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All Journal Jurnal Teknologi Industri Pertanian OPSI Jurnal Optimasi Sistem Industri OPERATION EXCELLENCE: Journal of Applied Industrial Engineering Jurnal Syntax Transformation JURNAL TEKNIK INDUSTRI Jurnal Pengabdian Masyarakat dan aplikasi Teknologi
Rina Fitriana
Department Of Industrial Engineering, Faculty Of Industrial Technology, Universitas Trisakti
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach Resty Ayu Ramadhani; Rina Fitriana; Anik Nur Habyba; Yun-Chia Liang
Jurnal Optimasi Sistem Industri Vol. 22 No. 2 (2023): Published in December 2023
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v22.n2.p197-214.2023

Abstract

Six Sigma is of paramount importance to organizations as it provides a structured and data-driven approach, fostering continuous improvement, minimizing defects, and optimizing processes to meet and exceed customer expectations. In response to the increasing defects of packaging product in a cosmetics industry in Indonesia, surpassing the specified 3% tolerance limit, this research conducts a thorough investigation into the root causes, corrective measures, and improvement proposals to elevate product quality. By leveraging the Six Sigma method and data mining techniques, the study systematically addresses the complexities associated with defect reduction in packaging for cosmetics product. The research methodology encompasses defining the problem through SIPOC and Critical to Quality (CTQ) diagrams, measuring via control charts and sigma level calculations, and analyzing using tools like pareto diagrams, Apriori algorithms, fishbone diagrams, and Fault Mode and Effect Analysis (FMEA). Key findings reveal a notable correlation between spot defects and varying colors, leading to pearl defects as identified by the Apriori algorithm. FMEA identifies critical failures, including suboptimal printing plate conditions, clumpy ink usage, and insufficient operator attention to ink filling. The improvement stage proposes practical solutions, such as implementing alarms and buzzers, color-indicator-adjusted ink storage labels, and a structured form for cleaning and monitoring printing plates. These findings carry significant implications, providing a tailored roadmap for enhancing the quality of cosmetic packaging. The anticipated implementation of proposed improvements aims to elevate customer satisfaction by addressing specific pain points in the production process. Furthermore, the research contributes valuable insights to the broader cosmetics industry, offering effective methodologies for defect reduction and quality enhancement in packaging processes.
Quality improvement of DB-CDP with integration of CRISP-DM and six sigma method Alfiandi, Ahmad; S.D, Triwulandari; Fitriana, Rina
Operations Excellence: Journal of Applied Industrial Engineering Vol. 16, No. 3 (2024): OE November 2024
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2024.v16.i3.126

Abstract

The DB-Customer Display Product (CDP) is a product that has a high level of defects with the type of attribute that the display light is off. The quality improvement is carried out using the integration of the Cross-Industry Standard Process Data mining (CRISP-DM) method with Six Sigma. The technique using classification technique with the CART algorithm to identify the leading causes of defects in the CDP and association processes using the Frequent Pattern-Growth Algorithm to make association rules between the combination of production support data sets. The results of both algorithms known attributes that cause high rejects are poor solder and solder Short. Implementation of proposed improvements made at the deployment stage, there are work instructions for re-soldering, tip checking forms, and Standard Operating Procedures for solder tip replacement. The result from implementation, was decrease in the value of Defects per unit to 0.0541, where previously it was worth 0.0628, and the value of Defects per million Opportunities decreased from 32.636 to 27.020, and converted into sigma level and obtained sigma value 3.80, before the implementation was at 3.74 sigma. The three indicators of DPU, DPMO, and Sigma level indicate that the proposed quality improvement is successful.
Proposed improvement of product support packaging material defects using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach Fitriana, Rina; Habyba, Anik Nur; Nabiha, Gina Almas; Mareta, Sannia
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.11803

Abstract

This research was conducted because the defect rate of packaging materials supporting lithos M products exceeded the Company's tolerance standard of 2%. This research aims to identify the causes and provide suggestions to improve the Quality of product support packaging materials. The methods used in data mining with the CRISP-DM (Cross-Industry Standard Process For Data Mining) approach. The Business Understanding stage determines the problem and research objectives, Power Business Intelligence, SIPOC (Supplier, Input, Process, Output, Customer) Diagrams, Operation Process Chart, QC Action, and CTQ (Critical to Quality). The Data Understanding stage creates a Control P Chart, calculates DPMO and the sigma level obtained by the unscramble machine dented bottle value 762.31 with a Sigma level of 4.66, Sticker 2nd defect Internal 187.47 with a sigma level of 5.06, Cap 2nd defect internal 67.18 with a sigma level of 5.32, and uses Fault Tree Analysis. The Data Preparation stage performs data cleaning, integration, transformation, and preprocessing. The Modelling stage makes classification with C4.5 and the Cart decision tree algorithm. The evaluation stage uses a Confusion Matrix accuracy of 78.8 percent and 89.4 percent, respectively. The Deployment stage produces improvement proposals by creating a Dashboard, Standard Operating Procedure, and Check Sheet.  
A Bibliometric Analysis of Undergraduate Theses in Industrial Engineering Undergraduate at Universitas Trisakti: Research Trends and Future Directions  Harahap, Elfira Febriani Harahap; Fitriana, Rina Fitriana; Adisuwiryo, Sucipto Adisuwiryo
JURNAL TEKNIK INDUSTRI Vol. 15 No. 1 (2025): March 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Indusri Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jti.v15i1.22492

