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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Sinkron : Jurnal dan Penelitian Teknik Informatika Angkasa: Jurnal Ilmiah Bidang Teknologi 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 Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) SWARNA Journal of Information Technology and Cyber Security Journal of Information Technology Journal of Vocational, Informatics and Computer Education Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Setiawan, Akas Bagus
Jurusan Teknologi Informasi, Politeknik Negeri Jember
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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The Waterfall Model in the Implementation of a Room Reservation Information System: A Case Study at a University Setiawan, Akas Bagus; Rizaldi, Taufiq
Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12248

Abstract

Politeknik Negeri Jember (National State of Jember Polytechnic), Indonesia, fully understand that infrastructure is one among various key factors to enhance the quality of the university. However, in the department of Information Technology, infrastructure management is a prominent issue. The primary cause of the issue appears to be a manual arrangement of room reservations, as room usage often crashes due to the scheduling mismatch. To tackle this issue, this study focuses on the development of a room reservation information system, known as “Sistem Informasi Peminjaman Ruangan dan Sarana Pembelajaran (SIPRu)”. The system is developed as an additional feature in the department website. It is developed using the Waterfall method, beginning with the requirements analysis (i.e., an identification of functional and non-functional requirements) and architectural processes design processes (i.e., the development of use case diagram, flowchart, data flow diagram, and database design). In this study, Laravel is used for the implementation, and black box testing is used to verify compliance with the functional requirements. During the testing process, 42 department stakeholders participated to validate the requirements and conduct user experience testing. The results indicated that the stakeholders have found the website to be sufficiently responsive to their needs. Furthermore, it is revealed that SIPRu is effective at assisting users in reserving rooms at the department of Information Technology, National State of Jember Polytechnic.
Proposing the Urgency of Consumer Protection Regulations and Freshness Detection Applications for Fish in Traditional Markets Using Technology Setiawan, Akas Bagus; Firmansyah, Akbar Maulana
Co-Value Jurnal Ekonomi Koperasi dan kewirausahaan Vol. 15 No. 10 (2025): Co-Value: Jurnal Ekonomi, Koperasi & Kewirausahaan
Publisher : Program Studi Manajemen Institut Manajemen Koperasi Indonesia Bandung

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Abstract

This study explores consumer knowledge and vendor practices related to the freshness of ikan tongkol (Euthynnus affinis) in traditional markets in Jember, Indonesia. Using a mixed-methods approach, data were collected from 271 consumers and 62 vendors through surveys and interviews. Consumers mostly relied on sensory indicators like color, smell, and texture—especially color—to assess freshness. Although many felt confident in judging freshness, around 20% were uncertain, revealing a knowledge gap. Social media and the Internet were key information sources, raising concerns about accuracy. Vendors commonly used ice storage, with limited access to refrigeration. Interviews highlighted vendor awareness of quality issues but also challenges due to limited infrastructure. The study reveals a mismatch between perceived and actual freshness, shaped by traditional knowledge and limited objective tools. Enhancing education, vendor practices, and introducing simple freshness indicators could improve transparency, safety, and market resilience. The findings offer insights for policy and food system improvements
Analisis Kualitas Sistem Informasi Akademik Berbasis Website di Universitas Bahauddin Mudhary Madura dengan Pendekatan PIECES Framework: Analysis of the Quality of Website-Based Academic Information Systems at Bahauddin Mudhary Madura University Using the PIECES Framework Approach Vindarosita, Babylina; Setyo Wibowo, Nugroho; Setiawan, Akas Bagus; Yuniar, Eka; Yudhistira, Prillinaya; Resya , Fachmi
Jurnal Ilmiah Inovasi Vol 25 No 1 (2025): April
Publisher : Politeknik Negeri Jember

