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Jurnal Responsive Teknik Informatika
Published by Universitas Ibnu Sina
ISSN : -     EISSN : 26147602     DOI : https://doi.org/110.36352/jr
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
JR: Jurnal Responsive Teknik Informatika is a scientific journal aimed at providing a platform for researchers, academics, and professionals to publish their latest research and thoughts in the field of responsive informatics engineering. This journal was established with the goal of being one of the leading sources of knowledge in informatics engineering and serving as a platform for sharing the latest findings and innovations. is a scientific journal aimed at providing a platform for researchers, academics, and professionals to publish their latest research and thoughts in the field of responsive informatics engineering. This journal was established with the goal of being one of the leading sources of knowledge in informatics engineering and serving as a platform for sharing the latest findings and innovations. With a focus on academic excellence and innovation, JR: Jurnal Responsive Teknik Informatika accepts contributions in various forms, including original research articles, literature reviews, technical notes, and conference papers. The scope of topics covered by this journal includes, but is not limited to: Software engineering Information systems Artificial intelligence Data analytics Cybersecurity Human-computer interaction Web and mobile development Internet of Things (IoT) Cloud computing And other related topics in informatics engineering JR: Jurnal Responsive Teknik Informatika is managed by an experienced editorial team committed to maintaining standards of excellence in published research. Each article undergoes a rigorous peer review process to ensure quality, originality, and relevance to the field of study. As a journal open to contributions from researchers worldwide, JR: Jurnal Responsive Teknik Informatika invites academics and professionals to actively participate in contributing new thoughts, findings, and ideas that can advance knowledge in the field of informatics engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 149 Documents
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Pengembangan Otomasi Inventaris Farmasi Rumah Sakit Gigi dan Mulut Berbasis YOLO Siti Salmiah; Khairul Abdi
Jurnal Responsive Teknik Informatika Vol 9 No 02 (2025): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v9i02.1481

Abstract

Abstrak Pengelolaan inventaris farmasi pada Rumah Sakit Gigi dan Mulut (RSGM) masih banyak dilakukan secara manual melalui pencatatan dan penghitungan stok satu per satu. Proses tersebut rentan terhadap kesalahan manusia, memerlukan waktu yang lama, serta menyulitkan deteksi dini terhadap kekurangan dan kelebihan stok obat maupun alat kesehatan. Penelitian ini bertujuan mengembangkan sistem otomasi inventaris farmasi berbasis algoritma You Only Look Once (YOLO) yang mampu mendeteksi, mengklasifikasikan, dan menghitung item farmasi secara otomatis dari citra rak penyimpanan. Dataset dibentuk dari 2.400 citra enam kelas item (tablet, sirup, ampul, kapsul, salep, dan alat kesehatan) yang telah dianotasi dan diaugmentasi, kemudian dibagi ke dalam data latih, validasi, dan uji dengan rasio 70:15:15. Model dilatih menggunakan pendekatan transfer learning dengan arsitektur YOLOv8 dan diintegrasikan ke dalam aplikasi web berbasis PHP dan MySQL. Hasil pengujian menunjukkan model mencapai mean Average Precision (mAP@0.5) sebesar 0,943, presisi 0,921, recall 0,914, dan skor F1 0,917, dengan kecepatan inferensi rata-rata 38 frame per detik. Penerapan sistem mampu menekan waktu penghitungan stok hingga 82% dibandingkan metode manual. Hasil ini membuktikan bahwa pendekatan berbasis YOLO layak diterapkan sebagai solusi otomasi inventaris farmasi yang akurat dan efisien di lingkungan RSGM.
Comparative Simulation of EfficientNetB0, ResNet50, and MobileNet for Cocoa Pod Disease Detection Okta Veza; Nofri Yudi Arifin; Sherly Agustini; Albertus Laurensius Setyabudhi
Jurnal Responsive Teknik Informatika Vol 9 No 01 (2025): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v9i01.1515

