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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 Ranah Research : Journal of Multidisciplinary Research and Development Management Studies and Business Journal Journal of Moeslim Research Technik
Pangastuti, Nova
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Analisa kualitas pelayanan menggunakan metode Service Quality (Servqual) dalam meningkatkan kepuasan nasabah (Study kasus: Bank Mandiri) Rahadian, Muhammad Rajiv; Pangastuti, Nova; Parningotan, Sepriandi
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 4 (2024): October
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i4.36269

Abstract

PT Bank Mandiri is a company engaged in banking, insurance, and e-commerce. This study employs factor analysis to process the data, where this technique is used to group measurement indicators in the service quality measurement tool (Servqual) based on the proximity of each tested indicator. This study uses SERVQUAL, which consists of five dimensions to measure customer satisfaction with service companies, namely: Reliability, Responsiveness, Assurance, Empathy, and Tangibles. The calculation of thegapvalue per dimension shows that the Responsiveness dimension has the highestgapvalue of 0.56, indicating that this dimension has met respondents' expectations. The overallgapcalculation shows that the overallgapvalue is 1.74, which means that the quality of service provided by PT Bank Mandiri Cabang Bekasi Juanda fully meets the needs and desires of the respondents. Based on the results of the research conducted to measure the service quality at PT Bank Mandiri Cabang Bekasi Juanda, it can be concluded that the calculation results using the Service Quality method show that the average actual value of customers is 71.32 and the average expected value of customers is 69.58, resulting in a positivegapvalue of 1.74, which indicates that customers are satisfied with the services provided by PT Bank Mandiri Cabang Bekasi Juanda
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; reject Abstrak: 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.  
Co-Authors Abu Hanifah Akmal, Fahrezi Aqmal, Muhammad Christoper, Djoshi William Intan, Clara Miwan Kurniawan Hidayat Muhammad Agung, Andi Nampira, Ardi Azhar Parnigotan, Sepriandi Rahadian, Muhammad Rajiv Ramadhan, Havis Rizky Sepriandi Parningotan Suhadi Suhadi Taufik Taufik Waruwu, Agustian Watmah, Sri Wiwit Zaki , Muhammad Zendrato, Ruth Veronika