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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik Industri Terintegrasi (JUTIN) Indonesian Journal of Industrial Engineering & Management INSANtek – Jurnal Inovasi dan Sains Teknik Elektro IMTechno: Journal of Industrial Management and Technology Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Ilmiah Sistem Informasi Ranah Research : Journal of Multidisciplinary Research and Development Management Studies and Business Journal Globe: Publikasi Ilmu Teknik, Teknologi Kebumian, Ilmu Perkapalan Venus: Jurnal Publikasi Rumpun Ilmu Teknik Advance Sustainable Science, Engineering and Technology (ASSET) Journal of Moeslim Research Technik
Pangastuti, Nova
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ANALISA PERBAIKAN TOP-BOTTOM REJECTION DALAM PROSES PRODUKSI MENGGUNAKAN METODE SIX SIGMA Intan, Clara; Suhadi, Suhadi; Pangastuti, Nova
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4521

Abstract

Abstract: Primary packaging is packaging that comes into direct contact with the product. This packaging serves to protect, preserve, and package the product, as well as provide important information to customers. As the main container, this packaging comes into direct contact with the product and maintains its quality until it reaches the customer. PT XYZ is a leading primary packaging manufacturer. The company produces three different variants of its flagship product, steel drums, which generate the most revenue. The oil, lubricant, food, and chemical sectors use steel drums. A large number of defects in finished products are caused by drum body rejection and top-bottom drum rejection. From the report data of 1,166 units produced in 2024, the most rejections came from the top-bottom production process, which consists of eight phases: pressing, opening, closing, puncture 1, puncture 2, fully open drum head puncture, head coating, and other processes. The results of this study show that UCL=0.00451 and LCL=0.00247. The sigma level is determined by converting the process after the process DPMO value is known. With a value of 436, the company's sigma level is 4.83 when the DPMO value is entered into the sigma-DPMO relationship table.Keyword: quality control; six sigma; DMAIC; rejectAbstrak: Kemasan primer merupakan kemasan yang bersentuhan langsung dengan produk, kemasan ini berfungsi untuk melindungi, menjaga, dan mengemas produk, serta menyediakan informasi penting bagi pelanggan. Sebagai wadah utama, kemasan ini bersentuhan langsung dengan produk dan menjaga kualitasnya hingga sampai ke pelanggan. PT XYZ, perusahaan yang bergerak di bidang produsen kemasan primer terkemuka, perusahaan tersebut memproduksi tiga varian berbeda dari produknya produk andalannya, drum baja, menghasilkan pendapatan terbesar. Sektor minyak, pelumas, makanan, dan kimia menggunakan drum baja. Jumlah cacat yang besar pada produk jadi disebabkan oleh penolakan badan drum dan penolakan atas-bawah drum. Dari data laporan sebanyak 1.166 unit yang diproduksi pada tahun 2024, penolakan terbanyak berasal dari proses produksi atas-bawah yang terdiri dari delapan fase yaitu penekanan, pembukaan, penutupan, tusukan 1, tusukan 2, tusukan kepala drum terbuka sepenuhnya, pelapisan kepala, dan proses lainnya. Hasil penelitian ini menunjukkan bahwa UCL=0,00451 dan LCL=0,00247. Tingkat sigma ditentukan dengan mengonversi proses setelah nilai DPMO proses diketahui. Dengan nilai 436, tingkat sigma perusahaan sebesar 4,83 ketika nilai DPMO dimasukkan ke dalam tabel hubungan sigma-DPMO.Kata kunci: pengendalian kualitas; Six Sigma; DMAIC; penolakan
AI-DRIVEN PREDICTIVE MAINTENANCE FOR SMART MANUFACTURING SYSTEMS: A CASE STUDY USING DEEP LEARNING ON SENSOR DATA Nampira, Ardi Azhar; Pangastuti, Nova; Wiwit; Taufik, Taufik
Journal of Moeslim Research Technik Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i3.2345

Abstract

The rapid advancement of Industry 4.0 has catalyzed the integration of artificial intelligence (AI) into smart manufacturing, with predictive maintenance emerging as a crucial application to reduce downtime and optimize operational efficiency. This study aims to develop and evaluate a deep learning-based predictive maintenance model by leveraging real-time sensor data from a smart factory environment. A convolutional neural network (CNN) architecture was implemented to detect anomalies and predict machinery failures in advance. The dataset, consisting of multivariate time-series signals from industrial sensors, was preprocessed and used to train, validate, and test the model’s predictive performance. Results indicate that the proposed deep learning model achieved a prediction accuracy of 94.6%, outperforming traditional statistical and machine learning methods in both precision and recall. The implementation of this AI-driven system enables proactive maintenance strategies, minimizing production losses and extending equipment lifespan. In conclusion, the research demonstrates the feasibility and effectiveness of deep learning in predictive maintenance applications for smart manufacturing systems and offers a scalable solution adaptable to diverse industrial settings.  
Designing a Human-Centered Smart Counter for Transjakarta Using the House of Quality to Improve Service Inclusivity Pangastuti, Nova; Oktariska Timbayo, Olivia; Pinasthika, Restu
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2405