Abstract

Industrial Engineering (IE) is a multidisciplinary field that evolves alongside technological advancements and global challenges. While bibliometric analyses are commonly used to assess journal publications, limited studies focus on the research trends within undergraduate theses, especially in Indonesia. Understanding these trends is essential for aligning academic curricula with emerging research areas. This study analyzes research trends in undergraduate theses at Universitas Trisakti's Industrial Engineering Department between 2021 and 2024. Data were collected from the campus repository and filtered using the keyword “industrial engineering.” A bibliometric analysis was conducted using Microsoft Excel for data processing and Python for computational mapping. K-means clustering was applied to identify active supervisors and research collaborations across laboratories. A total of 350 theses were analyzed, showing a peak in publications in 2021 (130 theses). The Quality Engineering Laboratory emerged as the most active contributor, with standard methodologies including FMEA, Six Sigma, Simulation, and Sustainable Practices. Key supervisors such as Triwulandari, Didien S, and Wawan K significantly shaped research directions. The study recommends integrating emerging technologies such as AI, machine learning, and Industry 4.0 to enhance future research relevance and industrial applications.
CUSTOMER SEGMENTATION WITH K-MEANS ALGORITHM AND BUSINESS STRATEGY BUSINESS INTELLIGENCE IN VEGETABLE ONLINE RETAILING Fitriana, Rina; Sugiarto, Dedy; Nurachman, Nurochman
Jurnal Teknologi Industri Pertanian Vol. 35 No. 2 (2025): Jurnal Teknologi Industri Pertanian
Publisher : Department of Agroindustrial Technology, Bogor Agricultural University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24961/j.tek.ind.pert.2025.35.2.118

Abstract

Most MSMEs still have obstacles to growing and developing at the business level. Applying a business intelligence system is expected to assist in making appropriate and quick business decisions so that MSMEs can grow and develop. This research aimed to determine customer segmentation based on product clustering that consumers demand. In addition, this study aims to determine the benefits of business intelligence in providing business performance information to make decisions. This research uses the k-means algorithm for clustering. Business intelligence uses Power BI software for visualisation. Based on the results of analysing product clustering with the k-means algorithm, the optimal number of clusters is 2 (k = 2). Determination of the value of k = 2 uses an average centroid distance of 121,624,275,127, and validation of the minimum DBI value = 0.052. Based on the clustering results, cluster 0 (28%) and cluster 1 (72%) are two consumer segments. Insights on the sales dashboard are daily sales fluctuations, the dominance of certain products in demand, and products with low sales. Strategy initiatives for the long term are customer segmentation for more personalized promos, focus on subscriptions and repeat orders, optimising digital marketing, and the use of predictive analytics to forecast sales trends. On the dashboard of production, order, and stock, information, such as daily production tends to exceed orders, leading to overstock, while orders fluctuate inconsistently. The key challenges are unbalanced production and demand, overstock on certain products, unstable orders, and underproduced products. Keywords: business intelligence, data analytics, k-means algorithm, Micro Small Medium Enterprise
Co-Authors Adisuwiryo, Sucipto Adisuwiryo Alfiandi, Ahmad Amal Witonohadi Anak Agung Istri Sri Wiadnyani Annisa Tri Wahyuni Daisman Aji Debby Kumala Sari Dedy Sugiarto Dedy Sugiarto Dian Mardi Safitri Habyba, Anik Nur Harahap, Elfira Febriani Harahap Jaquline Glenadys Siregar Lydia Sari Mareta, Sannia Miwan Kurniawan Hidayat Miwan Kurniawan Hidayat Nabiha, Gina Almas Nurachman, Nurochman Ratna Mira Yojana Resty Ayu Ramadhani S.D, Triwulandari Tania Theodora Taufik Djatna Triwulandari Satitidjati Dewayana Yun-Chia Liang Yunia Ningsih Yusri Eli Hotman Turnip