International Journal of Industrial Optimization http://journal2.uad.ac.id/index.php/ijio <table class="data" width="100%" bgcolor="#f0f0f0"> <tbody> <tr valign="top"> <td width="30%">Journal title</td> <td width="70%"><strong>International Journal of Industrial Optimization</strong></td> </tr> <tr valign="top"> <td width="30%">Initials</td> <td width="70%"><strong>IJIO</strong></td> </tr> <tr valign="top"> <td width="30%">Abbreviation</td> <td width="70%"><em><strong><strong>Int. J. Ind. Op.</strong></strong></em></td> </tr> <tr valign="top"> <td width="30%">Frequency</td> <td width="70%"><strong><a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/issue/archive" target="_blank" rel="noopener">2 issues per year</a> | <a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/issue/archive" target="_blank" rel="noopener">February and September</a></strong></td> </tr> <tr valign="top"> <td width="30%">DOI</td> <td width="70%"><strong>Prefix 10.12928</strong><strong> by <img src="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//public/site/images/dyoyo/CROSREFF_Kecil2.png" alt="" /></strong></td> </tr> <tr valign="top"> <td width="30%">ISSN</td> <td width="70%"><strong><a href="http://u.lipi.go.id/1568009637" target="_blank" rel="noopener">2714-6006</a> (printed)/<a title="e-ISSN" href="http://u.lipi.go.id/1568017487" target="_blank" rel="noopener">2723-3022</a> (online)</strong></td> </tr> <tr valign="top"> <td width="30%">Editor-in-chief</td> <td width="70%"><a href="https://www.scopus.com/authid/detail.uri?authorId=55489479200" target="_blank" rel="noopener"><strong>Hayati Mukti Asih</strong></a></td> </tr> <tr valign="top"> <td width="30%">Publisher</td> <td width="70%"><a href="https://uad.ac.id/en/"><strong>Universitas Ahmad Dahlan</strong></a></td> </tr> <tr valign="top"> <td width="30%">Citation Analysis</td> <td width="70%"><strong><a href="https://scholar.google.com/citations?hl=en&amp;user=gopOqDAAAAAJ" target="_blank" rel="noopener">Google Scholar</a></strong></td> </tr> </tbody> </table> <hr /> <div style="text-align: justify;">The <strong>International Journal of Industrial Optimization</strong> (IJIO) is an international journal published by Universitas Ahmad Dahlan, Indonesia. This is an open-access and peer-reviewed journal which is published twice a year (<a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/issue/archive" target="_blank" rel="noopener">2 issues/year</a>) in February and September. The first volume of IJIO was launched in February 2020. IJIO covers theoretical and empirical questions in industrial optimization. The span of coverage ranges from the basic implementation of production systems and simulation to advanced problems such as data mining and metaheuristics. Authors are encouraged to submit manuscripts that connect the gaps between research, development, and implementation.</div> <div style="text-align: justify;"> </div> <div style="text-align: justify;"> <div style="text-align: justify;"><strong>Before submission</strong>,<br />You have to make sure that your paper is prepared using the<a href="https://bit.ly/templateIJIO2021" target="_blank" rel="noopener"><strong> IJIO TEMPLATE </strong></a>and Carefully <strong><a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context//index.php/ijio/about/submissions#authorGuidelines" target="_blank" rel="noopener">read the submission guidelines</a>. </strong>Submit your paper <strong>ONLY in English. </strong>If you have problems with the journal, please contact us at <a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/context/mailto:hayati.asih@ie.uad.ac.id">hayati.asih@ie.uad.ac.id</a></div> </div> Universitas Ahmad Dahlan en-US International Journal of Industrial Optimization 2714-6006 <p><strong>License and Copyright Agreement</strong></p> <p>In submitting the manuscript to the journal, the authors certify that:</p> <ul> <li>They are authorized by their co-authors to enter into these arrangements.</li> <li>The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read the International Journal of Industrial Optimization (IJIO) Author Guidelines at <a href="http://journal2.uad.ac.id/index.php/ijio/management/settings/distribution//index.php/ijio/about/submissions#onlineSubmissions">http://journal2.uad.ac.id/index.php/ijio/about/submissions#onlineSubmissions</a></li> <li>That it is not under consideration for publication elsewhere,</li> <li>That its publication has been approved by all the author(s) and by the responsible authorities tacitly or explicitly of the institutes where the work has been carried out.</li> <li>They secure the right to reproduce any material that has already been published or copyrighted elsewhere.</li> <li>They agree to the following license and copyright agreement.</li> </ul> <p><strong>Copyright</strong></p> <p>Authors who publish with the International Journal of Industrial Optimization (IJIO) agree to the following terms:</p> <ol start="1"> <li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank" rel="noopener">Creative Commons Attribution License (CC BY-SA 4.0)</a> that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.