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Benazir Imam Arif Muttaqin
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INDONESIA
Journal of Advanced in Information and Industrial Technology (JAIIT)
Published by Universitas Telkom
ISSN : 27161935     EISSN : 27161927     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Journal of Advances in Information and Industrial Technology publishes peer-reviewed papers on all fields of information and industrial technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 90 Documents
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Business Process Improvement Using Quality Evaluation Framework and Root Cause Analysis at AMDK Company DC. Bali Pramesti, Anak Agung Istri Anindya; Githa, Dwi Putra; Susila, Anak Agung Ngurah Hary
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.703

Abstract

This study addresses critical inefficiencies in distribution operations at PT. AMDK Distribution Center Bali, where manual processes and system fragmentation result in document verification errors, delivery mismatches, and extended cycle times. We employed an integrated methodological framework combining the Quality Evaluation Framework (QEF) for systematic performance assessment, Root Cause Analysis (RCA) with Fault Tree Analysis (FTA) to identify underlying operational failures, and Business Process Improvement (BPI) for solution design. Process modeling was conducted using BPMN 2.0 and validated through Bizagi Modeler simulation. The QEF evaluation revealed five critical non-conforming indicators: road letter verification errors (Q6), product delivery inaccuracies (Q22), residual product inspection failures (Q23), LHPH documentation errors (Q26), and submission delays (Q28). Root cause analysis identified manual dependency, inadequate system integration, and insufficient SOPs as primary failure sources. The proposed To-Be model, incorporating automated validation, digital documentation workflows, and cross-system integration (Smartlog-SAP-OTM), achieved measurable improvements: a 17.61% time reduction in delivery operations and a 2.41% improvement in receiving processes. This research contributes a validated methodological framework for logistics process optimization in emerging market contexts, demonstrating how structured quality evaluation coupled with root cause-driven redesign can achieve sustainable operational improvements.
A Hybrid Neural Network-Time Series Regression Model for Intermittent Demand Forecasting Data Amri Muhaimin; Damaliana, Aviolla Terza; Muhammad Nasrudin; Riyantoko, Prismahardi Aji; Nabilah Selayanti; Putri, Shafira Amanda
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.704

Abstract

Forecasting is a vital tool that helps us make informed decisions by predicting future events based on past data. For forecasts to be accurate, it is important that the data is reliable, complete, and consistent. Yet, the intermittent data is a unique data that is challenging to forecast. Intermittent data contains a characteristic that the data has a lot of long zeros in some periods. The zero value will influence the model to generate a forecasting model. This study aims to tackle those problems by applying a hybrid approach. We integrate the regression model and neural network to create a novel approach for forecasting intermittent data. The dataset used for this data is from Kaggle, sales at Walmart supermarket for one category only. The sales data always produce an intermittent demand pattern, because not every day are the items always sold to customers. This irregular pattern makes the data difficult to forecast using a naïve approach, such as the Croston method, exponential smoothing, and ARIMA. To evaluate the performance of our model, some metrics were calculated. We use mean squared error, root mean squared error, and root mean squared scaled error. The result shows that our proposed method outperforms the benchmark model, with an RMSSE of 0.98, which is the lowest compared to other benchmark models in the root mean squared scaled error value. This result shows promise as an exciting solution for overcoming the challenges posed by irregular data in future forecasting tasks.
A Case study of Mitigating Risk of Invoice Payment Failure At Electricity Provider Company using the House of Risk Method Widyasari Retnaningtyas; Arvitrida, Niniet Indah
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.713

Abstract

Considering that a complicated or delayed invoice payment process can disrupt business operations, the company must establish mitigation measures to prevent such occurrences and minimize potential failures that may hinder future business processes. This context specifically involves collaboration with business partners or vendors providing services and materials. This research aims to map out the activities within the invoice payment business process, identify risk events and related risk agents, and formulate mitigation strategies to reduce their negative impact. The House of Risk (HOR) method is utilized to prioritize risks according to Aggregate Risk Potential (ARP) scores then the scores will be classified with a Pareto diagram. This study aims to offer actionable recommendations for risk mitigation, mostly in digitalization and automation of systems that can help lower the rate of document rejections and accelerate the overall payment cycle. Ultimately, these improvements will contribute to increased operational efficiency and reinforce vendor confidence, ensuring that high-value invoices are processed more reliably and promptly.
Design and Construction of the Action Role-Playing Game: Supply Odyssey Using the Game Development Life Cycle Method Dewanata, Enggaling Aji; Willis, Tito Bisma May
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.720

