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Lubis, Fitriani Surayya
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Analysis of Haura Mart's Marketing Strategy Using the Marketing Mix Method and the SOAR in Dealing with the Competition Sekar, Intan Ayu; Umam, Muhammad Isnaini Hadiyul; Lubis, Fitriani Surayya; Yola, Melfa; Hartati, Misra
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 6, No 3: October 2025
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v6i3.31592

Abstract

This research to analyze the marketing strategy of Haura Mart supermarket in facing competition, namely by using the Marketing Mix (7P) method and the SOAR (Strengths, Opportunities, Aspirations, and Results) if you want to increase sales. Marketing Mix consists of Product, Price, Place, Promotion, Physical Evidence, People, and Process which are used to identify the most dominant factors in marketing objectives. while the SOAR method is the result of the analysis of the most dominant or influential factors of the marketing mix strategy (7P). The results of this study found that the most influential on the Marketing Mix method was  from the results of the t-test, it was found that the factors that influenced sales volume were place with a t-value of 2.784, promotion with a t-value of 2.902, people with a t-value of 2.630, and process with a t-value of 2.133. This is because t-value > t-table (1.986). And SOAR calculations with the result of the total IFE matrix score is 3.518 and the total EFE matrix score is 3.737.So that the SA method, OA method, SR approach, and OR method marketing mixare the most effective strategies to increase Haura Mart supermarket sales in facing competition.
Fuzzy Single Depot mTSP Model using Robust Ranking Technique for Handling Deposit Carrying Bank Rahmawati, Rahmawati; Lubis, Fitriani Surayya; Efendi, Riswan; Resta, Anneke De
Jurnal Sains Matematika dan Statistika Vol 12, No 1 (2026): JSMS Januari 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v12i1.39038

Abstract

Multiple Traveling Salesman Problem (mTSP) of assignment-based consists of two types, namely the single-depot and multi-depot. This study aims to develop a single-depot mTSP assignment-based model with  fuzzy travel cost form. The single-depot mTSP model above was formulated using an objective function with trapezoidal fuzzy-coefficient form. The fuzzy forms above were converted into crisp using the Robust Ranking Technique for getting an optimal solution. The developed model above was applied to handle deposit-carrying problem at Mandiri Bank with 20 branches in Pekanbaru, Riau Province, Indonesia. In this problem, the main objective is to minimize the total travel cost by bank’s salesmen from initial depot to all destination branches. The result indicated that the developed fuzzy single-depot mTSP model  is capable to determine the minimum total cost above into IDR 70,980.00 with m= 4 salesmen,the upper boundand the lower bound . This developed model could be considered and enhanced in handling deposing-carrying problem from another sectors.
Implementation of Data Mining to Classify Potential Customers Using the C5.0 Algorithm Rizki, Muhammad; Maghfirah, Cintya Nil; Norhiza, Fitra Lestari; Nofirza, Nofirza; Lubis, Fitriani Surayya
JTI: Jurnal Teknik Industri Vol 11, No 1 (2025): JUNI 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v11i1.39167

Abstract

PT Pegadaian, as listed on its official website, is a fast-growing financial company. One of its key challenges is late installment payments, which can lead to financial losses. Using pawn customer data from 2013 to 2021, this study found that out of 534 customers, 68 were late in paying installments, and 10 did not pay. To address this issue, this research applies customer classification to identify borrowers who are more likely to pay on time. The classification model is developed using data mining with the C5.0 algorithm to generate decision-tree rules. Prior to modeling, the dataset is processed through the Knowledge Discovery in Databases (KDD) stages, including data selection, cleaning, and transformation. The proposed model produces 26 classification rules and achieves an accuracy of 87.04%. All data processing, modeling, and validation are conducted using RapidMiner Studio. Keywords: Classification, Decision Tree, C5.0 Algorithm, Data Mining
Controlling the Inventory of Boiler Ash Raw Materials in Organic Fertilizer Using the Minmax Method (Case Study: UMTR Belilas Organic Fertilizer) Aditama, Dhimas; Nurainun, Tengku; Lubis, Fitriani Surayya; Nofirza, Nofirza; Anwardi, Anwardi
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 7, No 1: February 2026
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v7i1.31728