Abstract

The selection of a convolutional neural network (CNN) architecture for cocoa (Theobroma cacao) pod disease detection involves a trade off between classification accuracy and computational efficiency that is decisive for eventual deployment on the mobile hardware available to smallholder farmers. This study presents a controlled comparative simulation of three widely used architectures, EfficientNetB0, ResNet50, and MobileNetV2, under identical, literature-grounded conditions. Rather than reporting field-validated results, a balanced synthetic dataset of 3,000 images spanning four classes (healthy, black pod, pod borer, frosty pod) was generated with class-conditional feature statistics parameterized from published references. All three models were initialized with ImageNet weights, fine-tuned with an identical training protocol and shared data splits, and evaluated on the same held-out test set. In simulation, EfficientNetB0 achieved the highest accuracy (93.8%) and macro F1 (0.938), followed by ResNet50 (92.7%, 0.926) and MobileNetV2 (91.1%, 0.909). When efficiency is considered, the ranking shifts: MobileNetV2 offered the smallest footprint and lowest latency, EfficientNetB0 delivered the best accuracy-per-parameter, and ResNet50 was the most resource-intensive without a commensurate accuracy gain. The dominant error mode across all models was confusion between pod borer and frosty pod. The results indicate that EfficientNetB0 offers the most favorable accuracy efficiency balance for this task, while MobileNetV2 is preferable under strict on-device constraints. All figures are framed explicitly as simulation outputs and discussed in light of the synthetic-to-real domain gap
RANCANG BANGUN SISTEM INFORMASI MONITORING ASET DI SMK 02 IBNU SINA BATAM DENGAN METODE EXTREME PROGRAMMING (XP) Weni Lestari Putri; SecondAuthor; Third Author
Jurnal Responsive Teknik Informatika Vol 4 No 01 (2020): Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

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

Abstract

Perkembangan teknologi informasi mendorong kebutuhan akan sistem yang dapat meningkatkan efisiensi pengelolaan aset dalam institusi pendidikan. SMK 02 Ibnu Sina Batam masih menggunakan metode manual dalam pencatatan dan monitoring aset, yang berisiko terhadap kesalahan pencatatan, kehilangan data, serta ketidakakuratan informasi. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi monitoring aset berbasis web menggunakan metode Extreme Programming (XP). XP dipilih karena mampu memberikan fleksibilitas dalam pengembangan sistem secara iteratif dan responsif terhadap perubahan kebutuhan pengguna. Metode penelitian meliputi wawancara, observasi, dan studi pustaka untuk mengumpulkan data kebutuhan sistem. Sistem diuji menggunakan metode Black Box Testing untuk memastikan fungsionalitasnya sesuai dengan spesifikasi yang ditetapkan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan dapat meningkatkan efisiensi pencatatan, pemantauan, serta pemeliharaan aset sekolah. Dengan sistem ini, pengelolaan aset di SMK 02 Ibnu Sina Batam menjadi lebih akurat, terstruktur, dan transparan, sehingga dapat mendukung operasional sekolah secara lebih optimal.
Dashboard Manajemen Donor Darah dengan Admin Panel dan Monitoring Stok Berbasis Web Meike Mandome
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1452

Abstract

The Blood Transfusion Unit (UTD) faces challenges in managing donor data and blood stocks, which are not yet fully computerized, making it vulnerable to recording errors and information delays. This study aims to design and implement a Blood Donor Management Dashboard with a Web-Based Admin Panel and Stock Monitoring to address these challenges. The system design was conducted using the waterfall method, which includes communication, planning, modeling, construction, and implementation. Functional requirements were obtained through literature studies, interviews with UTD officers, and direct observation of the donor data collection and blood stock recording processes. The system modeling is presented in use case diagrams, activity diagrams, sequence diagrams, and class diagrams. While the system construction uses the CodeIgniter framework with a MySQL database. The results of the study are a dashboard design and prototype that provides two levels of access rights, namely admin and donor, with features for verified donor registration, donor search, blood requests, and blood stock monitoring. The resulting design has the potential to support traceability of donor data and transparency of blood availability information. Empirical system testing and measuring its impact on UTD service performance are future research agendas.
Dashboard Eksekutif Sebagai Media Koordinasi dan Monitoring Pelayanan Informasi Publik Badan Pemerintah XYZ Abdul Hamid Arribathi; Esmeralda Esmeralda; Muhammad Ropianto
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1477

