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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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PENGENDALIAN KUALITAS PANGAN DENGAN PENERAPAN GOOD MANUFACTURING PRACTICES (GMP) PADA PROSES PRODUKSI DODOL BETAWI (STUDI KASUS UKM MC) Rina Fitriana; Wawan Kurniawan; Jaquline Glenadys Siregar
Jurnal Teknologi Industri Pertanian Vol. 30 No. 1 (2020): 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.2020.30.1.110

Abstract

Small and medium enterprise (SME) MC is a business in food industri which produces dodol betawi. Problems in the production process is using human who aren’t yet familiar with the attributes of cooking equipment as well as proper guidance in food production process. Workers haven’t recognized yet the importance of employee hygine regarding physical and bacterial hazards from the body so it doesn’t contaminate the food. The purpose of this study is to analyze the discrepancy of SME MC in implementing Good Manufacturing Practice (GMP) regulations so that discussing higher safety on products using the Failure Mode and Effect Analysis (FMEA) method can improve dodol food quality, and provide quality improvement.Based in laboratory tests of dodol betawi products, there are Escherichia coli bacteria provide evidence that storage errors in the drying process, when dodol already 1 week old, it causes the fungus Aspergillus flavus growth. The amount of Escherichia coli bacteria was found 25.000 bacteria/cc. The purpose of this research is to give improvement of sanitation operations standards for employee health in the production process of Dodol Betawi by using one of the Hazard Analysis and Critical Control Point (HACCP) methods, namely the application of Good Manufacturing Practice (GMP). The implementation of Good Manufacturing Practices (GMP) in this research is to provide cooking attribute equipment. The results of the implementation were able to reduce Escherichia coli bacteria by 10,000 bacteria / cc. The results of these improvements are expected to be able to make the MC UKM more trusted and widely known to the public. Keywords: good manufacturing practice (GMP), failure mode and effect analysis (FMEA), laboratory test, quality food
PERAN SISTEM INTELIJENSIA BISNIS DALAM MANAJEMEN PENGELOLAAN PELANGGAN DAN MUTU UNTUK AGROINDUSTRI SUSU SKALA USAHA MENENGAH Rina Fitriana, Eriyatno Taufik Djatna dan B.S. Kusmuljono TIP
Jurnal Teknologi Industri Pertanian Vol. 22 No. 3 (2012): Jurnal Teknologi Industri Pertanian
Publisher : Department of Agroindustrial Technology, Bogor Agricultural University

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Abstract

Business intelligence systems participate to deliver an accurate and useful information to appropriate decision makers within the necessary timeframe to support an effective decision making in dairy agro- industry. The objective of this study was to determine the role of business intelligence systems to support customer relationship management and quality in dairy-agro industry medium enterprise. The combine methods to develop this research included Unified Modeling Language (UML), Fuzzy, Failure Mode Effect Analysis (FMEA), Cube, On Line Analytical Processing (OLAP), Extract, Transform, Loading (ETL) and Data warehouse. Data warehouse model is supported by ETL process. Business Intelligence Model is an integration of Cube, Data warehouse Model and Fuzzy system and it helps for a fast and efficient transaction in the system. The design consisted of quality and CRM (Customer Relationship Management) sub model. The Role of In Quality Sub Model based on Fuzzy, FMEA, the highest Fuzzy Risk Priority Number (FRPN) was 692 with type of failure, sum of Total Plate Control, bigger than 1 million/mL. The CRM (Customer Relationship Management) Model with RFM (Recency Frequency Monetory) and Customer Life Value (CLV) methods with OLAP Cube, the highest rank CLV for dairy processing industry to get potential customer was at PT FFI. The integration quality and CRM models into BI System would make it quickly anticipate, adapt, and react to the changing business conditions.   Keywords: Business Intelligence (BI), Unified Modelling Language (UML), OLAP, cube
Quality Improvement of NH1X36B Pre-Printed Box with QM-CRISP DM Approach at PT X Anik Nur Habyba; Rina Fitriana; Tania Theodora
Operations Excellence: Journal of Applied Industrial Engineering Vol 13, No 3, (2021): OE November 2021
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2021.v13.i3.028