Abstract

The rapid development of the global gaming industry has made games not only serve as entertainment but also as a medium for learning, social interaction, and game-based entrepreneurship. One of the most widely favoured genres is the Action Role-Playing Game (Action RPG), which combines action elements with RPG mechanics. This research aims to design and develop a 2D video game titled “Supply Odyssey” in the Action RPG genre using the Godot Engine as the primary development platform. Godot was selected due to its open-source nature, free licensing, and strong support for both indie and professional developers. The research employs the Game Development Life Cycle (GDLC) methodology, incorporating the Line of Sight (LOS) technique to model enemy (NPC) behaviour. The implementation of LOS is expected to increase combat realism and provide greater tactical depth in real-time encounters by enabling players to strategize according to enemy visibility ranges, select optimal positions, and engage in more dynamic interactions. Supporting data were collected through observations on game platforms and developer and gamer community forums to identify relevant trends, preferred game concepts, and engaging gameplay patterns. The results of this research are expected to produce an Action RPG game that not only offers entertainment but also contributes to the development of Godot-based games and serves as a reference for applying GDLC and LOS methods in similar game development projects.
Disaster Management in the Palm Oil Industry Using Industrial Engineering Methods with Monte Carlo Simulation and Survival Analysis Suksmanatyo; Wahyudi, Brian Fitri; Hidayat, Muhammad Taufiq; Suheriyanto; Yulianto; Aulady, M. Ferdaus Noor
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.721

Abstract

The palm oil industry is a strategic sector that plays a significant role in foreign exchange earnings and national employment, but is highly vulnerable to disaster risks, both from natural (floods, fires) and technical (machine breakdowns, supply chain disruptions) factors. This study develops an industrial engineering-based disaster management framework by integrating Monte Carlo Simulation to estimate economic losses and Survival Analysis (Kaplan–Meier and Log-Rank Test) to assess the operational resilience of palm oil mills. The simulation results show an average annual loss of IDR 3.87 billion, with a 95% VaR of IDR 8.97 billion and a 95% CVaR of IDR 11.25 billion. Factors such as preventive maintenance, the location of the mill in a flood-prone area, and the availability of backup power sources significantly influence post-disaster recovery time. This study provides a quantitative basis for the allocation of financial risk reserves and strategic recommendations to improve the operational resilience of the palm oil industry to disaster uncertainty.
Exploring Logistics Process Improvement Possibility with SCOR Digital Standard and Lean Waste Analysis Adhie Prayogo; Curie Habiba; Ulkhaq, M. Mujiya; Dina Tauhida; Sitompul, Fachri Rizky
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.723

Abstract

Inbound logistics, including receiving goods, quality and physical checking, item inquiry, and stock-level checking are essential aspects within supply chain management in which the unresponsive operation may lead to inefficiency. This study aims to observed the ongoing operations in a mid-sized paper manufacturer using a combination of Business Process Modelling to map the current flow process, Lean Waste Analysis to identify possible wastes, and SCOR Digital Standard to offer improvement opportunities. The results show that waiting, motion, overprocessing, and inventory wastes are identified across the three logistics main processes. Additional waste, human skill, is observed in the stock-level checking procedure. Subsequently, SCOR DS recommends the firm to escalate the human skills of lean manufacturing, bar code handling & RFID, ERP system, automation tool, time management, and collaboration, to support the performance improvement. Finally, the study proposed metrics within four dimensions to validate the solution impact on the performance, including the responsiveness, reliability, asset management, and people.
Developing Strategies for a Seafood-Based Food Stall Using BMC, SWOT, and AHP Approaches Rohmah, Rofiqoh Nur; Zainida, Maya Revanola; Ramadhan, Gilang Titah
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.726

