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Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30318750     EISSN : 30318742     DOI : 10.61132
Core Subject : Science,
Computer Science & IT
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer memuat naskah hasil-hasil penelitian di bidang Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 278 Documents
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Hubungan Kelengkapan Fasilitas Bengkel dengan Hasil Belajar Praktik Sistem Rem Cakram Kelas XI Teknik Sepeda Motor (TSM) SMK Negeri Noemuti
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1248

Abstract

Vocational education requires a practice-based learning process supported by complete facilities so that students can master work competencies optimally. However, the condition of the Motorcycle Engineering workshop at Noemuti State Vocational High School shows that the completeness of the disc brake system practice facilities does not meet the ideal ratio standards, which affects the effectiveness of learning. This incompleteness has an impact on the low achievement of students' practical learning outcomes that have not reached the KKM. This study aims to describe the level of completeness of workshop facilities and the results of the disc brake system practice learning, and to determine whether there is a relationship between the two. The research approach uses a quantitative correlational method with a total sampling of 26 students. Data were obtained through observation, questionnaires, and documentation, then analyzed using descriptive statistics, normality tests, linearity tests, Pearson correlations, and coefficients of determination using SPSS 27. The results showed that the average completeness of workshop facilities was 42.27 and the results of practical learning were 42.81, both of which were in the good category. The Pearson correlation test produced a value of r = 0.960 with a significance of 0.000 (<0.05), indicating a very strong and significant relationship. The coefficient of determination (R² = 0.92) shows that 92% of the variation in practical learning outcomes is influenced by the completeness of workshop facilities. The more complete the workshop facilities, the higher the students' practical learning outcomes.
Analisis Pola Tanda Tangan untuk Identifikasi Kepribadian Diri Menggunakan Jaringan Syaraf Tiruan Backpropagation Berbasis Citra Digital
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1256

Abstract

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.
Bridging The Synthetic-To-Real Gap: A Model-Data Coevolution Approach With Stochastic Feature Decoupling For Ac Unit Fault Diagnosis
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1262

Abstract

The scarcity of real-world data in Air-Conditioning (AC) fault diagnosis necessitates the use of synthetic data; however, rule-based synthetic datasets often suffer from a significant sim-to-real domain gap. To address this, we propose a Model-Data Coevolution (MDC) framework that employs a Simulated Annealing (SA) controller to optimize augmentation parameters. We introduce a novel technique, Stochastic Feature Decoupling (SFD), which applies independent noise to raw and derived features, contrasting it with traditional Logically-Consistent Augmentation (LCA). Empirical results show that SFD significantly outperforms LCA, achieving a weighted F1-score of 0.93 and increasing NORMAL class recall to 82%. We demonstrate that by breaking deterministic feature links, SFD acts as a robust regularizer, utilizing "physically impossible" data to enhance generalization in complex real-world environments.
Penggunaan Transfer Learning Untuk Peningkatan Akurasi Deteksi Penyakit Tanaman Bunga
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1264

Abstract

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.
Desain dan Implementasi Website UMKM Desa sebagai Media Promosi Produk Lokal Menggunakan Framework Code Igniter
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1268

Abstract

This research aims to design and implement a village MSME website as a centralized digital promotional medium to overcome the limitations of conventional marketing and expand the market reach of local products more effectively and sustainably. The system was developed using the waterfall method, encompassing requirements analysis, design, implementation, testing, and maintenance. The system was developed using the PHP programming language and the CodeIgniter framework based on the Model-View-Controller (MVC) architecture to ensure a structured, efficient, and maintainable development process. The implementation resulted in a responsive and user-friendly website equipped with key features such as an informative product catalog, village MSME profiles, and a content management system via an admin dashboard that allows MSMEs to update data independently and flexibly. Functional testing demonstrated that all features functioned well and reliably according to user needs. Therefore, this village MSME website can be concluded as an effective digital solution for increasing the visibility of local products, strengthening MSME competitiveness, and supporting village economic growth through sustainable and integrated online promotion.
Memprediksi Dampak Anomali Cuaca Ekstrem terhadap Hasil Panen Padi Menggunakan Model Deret Waktu SARIMA
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1298

