Article Per Year (5 Year)
p-Index From 2021 - 2026
3.637
P-Index
This Author published in this journals
All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Sains Informatika Terapan (JSIT) Public Health and Safety International Journal (PHASIJ) Engineering and Technology International Journal (EATIJ) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Journal of Engineering and Technology Innovation MPKM
sherly agustini
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Rancang Bangun Sistem Informasi Inventori Barang Berbasis Web untuk Pengelolaan Aset di KPLI-B3 Kabil Azharye Putri Aulia; Okta Veza; Sherly Agustini
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.v9i02.1478

Abstract

Abstrak Kawasan Pengelolaan Limbah Industri Bahan Berbahaya dan Beracun (KPLI-B3) Kabil merupakan pusat pengelolaan limbah B3 di Pulau Batam yang membutuhkan pengelolaan inventori peralatan secara akurat. Pencatatan inventori yang masih menggunakan Microsoft Excel menimbulkan keterbatasan aksesibilitas data, sulitnya pembagian informasi secara real-time, serta tingginya risiko kesalahan akibat input manual sehingga proses pelaporan dan pengambilan keputusan menjadi lambat. Penelitian ini bertujuan merancang dan membangun sistem informasi inventori barang berbasis web untuk pengelolaan aset di KPLI-B3 Kabil. Metode pengembangan yang digunakan adalah Extreme Programming (XP) yang meliputi tahap planning, design, coding, dan testing. Perancangan sistem menggunakan Unified Modeling Language (UML), perancangan basis data MySQL, serta antarmuka berbasis web. Sistem yang dibangun menyediakan fitur pengelolaan data master barang, barang masuk, barang keluar, stok barang, data vendor, serta laporan. Pengujian dilakukan menggunakan metode black box dengan hasil seluruh fungsi berjalan sesuai dengan yang diharapkan. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan akurasi data, mempercepat proses stock opname, serta memudahkan proses monitoring dan pengambilan keputusan dalam pengelolaan inventori barang.
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
Pengaruh Aplikasi Inkubator Bisnis terhadap Keberlanjutan Usaha Rintisan: Kajian Literatur David Saro; Larisang Larisang; Sherly Agustini
Jurnal Responsive Teknik Informatika Vol 8 No 01 (2024): 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.v8i01.1469

Abstract

Usaha rintisan (startup) merupakan entitas bisnis yang menjadi pendorong utama inovasi dan pertumbuhan ekonomi di era digital. Namun, startup sering menghadapi tantangan signifikan dalam keberlanjutan dan pertumbuhan, dengan tingkat kegagalan yang tinggi. Inkubator bisnis hadir sebagai solusi untuk mendukung dan mempercepat pertumbuhan startup melalui penyediaan berbagai bentuk dukungan, seperti akses modal, bimbingan, pelatihan, dan fasilitas fisik. Penelitian ini bertujuan untuk mengevaluasi pengaruh inkubator bisnis terhadap keberlanjutan usaha rintisan melalui kajian literatur. Hasil penelitian menunjukkan bahwa inkubator bisnis memiliki pengaruh positif yang signifikan terhadap keberlanjutan startup, dengan beberapa faktor kunci yang mempengaruhi efektivitasnya. Tantangan yang dihadapi inkubator bisnis juga diidentifikasi, termasuk manajemen keuangan, pemilihan tim manajemen, dan adaptasi terhadap perubahan lingkungan bisnis. Dengan mengatasi tantangan ini, inkubator bisnis dapat meningkatkan efektivitas dalam mendukung ekosistem kewirausahaan lokal.
Rancang Bangun Sistem Informasi Monitoring Aset di SMK 02 Ibnu Sina Batam dengan Metode Extreme Programming (XP) Muhamad Yari Hanzalah; Atman Lucky Fernandes; Sherly Agustini; Revi illya Badri; Okta Veza
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.v9i01.1472

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.
Rancang Bangun Sistem Informasi Inventori Barang Berbasis Web untuk Pengelolaan Aset di KPLI-B3 Kabil Azharye Putri Aulia; Okta Veza; Sherly Agustini
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.v9i02.1478

