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Arnawan Hasibuan
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Faculty of Engineering, Universitas Malikussaleh Kampus Unimal Bukit Indah, Blang Pulo, Kec. Muara Satu Lhokseumawe
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Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Published by Universitas Malikussaleh
ISSN : -     EISSN : 26567520     DOI : -
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
The "Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)" is a scientific publication that compiles innovative works from researchers, academics, and practitioners in the field of multidisciplinary engineering. This proceeding serves as a platform to present cutting-edge research, studies, and discoveries shared during the ICOMDEN forum, organized by the international engineering community. The proceedings cover a wide range of disciplines in engineering, including but not limited to: Mechanical Engineering, Civil Engineering, Electrical and Electronics Engineering, Computer Science and Software Engineering, Materials Engineering, Industrial Engineering, Environmental Engineering, and other related fields. Each paper published in this proceeding undergoes a rigorous peer-review process to ensure high scientific quality and impactful contributions. By integrating perspectives from various engineering disciplines, the proceedings aim to foster cross-disciplinary collaboration and provide innovative solutions to complex challenges in the field of engineering. The ICOMDEN Proceedings highlight research and technological advancements relevant to industry and society, promoting the application of sustainable engineering practices. This publication is intended to be a key reference for researchers, students, and engineering professionals to expand their knowledge and generate new ideas in addressing global challenges in engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 119 Documents
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Comparison of the Results of Double Exponential Smoothing Method with Triple Exponential Smoothing for Predicting Chili Prices Nadia Saphira; Munirul Ula; Sujacka Retno
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Double Exponential Smoothing (DES) is a forecasting method that combines two components level and trend, used for data with a trend pattern that tends to increase or decrease over time. In contrast, Triple Exponential Smoothing (TES) incorporates three components: level, trend, and seasonality, making it suitable for data with trend and seasonal patterns. This study uses historical chili price data from 2020 to 2023, obtained from the Bank Indonesia website, managed by the National Strategic Food Price Information Center (PIHPS), to compare the effectiveness of DES and TES in predicting chili prices in Medan City. Prediction accuracy was evaluated using MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error). The study results show MAPE values for DES as follows: Large Red Chili 1.25%, Curly Red Chili 1.39%, Green Bird’s Eye Chili 1.14%, and Red Bird’s Eye Chili 1.13%. TES produced slightly lower MAPE values: Large Red Chili 1.25%, Curly Red Chili 1.38%, Green Bird’s Eye Chili 1.12%, and Red Bird’s Eye Chili 1.10%. The MAE values for DES are as follows: Large Red Chili 447.9, Curly Red Chili 494.83, Green Bird’s Eye Chili 430.92, and Red Bird’s Eye Chili 423.36. TES showed better accuracy with MAE values of Large Red Chili at 447, Curly Red Chili at 493.02, Green Bird’s Eye Chili at 416.2, and Red Bird’s Eye Chili at 409.36. The results conclude that Triple Exponential Smoothing performs better than Double Exponential Smoothing in predicting chili prices.
Cooperative Loan Eligibility Determination System Using The Evaluation Based on Distance form Average Solution (EDAS) Method Aina Rahmadani; Wahyu Fuadi; Ar Razi
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The rapid development of Decision Support Systems (DSS) has provided significant benefits, especially in supporting decision-making across various fields, including determining credit loan eligibility within cooperatives. Loans involve transactions in which lenders provide funds or assets to borrowers under agreed-upon terms. DSS is essential for enhancing objectivity and reducing the risk of non-performing loans arising from subjective evaluations. This study produces a web-based DSS capable of evaluating the loan eligibility of cooperative members by implementing the Evaluation Based on Distance from Average Solution (EDAS) method, combined with Rank Order Centroid (ROC) weighting. The system analyzes several criteria, such as collateral, membership status, loan amount, loan term, income, remaining loan balance, and payment status. The EDAS method generates a ranking based on eligibility, with alternative N0071 achieving the highest score of 0.907, followed by N0004 with a score of 0.830, and N0019 with a score of 0.762. The results of the system testing indicate that the loan eligibility calculations produced by the system are accurate and consistent with manual calculations, achieving a match of 95%. This ranking simplifies the cooperative's decision-making process. Furthermore, the system accelerates the loan eligibility determination process, enabling cooperatives to serve their members more effectively and efficiently.