Abstract

Transparansi penyelenggaraan pemerintahan merupakan fondasi utama tata kelola yang bertanggung jawab dan berorientasi pada kepentingan publik. Amanat Undang-Undang Nomor 14 Tahun 2008 menegaskan kewajiban setiap badan publik untuk membuka akses informasi kepada seluruh lapisan masyarakat secara proaktif dan responsif. Penelitian ini merancang dan membangun Executive Information Dashboard sebagai instrumen terpadu untuk mendukung koordinasi internal dan pemantauan pelayanan informasi publik di lingkungan instansi pemerintah, dengan mengacu pada tahapan model waterfall yang meliputi analisis kebutuhan, desain sistem, pengkodean, verifikasi, serta pemeliharaan. Hasil evaluasi sistem menunjukkan bahwa teknologi dashboard terbukti efektif dalam menyajikan data secara dinamis dan real-time, menghasilkan tampilan informasi yang mudah dipahami, serta memperkuat kapasitas pengambilan keputusan oleh pimpinan maupun staf operasional. Verifikasi sistem dilakukan melalui black box testing untuk memastikan kesesuaian fungsionalitas, serta user acceptance test guna mengukur derajat kepuasan pengguna akhir. Penelitian ini menegaskan bahwa dashboard eksekutif berbasis web yang diimplementasikan menggunakan PHP dan MySQL mampu beroperasi secara optimal dan memberikan kontribusi nyata dalam meningkatkan efisiensi koordinasi, ketepatan monitoring, serta akuntabilitas penyelenggaraan layanan informasi publik. Kata Kunci: Keterbukaan Informasi Publik, Dashboard Eksekutif, Monitoring, Waterfall, Badan Pemerintah
Kerangka kerja berbasis knowledge graph untuk memprediksi kebutuhan infrastruktur ruang kelas di Universitas Ibnu Sina Batam Choirul Anam; Abdul Hamid Arribathi
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1480

Abstract

Tujuan dari penelitian ini adalah untuk memprediksi kebutuhan infrastruktur ruang kelas di Universitas Ibnu Sina Batam dengan menggunakan kerangka kerja berbasis Knowledge Graph. Data utama yang digunakan dalam penelitian ini adalah data mahasiswa, program studi, jadwal kuliah, kapasitas ruang, dan penggunaan ruang kelas. Penelitian melibatkan proses pengumpulan data, pemrosesan dan integrasi data, pemodelan Gambar Pengetahuan, analisis kebutuhan ruang kelas, dan evaluasi hasil analisis. Hasil penelitian menunjukkan bahwa sistem berbasis Gambar Pengetahuan dapat mengintegrasikan berbagai sumber data ke dalam model pengetahuan yang terstruktur, yang memungkinkan analisis kebutuhan ruang kelas secara lebih sistematis. Kerangka kerja yang dibuat dapat digunakan sebagai dasar untuk membangun infrastruktur ruang kelas yang lebih efisien dan berkelanjutan.
Comparative Analysis of Classification Methods for Cyberattack Detection on Computer Networks Sherly Agustini; Mustakim Mustakim; Okta Veza
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1516

Abstract

The rapid growth of computer networks has increased the complexity and intensity of cyber threats, making machine learning based intrusion detection one of the most widely studied defense mechanisms. This study compares the performance of five classification methods Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes in detecting five categories of network activity: Normal, Denial of Service (DoS), Probing, Remote to Local (R2L), and User to Root (U2R). Due to limited access to sensitive real-world network traffic data, this study uses a small-scale simulated dataset of 500 samples generated programmatically using controlled statistical distributions to represent the characteristics of each category, including the class imbalance condition commonly found in real network traffic. The data was split into 70% training and 30% testing using a stratified scheme and evaluated using accuracy, precision, recall, F1-score, and computation time metrics. Results show that Decision Tree achieved the highest macro F1-score (85.96%) with 94.00% accuracy, slightly ahead of Naive Bayes (84.52% macro F1-score, 95.33% accuracy). Random Forest recorded the highest overall accuracy (96.67%), but its macro F1-score (84.06%) lagged due to low recall on the U2R class, which has very few samples. Feature-importance analysis indicates that srv_count, dst_host_count, and count are the main determinants for distinguishing attack categories. Naive Bayes and KNN recorded the fastest computation times, while Random Forest required the longest training time. The small dataset size causes performance estimates on minority classes (R2L and U2R) to be prone to fluctuation, so these findings should be regarded as a preliminary proof-of-concept study requiring further validation using a larger dataset or real world network traffic data.
Api Response Time Prediction Using a Deep Learning Model on Web Application Load Testing Simulation Data Defnizal Defnizal; Atman Lucky Fernandes; Aprizal Y; David Saro; Ghea Paulina Suri; Nofri Yudi Arifin; Romiko Afriantoni; Willy Rizki Perdana
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1539

