chatbot question scenarios (5 per intent). An "appropriate response" is operationally defined as the system correctly predicting the intended tag and retrieving factually matching information from the knowledge base. The chatbot achieved a scenario-based success rate of 93.3% (28 appropriate, 2 inappropriate). Technical classification metrics yielded a Macro-Average Precision of 0.94, Recall of 0.93, and F1-Score of 0.93, supported by Confusion Matrix analysis. The average response time was 1.20 seconds (Min: 0.85s, Max: 1.65s, Std. Dev: 0.22s). The two misclassifications were identified as False Negatives caused by unseen vocabulary variations outside the 128-feature BoW limit. These findings demonstrate the technical feasibility of the custom NLP implementation for regional tourism. However, the study is limited by the small dataset size and the absence of direct user satisfaction evaluation, highlighting the need for future research on larger independent test sets and user-based assessments
Bahasa Inggris Ellen Cipta Puthu Merinda; 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.v18i3.3278

Abstract

This study analyzes the Gojek platform work system from the perspective of driver partners using the PIECES Framework. The main issues examined include application performance, information clarity, economic impact, account security, work process efficiency, and support service quality. This study used a descriptive qualitative approach with a constructivist paradigm. Data were collected through observation, in-depth interviews with ten Gojek driver partners in Karawang Regency, documentation, and literature study. The data were analyzed through data reduction, data display, and conclusion drawing based on the six PIECES dimensions. The findings show that the Gojek platform supports driver partners through order acceptance, navigation, fare information, account security, and help center services. However, driver partners still face several problems, including system interruptions, unequal order distribution, inaccurate pickup points, fictitious orders, application deductions, paid programs, long merchant waiting times, and complaint handling that is not always fast. The novelty of this study lies in its integrated application of the six PIECES dimensions to examine the platform work system through the lived experiences of drivers partners, connecting technical and informational performance with economic conditions, control mechanisms, operational efficiency, and support services. These findings provide an evaluation basis for improving the platform work system to be more fair, stable, transparent, and responsive to the needs of driver partners.
ANALISIS SENTIMEN PENGGUNA APLIKASI BLIBLI MENGGUNAKAN TEXT MINING BERDASARKAN ULASAN GOOGLE PLAY STORE Nabilla Meilyta; Ahmad Ferdian Shobur
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.v18i3.3316

Abstract

The growth of e-commerce in Indonesia has driven an increase in the volume of user reviews that openly express satisfaction and complaints. Manual analysis of large-scale reviews is inefficient, necessitating an automated computational approach. This study aims to classify user sentiment of the Blibli application based on 1,060 Google Play Store reviews using text mining and the Multinomial Naive Bayes algorithm with TF-IDF weighting, as well as to identify sentiment patterns per service aspect. Of the 350 manually labeled reviews, 245 were used for training and 105 for testing. Model evaluation results show an accuracy of 86.67%, precision of 82.54%, recall of 94.55%, and F1-score of 88.14%. The overall sentiment distribution across 1,060 reviews indicates 61.1% positive and 38.9% negative. Aspect-based analysis reveals that UI/UX is the only aspect with dominant positive sentiment (70.6%), while Application Performance (57.1%), Payment Features (63.9%), Customer Service (66.7%), and Delivery Experience (67.3%) are dominated by negative sentiment. These findings can serve as data-driven evaluation material for improving Blibli application services.
SISTEM KLASIFIKASI STATUS GIZI BURUK PADA BALITA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE PADA PUSKESMAS SIMPANG TERITIP Davin Eza Saputra; Iski zaliman; Fransiskus Panca Juniawan
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.v18i3.3372

Abstract

Children's nutritional status is an important indicator of growth and development. The determination of nutritional status at the Simpang Teritip Community Health Center is currently still performed manually and often faces the obstacle of unstable internet networks in the field, potentially leading to errors and delays in decision-making. This study aims to develop a toddler nutritional status classification system using a web-based Support Vector Machine (SVM) algorithm equipped with offline capabilities. The data used consists of anthropometric data, including weight-for-age (BB/U), height-for-age (TB/U), and weight-for-height (BB/TB), based on WHO standards. The research process includes data preprocessing, normalization, SVM modeling with an RBF kernel, and evaluation using a confusion matrix. The results show that the SVM algorithm is capable of classifying the nutritional status of toddlers with high accuracy, achieving 98.20% for the BB/U indicator, 97.11% for the TB/U indicator, and 91.54% for the BB/TB indicator. This system is expected to assist health workers in conducting early detection directly in the field regardless of network constraints, as well as improving the effectiveness of child health services.
PENGARUH FITUR LIVE COMMERCE DAN SOCIAL INFLUENCE TERHADAP MINAT BELI PADA PENGGUNA TIKTOK LIVE SHOPPING Dian Angrianis; Setiawan Assegaff; Joni Devitra; Ahmad Fadhil Yusof
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.v18i3.3450