Contagion Analysis of Plantation Commodity Producing Regions in Aceh Province Using Bayesian Inference Juliawati; Mukti Qamal; Said Fadlan Anshari
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The commodity-producing region is one of the plantation sectors with significant potential for economic growth in Aceh Province. The spread level between commodities owned by regions within the network is called “contagion,” which means that one commodity will influence a region, leading to a greater focus on that commodity within the network, and a region will influence other regions. With the diversity of commodities across various areas, a comprehensive analysis and visualization of the network formed among commodity producing regions are conducted using a Social Network Analysis (SNA) approach. Thus, Bayesian inference can reveal the network of each region that has relationships among the variables used to form a graph with the desired representation. This network analysis result can provide an overview of Aceh Province's plantation data through the network graph visualization among commodity-producing regions and the network graph of commodity production levels by region. Keywords: Aceh; Contagion Analysis; Social Network Analysis
Application Of Data Mining For Classification Of BLT-DD Recipients Using The Support Vector Machine Method Meliza Putri; Bustami; Ar Razi
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This study focuses on the application of the Support Vector Machine (SVM) algorithm for classifying recipients who are eligible to receive Direct Cash Assistance from the Village Fund (BLT-DD) in Nurussalam District, East Aceh Regency. The background of this research is the difficulty in identifying households eligible for BLT-DD due to increasing poverty and economic inequality exacerbated by the COVID-19 pandemic. This study aims to address this issue by utilizing the SVM algorithm, which can separate household data into two categories: "Eligible" and "Not Eligible." A total of 550 data points from Nurussalam District were used in this study, with 400 data points for training and 150 data points for testing. In the training data, 322 households (80.5%) were classified as "Eligible," while 78 households (19.5%) were categorized as "Not Eligible." The data collected includes variables such as household income, type of employment, education level, history of chronic disease, and home ownership status. After preprocessing the data, such as normalization and encoding, the SVM model was trained to classify BLT-DD recipients. In the testing data, 128 data points (85.33%) were classified as "Eligible," while 22 data points (14.67%) were classified as "Not Eligible." Further analysis of village distribution in Nurussalam District shows that some villages have a high percentage of eligible recipients, such as Blang Rambong and Alue Jagat, with 100% of recipients classified as "Eligible." Other villages, such as Arul Pinang and Alue Dua Muka O, show more varied eligibility rates, with 71.43% and 72.73% classified as "Eligible," respectively. In conclusion, the SVM algorithm provides an effective approach in determining the eligibility of BLT-DD recipients, helping the government to distribute assistance more accurately and efficiently in Nurussalam District.
Application of the Naïve Bayes Method in Optimizing Marketing Performance at PT. Semen Indonesia Mahesa Reglisalo; Dahlan Abdullah; Yesy Afrillia
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This study examines the application of the Naïve Bayes method to improve marketing performance at PT. Semen Indonesia. In an increasingly competitive business environment, effective data management is crucial for strategic decision-making. Currently, PT. Semen Indonesia utilizes the SAP system to manage sales and financial data, but it lacks an automated system to analyze marketing performance. This research aims to develop a Naïve Bayes-based classification system to monitor marketing performance, considering attributes such as profit, market share, sales volume, and customer satisfaction. The Naïve Bayes method was chosen for its accuracy in handling large-scale data and its ability to provide fast and efficient predictions. Marketing performance data is processed using this method to categorize marketing performance as “good” or “poor.” The analysis results show that the developed system achieves a classification accuracy of 43.75% for the “good” category and 56.25% for the “poor” category. This system assists management in designing more effective marketing strategies by leveraging historical data to predict trends and market needs. Keywords: Naïve Bayes, marketing performance, PT. Semen Indonesia, data analysis, classification system, profit, market share
Implementation of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) Algorithm for Rice Price Prediction Ezra Sasqia Syahna; Zara Yunizar; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Abstrak Studi ini mengimplementasikan model Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) untuk memprediksi harga beras ( gabah ) berdasarkan data historis dari tahun 2020 hingga 2024. Dengan memanfaatkan data yang diperoleh dari Investing.com, penelitian ini mengintegrasikan variabel eksternal utama seperti suhu, harga pupuk, dan tingkat produksi untuk meningkatkan akurasi prediksi. Metodologi ini terdiri dari langkah-langkah sistematis, termasuk pengumpulan data, pemrosesan, dan evaluasi model, dengan menggunakan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE) untuk menilai kinerja. Temuan tersebut mengungkapkan korelasi yang kuat antara harga pasar yang diprediksi dan aktual, khususnya dalam kategori harga penutupan, yang mencapai MAPE sebesar 1,354%. Metrik evaluasi selanjutnya mengonfirmasi kekokohan model, dengan harga penutupan menunjukkan MSE terendah sebesar 299.629,64 dan RMSE sebesar 547,38. Meskipun kategori harga tertinggi menunjukkan MAPE yang sedikit lebih tinggi, yaitu 2,007%, semua kategori tetap berada di bawah ambang batas yang dapat diterima, yaitu 2%, yang menunjukkan akurasi prediksi yang memuaskan. Sebagai kesimpulan, model SARIMAX menunjukkan efektivitas yang signifikan dalam peramalan harga beras, yang memberikan wawasan berharga bagi para pemangku kepentingan di pasar pertanian. Implementasi dalam aplikasi web memfasilitasi prediksi secara real-time, yang mendukung pengambilan keputusan yang tepat, dan meningkatkan strategi pasar. Kata kunci : SARIMAX; harga beras; model prediksi; MAPE; pasar pertanian; analisis deret waktu.
