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All Journal JURNAL PENDIDIKAN TAMBUSAI Journal of Computer Networks, Architecture and High Performance Computing Jurnal Sains Informatika Terapan (JSIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Engineering and Technology International Journal (EATIJ) Jurnal Pengabdian West Science JT-IBSI (Jurnal Teknik Ibnu Sina) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika JURNAL LIGA ILMU SERANTAU Journal of Engineering and Technology Innovation MPKM
Nofri Yudi Arifin
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Other

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Perancangan Sistem Informasi Manajemen Kearsipan Dokumen di PT. Glori Global Sukses Novi Widiana Putri; Atman Lucky Fernandes; Al Rusman; Aprizal Y; Nofri Yudi Arifin
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.v9i01.1471

Abstract

Dalam era digital, pengelolaan dokumen menjadi aspek penting bagi perusahaan untuk meningkatkan efisiensi dan mengurangi risiko kehilangan data. PT. Glori Global Sukses, yang bergerak di bidang perdagangan internasional, masih mengelola dokumen secara manual, sehingga mengalami kendala dalam pencarian, penyimpanan, dan keamanan data. Penelitian ini bertujuan untuk merancang dan mengembangkan Sistem Informasi Manajemen Kearsipan Dokumen berbasis Rapid Application Development (RAD) guna meningkatkan efisiensi dan efektivitas pengelolaan dokumen. Proses pengembangan sistem dilakukan melalui tiga tahapan utama: Requirement Planning, Design Workshop, dan Implementation. Hasil penelitian menunjukkan bahwa sistem yang dirancang dapat mengotomatisasi proses pengarsipan, mempercepat pencarian dokumen, serta meningkatkan akurasi dan keamanan data. Dengan demikian, implementasi sistem ini dapat menjadi solusi bagi PT. Glori Global Sukses dalam mengelola dokumen secara lebih efisien dan terstruktur.
Data Quality Management dalam Data Warehouse: Tinjauan Literatur Tiara Aziza; David Saro; Nofri Yudi Arifin; Romiko Afriantoni; Aprizal Y; Atman Lucky Fernandes
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.v9i02.1476

Abstract

This study presents a systematic literature review of Data Quality Management (DQM) in data warehouse environments, aiming to map key dimensions, processes, and architectural/technological enablers, and to identify research gaps. Searches were conducted across Scopus, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar (as a complement) for the period 2009–2025, following PRISMA 2020. Of 200 initial records, 133 were excluded during the first screening, 67 underwent further assessment, and 6 studies met the inclusion criteria for in-depth analysis. Thematic synthesis indicates that effective DQM rests on four integrated pillars: (1) standardized quality dimensions and metrics (accuracy, completeness, consistency, timeliness, and traceability), (2) prevention–detection–correction processes embedded along the ETL/ELT pipeline (including consistent SCD policies and handling of late-arriving data), (3) architectural/technological support (automated data tests within CI/CD, catalogs/metadata, data lineage, observability, and data contracts), and (4) governance that clarifies roles and accountability (data owners/stewards) with incident-response procedures. Practically, organizations should start from critical data elements and high-priority consumption paths, translating SLA/SLI into executable rules. Limitations include the small number of included studies and contextual heterogeneity, motivating further work on cross-domain metric standardization, open DQM benchmarks, cost–benefit evaluations of observability/contract enforcement, and the impact of data quality on analytic/AI performance in near real-time settings.
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
Analisis Kinerja Sistem Informasi Pengarsipan Dokumen Berbasis Web Doni Syofiawan; Alex Sandri Sikumbang; Al Rusman; Nofri Yudi Arifin; Okta Veza
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.v8i01.1470

Abstract

Perkembangan Teknologi Informasi (TI) saat ini telah mencapai puncaknya, mengubah cara fundamental dalam kehidupan sehari-hari. TI telah merevolusi berbagai sektor, mulai dari bisnis hingga tata kelola, dengan mengotomatisasi proses yang sebelumnya manual, meningkatkan akurasi dan kecepatan pengambilan keputusan, serta memperluas akses terhadap informasi mutakhir. Manajemen informasi yang efektif menjadi krusial bagi setiap organisasi dalam mencapai tujuan operasional dan strategisnya. CV. Inti Jembar, perusahaan pengendalian hama di berbagai sektor, telah mengadopsi sistem informasi pengarsipan dokumen berbasis web untuk meningkatkan efisiensi dalam pengelolaan arsip perusahaan. Penelitian ini bertujuan untuk menganalisis kinerja sistem informasi pengarsipan dokumen berbasis web di CV. Inti Jembar, dengan fokus pada identifikasi kelebihan dan potensi pengembangan lebih lanjut. Melalui pemahaman mendalam terhadap sistem yang ada dan tinjauan literatur yang relevan, penelitian ini memberikan rekomendasi konkret untuk meningkatkan efektivitas pengelolaan arsip perusahaan.
Perancangan Sistem Informasi Manajemen Kearsipan Dokumen di PT. Glori Global Sukses Novi Widiana Putri; Atman Lucky Fernandes; Al Rusman; Aprizal Y; Nofri Yudi Arifin
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.v9i01.1471

