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All Journal Jurnal Ilmu Komputer dan Informasi Majalah Ilmiah Pengkajian Industri FIRM: Journal of Management Studies
Supriyadi, Muhamad Rodhi
Unknown Affiliation
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences Transportation

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APPLICATION OF INSTRUMENTATION AND CONTROL SYSTEM FOR BIOGAS POWER GENERATION COMMISSIONING AT PTPN V KAMPAR PALM OIL MILL Salehah, Nur Azimah; Prasetyo, Dwi Husodo; Senda, Semuel Pati; Supriyadi, Muhamad Rodhi; Adeliaa, Nesha; Samodra, Bayu; Adiprabowo, Arya Bhaskara; Muharto, Bambang; Anindita, Hana Nabila
Majalah Ilmiah Pengkajian Industri Vol 14, No 1 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/mipi.v14i1.3865

Abstract

Biogas Power Plant (PLT) from palm oil mill effluent had been commissioned by a team from the Center of Technology for the Energy Resources and Chemical Industry, Agency for the Assessment and Application of Technology (PTSEIK-BPPT). The biogas power plant is located in PTPN V Kampar, Riau Province. A PLC (Programmable Logic Controller) has been implemented to support the operation of biogas power plant. Proper sensor selection has been done for each measurement applications. A computer and mimic panel is used as an interface for the operation of PLC. The master control system communicates with the slave control systems and Human Machine Interface (HMI) by means of ethernet communication protocol. Commissioning phase is carried out for 2 hours with a load of 450 kW. Instrumentation and control system is able to measure important variables such as fluctuation in methane numbers, pressures, and biogas flow rate to check the suitability of biogas supply in accordance to gas engine specification.
Telemedicine and AI in Occupational Skin Disease Management: A Contemporary Review Purwoko, Reza Yuridian; Muliadi, Jemie; Roestam, Rusdianto; Wan Sen, Tjong Wan Sen; Pamungkas, Lukas Sangka; Nugroho, Anto Satriyo; Armi, Nasrullah; Supriyadi, Muhamad Rodhi; Melati, Rima; Alfaqih, Muhammad Subhan; Montolalu, Ivan Adrian; Ruhdiat, Rudi; Ferianasari, Inneke Winda; Aryanti, Evy Aryanti; Saputra, Silvan; Asmail, Asmail; Rahayaan, Manuela; Hi Rauf, Siti Nuraini
FIRM Journal of Management Studies Vol 8, No 2 (2023): FIRM JOURNAL OF MANAGEMENT STUDIES
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/firm.v8i2.5802

Abstract

Occupational skin diseases present significant challenges to workplace health, impacting both productivity and quality of life. The integration of telemedicine and artificial intelligence (AI) has transformed dermatological care by facilitating remote consultations, enabling early diagnosis, and supporting continuous monitoring. The COVID-19 pandemic has accelerated the adoption of digital health solutions, underscoring their potential to enhance accessibility and efficiency in occupational dermatology.AI-driven innovations, including machine learning algorithms and wearable technologies, have further improved diagnostic accuracy and patient management. However, challenges such as healthcare disparities, technological limitations, and workplace-specific factors continue to hinder widespread implementation. This review explores the evolving role of telemedicine and AI in managing occupational skin diseases, highlighting key challenges, emerging opportunities, and policy considerations for enhancing workplace health outcomes.
Classification of Clove Leaf Blister Blight Disease Severity Using Pre-trained Model VGG16, InceptionV3, and ResNet Pramesti, Putri Ayu; Supriyadi, Muhamad Rodhi; Alfin, Muhammad Reza; Noveriza, Rita; Wahyuno, Dono; Manohara, Dyah; Melati; Miftakhurohmah; Warman, Riki; Hardiyanti, Siti; Asnawi
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1237

Abstract

Clove is one of the precious plants produced in Indonesia. Clove has many benefits for humans, but clove cultivation often experiences problems due to disease attacks, including Leaf Blister Blight Disease(CDC). The handling of CDC disease is carried out based on the severity of the symptoms that can be seen on the affected leaves. This research was conducted to obtain a CDC disease classification model, so appropriate treatment can be carried out. This study used the pre-trained VGG16, InceptionV3, and ResNet models for classification. VGG16 got the highest average accuracy of 96.7%. Aside from that, k-fold cross validation improved the model's accuracy.
Co-Authors Adeliaa, Nesha Adiprabowo, Arya Bhaskara Alfaqih, Muhammad Subhan Alfin, Muhammad Reza Anto Satriyo Nugroho, Anto Satriyo Aryanti, Evy Aryanti Asmail, Asmail ASNAWI Dono Wahyuno Dyah Manohara Ferianasari, Inneke Winda Hana Nabila Anindita, Hana Nabila Hi Rauf, Siti Nuraini Melati Miftakhurohmah Montolalu, Ivan Adrian Muharto, Bambang Muliadi, Jemie Nasrullah Armi Pamungkas, Lukas Sangka Prasetyo, Dwi Husodo Purwoko, Reza Yuridian Putri Ayu Pramesti, Putri Ayu Rahayaan, Manuela Rima Melati Rita Noveriza Ruhdiat, Rudi Rusdianto Roestam Salehah, Nur Azimah Samodra, Bayu Saputra, Silvan Semuel Pati Senda Siti Hardiyanti Wan Sen, Tjong Wan Sen Warman, Riki