Abstract

PT X is a manufacturer of cardboard box whose products are indispensable for various fields. The problem identified in NH1X36B pre-printed box, which is a shoes box, is the high defect rate that exceed company target of 2%. This study aims to reduce the defect rate of the product. The Quality-Management (QM) and Cross Industry Standard Process for Data Mining (CRISP-DM) approach was conducted by integrating Six Sigma and data mining. The Business Understanding phase was intended to define business and data mining objectives, SIPOC (Supplier-Input-Process-Output-Customer) diagram, and Critical-to-Quality (CTQ). In Data Understanding phase, it is known that the Defects Per Million Opportunities (DPMO) value is 1210.12. Data preparation phase was carried out with data cleaning, reduction, and discretization. Based on the Modeling result using Decision Tree C4.5 and FP-Growth algorithm, it is known that the dominant attributes causing high rejection are smeared ink, white spots, uneven varnish, and delamination. Decision Tree model accuracy of 90.24% indicates that the model is performing well. Analysis using FMEA yielded priority correction to the causes of smeared ink, uneven varnish, and delamination. Process improvement in Deployment phase was the application of plate cleaning and mounting form, printing process checklist, and SOP for sheet inspection. The improvement plans managed to improve the quality by rising sigma level from 4.533 to 4.648 sigma and decrease defect rate to 1.559%.
Sistem Pendukung Keputusan Rantai Pasok Koperasi Pengolahan Susu X Di Jawa Barat Rina Fitriana; Taufik Djatna
JURNAL TEKNIK INDUSTRI Vol. 1 No. 2 (2011): Volume 1 No 2 Juli 2011
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Indusri Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.472 KB) | DOI: 10.25105/jti.v1i2.7004

Abstract

A decision support system is a computerized information system, designed to support business and organizational decision-making activities. Agroindustrial Supply Chain Management (Agro-SCM) is the management of the entire set of production, transformation/processing, distribution and marketing activities in agroindustry by which a consumer is supplied with a desired product. Milk Processing Cooperation has a strategic role to support the milk industry development in Indonesia. The purpose of this research is to make a proposal supply chain decision support system of Milk Processing Cooperative X in West Java. The first sub model is Sales and Purchase. The second sub model is a Quality Risk. Third sub model is the Forecasting. The fourth sub model is Transportation. The Fift sub model is Supply Chain Management. Validation and Verification of Decison Support System conducted through case studies with empirical data in Milk Processing Cooperative X in West Java
Analisis Keberhasilan Program Pertukaran Mahasiswa Fakultas Teknologi Industri Universitas Trisakti Rina Fitriana; Dian Mardi Safitri; Ratna Mira Yojana; Amal Witonohadi; Lydia Sari; Daisman Aji; Yunia Ningsih
JURNAL TEKNIK INDUSTRI Vol. 12 No. 1 (2022): VOLUME 12 NO 1 MARET 2022
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Indusri Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.31 KB) | DOI: 10.25105/jti.v12i1.13956

Abstract

Intisari— Program pertukaran mahasiswa merupakan bagian dari program Kementerian Pendidikan dan Kebudayaan PendidikanTinggi, yaitu Merdeka Belajar Kampus Merdeka (MBKM). Prodi Teknik Industri, Prodi Teknik Elektro, Prodi Teknik Mesin danProdi Teknik Informatika Fakultas Teknologi Industri telah melaksanakan program pertukaran mahasiswa pada semester ganjil2021/2022. Tujuan penelitian adalah untuk mengevaluasi pelaksanaan Pertukaran mahasiswa yang berjalan di Fakultas TeknologiIndustri Universitas Trisakti dan merancang perbaikan untuk pelaksanaan program pertukaran mahasiswa pada semester yang akandatang. Berdasarkan hasil pengolahan data kuesioner dengan metode Importance Performance Analysis (IPA), poin yang penting untuk diperbaiki dalam program pertukaran mahasiswa untuk kuesioner mahasiswa dan dosen adalah pengembangan Student Information System (SIS), pengembangan penggunaan Learning Management System (LMS). Abstract— The Student Exchange Program is part of the government program, namely Independent Learning Independent Campus.Industrial Engineering Department, Electrical Engineering Department, Mechanical Engineering Department and InformaticsEngineering Department, Faculty of Industrial Technology has implemented student exchange programs in the odd semester 2021/2022. The purpose of the research is to evaluate the implementation of student exchanges that is being run at the Faculty of Industrial Technology of Trisakti University and design improvements for the implementation of student exchange programs in the coming semester. Based on the results of processing questionnaire data with important performance analysis (IPA) methods, important points to improve in student exchange programs are student information system (SIS) development, learning management system (LMS) development and socialization.
PENERAPAN SISTEM INTELIJENSIA BISNIS DAN K-MEANS CLUSTERING UNTUK MEMANTAU PRODUKSI TANAMAN OBAT Miwan Kurniawan Hidayat; Rina Fitriana
Jurnal Teknologi Industri Pertanian Vol. 32 No. 2 (2022): 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.2022.32.2.204