Abstract

This study evaluates the inventory management of boiler ash raw materials in an independent organic fertilizer company, UMTR Belilas, using the Min-Max method supported by forecasting. The main problem in this study is that consumer demand is often not met on time due to suboptimal inventory management. The purpose of this research is to maintain the availability of raw materials in order to meet consumer demand optimally while reducing the risk of out-of-stock and overstocking. The results show that the application of the Min-Max method results in a minimum stock limit of 88.5 kg and a maximum stock of 171 kg, with an optimal purchase quantity of 82.5 kg per order. The safety stock level is calculated at 6.1 kg, while the Reorder Point (ROP) is set at 88.5 kg. With an order frequency of 120 times per year, this method has succeeded in optimizing storage costs through faster stock turnover. The combination of the Min-Max method and forecasting has proven to be effective in responding to fluctuations in demand, ensuring the availability of raw materials on time, and supporting the operational sustainability of organic fertilizer production.
Analysis of Burger Main Raw Material Inventory Control Using the Economic Order Quantity (EOQ) Method and the Just in Time (JIT) Method Rahmadini, Reny; Nurainun, Tengku; Hartati, Misra; Lubis, Fitriani Surayya; Nur, Muhammad
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 7, No 1: February 2026
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v7i1.31502

Abstract

Burger Fandawa is an MSME in Pekanbaru that faces problems in managing the inventory of the main raw materials, namely bread, meat, and sauce. The main problem is the uncertainty of demand and inventory irregularities that cause shortages or excess stock, thus disrupting smooth production and cost efficiency. This study aims to analyze the main raw material inventory management method using the Economic Order Quantity (EOQ) and Just In Time (JIT) approaches to determine the most optimal method. The research method used is quantitative analysis based on demand data, ordering costs, storage costs, and lead time of the main raw materials during 2023. The results of the analysis show that the optimal purchase quantity according to EOQ is 236.505 packs of bread, 93.510 kg of meat, and 118.866 kg of sauce, with a total inventory cost of IDR. 2,763,809,416. Meanwhile, the JIT method results in a lower total inventory cost of IDR. 1,142,030,936, with a savings difference of IDR. 1,621,778.48. Based on these results, the JIT method is considered more optimal for managing raw material inventory at Burger Fandawa. The JIT method is more efficient in reducing inventory costs and increasing the effectiveness of managing the main raw material stocks.
Optimization of Blood Clam Supply Control Using the Artificial Neural Network (ANN) Method Suardi, Syafarudin; Hartati, Misra; Lubis, Fitriani Surayya; Nurainun, Tengku; Taslim, Rika
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 7, No 1: February 2026
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v7i1.33669

Abstract

Mr. Badul MSME faces problems in managing blood clam inventory, namely excess and shortage of stock. To overcome this, research was conducted to design an inventory prediction system using the Artificial Neural Network (ANN) method with the Backpropagation algorithm. The ANN model used has an architecture with 10 input neurons, 10 hidden neurons, and 1 output neuron. The inventory data is normalized before the training process, then the results are denormalized to get the actual prediction. The developed model shows good performance with a very low Mean Squared Error (MSE) value of 2.7359e-06, as well as a correlation coefficient of 0.91478, which shows a strong relationship between predictions and actual data. The prediction results cover the period from January 2023 to December 2024. In January 2023, the inventory was predicted to be 96,050 kg, declining in February to 89,205 kg, and dropping sharply to 68,670 kg in March and April. Inventory increases again in May to August with fluctuations from 75,515 kg to 89,205 kg. A similar pattern occurs in 2024, starting with 96,050 kg in January, decreasing in March and April, then increasing again in the middle of the year, and decreasing again towards the end of the year, with the lowest inventory of 65,933 kg in November and December.
Penerapan Metode Human Resource Scorecard (HRSC) dan Analytical Hierarchy Process (AHP) dalam Penilaian Kinerja Karyawan pada PT. Laundry Kotak Indonesia Kanwil Pekanbaru Pangestu, Bima Okta; Kusumanto, Ismu; Lubis, Fitriani Surayya; Yola, Melfa
Industrika : Jurnal Ilmiah Teknik Industri Vol. 10 No. 2 (2026): Industrika: Jurnal Ilmiah Teknik Industri
Publisher : Fakultas Teknik Universitas Tulang Bawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37090/ndvxsm13