Implementation Of The Adaboost Method On Linear Kernel Svm For Classifying Pip Assistance Recipients At SMP Negeri 2 Kejuruan Muda Muhammad Fahri Al Fikri; Asrianda Asrianda; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Abstract: This study examines the application of the AdaBoost algorithm to a Linear Kernel Support Vector Machine (SVM) for determining student eligibility for the Indonesian Smart Program (PIP) at SMP N 2 Kejuruan Muda. The main objective is to improve the accuracy and fairness of the PIP aid distribution using advanced machine learning techniques. The dataset used comprises 500 student records, which include demographic, academic, and economic factors. The dataset was divided into training and testing sets, with the AdaBoost algorithm applied to enhance the SVM model’s performance. The study found that the SVM model optimized with AdaBoost was able to classify 91 students as eligible for PIP aid, achieving an impressive accuracy rate of 97.85%. Only 2 students were classified as ineligible, representing 2.15% of the total sample. When compared to the standard SVM model, which also classified 91 students as eligible, the key advantage of AdaBoost lies in its ability to handle borderline data more effectively. AdaBoost improves the classification of students whose eligibility was less clear by reinforcing the importance of difficult-to-classify instances. The model’s higher precision on edge cases indicates that AdaBoost offers a significant improvement over traditional SVM models in handling complex classification tasks. This research concludes that incorporating AdaBoost into SVM models provides a more robust and accurate method for determining student eligibility for government aid programs such as PIP. Keywords: AdaBoost, SVM, Indonesian Smart Program, PIP aid, machine learning, student eligibility, classification.
Comparison Of Maximal Marginal Relevance ( MMR) And Textrank Automatic Text Summarization Methods In Jurnal Muhammad Alif Al Fattah; Rizal Rizal; Rini Meiyanti
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The purpose of this study is to find out the application of the Maximal Marginal Relevance (MMR) and TextRank methods in automatic text summarization in journals in viewing the best model value in text automatically. This study can also implement an automatic text summarization application and find out the comparison between MMR and TextRank in the text summarization process in journals. Next research will evaluate the performance of the two models in producing relevant and informative text summaries. The problem of this research is how to overcome the problem of summarizing text with the basic concept of summary in providing the essence or overall content text in a journal. The main focus of this research is to display significant and relevant information in a more organized form and to display values for the efficiency of which model is the best after comparing the two models. The results of this research are a decision support system for determining the quality of poor rice using the fuzzy madm yager model with a value of (MMR) 0.4 and top N(3). The results of the comparison of the Maximal Marginal Relevance (MMR) similarity values are 0.510 and the score is 0.510, while the TextRank similarity is 0.510 and the score is 0.015. Based on testing of the two models, the best value was obtained from the rextrank model with a score of 0.015. Keywords: maximal marginal relevance (mmr), textrank, summary
Predictive Analysis of Retail Promotion Strategies in the Context of Consumer Shopping Behavior Ima Pratiwi; Muhammad Fikry; Sujacka Retno
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

In this paper, we examine the impact of various promotional strategies on consumer shopping interest, focusing on the Alfamart retail chain in Lhokseumawe City, Indonesia, which saw rapid expansion from five to fifteen stores between 2017 and 2023. Despite this growth, expected sales increases have not been met, raising concerns about the effectiveness of current promotional tactics. Utilizing multiple linear regression analysis, we investigate the influence of three specific strategies, Promo Spesial Mingguan, Serba Gratis, and Tebus Murah on shopping interest across the 15 stores. Findings reveal that Tebus Murah is the most effective strategy in boosting shopping interest, showing the smallest error margin between predictive and actual sales figures. This study provides comprehensive insights into the broader effects of promotional strategies on consumer interest, highlighting the need for Alfamart to focus on optimizing the Discounted Redemption approach to maximize sales. The predictive system developed serves as a strategic tool for identifying effective promotions, forecasting sales, calculating return on investment, and analyzing consumer behavior. Our results underscore the value of predictive analysis in refining promotional strategies, enabling Alfamart to adopt a more targeted and efficient marketing approach to enhance sales performance.
Place Dependence in the Coffee Truck Area: A Case Study on Jalan Teuku Hamzah Bendahara, Lhokseumawe City Wahyu Aulia; Cut Azmah Fithri; Hendra Aiyub; Yenny Novianti
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The phenomenon of coffee trucks has rapidly developed in various cities across Indonesia, including on Jalan Teuku Hamzah Bendahara in Lhokseumawe City. This area utilizes pedestrian pathways as a dynamic public interaction space, attracting numerous visitors to engage in activities and fulfill their social and consumption needs. This study aims to measure the level of place dependence and examine the relationships between various variables such as perceived quality, need fulfillment, loyalty, effort to stay, and effort to return in the coffee truck area. The methodology employed is a quantitative survey using questionnaires completed by 100 respondents, with data analyzed using SPSS. The findings show that the level of place dependency is quite high, indicating a strong attachment to this location. User satisfaction regarding the quality of the space provided by the coffee truck shows a strong and significant relationship, contributing notably to enhancing the perception of this location as the best in meeting visitors' needs and goals overall. In conclusion, the coffee truck area not only fulfills users' functional needs but also fosters emotional connections, thereby strengthening their dependence on the location. The results of this study offer valuable insights for managing similar public spaces, particularly in creating places that can establish long-term engagement with users and communities.

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