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All Journal Jurnal Ilmu Komputer dan Informasi Majalah Ilmiah Pengkajian Industri FIRM: Journal of Management Studies Computer Science and Information Technologies
Supriyadi, Muhamad Rodhi
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering 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.
Transfer learning: classifying balanced and imbalanced fungus images using inceptionV3 Supriyadi, Muhamad Rodhi; Alfin, Muhammad Reza; Karisma, Aulia Haritsuddin; Maulana, Bayu Rizky; Pinem, Josua Geovani
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p112-121

Abstract

Identifying the genus of fungi is known to facilitate the discovery of new medicinal compounds. Currently, the isolation and identification process is predominantly conducted in the laboratory using molecular samples. However, mastering this process requires specific skills, making it a challenging task. Apart from that, the rapid and highly accurate identification of fungus microbes remains a persistent challenge. Here, we employ a deep learning technique to classify fungus images for both balanced and imbalanced datasets. This research used transfer learning to classify fungus from the genera Aspergillus, Cladosporium, and Fusarium using InceptionV3 model. Two experiments were run using the balanced dataset and the imbalanced dataset, respectively. Thorough experiments were conducted and model effectiveness was evaluated with standard metrics such as accuracy, precision, recall, and F1 score. Using the trendline of deviation knew the optimum result of the epoch in each experimental model. The evaluation results show that both experiments have good accuracy, precision, recall, and F1 score. A range of epochs in the accuracy and loss trendline curve can be found through the experiment with the balanced, even though the imbalanced dataset experiment could not. However, the validation results are still quite accurate even close to the balanced dataset accuracy.
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 Karisma, Aulia Haritsuddin Maulana, Bayu Rizky Melati Miftakhurohmah Montolalu, Ivan Adrian Muharto, Bambang Muliadi, Jemie Nasrullah Armi Pamungkas, Lukas Sangka Pinem, Josua Geovani 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