Abstract

Indonesia has biodiversity including medicinal plants. The medicinal plant business can be a profitable business prospect because it has high export opportunities. Based on the benefits obtained, the production of medicinal plants needs to be considered by monitoring and evaluating production results to increase productivity, especially for areas with low production levels. The Department of Food Crops and Horticulture of West Java Province through the website https://opendata.jabarprov.go.id has provided production datasets for each type of medicinal plant, but it has not yet become a dataset with various types of medicinal plants. The purpose of this study was to design an integrated data storage model in the form of a data warehouse, grouping medicinal plant production areas using data mining and designing data visualization in business intelligence systems. Business intelligence system design was carried out through several stages, namely system requirements analysis, identification of data and information needs, data warehouse design, data warehouse filling, data mining processes, data visualization, and system performance evaluation. The results of the research were the application of a data warehouse using a dimensional model with a star schema; a grouping of production areas using the KMeans algorithm with optimal k=3 and the number of elements produced in each cluster is 24 regions in cluster 0, 1 region in cluster 1, and 2 regions in cluster 2; a business intelligence system is implemented using a dashboard to show information on the amount of production and display the results of grouping potential areas for producing medicinal plants.Keywords: business intelligence, data warehouse, dimensional models, visualization, medicinal plants
IMPLEMENTASI K-MEANS DAN K-MEDOIDS DALAM PENGELOMPOKAN WILAYAH POTENSIAL PRODUKSI DAGING AYAM Miwan Kurniawan Hidayat; Rina Fitriana
Jurnal Teknologi Industri Pertanian Vol. 32 No. 3 (2022): 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.2022.32.3.239

Abstract

Livestock is the main sector in the effort to fulfill food needs for people in Indonesia and has the potential to maintain the availability of animal food. Guidance and socialization to provide information and knowledge in the field of food production from animals, especially in areas with low levels of chicken meat production need to be done. The research objectives were the use of the K-Means and K-Medoids algorithms for grouping chicken meat production areas in the province of West Java and the use of the Davies Bouldin Index (DBI) value in choosing the best algorithm. The application of K-Means and K-Medoids was carried out through the data mining process phase, namely data collection, data preprocessing, data mining implementation, evaluation of the number of clusters, determination of the best algorithm, and clustering results. The K-Means algorithm with 5 clusters can optimally classify potential areas for chicken meat production in West Java province with a DBI value of 0.273. The results of clustering can be used in business processes related to information on the amount of chicken meat production in the West Java region as a reference in the pattern of guidance to increase animal food production, develop chicken farming potential, and develop animal feed distribution potential. Keywords: clustering, chicken meat k-means, data mining, k-medoids
CLUSTERING KABUPATEN BERDASARKAN LUAS HUTAN MENGGUNAKAN METODE K-MEANS DI PROVINSI JAWA TENGAH Yusri Eli Hotman Turnip; Rina Fitriana
Jurnal Teknologi Industri Pertanian Vol. 33 No. 1 (2023): Jurnal Teknologi Industri Pertanian
Publisher : Department of Agroindustrial Technology, Bogor Agricultural University