Abstract

The instability of the climate is becoming increasingly prominent across Southeast Asia, creating uncertainty in agricultural systems that are highly dependent on seasonal weather patterns. Indonesia, where rice remains the primary staple food, is particularly vulnerable to the effects of rising temperatures and rainfall deficits. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict rice production while incorporating indicators of extreme climate anomalies. Using publicly available datasets, including FAOSTAT production statistics, NOAA rainfall and temperature anomalies, and climate indices from the World Bank, this model was developed following the Box-Jenkins procedure. Among the configurations tested, the SARIMA model (1,1,1)(0,1,1)₁₂ showed the strongest performance, reflected in a MAPE of 4.62% and low RMSE values. The model indicates that significant El Niño events can reduce annual rice production by 3–7%, while wetter La Niña conditions may support production recovery. These findings highlight the importance of integrating climate-sensitive data into agricultural forecasting. The model presented here could support early warning systems, adaptive farming strategies, and long-term food security planning in Indonesia.
Pengaruh Fraksi Volume Polyurethane terhadap Uji Tarik dan Lentur Komposit Pegas Daun
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 4 No. 1 (2026): Februari: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v4i1.1307

Abstract

Leaf springs serve as vehicle weight supports and vibration dampers from uneven roads. Reducing vehicle weight can support fuel consumption reduction. The use of composite materials allows for a reduction in leaf spring weight without reducing load capacity and stiffness. The purpose of this study was to find the composition of composite leaf springs with a polyurethane matrix that were resistant to tensile and flexural tests using e-glass, epoxy, and polyurethane materials. This study used an experimental method, in which specimens were tested using a tensile and flexural testing machine. The variations included polyurethane matrices of 10%, 20%, and 30%. The data was statistically analyzed using Excel to determine the significant effect of the variables. The results showed the effect of polyurethane variation on the composite. The tensile test showed that the greatest tensile stress was on the 30% polyurethane specimen at 1.574 N/mm² and the smallest was on the 10% specimen at 7.007 N/mm². In the flexural test, the greatest effect on flexural strength was observed in the 30% specimen at 14.36 MPa and the smallest in the 10% specimen at 25.82 MPa. Without the addition of polyurethane, the tensile stress was 39.678 N/mm² and the flexural strength was 157.09 MPa. Conclusion: The addition of polyurethane reduces the mechanical strength of composite leaf spring material without polyurethane addition.
Perancangan dan Analisis Kinerja Sistem Penghitung Lalu Lintas Otomatis Berbasis YOLOv8
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1311

Abstract

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  
Implementasi Algoritma Jaringan Syaraf Tiruan Backpropagation untuk Prediksi Penyakit Diabetes
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1312

Abstract

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.
Implementasi Sistem Informasi Eksekutif untuk Evaluasi Kinerja Penjualan Tiket Kapal Menggunakan Metode Visualisasi Data dan Drill-Down (Studi Kasus: Seapass)
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 6 (2025): Desember: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i6.1313

Abstract

The rapid development of the sea transportation industry produces a massive and complex volume of transaction data, requiring strategic management to support managerial decision-making. This research aims to implement the Executive Information System on SeaPass in order to evaluate the performance of ship ticket sales. The research method uses data visualization with a two-level drill-down mechanism, which allows the presentation of information hierarchically from general summaries to specific details. The methodological stages include needs analysis, user interface (UI) design using Figma, front-end implementation with HTML, CSS, and JavaScript, database integration, and system testing through Black Box Testing. The results showed that the SIE implementation successfully integrated operational data, including schedules, ships, and manifests, into an interactive dashboard. The two-level drill-down feature provides the ability for executives to identify operational anomalies and market fluctuations in real-time. In conclusion, the system significantly enhances executive data analysis capabilities, transforming complex transaction data into accurate strategic information, thereby supporting more precise business decision-making and adaptive to the dynamics of the marine transportation market.

Page 25 of 28 | Total Record : 278

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