Abstract

Web application performance, particularly application programming interface (API) response time, is one of the primary determinants of user experience and system reliability, especially under high-load conditions. Conventional load testing is reactive, as it can only measure actual response time after a given load has been applied to the system, creating a need for a predictive approach capable of estimating API response time from load and server-resource conditions. This study applies a deep learning model in the form of a Multi-Layer Perceptron (MLP) with three hidden layers to predict API response time, and compares its performance against two baseline models, namely Linear Regression and Random Forest Regressor. Due to limited access to sensitive real-world production data, this study uses a simulated load-testing dataset of 500 samples generated programmatically, comprising ten load and server-resource features such as concurrent users, requests per second, payload size, database query count, cache hit ratio, and CPU and memory utilization, with non-linear patterns representing resource-contention effects under high load. The data was split into 70% training and 30% testing, then evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Experimental results show that the Deep Learning (MLP) model achieved the best predictive performance with an RMSE of 24.86 ms, MAPE of 19.33%, and R² of 0.9009, outperforming Linear Regression (RMSE 26.25 ms, R² 0.8895) and Random Forest (RMSE 27.90 ms, R² 0.8751), although Linear Regression's MAE was slightly lower (19.34 ms versus 19.53 ms). Feature-importance analysis indicates that the number of concurrent users, database query count, and CPU utilization are the most dominant factors influencing API response time. To ground the simulated scenario in a concrete example, a small real demonstration web application was also built and load-tested; its qualitative behavior (response time increasing with concurrency) is consistent with the assumptions underlying the simulated dataset. These findings indicate that the deep learning model is better able to capture non-linear patterns caused by resource contention compared to a linear model or a tree-based ensemble, suggesting potential use as a predictive component in auto-scaling strategies or web-application capacity planning, with the caveat that these results remain a preliminary study requiring further validation using real production data.
Chatbot Berbasis Natural Language Processing untuk Layanan Pendidikan Andi Amin Amirul Mukminin; Revi Illya Badri; Nopan Aswandi
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.150900023

Abstract

Perkembangan pesat kecerdasan buatan, khususnya model bahasa besar (LLM) generatif, mendorong meluasnya penggunaan chatbot sebagai kanal komunikasi alternatif antara institusi pendidikan dan penggunanya. Artikel ini menyajikan kajian literatur mengenai penerapan chatbot berbasis teknologi Natural Language Processing (NLP) dalam layanan pendidikan. Kajian bertujuan memetakan kategori, fungsi, dan teknologi yang mendasari chatbot pendidikan, serta menyintesis manfaat dan tantangan yang dilaporkan pada publikasi terindeks terkini. Metode yang digunakan adalah kajian literatur naratif terhadap tiga belas artikel yang telah melalui tinjauan sejawat dan terindeks Scopus maupun Web of Science, terbit antara tahun 2023 hingga 2025, ditelusuri dari Scopus, Web of Science, Google Scholar, ScienceDirect, dan SpringerLink dengan kata kunci “chatbot”, “natural language processing”, “large language model”, dan “pendidikan”. Hasil kajian menunjukkan bahwa chatbot pendidikan dapat dikategorikan menjadi chatbot FAQ berbasis aturan, tutor virtual, chatbot pembelajaran bahasa, dan chatbot generatif berbasis model bahasa besar. Manfaat utama yang teridentifikasi meliputi ketersediaan layanan yang berkelanjutan, personalisasi pembelajaran, dan performa akademik yang kompetitif, sedangkan tantangannya meliputi risiko ketergantungan kognitif, isu integritas akademik, persoalan privasi dan etika data, serta keterbatasan landasan pedagogis pada sebagian besar chatbot pendidikan yang ada. Kajian ini menyimpulkan bahwa diperlukan penelitian empiris lanjutan untuk memvalidasi efektivitas chatbot berbasis NLP dalam konteks pendidikan tinggi di Indonesia serta merumuskan kebijakan institusional bagi penggunaannya secara bertanggung jawab.

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