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Abstract

Penelitian ini bertujuan untuk menganalisis kualitas Sistem Informasi Akademik (SIAKAD) Universitas Bahauddin Mudhary Madura berdasarkan persepsi kepuasan pengguna (mahasiswa) menggunakan metode PIECES framework. Metode PIECES framework digunakan untuk mengevaluasi enam aspek kualitas sistem informasi, yaitu Performance, Information and Data, Economics, Control and Security, Efficiency, dan Service. Pengumpulan data dilakukan melalui kuesioner yang disebarkan kepada mahasiswa pengguna SIAKAD. Hasil penelitian diharapkan dapat memberikan gambaran yang komprehensif mengenai kualitas SIAKAD saat ini, mengidentifikasi area-area yang memerlukan perbaikan, serta memberikan rekomendasi yang konstruktif untuk pengembangan SIAKAD di masa depan sehingga dapat meningkatkan kepuasan mahasiswa sebagai pengguna. Penelitian ini diharapkan dapat memberikan kontribusi bagi Universitas Bahauddin Mudhary Madura dalam meningkatkan kualitas layanan akademik berbasis teknologi informasi.
Implementasi Naïve Bayes dalam Flask Framework untuk Sistem Informasi Klasifikasi Penyakit Jantung Bagus Setiawan, Akas; Nasyatha Adlin, Dzakiyyan; Hermansyah, Mas'ud
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 5 (2025): EDISI SEPTEMBER 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i5.11494

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian di Indonesia, dengan tren kasus yang terus meningkat setiap tahunnya. Deteksi dini sangat penting untuk mencegah risiko yang lebih parah, namun keterbatasan akses terhadap layanan kesehatan menjadi kendala di beberapa wilayah. Penelitian ini bertujuan untuk membangun sistem klasifikasi penyakit jantung berbasis algoritma Naive Bayes yang diimplementasikan dalam API menggunakan Flask Framework Python sebagai backend cerdas. Dataset yang digunakan diperoleh dari Kaggle dan telah melalui tahapan preprocessing, seleksi atribut, transformasi data, serta pembagian data latih dan uji. Model Naive Bayes dipilih karena kesederhanaannya serta kemampuannya dalam menangani data berskala besar. Evaluasi model menunjukkan performa yang cukup baik, dengan accuracy mencapai 73,77%, precision 67,57%, dan recall 86,21%. Sistem yang dikembangkan diintegrasikan dalam layanan web dan mobile, sehingga dapat diakses oleh pengguna secara luas. Hasil penelitian ini menunjukkan bahwa integrasi algoritma machine learning dan API dapat memberikan solusi deteksi dini penyakit jantung yang ringan, cepat, dan mudah digunakan. Penelitian ini diharapkan dapat menjadi dasar pengembangan sistem pendukung keputusan dalam bidang kesehatan digital.
Line-of-Sight Dominance Over Vegetation: Simulation-Based LoRa Performance in Tropical Forest Terrain Atmoko, Rachmad; Rahmat Hidayatullah, Rifqi; Nur Na’im, Septian Ghuslal; Izzun Ni`am, Muhammad; Bagus Setiawan, Akas
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15627