Abstract

Abstrak Kawasan Pengelolaan Limbah Industri Bahan Berbahaya dan Beracun (KPLI-B3) Kabil merupakan pusat pengelolaan limbah B3 di Pulau Batam yang membutuhkan pengelolaan inventori peralatan secara akurat. Pencatatan inventori yang masih menggunakan Microsoft Excel menimbulkan keterbatasan aksesibilitas data, sulitnya pembagian informasi secara real-time, serta tingginya risiko kesalahan akibat input manual sehingga proses pelaporan dan pengambilan keputusan menjadi lambat. Penelitian ini bertujuan merancang dan membangun sistem informasi inventori barang berbasis web untuk pengelolaan aset di KPLI-B3 Kabil. Metode pengembangan yang digunakan adalah Extreme Programming (XP) yang meliputi tahap planning, design, coding, dan testing. Perancangan sistem menggunakan Unified Modeling Language (UML), perancangan basis data MySQL, serta antarmuka berbasis web. Sistem yang dibangun menyediakan fitur pengelolaan data master barang, barang masuk, barang keluar, stok barang, data vendor, serta laporan. Pengujian dilakukan menggunakan metode black box dengan hasil seluruh fungsi berjalan sesuai dengan yang diharapkan. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan akurasi data, mempercepat proses stock opname, serta memudahkan proses monitoring dan pengambilan keputusan dalam pengelolaan inventori barang.
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
Simulation Study of EfficientNetB0 Performance for Cocoa Pod Disease Classification Using Literature Based Synthetic Data Okta Veza; Sherly Agustini; Nofri Yudi Arifin; Albertus Laurensius Setyabudhi
Engineering and Technology International Journal Vol 7 No 03 (2025): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v7i03.1335

Abstract

Automated detection of cocoa (Theobroma cacao) pod diseases such as black pod, pod borer infestation, and frosty pod rot is critical for safeguarding yield, yet the development of deep-learning classifiers is frequently constrained by the scarcity of curated, well-balanced image datasets. This study presents a controlled simulation that evaluates the expected performance envelope of an EfficientNetB0 classifier under idealized, literature-grounded conditions before field data collection is undertaken. Rather than asserting empirical field results, a synthetic dataset is constructed whose per-class feature distributions (color, texture, and lesion morphology) are parameterized from values reported across six core references. A balanced corpus of 3,000 synthetic images spanning four classes (healthy, black pod, pod borer, frosty pod) was generated and partitioned using a stratified 70/15/15 split. EfficientNetB0, initialized with ImageNet weights and fine-tuned with standard augmentation, achieved a simulated test accuracy of 93.8%, a macro-averaged F1-score of 0.926, and balanced per-class precision and recall in the 0.90-0.95 range. The confusion matrix indicates that the principal source of error is morphological overlap between pod borer and frosty pod presentations. The results delineate a plausible upper-bound performance band to guide sample-size planning, augmentation strategy, and architecture selection for a subsequent field study. All reported figures are framed explicitly as simulation outputs.
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.
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.
Peran IoT dalam Transformasi Jaringan Multimedia: Tinjauan Literatur Sistematis Ghea Paulina Suri; Atman Lucky Fernandes; Hidayatul Ikhsan; Putri Devina; Ledi Alde Nopa; Sherly Agustini
Jurnal Responsive Teknik Informatika Vol 8 No 02 (2024): 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.v8i02.1430

Abstract

Integrasi Internet of Things (IoT) dengan jaringan multimedia telah mengubah paradigma pengelolaan, pengiriman, dan pemrosesan konten multimedia secara fundamental. Penelitian ini bertujuan mensintesis perkembangan terkini melalui tinjauan literatur sistematis terhadap 25 artikel ilmiah yang dipublikasikan antara 2019–2024, bersumber dari Scopus, IEEE Xplore, dan Google Scholar, mengikuti protokol PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Hasil tinjauan mengungkap empat tema utama: (1) arsitektur jaringan berbasis IoT memanfaatkan edge computing dan 5G untuk optimasi latensi; (2) manajemen Quality of Service (QoS) secara adaptif; (3) keamanan data dan privasi dalam ekosistem perangkat heterogen; serta (4) penerapan kecerdasan buatan untuk otomasi pengelolaan data multimedia. Analisis sintesis menunjukkan adopsi 5G mampu mengurangi latensi hingga 85% dibanding 4G, sementara model keamanan berbasis blockchain menjadi solusi yang paling banyak dikaji. Gap penelitian teridentifikasi pada minimnya studi tentang standarisasi interoperabilitas antar-vendor dan model konsumsi energi IoT skala besar.
Co-Authors Albertus L. Setyabudhi Albertus Laurensius Aprizal Y Azharye Putri Aulia David Saro Fernandes, Atman Lucky Ghea Paulina Suri Hermansyah Hidayatul Ikhsan Larisang Laurensius Setyabudhi, Albertus Ledi Alde Nopa Mahdi Aulia Suheri Muhamad Yari Hanzalah Mustakim Mustakim Nofri Yudi Arifin Okta Veza Okta Veza Putri Devina Revi Illya Badri Romiko Afriantoni Willy Rizki Perdana