</li> <li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</li> <li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</li> </ol> A dai-liao hybrid conjugate gradient method for unconstrained optimization http://journal2.uad.ac.id/index.php/ijio/article/view/4100 One of todays’ best-performing CG methods is Dai-Liao (DL) method which depends on non-negative parameter  and conjugacy conditions for its computation. Although numerous optimal selections for the parameter were suggested, the best choice of  remains a subject of consideration. The pure conjugacy condition adopts an exact line search for numerical experiments and convergence analysis. Though, a practical mathematical experiment implies using an inexact line search to find the step size. To avoid such drawbacks, Dai and Liao substituted the earlier conjugacy condition with an extended conjugacy condition. Therefore, this paper suggests a new hybrid CG that combines the strength of Liu and Storey and Conjugate Descent CG methods by retaining a choice of Dai-Liao parameterthat is optimal. The theoretical analysis indicated that the search direction of the new CG scheme is descent and satisfies sufficient descent condition when the iterates jam under strong Wolfe line search. The algorithm is shown to converge globally using standard assumptions. The numerical experimentation of the scheme demonstrated that the proposed method is robust and promising than some known methods applying the performance profile Dolan and Mor´e on 250 unrestricted problems. Numerical assessment of the tested CG algorithms with sparse signal reconstruction and image restoration in compressive sensing problems, file restoration, image video coding and other applications. The result shows that these CG schemes are comparable and can be applied in different fields such as temperature, fire, seismic sensors, and humidity detectors in forests, using wireless sensor network techniques. Nasiru Salihu Mathew Remilekun Odekunle Mohammed Yusuf Waziri Abubakar Sani Halilu Suraj Salihu Copyright (c) 2021 Nasiru Salihu, Mathew Remilekun Odekunle, Mohammed Yusuf Waziri, Abubakar Sani Halilu, Suraj Salihu 2021-09-01 2021-09-01 2 2 69 84 10.12928/ijio.v2i2.4100 Marketing strategy planning at alfamart lodadi stores using the clustering, ahp, and ar-mba method http://journal2.uad.ac.id/index.php/ijio/article/view/4361 Nowadays, people are very facilitated by the existence of various shopping centers, including retail. Because many retailers are close to each other, Alfamart Lodadi must have a good marketing strategy. So far, the strategy used is sometimes inaccurate because it is not based on customer segmentation.  Therefore, the purpose of this research is to help retail owners to make decisions regarding the right marketing strategy with three methods so that Alfamart Lodadi can compete and increase sales. The Analytical Hierarchy Process (AHP) is employed to find the priority variables of customer segmentation; meanwhile, the K-Means Clustering is used to group customers based on the similarity of predetermined characteristics. AR-MBA is used to find out the best rules, and products are rarely, sufficient, and frequently purchased.  The results of this research, based on AHP, obtained five segmentation priority variables based on the largest eigenvector values. There are income, age, expenditure, distance, and shopping intensity with each eigenvector value of 0.13; 0.16; 0.12; 0.12; 0.17. From clustering, there are three customer clusters with different strategies, including free shipping when shopping, product discounts for certain periods, and providing catalogs and discounts on each transaction and offer notifications. Then, this research also obtained three strategies based on AR-MBA. These include making a catalog by bringing frequently purchased products closer together, choosing a layout for shopping places by bringing frequently purchased products closer together, and making shopping coupons for rarely purchased products. With several strategic choices, companies can make decisions appropriately according to the desired criteria. Fariza Halidatsani Azhra Najib Fadhlurrohman Bagas Swardhana Putra Faisal Ibrahim Copyright (c) 2021 Fariza Halidatsani Azhra, Najib Fadhlurrohman, Bagas Swardhana Putra, Faisal Ibrahim 2021-09-01 2021-09-01 2 2 85 98 10.12928/ijio.v2i2.4361 Multi-item inventory policy with time-dependent pricing and rework cost http://journal2.uad.ac.id/index.php/ijio/article/view/4370 The price of broiler chickens at the consumer level varies daily. The price can be very low or otherwise. The price has resulted from the imbalance between the availability of chicken from suppliers and the market demand. As a result, demand will also fluctuate because it is influenced by consumer purchasing power. When the price of live chickens is low, the carcass company will usually buy in large quantities and expect to sell them at a higher price. The problem arises when the chicken overstock company will risk product damage due to product buildup in the refrigerated warehouse, so rework is necessary. In this paper, we will be developed a multi-item inventory model that considers material prices that vary to time, probabilistic demand, and rework costs. The aim is to determine the right policy for controlling frozen chicken products' inventory to minimize losses and total inventory costs. This model can evaluate the best time to order broiler chickens, how much to order, how long the interval between orders, and the optimal number of orders, resulting in minimum total inventory cost per period. The model solution is carried out with an optimization approach based on the parameters that affect the model. A numerical example is given at the end of this paper for model validation