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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Inovasi G-Tech : Jurnal Teknologi Terapan International Journal of Industrial Optimization (IJIO) Jurnal Sistem Informasi dan Sains Teknologi Jurnal Media Informatika Co-Value : Jurnal Ekonomi, Koperasi & Kewirausahaan Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) SWARNA Journal of Information Technology and Cyber Security KOMPUTASI Journal of Information Technology Journal of Vocational, Informatics and Computer Education
Setiawan, Akas Bagus
Politeknik Negeri Jember, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Other

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Decision Support System for Selecting the Best Rental House with Weight Product in Sidokare District, Sidoarjo Regency Setiawan, Akas Bagus; Yuniar, Eka; Hermansyah, Mas'ud; Mujiono, Mujiono; Ariyadi, David Juli
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8865

Abstract

The demand for rental housing in high-mobility areas such as Sidokare District, Sidoarjo Regency, continues to increase along with the region's economic development. However, prospective tenants often face difficulties in selecting the right rental house due to unstructured information and subjective decision-making processes. This study aims to develop a web-based Decision Support System (DSS) capable of providing objective and measurable rental housing recommendations. The method used is the Weighted Product (WP), a multicriteria decision-making technique that normalizes weights through multiplication operations. This system evaluates five rental housing alternatives based on eight main criteria, including price, location, facilities, security, and comfort. The results show that Sukun Rental House is the best alternative with the highest preference value of 0.0769. The practical implication of this study is the availability of an efficient digital tool for residents and students in Sidokare to compare various housing options transparently and quickly. This system successfully minimizes subjectivity in housing selection and helps users find the housing that best suits their financial priorities and functional needs.
Optimization of real-time forest monitoring system using yolo v9 object detection and 2.4 ghz wireless network: resource allocation, energy efficiency, and industrial deployment strategies Atmoko, Rachmad Andri; Hidayatullah, Rifqi Rahmat; Na’im, Septian Ghuslal Nur; Setiawan, Akas Bagus
International Journal of Industrial Optimization Vol. 7 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v7i1.11899

Abstract

Large forest areas are increasingly exposed to illegal activities and environmental threats, while conventional monitoring systems suffer from limited coverage, high energy consumption, and delayed response. To address these challenges, this study proposes an optimized real-time forest monitoring system designed for industrial-scale deployment in remote environments. The primary objective is to enhance surveillance efficiency by integrating AI-based object detection, long-range wireless communication, and resource-efficient system design. The proposed system employs ESP32-CAM sensor nodes integrated with 2.4 GHz CPE wireless links and a gateway-based YOLOv9 object detection framework. Bandwidth utilization is optimized through selective transmission of processed detection metadata instead of raw images, while deployment parameters are optimized using simulation-based planning. A web-based monitoring platform with an optimized REST API supports real-time visualization and alert generation. Experimental results show that the system achieves reliable communication up to 500 m with packet loss below 5% and latency under 50 ms at distances up to 300 m. Human detection accuracy reaches 98.5% under optimal conditions, with performance degradation observed in dense vegetation and low-light environments. Energy evaluation confirms sustainable operation, with ESP32 nodes consuming 160 mA and the gateway operating at 3.7 W. Comparative analysis indicates reductions of 37% in deployment cost, 24% in energy consumption, and 51% in latency compared to similar systems. This study concludes that the proposed architecture effectively balances accuracy, scalability, cost, and energy efficiency. The novelty lies in the integrated optimization of edge-based AI detection, selective data transmission, and simulation-driven deployment for industrial forest monitoring.
Multimodal Detection Models for Poultry Fraud Monitoring on Jetson Nano Atmoko, Rachmad; Perdana, Rizal Setya; Wijaya, Fariz Rizky; Setiawan, Akas Bagus
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15884