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Abstract

Indonesia is one of the countries with the largest forest in the world. The tropical climate and high rainfall cause a lot of biodiversity in Indonesia’s forests. The existence of these forests can be utilized by many parties, both the government and the community in accordance with their functions to improve welfare. The government through the Central Statistic Agency has provided data information related to the forest area in various regions, one of which is Central Jawa Province but still requires development to obtain important information in the data. This study aims to divide the district based on forest area including protected forest, protected area, area for production, and area for other users in Central Java province using the K-Means Data Mining method. The data is obtained from Central Statistic Agency for the Central Java area, where four types of forest are to be grouped. The results of this study indicate that the grouping of districts based on the area of forest owned is based on the smallest Davies Bouldin (DB), which is 0.436 in the grouping with 2 clusters. The two clusters are distinguished based on the value of the proximity of the forest type attribute with the centroid point in each cluster. The clustering process grouped 26 districts in the province of Central Java into cluster 1, while cluster 2 consisted of 3 districts in Central Java, namely Grobogan, Blora, and Brebes districts. Keywords: clustering, forests, K-Means
Pelatihan Manajemen Kualitas untuk Usaha Mikro Kecil Menengah di Depok Ratna Mira Yojana; Rina Fitriana; Debby Kumala Sari; Dedy Sugiarto
Jurnal Pengabdian Masyarakat dan aplikasi Teknologi (Adipati) Vol 2, No 1 (2023)
Publisher : Institut Teknologi Adhi Tama Surabaya

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Abstract

Wabah Covid-19 pada awal tahun 2020 memberikan dampak besar terhadap sektor perkonomian Indonesia, termasuk pada UMKM Kota Depok. Kebijakan work form home (WFH) dan menutup sebagian besar pusat perbelanjaan serta tempat wisata pada awal terjadinya wabah berdampak pada terputusnya rantai distribusi pemasaran produk UMKM. Upaya untuk membangkitkan kembali pasar UMKM dilakukan oleh pemerintah, salah satunya dengan menjalin kerjasama dengan perguruan tinggi. Perguruan tinggi dianggap mampu memberikan konsep secara teori maupun praktis untuk pengembangan UMKM. Teknik Industri Universitas Trisakti melakukan penyuluhan mengenai nilai penting keamanan pangan dan manajemen kualitas sebuah produk. Tema ini diambil dengan alasan sebagian besar UMKM Kota Depok memproduksi makanan, sehingga penilaian terhadap keamanan pangan dari produk yang dihasilkan menjadi sangat penting dalam poin penilaian kualitas produk. Penyuluhan dilakukan dengan penyampaian materi dan kegiatan tanya-jawab dengan peserta penyuluhan. Evaluasi dilakukan dengan pemberian soal pre-test dan post-test kepada peserta, serta pengisian feedback oleh peserta. Hasil rekap evaluasi memperlihatkan bahwa terjadi peningkatan pengetahuan peserta terhadap materi mengenai keamanan pangan dan manajemen kualitas. Harapannya materi tersebut dapat membantu pengembangan usaha para peserta.
Factor Analysis of Increasing Customer Loyalty in The Automotive Industry Annisa Tri Wahyuni; Triwulandari SD; Rina Fitriana
Jurnal Syntax Transformation Vol 4 No 11 (2023): Jurnal Syntax Transformation
Publisher : CV. Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jst.v4i11.846

Abstract

This study aims to analyze the factors that influence loyalty in the automotive industry. The population of this study are car owners who live in Jabotabek . Using two types of questionnaires, namely the screening questionnaire and the main questionnaire consisting of 27 indicators. Probability sampling was used in determining the sample so that 135 samples were obtained which were dominated by Toyota and Honda brand users with the MVP segment. Factor analysis was carried out using the SEM (structural equation model) method with Smart PLS software and tested the validity, Cronbach's Alpha reliability test and goodness of fit to test the structural model. The results showed that among the 3 variables, namely brand image, product and staff, only brand image proved to have a positive and significant effect on loyalty (50.1%), while the product and staff variables had no significant effect. With the results of this study, it is hoped that ATPM can maintain the loyalty of their customers by continuing to improve their brand image by paying attention to 7 indicators, namely reliable, advanced, challenging, creative, sophisticated, luxury and sustainable.
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