and illustrates the model solving algorithm. Laila Nafisah Nabilla Clara Devi Maharani Yuli Dwi Astanti Muhammad Shodiq Abdul Khannan Copyright (c) 2021 Laila Nafisah, Nabilla Clara Devi Maharani, Yuli Dwi Astanti, Muhammad Shodiq Abdul Khannan https://creativecommons.org/licenses/by-sa/4.0 2021-09-01 2021-09-01 2 2 99 112 10.12928/ijio.v2i2.4370 Ceramic supplier selection using analytical hierarchy process method http://journal2.uad.ac.id/index.php/ijio/article/view/4406 This study tried to implement the Analytical Hierarchy Process (AHP) and the weights of the criteria and sub-criteria to find the best supplier. According to QCDFR (quality, cost, delivery, flexibility, and responsiveness). This study took place in one of the biggest tile producers, ranks fifth in the world and the first in Indonesia. However, the company currently only uses quality, cost, and delivery methods to choose the best supplier of raw material, namely feldspar. This research tries to use the systematic method to find the best supplier based on the importance of the criteria. The method used the quantitative approach to enumerate the data to analyze the information. The company analyzed six suppliers. The primary tool used in this research is a Super Decision Software version 3.2 to create and manage the AHP model, enter the judgments, get results, and perform sensitivity analysis on the results. The result found that Semarang is the best supplier. The company will choose Semarang to become the company's business partner compared to the other suppliers because Semarang has met the criteria that the company prioritizes the most. By having the best supplier selection, the company can provide the right material consistency and suitable material suitability. Filda Rahmiati H.M Yani Syafei Purwanto Purwanto Jonathan Andianto Copyright (c) 2021 Filda Rahmiati, H.M Yani Syafei, Purwanto Purwanto, Jonathan Andianto 2021-09-01 2021-09-01 2 2 113 124 10.12928/ijio.v2i2.4406 Analysis of marketing strategy at setia stores using ahp, clustering, and ar-mba method http://journal2.uad.ac.id/index.php/ijio/article/view/4369 <p><span style="font-size: 10pt; line-height: 107%; font-family: Arial, sans-serif;">A company can survive and thrive when the strategies and processes applied in its business are correct. One of the processes in determining strategy in decision making. The owner of Setia Store has difficulty in choosing a marketing strategy. The product layout shows this in the Setia Store, which confuses customers. Setia Store also rarely offers a promotion, making it difficult to compete with competitors. This study aims to help Setia Store increase sales by determining the right marketing strategy. To determine the right marketing strategy, there are three methods developed. First of all, the analytical hierarchy process (AHP) is employed to find the customer priorities. Then, clustering is proposed to find potential marketing targets that have similar characteristics from the results of the AHP method. Third, association rule-market basket analysis (AR-MBA) is developed to find the best rules for product marketing strategy. The first method shows that the housewives (EV=0.6270) are Setia Store's priority customers from the three methods. Second, cluster 3 (which has three characteristics in common) is a very potential target market. Third, the best rule is to buy products from departments 2 and 3 (Confidence 60%, Support 12%). From these results, the right marketing strategy is to create a buy 1 get 1 promo banner or label for products that are rarely purchased, such as household appliances. Then, create a catalog by bringing together frequently purchased products such as spices and food ingredients. Finally, improve the layout by bringing the departmental shelves closer to frequently purchased products</span><span style="font-size: 11pt; line-height: 107%; font-family: Arial, sans-serif;">.</span></p> Faisal Ibrahim Bagas Swardhana Putra Fariza Halidatsani Azhra Najib Fadhlurrohman Copyright (c) 2021 Faisal Ibrahim, Bagas Swardhana Putra, Fariza Halidatsani Azhra, Najib Fadhlurrohman 2021-09-01 2021-09-01 2 2 125 140 10.12928/ijio.v2i2.4369 The hybrid design of supervised learning algorithm for design and development in classifications a defect in clay tiles http://journal2.uad.ac.id/index.php/ijio/article/view/4449 The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards.  One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight.  It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods.  Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify. Murman Dwi Prasetio Rais Yufli Xavier Haris Rachmat Wiyono Wiyono Denny Sukma Eka Atmaja Copyright (c) 2021 Murman Dwi Prasetio, Rais Yufli Xavier, Haris Rachmat, Wiyono Wiyono, Denny Sukma Eka Atmaja 2021-09-01 2021-09-01 2 2 141 150 10.12928/ijio.v2i2.4449 Sentiment analysis on myindihome user reviews using support vector machine and naive bayes classifier method http://journal2.uad.ac.id/index.php/ijio/article/view/4437 In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample.  The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%.  Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product. Sulton Nur Hakim Andika Julianto Putra Annisa Uswatun Khasanah Copyright (c) 2021 Sulton Nur Hakim, Andika Julianto Putra, Annisa Uswatun Khasanah https://creativecommons.org/licenses/by-sa/4.0 2021-09-01 2021-09-01 2 2 151 164 10.12928/ijio.v2i2.4437