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All Journal Journal of Applied Data Sciences JURNAL POLI-TEKNOLOGI Prosiding Seminar Nasional Inovasi Teknologi Terapan
Fajar Aswin
Politeknik Manufaktur Negeri Bangka Belitung
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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PENERAPAN METODE FAILURE MODE AND EFFECT ANALYSIS (FMEA) PADA MESIN CNC TURNING MORI SEIKI SL-25 Fahira Tamimy; Fajar Aswin; Husman
Prosiding Seminar Nasional Inovasi Teknologi Terapan Vol. 2 No. 02 (2022): Prosiding Seminar Nasional Inovasi Teknologi Terapan
Publisher : Politeknik Manufaktur Negeri Bangka Belitung

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Abstract

The Bangka Belitung State Manufacturing Polytechnic has machine tools such as conventional machines in the form of lathes, milling, scrap, drills and also automatic machines such as CNC lathes, milling. The machining process used in the production process is a machining process that must comply with the demands of the production quality of CNC Turning Machines at the Polman Babel Mechanical Engineering Laboratory, which often experience damage and disrupt the performance of the machine. The purpose of this research is to identify failure modes, causes of damage, and efforts to minimize and prevent damage that can be done to improve the performance of CNC machines at the Polman Babel Mechanical Engineering Laboratory. The method used to identify and analyze possible damage to the machine uses the Filure Mode and Effect Analysis (FMEA) method and is used to find the priority of the problem through the Risk Priority Number (RPN). The results of this study indicate that there is damage to the components in the form of damage to the oil seal, damage to the en-coder, blown fuse and damage to the front door switch. The results of the analysis show that the highest RPN value is the most risky cause of failure and the main cause of damage. Proposed corrective action is to immediately replace components that are no longer functioning properly by optimizing component maintenance according to applicable instructions and rules.
IMPLEMENTASI SENSOR MEMS AKSELEROMETER SEBAGAI ALAT PENGUKUR GETARAN PADA TURBIN ANGIN Fajar Aswin; Zaldy Sirwansyah Suzen
Jurnal Poli-Teknologi Vol. 18 No. 3 (2019)
Publisher : Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/pt.v18i3.2340

Abstract

Many techniques are available in the market to measure and analyze vibrations caused by rotating machines and the structure of the machine itself, but require expensive resources to identify the problem of damage. Generally these expensive equipment use conventional sensors such as eddy-current sensors, swing coil velocity sensors and piezoelectric transducer sensors to measure vibration. The implementation of a vibration measuring device in the form of an accelerometer sensor with a type of Micro Electro Mechanical System (MEMS) and a signal processing unit (Arduino Mega microcontroller) is used to measure vibrations that occur in small-scale horizontal type wind turbine blades. Experiments were carried out to check the accelerometer's ability to detect vibration response on wind turbine blades with the free vibration position. Then the signal in the form of the time domain will be converted into the frequency domain to get the dominant frequency value of the turbine blade, which is then analyzed to be compared with the B & K VibroPort 80 vibration meter. The experimental results show that the MEMS type accelerometer sensor can be applied to measure the free vibration conditions on wind turbine blades with an error percentage below 6%.
ROOT CAUSE ANALYSIS KERUSAKAN SISTEM PENGEREMAN SPINDEL MESIN FRAIS AJAX MODEL N2A MKV MENGGUNAKAN METODE 5 WHY Riswanda Riswanda; Niko Julianto; Masdani Masdani; Fajar Aswin
Prosiding Seminar Nasional Inovasi Teknologi Terapan Vol. 6 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Terapan
Publisher : Politeknik Manufaktur Negeri Bangka Belitung