Abstract

Indonesia holds vast marine potential that remains underutilized, particularly in downstream processing within the micro-scale seafood culinary sector. Seafood-based eateries play a critical role in strengthening local economies and supporting the implementation of the blue economy. However, these businesses still face structural challenges, including limited technological adoption, market access barriers, and unstructured business models. This study aims to formulate and prioritize development strategies for seafood-based microenterprises using a combination of the Business Model Canvas (BMC), SWOT analysis, and the Analytic Hierarchy Process (AHP). A mixed-method approach was employed, incorporating interviews, field observations, and expert surveys. The BMC analysis showed that the business had some major strengths, such as a wide range of menu items, the use of fresh ingredients every day, and a high level of customer loyalty. At the same time, weaknesses included limited visibility in locations, limited digital integration, and conventional operational systems. The SWOT analysis generated five strategic alternatives, which were evaluated using AHP based on five criteria: sustainability, technical feasibility, local economic impact, business competitiveness, and implementation cost. The findings indicate that the internal strengthening strategy—comprising staff training, business expansion, and supplier selection—is the most feasible and prioritized approach. However, this strategy does not fully embody the core principles of the blue economy. Therefore, policy interventions and cross-sector collaboration are essential to integrate micro-scale seafood businesses into a sustainable and inclusive maritime ecosystem.
IoT-Based Water Quality Monitoring System to Enhance Sustainability and Business Performance in Koi Fish Cultivation Sugiarto; Nugraha, Isna; Fahrudin, Tresna Maulana; Rizqina, Azza; Agvenia, Keisya
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.730

Abstract

Water quality is a critical factor that determines the survival and productivity of koi fish cultivation. Fluctuations in key parameters, such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and turbidity, can induce stress and lead to mass fish mortality, resulting in substantial financial losses for farmers. This study proposes an IoT-based water quality monitoring system designed to enhance both environmental sustainability and business performance in koi aquaculture. The system integrates four sensors (pH, DO, TDS, and turbidity) connected to an ESP32 microcontroller, which transmits real-time data via Wi-Fi to cloud platforms (Firebase and Blynk). A dedicated dashboard provides continuous monitoring, historical trend visualization, and real-time alerts when parameter thresholds are exceeded. The prototype was validated in an operational koi pond and achieved an average accuracy of 96.5%. User testing involving 10 koi farmers showed an 89% satisfaction rate, demonstrating the system's practicality and usability. Economically, the solution reduced manual monitoring costs by 40%, water replacement volume by 25%, and increased fish survival rates by 12%. These results indicate that IoT implementation in aquaculture not only improves environmental control but also increases operational efficiency and overall profitability, contributing to sustainable, data-driven aquaculture practices.
An Extended TAM Approach to Understanding Behavioral and Institutional Drivers of Drone Technology Adoption in Indonesia’s Green Economy Khofiyah, Nida; Isna Nugraha; Hasyrani Windyatri
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.732

Abstract

Indonesia’s transition toward a green economy requires the adoption of efficient and environmentally friendly technologies, including drone systems that offer substantial benefits across agriculture, forestry, and green logistics. However, adoption remains limited due to behavioral and institutional barriers. This study applies an extended Technology Acceptance Model (TAM) to examine how Environmental Concern (EC), Government Support (GS), Facilitating Conditions (FC), Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) shape Behavioral Intention (BI) toward drone adoption in Indonesia. Using SEM-PLS and data from 112 practitioners and stakeholders, the results reveal that FC has a strong and significant effect on PEOU, while EC and GS do not significantly influence PU, indicating limited perceived relevance of environmental awareness and government policy in shaping usefulness perceptions. Furthermore, PEOU significantly affects PU and BI, and PU significantly enhances BI, confirming the central role of usability and functional benefits in driving adoption. These findings highlight that effective infrastructure, operational support, and usability improvements are more influential than environmental or regulatory factors in promoting drone technology. The study provides strategic recommendations for policymakers and industry actors to strengthen institutional facilitation, improve capacity building, and enhance the practical value of drones to advance Indonesia’s green economic transformation.
Implementation of the YOLOv8n Model for Automatic Owl Detection in Swiftlet Farming Buildings Putra, Iqbal Kurniawan Asmar; Apriska Prameswari; Fikri, Muhammad Ainul; Suhari, Ahmad Riznandi
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.733

Abstract

Object detection based on digital images is a rapidly developing field in the application of intelligent systems. This study aims to create an automatic owl detection system utilizing the YOLOv8 deep learning model as a pest mitigation measure in the swiftlet farming industry. Owls are known to enter swiftlet houses at night and prey on the birds, causing economic losses. Owl image datasets were obtained from the Roboflow platform and annotated in YOLO format. The model was trained using the YOLOv8-nano architecture with a 640×640 pixel input resolution. The evaluation results showed that the model achieved a mAP@0.5 of 96.82% and mAP@0.5:0.95 of 70.5%, with a precision of 97.2% and a recall of 93.38%. These results indicate that the YOLOv8 model performs well and has the potential to be implemented as an automatic monitoring system in swiftlet farming environments.

Page 9 of 9 | Total Record : 90

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