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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Pendas : Jurnah Ilmiah Pendidikan Dasar JURNAL PENDIDIKAN MIPA Jurnal Ilmiah Profesi Pendidikan Mandalika Mathematics and Educations Journal Journal of Classroom Action Research Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Griya Journal of Mathematics Education and Application Rengganis Jurnal Pengabdian Masyarakat Journal Research of Social Science, Economics, and Management Jurnal Pendidikan dan Pengabdian Masyarakat Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora Prosiding Seminar Nasional Unimus Jurnal Pendidikan MIPA Sigma&Mu: Journal of Mathematics, Statistics and Data Science Journal of Science Technology and Education Media Pendidikan Matematika Indonesian Journal of STEM Education
Nani Kurniati
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Physics Public Health Social Sciences Other

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Optimization of Gas Turbine Operational Parameters Using Machine Learning Hadi, Safwanul; Kurniati, Nani
Journal Research of Social Science, Economics, and Management Vol. 5 No. 5 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i5.1264

Abstract

Cogeneration Gas turbines are the main equipment in the electrical grid system. To meet the load needs on the grid, power plants have several gas turbine units, with the same or different capacities. The load adjustment for each gas turbine unit is carried out by numerical calculation based on load needs, operating parameters, fuel consumption and steam production. In Fact, the recommended value of the numerical calculation is always above the turbine gas operation, resulting in inefficient fuel consumption. This research reformaltes a gas turbine dispatch problem into a data-driven optimization task. Researcher develop an Artificial Neural Network (ANN) on Multi Layer Preceptron (MLP) model using parameter data from 2024 with filters baseload-efficient condition. The Model produces unit capability rankings and validated within <2% error. Compared to dispatcher recommendations, average deviation ~7% with the model, enabling measurable fuel saving and increased steam production.
Implementation of Text Mining-Based Linear Programming in Maintenance Scheduling in Power Plant Septia Yazid, Fajri; Kurniati, Nani
Journal Research of Social Science, Economics, and Management Vol. 5 No. 5 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i5.1271

Abstract

Weekly maintenance scheduling is an important activity in maintaining the reliability of operating facilities, especially in the Power Plant area in the oil and gas industry sector. This study aims to optimize the maintenance planning and scheduling process by utilizing the text mining approach through Latent Dirichlet Allocation (LDA) modeling to manage and group maintenance data, and integrate it with the Linear Programming (LP) model as the basis for the preparation of an optimal Work Order (WO) Scheduler. The LDA model is used to categorize work based on Fixed Reference Activities (FRA), resulting in a more structured classification of maintenance activities. The output of this category is then an input for the LP model that compiles labor allocation, duration, and work priorities according to the available weekly time limits. Sensitivity analysis was carried out on the parameters of the number of labor, work priority, and length of time horizon with variations of ±10%, ±20%, and ±30%. The results show an increase in the WO completion rate, a reduction in the backlog, and a more accurate understanding of labor utilization. The model has proven to be sensitive to changes in workforce capacity so that human resource management is the dominant factor in the successful implementation of schedulers.
Analisis Aktivitas Pemeliharaan Preventif Berdasarkan Reliability Growth Pada Dryer Kiln Achirudin, Zulfikar; Kurniati, Nani; Suef, Mokhamad
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v11i1.63622

