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All Journal Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Prosiding SNTTM
Anggriawan, Akbar
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Deteksi Kerusakan Motor Induksi Dengan Menggunakan Sinyal Suara Anggriawan, Akbar; Huda, Feblil
Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Vol 5, No 1 (2018): Wisuda April Tahun 2018
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Induction motor plays an important role in the industrial realm that serves as a driving parts such as the conveyor, lathes and others. Regarding to this important role, the early detection of induction motor damage becomes important for system operation, so that it could not stopped without scheduled. The types of damage that often occur in induction motor are mechanical unbalance, rotor damage and bearing damage. The usual detection method which uses vibration measurments has some disadvanteages such as it is very expensive, physical contact with the induction motor that occurs and the sensors are less heat resistant on induction motor. The author uses sound signal detection method that is cheaper regarding to the cost, it has no physiscal contact with the induction motor and heat resistant. The sound signal is generated by the exicitation of the artificial damage which is provided to the induction motor. Artificial damage is given by damaging the bearing, rotor and mass unbalance. Sound signal data from artificial damage test result on induction motor is processed by using fast fourier transform method. The result of the research obtains the amplitude increased number for one time motor rotation 2970 rpm (1xrpm) is 49.5 Hz, 144.5 Hz for bearing damage frequency and 78.5 Hz for the rotor damage frequency.Keywords : Induction motor, mechanical unbalance, bearing, rotor, signal sound, fast fourier transform
Optimizing Steam to Electricity Ratio in Crude Palm Oil Refinery Captive Power Plant: A Six Sigma-DMAIC Capability Assessment Anggriawan, Akbar; Susilawati, Anita; Mainil, Rahmat Iman
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 69 No 3 (2025): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v69i3.552

Abstract

This study applies the Six Sigma-DMAIC (Define, Measure, Analyze, Improve, Control) methodology combined with process capability analysis to enhance energy efficiency, specifically by reducing the steam to electricity ratio of a steam turbine. Initial measurements indicated a steam-to-electricity ratio of 4.5 to 5.34 kg/kWh, highlighting high steam consumption and poor efficiency. The process was unstable, with Cp and Cpk values of 0.30 and -0.16, and a defect rate exceeding 560,000 DPMO. Using an Ishikawa diagram, a vacuum leak in the steam turbine condenser was identified as the main cause of excessive steam consumption. After repairing the condenser, monitoring showed significant improvements, with the steam to electricity ratio reducing to 3.0 – 4.0 kg/kWh. Process capability improved, with Cp increasing to 1.39, Cpk to 1.02, and Z-bench to 3.05 (equivalent to 1,143 DPMO). The Anderson-Darling test confirmed a normal distribution (p-value = 0.464). Six Sigma-DMAIC effectively optimized steam turbine performance.
Implementasi analytic hierarchy process dalam penentuan bobot key performance indicators pada pembangkit listrik turbin uap Anggriawan, Akbar; Susilawati, Anita; Arief, Dodi Sofyan; Nazaruddin
Prosiding SNTTM Vol 23 No 1 (2025): SNTTM XXIII October 2025
Publisher : BKS-TM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71452/6yw8w905

Abstract

Optimalisasi Key Performance Indicators (KPI) pada pembangkit listrik turbin uap sangat penting untuk meningkatkan efisiensi operasional dalam industry pengolahan kelapa sawit. Penelitian ini menerapkan metode Analytic Hieararchy Process (AHP) untuk menentukan bobot relative setiap KPI, sehingga memfasilitasi pengambilan keputusan berbasis data untuk peningkatan kinerja. Empat KPI krusial dievaluasi melalui perbandingan berpasangan berdasarkan keahlian para pakar. Hasil penelitian menunjukkan bahwa power output (47,16%) merupakan KPI paling signifikan, diikuti oleh availability factor (38,58%), steam consumption (9,69%), dan capacity factor (4,58%). Nilai consistency ratio (CR) dari seluruh penilaian pakar berada di bawah 0,10, yang menunjukan bahwa hasil AHP tersebut dapat diandalkan.
Implementation of the Analytic Hierarchy Process (AHP) to Determine Key Performance Indicator (KPI) Weights for Steam Turbine Power Plant Using Python Anggriawan, Akbar; Nazaruddin, Nazaruddin; Susilawati, Anita
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 70 No 1 (2026): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v70i1.582

Abstract

The optimization of Key Performance Indicators (KPIs) in steam turbine power plants is crucial for enhancing operational efficiency in the palm oil processing industry. This study applies the Analytic Hierarchy Process (AHP) to determine the relative weights of KPIs, thereby supporting data-driven decision making for performance improvement. Four critical KPIs were evaluated through pairwise comparisons expertise. A Python based computational model was developed to automate AHP calculations, ensuring accuracy and efficiency in deriving priority weights. This study reveals power output (47.16%) is the most significant KPI, followed by availability factor (38.58%), steam consumption (9.69%), and capacity factor (4.58%). The consistency ratio (CR) for all expert judgments was below 0.10, validating the reliability of the AHP outcomes. This research demonstrates that integrating AHP with Python programming provides a robust framework for KPI prioritization. The findings offer practical insights for industry stakeholders to optimize steam turbine performance and reduce operational inefficiencies.
Co-Authors Anita Susilawati Dodi Sofyan Arief, Dodi Sofyan Feblil Huda N. Nazaruddin Nazaruddin Rahmat Iman Mainil