Abstract

Low-Power Wide-Area Network (LPWAN) technologies, especially LoRa, are receiving considerable interest for applications involving environmental monitoring in difficult terrain conditions. However, existing research predominantly examines vegetation attenuation or terrain elevation effects separately, leaving a critical research gap in understanding their combined and interactive impacts on LoRa connectivity in tropical forest environments. Furthermore, most studies rely on simplified propagation models that inadequately represent the complex radio environment of tropical forests, and few investigations systematically compare the relative importance of vegetation density, elevation, and line-of-sight conditions. This work addresses these gaps through an in-depth simulation-based investigation of LoRa network behavior in the University of Brawijaya (UB) Forest, which serves as a typical tropical forest setting in Indonesia. We performed detailed simulations using Python and LoRaSim, employing fine-resolution elevation datasets and precise vegetation classification to examine how dense vegetation, medium vegetation, and elevation parameters influence LoRa communication performance. Our findings indicate that, in contrast to traditional propagation models, nodes located in dense vegetation zones reached a 90.0% success rate, as opposed to 65.0% in zones without vegetation. Additional investigation shows that line-of-sight presence (28.6% versus 0.0% success rate) and relative elevation relative to the gateway (11.1% versus 27.3% success rate for nodes positioned above and below the gateway, respectively) represent more crucial factors for connectivity compared to vegetation attenuation by itself. These outcomes offer important guidance for enhancing LoRa-based environmental monitoring systems in tropical forest settings through strategic node positioning that considers elevation characteristics and line-of-sight availability.
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
An End-to-End Machine Learning Pipeline for Online Purchase Intention Prediction Using Random Forest and MLOps Practices Akas Bagus Setiawan; Hendra Yufit Riskiawan; Hermawan Arief Putranto; Taufiq Rizaldi; Rachmad Andri Atmoko
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 18, No 1 (2026): Februari
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v18i1.3841

Abstract

Predicting online shoppers' purchase intention is a key issue in e-commerce because it directly affects conversion and marketing effectiveness. The main focus of this article is a Random Forest purchase-intention model accompanied by an end-to-end MLOps implementation to ensure production readiness. The dataset used is Online Shoppers Intention with 12,330 samples and 18 features representing administrative, informational, and product-related characteristics, along with behavioral metrics. Preprocessing includes missing-value imputation, numerical feature standardization, categorical feature encoding, and outlier removal using the z-score method. The model is optimized with GridSearchCV and 3-fold cross-validation. Test results show 91.38% accuracy with 73.60% precision, 56.64% recall, and 64.02% F1-score for the positive class. MLOps implementation uses MLflow for experiment tracking, Prometheus-Grafana for monitoring, and a GitHub Actions-based CI/CD pipeline for deployment automation. Overall, the Random Forest model delivers strong predictive performance on e-commerce data and is supported by an MLOps pipeline that improves reproducibility, deployment, and production monitoring
Co-Authors Aji Seto Arifianto Annisa Fitri Maghfiroh Harvyanti Atmoko, Rachmad Beni Widiawan, Beni David Juli David Juli Ariyadi David Juli Ariyadi Devasis Pradhan Dwi Putro Sarwo Setyohadi Dwi Putro Sarwo Setyohadi, Dwi Putro Sarwo Eka Yuniar Eka Yuniar Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Hendra Yufit Riskiawan Hendra Yufit Riskiawan Hermansyah, Mas'ud Hermansyah, Masud Hermansyah, Mas’ud Hermawan Arief Putranto I Gede Wiryawan Ismail Rakip Karas Izzun Ni`am, Muhammad Jannah, Annisa Nurul Hidayati Khen Dedes Khen Dedes M Faiz Firdausi M. Faiz Firdausi Mas&#039;ud Hermansyah Mas&#039;ud Hermansyah Mas’ud Hermansyah Mas’ud Hermansyah Maulana Firmansyah, Akbar Mujiono Mujiono Mujiono Mujiono Mujiono Mujiono Mujiono Mujiono Nasyatha Adlin, Dzakiyyan Nur Na’im, Septian Ghuslal Pascawati Savitri Universitasari Rachmad Andri Atmoko Rachmad Andri Atmoko Rachmad Andri Atmoko Rahmat Hidayatullah, Rifqi Resya , Fachmi Reza Putra Pradana Rifqi Rahmat Hidayatullah Rizal Setya Perdana Saiful Anwar Salnan Ratih Asriningtias, Salnan Ratih Septian Ghuslal Nur Na’im Setyo Wibowo, Nugroho Sherin Ramadhania Syamsul Arifin Taufiq Rizaldi Taufiq Rizaldi Vindarosita, Babylina Wijaya, Fariz Rizky Yudhistira, Prillinaya Yuniar, Eka