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All Journal Jurnal TIN Universitas Tanjungpura Jurnal Rekayasa Sistem Industri SPEKTRUM INDUSTRI OPSI OPERATION EXCELLENCE: Journal of Applied Industrial Engineering JTERA (Jurnal Teknologi Rekayasa) GERVASI: Jurnal Pengabdian kepada Masyarakat ULIN: Jurnal Hutan Tropis Manhaj: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Abdi Insani Jurnal Media Teknik dan Sistem Industri JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Rahmahwati, Ratih
Universitas Tanjungpura
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Simulation of Queue System of Retirement Fund Retrieval at The Sanggau Post Office During the Covid-19 Pandemic Using Arena Software Yulianti Wulandari; Tri Wahyudi; Ratih Rahmahwati; Silvia Uslianti; Febri Prima
OPSI Vol 14, No 1 (2021): ISSN 1693-2102
Publisher : Jurusan Teknik Industri Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v14i1.4776

Abstract

PT. Pos Indonesia (Persero) entered into a collaboration with PT. Taspen (Persero) in the payment of pension funds. The Sanggau Post Office is a place of public service, where during the Covid-19 pandemic there were still many people queuing to collect pension funds. The number of customers who took pension funds was 208 customers, so there was a long queue which resulted in the crowds of customers queuing up. The application of physical distancing as an effort to prevent the spread of Covid-19 by implementing health protocols while queuing. According to WHO, the physical distancing distance is one to two meters. The need to reduce the queues that occurred during the Covid-19 pandemic can be designed a queuing system model in accordance with existing conditions and an alternative proposed improvement for the optimal queuing system using a simulation method with the help of Arena software. The results showed that the average total time was 10.9 minutes and the average waiting time was 33.9 minutes. Based on the results of the repair scenario, the best scenario for improvement is scenario 2, in the form of a proposed improvement for the addition of 2 counters. Scenario 2 obtains an average total time of 4.9 minutes, while the average waiting time is 5.04 minutes. Therefore, the Sanggau Post Office can add 2 service counters, so there are 3 counters that can serve customers taking pension funds. 
Rancang Bangun Pengering Buah Pinang Otomatis Menggunakan Pendekatan Quality Function Deployment (QFD) Tri Wahyudi; Ratih Rahmahwati; Silvia Uslianti
OPSI Vol 15, No 2 (2022): ISSN 1693-2102
Publisher : Jurusan Teknik Industri Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v15i2.6289

Abstract

The processing of dried betel nut by the people of West Kalimantan consists of 2 stages, namely drying and stripping the betel nut. The drying process usually carried out by farmers is drying directly in the sun, and the smoking process uses fire to heat. The method of drying the areca nuts takes about 14-15 days. This study will examine the problems faced by farmers in the process of drying areca nuts. Based on the issue of drying areca nuts, it is necessary to have an alternative method of drying that is more efficient. Areca nut drying machine is an alternative to obtain dried betel nut relatively quickly with well-maintained fruit quality. The design of the areca nut drying machine is made based on the identification of farmers' needs. Identifying farmers' needs or desires will be an attribute of product development, and technical parameters are determined to meet these product attributes. The method that will be used is the Quality Function Deployment (QFD) method. The study results that priority attributes must be completed based on calculations in the planning matrix using QFD. The design of the areca nut has dimensions of 260 mm x 490 mm, with the specifications of the tool consisting of two main parts, namely an inner filter (a place for betel nuts) and a betel nut dryer.
Utilizing machine learning for predictive maintenance of production machinery in small and medium enterprises Prawatya, Yopa Eka; Djanggu, Noveicalistus H; Rahmahwati, Ratih
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.v18i1.13479

Abstract

Predictive maintenance involves the early detection of potential machine failures and subsequent maintenance to prevent such failures. Machine learning is a pertinent statistical method for predictive maintenance, enabling the early detection of machine failures and the implementation of preventive measures through a model. The development of the machine learning model commences with data collection from the machine, encompassing vibration, acceleration, machine temperature, and machine sound, facilitated by a microcontroller equipped with sensors. Subsequently, the data undergoes cleaning, including removing outliers or missing values and standardization. Data is partitioned into 70% allocated for training and 30% for testing. After determining hyperparameters and their values through hyperparameter tuning, the training data is utilized to train machine learning models, such as K-nearest neighbor, decision tree, and random forest models. Post-training, the models are evaluated using the remaining test data, employing performance metrics such as accuracy, precision, recall, and F1-score. The random forest model excels due to its utilization of a substantial number of trees for predictions and the full exploitation of the variables which F1-score is 91.22%. The best-performing model is subsequently deployed into a monitoring system, providing real-time machine condition predictions. The deployment results validate the accurate prediction of machine failures.
Co-Authors Adelia Tamala Andang Firmansyah Aprilliadi, Elgi Asriah Nurdini M. Ateng, Verico Ekawijaya Bartolomeus Lumbantoruan Briliantino, Adjie Citra, Rya Widya Dwija Saptahadi Eka Priadi Febri Prima Fenyvian, Clara Clarita Gusti Hardiansyah Helen Sasty Pratiwi Hemri Yansa Hermayudi, Hermayudi Ikhsan, FadiI Ivan Sujana Ivan Sujana Ivan Sujana Japri, Bryan Anthony Lumbantoruan, Bartolomeus M. Taufiqurrahman Meliyani, Winda Muhammad Akmal Nasher, Ammarullah Rusdyllah Ethwear Servac Noveicalistus H Djanggu Pandia, Muhammad Iqbal Putri, Famelga Clea Ramadhan, Restu Nur Ratna Herawatiningsih Riadi Budiman SANDI KURNIAWAN Shenny Oktoriana Silvia Uslianti Silvia Uslianti Silvia Uslianti Silvia Uslianti Silvia Uslianti Subarjo Subarjo Tri Wahyudi Tri Wahyudi Ulli Kadaria Wahyudi, Tri Yanti Hikma Yopa Eka Prawatya Yulianti Wulandari