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Abstract

The spindle braking system is one of the most important components in a milling machine because it stops spindle rotation immediately after machining. Failure of the braking system reduces operational safety, decreases machining efficiency, and accelerates component wear. This study aims to identify the root cause of spindle braking system failure in an Ajax Model N°2A MKV milling machine using the Root Cause Analysis (RCA) method with the 5 Why approach. Data were collected through direct observation, machine manual review, electrical testing using a multimeter, and systematic root cause analysis. The results indicate that the braking system failed due to a short circuit in the electromagnetic motor coil, causing excessive current in the AC-to-DC braking circuit. Consequently, the bridge rectifier diode and fuse repeatedly failed. The root cause was identified as coil deterioration caused by prolonged service life. The 5 Why method effectively identified the root cause and provided a basis for corrective actions and preventive maintenance to improve machine reliability.
PENERAPAN METODE 5 WHYS DALAM MENGIDENTIFIKASI PENYEBAB GANGGUAN SISTEM KELISTRIKAN PADA MESIN FRAIS ACIERA F3 Aditia Akbar Rianto; Aan Aji Saputra; Fajar Aswin; Hasdiansah Hasdiansah
Prosiding Seminar Nasional Inovasi Teknologi Terapan Vol. 6 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Terapan
Publisher : Politeknik Manufaktur Negeri Bangka Belitung

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Abstract

The Aciera F3 milling machine is a vital tool in practical and production activities at the Mechanical Engineering Laboratory of the Bangka Belitung State Manufacturing Polytechnic. Damage to the electrical system resulted in the machine being unusable, which hampered the learning process. This study aims to identify the source of the damage and determine the appropriate repair method. The strategy implemented is Root Cause Analysis (RCA) with the 5 Whys method. Data were collected from interviews with technicians, literature studies, maintenance and repair records, and manuals. The research findings indicate damage to the vulnerable auxiliary contactor and broken terminals, resulting in a power outage to the control panel. The 5 Whys analysis indicates that the old component life and lack of regular replacement are suspected to be the main causes of the auxiliary contactor damage.
A Stacking Ensemble Model for Predicting Student High School Graduation Outcomes Fitriyani Fitriyani; Ari Amir Alkodri; Fajar Aswin
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1067

Abstract

This study develops and evaluates machine learning models to predict high school graduation outcomes and identify at-risk students for early intervention. Using a quantitative approach, data from 1,017 students across three public high schools were analyzed, encompassing academic performance (average yearly scores), behavioral factors (attendance rates and extracurricular participation), and socio-economic background (proxied by parental occupation). A comparative modeling strategy was applied, beginning with a Decision Tree baseline and advancing to a Stacking Ensemble model that integrated three heterogeneous base learners—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree—combined through a Logistic Regression meta-model. Both models were optimized using GridSearchCV and adjusted for class imbalance between graduates (93.4%) and at-risk students (6.6%). The results showed that academic variables, particularly third-year average scores (mean = 82.6, SD = 6.4) and attendance rate (mean = 94.3%), were the strongest predictors of graduation, while socio-economic indicators had minimal impact. The Stacking Ensemble achieved a notable improvement over the Decision Tree, reaching an accuracy of 99.6%, precision of 0.909, recall of 1.000, F1-score of 0.952, and AUC of 1.000, compared to the baseline accuracy of 94.9% (F1-score = 0.519, AUC = 0.83). These findings indicate the superior predictive capability of the ensemble model in identifying students at risk of non-graduation. The study’s novelty lies in combining interpretable and high-performance models to construct a practical early-warning framework that can guide educators and policymakers in targeted academic interventions. However, the near-perfect metrics also suggest potential overfitting, emphasizing the need for validation using external datasets before broader application. Overall, this research contributes a robust, data-driven methodology for improving student retention through predictive analytics in educational settings.
Co-Authors Aan Aji Saputra Aditia Akbar Rianto Ari Amir Alkodri Fahira Tamimy Fitriyani Fitriyani Hasdiansah, Hasdiansah Husman Husman Masdani, Masdani Niko Julianto Riswanda Riswanda Suzen, Zaldy Sirwansyah