Abstract

The rapid population growth in urban areas has increased the demand for Laundry services. PT. Laundry Kotak Indonesia, Pekanbaru Branch, plays a key role in managing and developing its workforce to ensure efficient operations and quality customer service. However, issues such as tardiness, safety negligence, lack of skills, and high employee turnover have negatively affected employee performance and overall productivity. Despite these challenges, no formal performance measurement system for employees has been implemented. This study aims to evaluate employee performance using the Human Resource Scorecard (HRSC) and Analytical Hierarchy Process (AHP) methods. The objective is to improve employee performance to support the company’s vision and competitiveness. The findings identify 14 strategic performance indicators, with the Financial perspective receiving the highest weight (0.437), providing a basis for more focused HR strategies, policy development, training resource allocation, and continuous performance evaluation. Keywords:, AHP, HRSC, Employee Performance Measurement, Performance
THE INFLUENCE OF MARKETING MIX ON CONSUMER PURCHASE PATTERNS USING THE APRIORI DATA MINING ALGORITHM Umam, Muhammad Isnaini Hadiyul; Kurniawan, Muhammad Ilham; Lubis, Fitriani Surayya; Rizki, Muhammad
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2357

Abstract

The increasing volume of sales transaction data in retail and marketplace environments presents an opportunity to extract valuable insights for decision-making; however, such data are often underutilized. This study aims to analyze consumer purchasing patterns using the Apriori algorithm and to examine the influence of the marketing mix (product, price, place, and promotion) on purchasing decisions that shape these patterns. This research employs a quantitative approach by integrating data mining and statistical analysis. Transaction data are processed using the Apriori algorithm through RapidMiner to generate association rules and identify frequent itemsets. In addition, questionnaire data are analyzed using multiple linear regression to evaluate the effect of marketing mix variables on purchasing decisions. The results show that product, price, place, and promotion simultaneously have a significant effect on purchasing decisions. Partially, product (t = 2.622; p = 0.011), price (t = 4.738; p = 0.000), and place/distribution (t = 2.239; p = 0.029) have a significant positive effect, while promotion does not have a significant effect (t = 1.486; p = 0.143). The Apriori analysis reveals dominant purchasing patterns that can be translated into practical marketing strategies, such as product bundling and layout optimization. This study contributes by integrating association rule mining with marketing mix analysis to provide both predictive patterns and explanatory insights. However, the findings should be interpreted with caution due to data limitations, including a relatively small sample size (n = 148) and a short observation period of three months during peak season, which may limit generalizability. Despite these constraints, the results offer practical implications for optimizing marketing strategies and contribute theoretically to interdisciplinary research in data mining and consumer behavior.
Co-Authors Aditama, Dhimas Al-As'ad, Faiz Al-Ghozy, Athaullah Ahmad Amanda, Suci anwardi, anwardi Burrahman, Habi Dharma, Satrio Arya Dwi Arini, Jean Retno Efendi, Riswan Frenita Zurids, Adilla Gusti Fara Hitari, Belia Hadiyul Umam, Isnaini Hamdy, Muhammad Ihsan Handika, Puri Tri hanifah hanifah Harpito, Harpito Hartati, Misra Hidayat, Ferdi Gusman Ismu Kusumanto Kurniawan, Muhammad Ilham Lestari, Fitra Lisnasari, Srie Faizah Maghfirah, Cintya Nil Mayuta, Fadhila Putra Mayuta, Fadhila Putri Melfa Yola, Melfa Muhammad Muhammad Nur Muhammad Rizki Muhammad Rizki N. Nazaruddin Nazaruddin Nofirza Nofirza Nofirza Pangestu, Bima Okta Rahmadini, Reny Rahmawati, Rahmawati Resta, Anneke De Rifki Alfarizi Rika Taslim Saputri, Anggi Lisa Sekar, Intan Ayu Sri Wahyuni, Annisa Suardi, Syafarudin Suherman, Suherman Syafnan Syafnan Syalsabila, Nadya Tengku Nurainun Umam, Muhammad Isnaini Hadiyul Vera Devani Wahab, Mohammad Ikbar Bin Abdul Wilda, Yulia Wulandari, Ayuni