Abstract

Jakarta's increasing vehicle usage has exacerbated air pollution, and as a result, the initiative to advance sustainable urban mobility and a target to achieve Net Zero Emissions by 2050. Nevertheless, public satisfaction with Transjakarta remains low due to inconsistent service quality and no real-time information for riders. This study puts forward the SmartCounter, a smart passenger-counting system developed by a Human-Centered Design (HCD) process with support from the SERVQUAL approach and House of Quality (HoQ) analysis. The research employs a mixed-method methodology using gap analysis, semi-structured interviews, and focus group discussions to comprehensively gather and convert user requirements into technical specifications. Critical parameters that are elicited from SERVQUAL then propel the Voice of Customer and subsequently get mapped to ranked technical needs with the help of the HoQ. SmartCounter utilizes cutting-edge sensing technology (Time-of-Flight or AI-integrated cameras) with onboard edge computing to enable automatic, real-time, and privacy-respecting passenger counting. The HoQ study prioritized three main technical imperatives: sensor accuracy (score 123), casing robustness (score 111), and real-time transmission (score 109). Other aspects include embedded processors (score 103), display units and operator dashboards (scores 84), and power systems (score 71). Overall, the SmartCounter actively addresses both passenger and operational needs, advancing Jakarta's goals towards a more sustainable, efficient, and inclusive urban transport system for Net Zero Emissions 2050.
Penerapan Metode AHP Dalam Pemilihan Supplier di PT. Qian Hu Joe Aquatic Indonesia Ruth Veronika Zendrato; Nova Pangastuti; Miwan Kurniawan Hidayat
INSANtek Vol. 4 No. 2 (2023): November 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/insantek.v4i2.2399

Abstract

PT. Qian Hu Joe Aquatic Indonesia merupakan perusahaan ekspor impor dimana ikan hias air tawar menjadi komoditi utamanya. Hingga saat ini perusaaan memiliki pasar ekspor di beberapa negara dari 4 benua. Untuk memenuhi permintaan konsumen perusahaan bekerja sama dengan banyak supplier ikan hias dari berbagai daerah di Indonesia. Meskipun memiliki banyak supplier, perusahaan masih mengalami permasalahan dalam memenuhi permintaan konsumen. Ketidaksesuaian ikan hias dari supplier terhadap standar perusahaan sangat berpengaruh terhadap pemenuhan permintaan pasar. Oleh sebab itu, penting bagi perusahaan untuk mengevaluasi kinerja supplier dan menyeleksi dari banyaknya supplier manakah yang paling sesuai dengan standar perusahaan dan layak menjadi supplier prioritas perusahaan. Setelah melakukan pengumpulan data melalui observasi, wawancara dan penyebaran kuesioner, data kemudian diolah dengan metode Analytical Hierarchy process (AHP). Berdasarkan hasil pengolahan data didapatkan kriteria yang paling diperhatikan perusahaan dalam pemilihan supplier secara berurutan yaitu: kualitas, harga, pengiriman, layanan dan terakhir hubungan supplier. Adapun supplier  yang dapat dijadikan sebagai prioritas oleh perusahaan secara berurutan yaitu: supplier Catur dengan bobot 0,363, supplier Laksana Aquarium dengan bobot 0,316, supplier  Jumali dengan bobot 0,315, supplier Rodi dengan bobot 0,301, dan prioritas kelima adalah supplier Argo dengan bobot 0,240
Co-Authors Abu Hanifah Agustian Waruwu Akmal, Fahrezi Aqmal, Muhammad Christoper, Djoshi William Danang Hadi Wibowo Destiana Putri Fajri, Erland Alyansa Intan, Clara Irawati , Dwi Miwan Hidayat Miwan Kurniawan Hidayat Muhammad Agung, Andi Nampira, Ardi Azhar Oktariska Timbayo, Olivia Parnigotan, Sepriandi Pinasthika, Restu Rahadian, Muhammad Rajiv Ramadhan, Havis Rizky Ruth Veronika Zendrato Sepriadi Paningotan Sepriandi Parningotan Sofyan Wahyu Sri Watmah Suhadi Suhadi sunardi sunardi Taufik Taufik Waruwu, Agustian Wiwit Yuni Siti Nuraeni Zaki , Muhammad Zendrato, Ruth Veronika