Abstract

TikTok Live Shopping menggabungkan fitur platform interaktif dengan lingkungan sosial yang dapat membentuk kepercayaan konsumen dan niat pembelian. Studi ini meneliti hubungan antara Fitur Live Commerce, Pengaruh Sosial, Kepercayaan, dan Niat Pembelian menggunakan kerangka kerja Stimulus-Organism-Response (SOR). Survei lintas sektoral dilakukan di antara perempuan Generasi Z yang tinggal di Kota Jambi. Dari 112 tanggapan yang dikumpulkan dari Maret hingga Juni 2026, 100 tanggapan lengkap dianalisis menggunakan PLS-SEM di SmartPLS 4, dengan bootstrapping berdasarkan 5.000 subsampel. Fitur Live Commerce berasosiasi positif dengan Kepercayaan (β = 0,514, p < 0,001), sedangkan Pengaruh Sosial tidak berasosiasi secara signifikan dengan Kepercayaan (β = 0,047, p = 0,657). Kepercayaan berasosiasi positif dengan Niat Pembelian (β = 0,677, p < 0,001), dengan model menjelaskan 45,8% dari variansnya. Hubungan tidak langsung melalui Kepercayaan signifikan untuk Fitur Perdagangan Langsung (β = 0,347, p < 0,001) tetapi tidak untuk Pengaruh Sosial (β = 0,032, p = 0,662). Temuan ini mendukung jalur berbasis kepercayaan yang telah ditentukan untuk persepsi terkait platform dalam sampel ini, sementara jalur sosial yang terkait tetap tidak didukung. Desain lintas-seksional dan pengambilan sampel bertujuan membatasi interpretasi kausal dan generalisasi.
Penerapan machine learning untuk mendeteksi serangan mirai infection pada jaringan komputer menggunakan dataset gotham 2025 Ravi Kumar Saidala; Priyanti; Bunga Intan; Sri Tita Faulina
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.v18i3.3460

Abstract

The expansion of networks increases the likelihood of cyberattacks, particularly those involving Mirai infections. This study aims to design and analyze a machine learning model capable of identifying Mirai infection attack anomalies within network infrastructure using the gotham2025 dataset. This dataset encompasses both normal traffic and various forms of Mirai infection attacks, such as ingress tool transfers, file downloads, Mirai C&C communication, reporting, and Merlin activity. Data imbalance was addressed using balancing methods—specifically the SMOTE technique—to prevent model bias. Algorithms such as Random Forest and Support Vector Machine (SVM) were implemented and evaluated based on accuracy, precision, recall, F1-score, confusion matrix, MCC, Kappa, ROC-AUC. The results demonstrate that Random Forest achieved 100% accuracy, while SVM attained 99.95%. Overall, both algorithms exhibited the most consistent performance in detecting the various forms of Mirai infection attacks. This research contributes to the development of machine learning-based intrusion detection systems aimed at enhancing network security.
ANALISIS KAUSAL FAKTOR RISIKO DIABETES MENGGUNAKAN METODE GREEDY EQUIVALENCE SEARCH (GES) Nurhaeka Tou; Putri Mentari Endraswari; A. Taqwa Martadinata; Iski Zaliman
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.v18i3.3528

Abstract

Diabetes mellitus is a chronic disease influenced by multiple interrelated factors, making the identification of causal relationships among risk factors important for understanding the mechanisms underlying diabetes. This study aims to analyze the causal structure of diabetes risk factors using the Greedy Equivalence Search (GES) algorithm. The dataset consists of nine variables, namely gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, blood glucose level, and diabetes. The research stages include literature review, data preprocessing, Formation of True DAG, causal modeling using GES, and evaluation by comparing the estimated structure with the True Directed Acyclic Graph (True DAG). The implementation was conducted using the R programming language with the pcalg package and the GaussL0penObsScore scoring function. The results show that GES successfully identified 14 out of 15 edges in the True DAG, with 8 false positives and 1 false negative. The evaluation yielded a precision of 63.64%, recall of 93.33%, F1-score of 75.4%, and accuracy of 87.50%. The GES-derived structure also identified several relationships relevant to diabetes, particularly involving HbA1c level, blood glucose level, BMI, age, and smoking history. These findings indicate that GES can be used to identify the causal structure of diabetes risk factors based on simulational data.
SEGMENTASI SDM MENGGUNAKAN K-MEANS CLUSTERING BERDASARKAN STATUS KERJA, PENGALAMAN, DAN KINERJA PADA PT XYZ Jovita Angelia Febriani; Johan Tambotoh
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.v18i3.3577

Abstract

PT XYZ is a systems integration company with a diverse workforce comprising permanent employees, university interns, and vocational high school (VHS) interns. Differences in employment status and individual characteristics provide a context for exploring human resource segmentation based on individual experiences and performance assessments. This exploratory study aims to identify human resource segments using the K-Means Clustering algorithm and describe the distribution of cluster membership across employment status categories. The study involved 18 respondents. Before clustering was performed, the data were tested for validity and reliability, yielding Cronbach’s alpha coefficients of 0.946 for X1 and 0.767 for X2. The optimal number of clusters was determined using the Elbow Method and evaluated using the Silhouette Score. The results revealed four clusters representing the following performance categories: Excellent, Very Good, Fairly Good, and Needs Improvement. Cross-tabulation analysis was subsequently used to describe the distribution of employment status across the resulting clusters. These findings provide preliminary insights into workforce segmentation among respondents at PT XYZ and may serve as a reference for further discussions on human resource management practices

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