Abstract

Penelitian ini bertujuan untuk mengevaluasi pengaruh implementasi predictive maintenance terhadap pertumbuhan keandalan sistem Dryer Kiln serta mengoptimalkan interval preventive maintenance yang efisien, ekonomis, dan tetap menjamin keberlanjutan operasi pada fasilitas pengolahan nikel PT Vale Indonesia Tbk. Analisis keandalan dilakukan pada enam komponen utama dryer, yaitu Pinion & Bull Gear, Main Drive, Burner System, Internal, Apron Feeder, dan Shell & Drum, menggunakan parameter MTBF, MTTR, dan physical availability berdasarkan data operasi periode 2020–2024. Model Crow-AMSAA digunakan untuk menilai reliability growth masing-masing komponen dan sistem, sedangkan konfigurasi keandalan ditentukan melalui evaluasi 406 kombinasi seri–paralel dengan pemilihan berdasarkan Root Mean Square Error (RMSE). Hasil penelitian menunjukkan bahwa implementasi predictive maintenance berdampak positif terhadap peningkatan pertumbuhan keandalan. Profil keandalan dengan preventive maintenance memperlihatkan peningkatan stabilitas dan penurunan frekuensi kegagalan dibandingkan tanpa pemeliharaan. Optimisasi interval preventive maintenance terhadap 729 kombinasi waktu menunjukkan bahwa interval minimum memberikan performa keandalan terbaik untuk lima komponen utama, sementara Shell & Drum dapat diperpanjang hingga 300 hari tanpa menurunkan Overall Equipment Effectiveness (OEE) atau meningkatkan biaya Cost per Unit Time (CPUT) secara signifikan. Hasil ini menegaskan bahwa optimisasi interval pemeliharaan mampu mencapai keseimbangan antara biaya pemeliharaan yang minimal dan tingkat keandalan sistem yang tinggi. Hasil penelitian ini merekomendasikan peningkatan sistem predictive maintenance berbasis monitoring kondisi, evaluasi berkala interval pemeliharaan, strategi perawatan khusus untuk komponen termal kritis pada dryer, serta integrasi model keandalan ke dalam sistem digital twin untuk mendukung pengambilan keputusan pemeliharaan berbasis data.
Co-Authors Achirudin, Zulfikar Afandi, Noval Agustina Agustina Agustina, Arianti Agutina, Ripki Amalia Ainun, Yusri Ambia, Nurul Amelia Kusuma Amrullah Amrullah Amrullah Anisah Anwariyah, Sofiyatil Apriani, Amelia Aprilia, Ririn Arjudin Arjudin Arjudin Azmi, Ema Suryani Baehaqi Baidowi Baidowi Baidowi Baidowi Baiq Julia Setyaningrum Cahyani, Nurriza Indah Chaerunnisa Sumiatun Efendi Chopifah Rosdianti Dhuha, Shela Hadri Dhuha Dina, Yana Roza Eka Kurniawan Erika Fitriana Erly Ekayanti Rosyida Ezi Julianto Fadila, Laila Fahmi Firdaus Firnanda, Riska Faya Hadi, Safwanul Harry Soeprianto Haryani, Fitria I Nyoman Pujawan Ida Royani Intan Kertiyani, Ni Made Irwadi Saputra Iryanto, Rizal Ismi Andriyani, Ismi Julianto, Ezi Junaidi Junaidi Junaidi Junaidi Kayanti, Kusamantha Dwi Ketut Sarjana KETUT SARJANA, KETUT Kurabi, M.Kasyiful Kurniawan, Eka Laila Hayati Laila Hayati, Laila Lu’luilmaknun, Ulfa Lu’luilmaknun, Ulfa Miftahul Janah Muhammad Turmuzi Mulya Lestari Muniro, Nisrina Nabila Aulia Priangka Ni Kadek Nirmala Wilwatikta Ni Made Intan Kertiyani Nia Agustina Nilza Humaira Salsabila Nourma Paramestie Wulandari Nourma Pramestie Wulandari Nurfahrani, Nurfahrani nurul ambia Nurul Fatmawati Nurul Hikmah NURUL HIKMAH Nuryadin, Nanang Nyoman Sridana Nyoman Sridana Prasetyo, Erik Bagus Rabiyatil Husniyati Ratna Dwi Aryani Ratna Yulis Tyaningsih Rima Guntari Lyana Rosmala Dewi, Lale Amrini Salsabila, Nilza Humaira Sapitri, Evi Sasa Apriliska Septia Yazid, Fajri SITI ANISA RUSMANIA Soepriyanto, Harry Sri Subarinah Sri Subarinah, Sri Sridana Sridana Sripatmi Sripatmi Sripatmi Sripatmi Sripatmi, Sripatmi Sudi Prayitno Suef, Mokhamad Sujaya, Ketut Ary Sulisyani, Anis Suliyani, Anis Syafitri, Rekha Hesti Syahril Ramdhan Syahrul Azmi Syahrul Azmi Tabita Wahyu T Tabita Wahyu Triutami Thohira, Nur Triutami, Tabita Wahyu Ulfa Lu'luilmaknun Ulfa Lu’luilmaknun ULUL AZMI Utami, Davina Putri Wafasari Wahidaturrahmi Wahidaturrahmi Wahidaturrahmi, Wahidaturrahmi Wulandari, Nourma Pramestie Yulis Tyaningsih, Ratna Yunita Septriana Yunita Septriyana Anwar Zalty, Rani Febriana Zulhijri