Abstract

This study defines an indoor commercial poultry-house scenario with no Global Positioning System (GPS) signal, variable bird density, illumination shifts, occlusion, and normal versus fraud episodes characterized as abnormal poultry population behavior (an unauthorized deviation between observed bird count and expected inventory baseline). We evaluate an unmanned aerial vehicle (UAV) to an edge-computing pipeline on Jetson Nano by comparing three models: You Only Look Once version 11 (YOLOv11) with red-green-blue (RGB) input, YOLOv11 with RGB and thermal late fusion, and a convolutional neural network (CNN) backbone with a support vector machine (SVM) classifier. The dataset contains 12,000 frames with synchronized RGB-thermal augmentation to preserve modality alignment. Evaluation covers mean Average Precision (mAP), precision, recall, F1-score, counting errors via mean absolute error (MAE) and root mean square error (RMSE), and edge metrics including frames per second (FPS), latency, and memory. YOLOv11 RGB+thermal records mAP@0.5 of 0.94 (Table 4a), MAE of 1.4, and RMSE of 2.0 (Table 4b), compared with YOLOv11 RGB at 0.91, 1.8, and 2.5 and CNN-SVM at 0.85, 2.6, and 3.4 (Table 4a-4b). For edge throughput, CNN-SVM reaches 28 FPS, while YOLOv11 RGB reaches 18 FPS and YOLOv11 RGB+thermal reaches 14 FPS (Table 8). As a scenario study, these metric-supported results indicate that YOLOv11 RGB+thermal is accuracy-first, CNN-SVM is speed-first, and YOLOv11 RGB is a balanced option for real-time poultry fraud monitoring.
Field Evaluation of an IoT-VFD Smart Ventilation System for Energy-Efficient Rice Seed Storage Riskiawan, Hendra Yufit; Anwar, Saiful; Setyohadi, Dwi Putro Sarwo; Arifin, Syamsul; Widiawan, Beni; Jannah, Annisa Nurul Hidayati; Setiawan, Akas Bagus
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15962

Abstract

Stable storage conditions are required in Rice Seed Storage to preserve seed quality and suppress fungal contamination, yet many warehouse ventilation systems still rely on inefficient on-off operation with limited responsiveness to changing temperature and humidity conditions. This study addresses the lack of integrated IoT-VFD control with field-validated energy and microclimate performance in seed warehouses. It proposes an IoT-based Ventilation Control architecture that combines ESP32, MQTT communication, and a Variable Frequency Drive to regulate a three-phase exhaust fan in both offline and online operating modes. The novelty of this work lies in integrating variable-speed control, real-time supervision, and field-based performance validation within a single seed warehouse deployment. The prototype was implemented in a 900 m3 warehouse at Politeknik Negeri Jember and evaluated through a 7-day field trial with continuous monitoring of temperature, humidity, and motor speed. The controlled system brought warehouse conditions closer to the intended storage setpoints and produced statistically significant improvements in both temperature and humidity (p < 0.001). Control performance was stable, with high target-hit accuracy and low RMSE, while energy testing showed lower electricity consumption than conventional non-VFD operation. Over an equivalent 2-hour operating period, energy use was reduced by 30.4%. The system also maintained 99.64% MQTT uptime, and no mold incidence was observed during controlled operation. These findings indicate that the proposed IoT-VFD architecture is a practical approach for improving microclimate stability, reducing energy use, and supporting fungus-preventive seed warehouse management.
Segmentation and Prediction of Store Performance on the Shopee Marketplace Using a Hybrid Clustering Approach, Spatial Analysis, and Feature Importance Eka Yuniar; Sherin Ramadhania; Pascawati Savitri Universitasari; Mas&#039;ud Hermansyah; Akas Bagus Setiawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2256

Abstract

Marketplace platforms have become a central component of digital commerce, particularly in Southeast Asia where Shopee has emerged as one of the dominant e-commerce ecosystems. The increasing number of sellers on the platform intensifies competition and requires data-driven approaches to understand store performance patterns. This study aims to analyze and predict the performance of Shopee stores using a hybrid data mining approach that integrates clustering, spatial analysis, and feature importance evaluation. The dataset consists of 655 Shopee stores collected on February 18, 2026, including attributes such as number of products, chat response rate, follower count, store rating, store tenure, promotional activity, and seller address. K-Means clustering is applied to segment store performance, while spatial analysis examines the geographic distribution of clusters across Indonesian provinces. Furthermore, a Random Forest classifier is used to predict performance categories and identify influential features affecting store competitiveness. The clustering results reveal three distinct store performance groups representing low, medium, and high activity levels. Spatial analysis indicates that provinces with stronger digital ecosystems, particularly West Java and Jakarta, contain a higher concentration of active stores. Feature importance analysis shows that promotional activity, chat responsiveness, and follower count significantly influence store performance classification. The findings contribute to the development of hybrid data mining frameworks for marketplace analysis and provide practical insights for improving seller competitiveness in digital commerce ecosystems.
Explainable Clinical-Operational Intelligence for Hospital Length of Stay Prediction Using Integrated Multi-Source Admission Data with Time-Based Evaluation Dwi Putro Sarwo Setyohadi; Hendra Yufit Riskiawan; Aji Seto Arifianto; I Gede Wiryawan; Akas Bagus Setiawan
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.507