Abstract

Dalam era digital, pengelolaan dokumen menjadi aspek penting bagi perusahaan untuk meningkatkan efisiensi dan mengurangi risiko kehilangan data. PT. Glori Global Sukses, yang bergerak di bidang perdagangan internasional, masih mengelola dokumen secara manual, sehingga mengalami kendala dalam pencarian, penyimpanan, dan keamanan data. Penelitian ini bertujuan untuk merancang dan mengembangkan Sistem Informasi Manajemen Kearsipan Dokumen berbasis Rapid Application Development (RAD) guna meningkatkan efisiensi dan efektivitas pengelolaan dokumen. Proses pengembangan sistem dilakukan melalui tiga tahapan utama: Requirement Planning, Design Workshop, dan Implementation. Hasil penelitian menunjukkan bahwa sistem yang dirancang dapat mengotomatisasi proses pengarsipan, mempercepat pencarian dokumen, serta meningkatkan akurasi dan keamanan data. Dengan demikian, implementasi sistem ini dapat menjadi solusi bagi PT. Glori Global Sukses dalam mengelola dokumen secara lebih efisien dan terstruktur.
Data Quality Management dalam Data Warehouse: Tinjauan Literatur Tiara Aziza; David Saro; Nofri Yudi Arifin; Romiko Afriantoni; Aprizal Y; Atman Lucky Fernandes
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.v9i02.1476

Abstract

This study presents a systematic literature review of Data Quality Management (DQM) in data warehouse environments, aiming to map key dimensions, processes, and architectural/technological enablers, and to identify research gaps. Searches were conducted across Scopus, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar (as a complement) for the period 2009–2025, following PRISMA 2020. Of 200 initial records, 133 were excluded during the first screening, 67 underwent further assessment, and 6 studies met the inclusion criteria for in-depth analysis. Thematic synthesis indicates that effective DQM rests on four integrated pillars: (1) standardized quality dimensions and metrics (accuracy, completeness, consistency, timeliness, and traceability), (2) prevention–detection–correction processes embedded along the ETL/ELT pipeline (including consistent SCD policies and handling of late-arriving data), (3) architectural/technological support (automated data tests within CI/CD, catalogs/metadata, data lineage, observability, and data contracts), and (4) governance that clarifies roles and accountability (data owners/stewards) with incident-response procedures. Practically, organizations should start from critical data elements and high-priority consumption paths, translating SLA/SLI into executable rules. Limitations include the small number of included studies and contextual heterogeneity, motivating further work on cross-domain metric standardization, open DQM benchmarks, cost–benefit evaluations of observability/contract enforcement, and the impact of data quality on analytic/AI performance in near real-time settings.
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.
Deep Learning Approaches for Cocoa Pod Disease Classification A Literature Review Okta Veza; Nofri Yudi Arifin; Albertus Laurensius Setyabudhi
Engineering and Technology International Journal Vol 6 No 03 (2024): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v6i03.1337

Abstract

Cocoa (Theobroma cacao) is a cornerstone of many tropical economies, yet its yield is persistently threatened by pod diseases such as black pod rot, frosty pod rot, and cocoa pod borer infestation. Over the past decade, deep learning, and convolutional neural networks (CNNs) in particular, has emerged as a powerful tool for automated plant disease diagnosis from images. This paper presents a structured literature review of deep-learning approaches applied, directly or by close analogy, to cocoa pod disease classification. Following a PRISMA style protocol, 41 studies published between 2016 and 2025 were selected from major databases and synthesized along five dimensions: data sources and dataset construction, preprocessing and augmentation, network architectures, training and transfer-learning strategies, and evaluation methodology. The review finds that transfer learning with compact architectures, notably ResNet, MobileNet, and EfficientNet variants, dominates recent work and consistently achieves reported accuracies above 90% on related tasks. Three persistent gaps are identified: the scarcity of large, balanced, and openly available cocoa specific image datasets; limited validation under realistic field conditions; and inconsistent reporting of evaluation metrics. The review concludes by outlining research directions, including domain adaptation, lightweight on device inference, explainability, and standardized benchmarking, to move cocoa pod disease classification from controlled experiments toward deployable tools for smallholder agriculture.
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.
Co-Authors Adi Nugroho Susanto Putro Ajat Sudrajat Al Rusman Albertus L. Setyabudhi Albertus Laurensius Alex Sandri Sikumbang Aprizal Y David Saro Defnizal Defnizal Deo Bagus Prayoga Devega, Army Trilidia Devin Mahendika Didik Cahyono Didin Setyawan Doni Syofiawan Dra. Nur Iffah Elly Prasetyo Eri Mardiani Etika, Erdyna Dwi Fernandes, Atman Lucky Ghea Paulina Suri Hadi, Anto J. Hasan Bashori Helmi Ali Hermansyah Ikhsan Amar Jusman Jasiah Jasiah Kristiana Lylya Larisang Laurensius Setyabudhi, Albertus Legito . Mardiati Mardiati Moh. Erkamim Mutiara Ayu Mawaddah Nanda Jarti Nindi Permata Riau Novi Widiana Putri Okta Veza Okta Veza Revi Illya Badri Rian Dwi Guntoro Romiko Afriantoni Sepriano Sepriano sherly agustini Tiara Aziza Villia Anggraini Willy Rizki Perdana Yulia Suryani Zainol Hasan