Abstract

Purpose - Hospital length of stay (LOS) affects bed turnover, discharge planning, staffing, and capacity. Integrated hospital data can strengthen LOS prediction and support decision-making. This study developed an explainable clinical-operational intelligence framework for LOS prediction using integrated admission data. Methods - The dataset comprised 45,000 admissions with supporting patient, diagnostic, prescription, billing, ward, bed, staff, and insurance records. It is based on a structured simulation designed to resemble the operational data of hospitals. An admission-level master table was constructed from demographic, temporal, clinical, pharmaceutical, insurance, operational, and patient history features. Length of stay (LOS) regression and high-risk LOS classification were evaluated using a temporal split of 2020-2023 for training, 2024 for validation, and 2025 for testing. Ridge, Random Forest, XGBoost, and CatBoost were compared, followed by threshold optimization, label screening, and SHAP analysis. Findings – CatBoost achieved the best LOS regression performance, with a test MAE of 1.606, an RMSE of 2.028, and an R2 of 0.614. For classification, very_high_los_q90 produced the most balanced extreme-risk formulation, with an accuracy of 0.885 and ROC-AUC of 0.802, whereas high_los_q75 yielded a recall of 0.998 and an F1-score of 0.604. SHAP indicated that prior admission history, diagnostic burden, medication-related features, and ward-level context were prominent drivers of LOS. Research implications – Integrated hospital data are useful for detecting prolonged and extreme LOS, supporting better hospital planning and resource management Originality – This study offers an explainable modeling approach using integrated admission data to support LOS prediction and hospital analytics
BERT Sentimen: Fine-Tuning Multibahasa untuk Ulasan Bahasa Indonesia Khen Dedes; Fatimatuzzahra; Mas'ud Hermansyah; Akas Bagus Setiawan; Reza Putra Pradana; Annisa Fitri Maghfiroh Harvyanti
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.585

Abstract

Penelitian ini mengevaluasi pengaruh teknik augmentasi dan fine‑tuning terhadap kinerja model BERT multibahasa pada tugas klasifikasi sentimen ulasan film berbahasa Indonesia. Dataset awal terdiri dari 1.200 ulasan; 80% digunakan untuk pelatihan dan validasi (n = 960) dan 20% untuk pengujian (n = 240). Data pelatihan diperluas melalui augmentasi menjadi 2.880 sampel sintetis untuk keperluan fine‑tuning. Model kemudian di‑fine‑tune pada korpus yang diperluas dan dievaluasi menggunakan metrik akurasi, precision, recall, dan F1. Pada set pengujian diperoleh akurasi 82,5%, precision untuk kelas positif 76,0%, recall 95,0%, dan F1‑score 84,44%. Matriks kebingungan menunjukkan TP = 114, FN = 6, FP = 36, dan TN = 84, yang mengindikasikan sensitivitas tinggi terhadap ulasan positif namun terdapat proporsi false positive yang relatif besar. Temuan ini mengindikasikan bahwa augmentasi meningkatkan kemampuan model dalam menangkap sinyal positif (tingginya recall), namun memerlukan penyesuaian lebih lanjut untuk mengurangi kesalahan prediksi positif (meningkatkan precision). Secara keseluruhan, hasil penelitian menyediakan bukti bahwa BERT multibahasa mampu menangani tugas sentimen berbahasa Indonesia dengan performa memadai apabila didukung strategi augmentasi dan prosedur validasi yang tepat.
Co-Authors Aji Seto Arifianto Annisa Fitri Maghfiroh Harvyanti Atmoko, Rachmad Beni Widiawan, Beni David Juli Ariyadi Dedes, Khen Dwi Putro Sarwo Setyohadi Dwi Putro Sarwo Setyohadi, Dwi Putro Sarwo Eka Yuniar Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Hendra Yufit Riskiawan Hermansyah, Mas'ud Hermansyah, Masud Hermansyah, Mas’ud Hidayatullah, Rifqi Rahmat I Gede Wiryawan Izzun Ni`am, Muhammad Jannah, Annisa Nurul Hidayati Juli, David Khen Dedes M Faiz Firdausi Mas&#039;ud Hermansyah Mas'ud Hermansyah Maulana Firmansyah, Akbar Mujiono Mujiono Nasyatha Adlin, Dzakiyyan Na’im, Septian Ghuslal Nur Nur Na’im, Septian Ghuslal Pascawati Savitri Universitasari Rachmad Andri Atmoko, Rachmad Andri Rahmat Hidayatullah, Rifqi Resya , Fachmi Reza Putra Pradana Rizal Setya Perdana Saiful Anwar Setyo Wibowo, Nugroho Sherin Ramadhania Syamsul Arifin Taufiq Rizaldi Vindarosita, Babylina Wijaya, Fariz Rizky Yudhistira, Prillinaya Yuniar, Eka