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Infotekmesin
Published by Politeknik Negeri Cilacap
ISSN : INFOTEKMES     EISSN : 26859858     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering
INFOTEKMESIN is a peer-reviewed open-access journal with e-ISSN 2685-9858 and p-ISSN: 2087-1627 published by Pusat Penelitian dan Pengabdian Masyarakat (P3M) Politeknik Negeri Cilacap. The journal invites scientists and engineers to exchange and disseminate theoretical and practice-oriented in the various topics include, but not limited to Informatics, electrical Engineering, and mechanical Engineering.
Arjuna Subject : -
Articles 669 Documents
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Pengendalian Emergency Lowering System (ELS) Pada Lift 3 Lantai Hasby Mukamal, Irham; Sumardiono, Arif; Artdhita Fajar Pratiwi
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2928

Abstract

Power outages in multi-storey buildings can cause elevators to stop operating and pose safety risks to users. One effective solution to address this issue is the implementation of an Emergency Lowering System (ELS), which enables the elevator to move to the nearest floor when the main power supply fails. This study aims to design and evaluate an Emergency Lowering System (ELS) for a three-floor elevator based on an Arduino Mega, equipped with a backup power system using an inverter and battery, as well as load monitoring using a load cell sensor. The research methodology includes hardware and software design, load cell sensor calibration, and system testing under various load conditions and power outage scenarios. The experimental results show that the ELS is capable of transferring the power source with an average switching time of less than 5 seconds. The load cell measurement error was recorded at 0.16%, indicating reliable load detection. Consequently, the elevator can be safely lowered to the nearest lower floor during a power failure. These results demonstrate that the developed ELS system has strong potential to enhance the safety and operational reliability of small-scale elevators, particularly in low-rise buildings.
Optimasi Friction Stir Spot Welding Dengan Stabilizer Interlayer Pasta Zink Pada Material Dissimilar Aa5052-Ss400 Nota, Nota Ali Sukarno; Lingga Arti Saputra; Abdul Aziz
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2943

Abstract

Joining aluminum and steel materials using Friction Stir Spot Welding (FSSW) still faces challenges, particularly regarding interlayer stability and the limitations of joint mechanical properties. Previous studies generally focused on FSSW without an interlayer or used an interlayer without an adequate stabilization mechanism, so the joint quality has not been optimal. Therefore, this study aims to investigate the effect of using a zinc (Zn) paste interlayer stabilizer on the mechanical properties of dissimilar Al 5052–SS400 joints. The experimental method was carried out by comparing FSSW joints without an interlayer and with a Zn interlayer stabilizer. Evaluation was conducted through shear tensile testing and hardness measurements. The research results show that the Zn interlayer stabilizer is able to significantly improve joint performance, with a maximum hardness value of 119 HV and the highest shear tensile strength of 4.8 kN at a dwell time of 12 seconds. These findings fill a research gap related to the role of interlayer stabilization in enhancing the quality of FSSW joints of dissimilar materials.
Pengembangan Media Pembelajaran Berbasis Motion Graphics untuk Edukasi Kesiapsiagaan Tsunami dengan Metode ADDIE Susanto, Agus; Annas Setiawan Prabowo; Sriadji, Rubino
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2950

Abstract

Indonesia is geographically located in the Pacific Ring of Fire, making it a country with a high risk of natural disasters, especially tsunamis. Cilacap Regency, as one of the disaster-prone areas, has a low level of preparedness, thus requiring increased public knowledge about disaster risks. This study aims to improve the preparedness of educational units in facing tsunami disasters through educational media based on motion graphics animation. The method used is the development of ADDIE, which has five stages: Analysis, Design, Development, Implementation, and Evaluation, which is designed to increase understanding and awareness of the importance of disaster mitigation. The results of this study are an increase in students' response abilities in facing emergency situations, as evidenced by the average score of students' pre-test of 58.4, increasing to 82.6 in the post-test. The increase in the score of 24.2 points resulted in an N-Gain value of 0.58, which is in the moderate category, indicating that the use of educational animation media based on motion graphics is effective in increasing tsunami preparedness. Thus, this technology-based educational approach can be an innovative solution in supporting the Disaster Safe Education Unit (SPAB) program and creating a more disaster-resilient society.
Rancang Bangun dan Uji Kinerja Mesin Oil Skimmer Disk Type Berdasarkan Volume dan Kadar Ph Pada Cairan Pendingin Mesin CNC Milling Hurco Seri Vm10 Kurniawan, Ipung; Pujono; Pribad, Joko Setia Pribad; Santoso, Agus; Ibrahim, Ariq Rafi; Adha, Hilmam Zulmi
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2953

Abstract

Modern machining processes heavily rely on liquid coolants to extend the life of cutting tools; however, water-soluble coolants are susceptible to oil contamination, which degrades their quality. This research aims to design the form and details of a disc-type oil skimmer machine, perform the necessary machine element calculations, and test the results. The design process utilizes the VDI 2222 systematic approach with the aid of Autodesk Inventor 2025 software, supported by literature and field studies. The calculation results indicate a required motor power of 0.042 HP, a minimum shaft diameter of 7.2 mm, and a dynamic bearing load of 5.868 N. The frame strength analysis also ensures safety, with a maximum stress of 15.19 N/mm², well below the allowable limit of 79,2 N/mm². In conclusion, this design yields a viable and safe oil skimmer for implementation on the Hurco VM10 series CNC Milling machine, complete with detailed working drawings and an optimal speed of 80 rpm for separating oil from the coolant.
Pengembangan Pelet Komposit Biomassa-Cangkang Kerang sebagai Bahan Bakar dan Bahan Baku Alternatif untuk Co-processing Semen Dian Prabowo; Wardani, Nur Indah; Unggul Satria Jati; Andika Prastya; Ali Basroh
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2972

Abstract

This study evaluates Biomass-Shell Composite Fuel (BSCF) from lignocellulosic and seashell waste via fermentation and densification. Results show pellet moisture (2.64–11.0%) meets ISO 17225-6:2021 standards. The highest heating value was 12.11 MJ/kg in the mixed variant, though it was below the 14.5 MJ/kg minimum. Bulk density (0.12–0.37 g/cm³) and length (33–50 mm) were also below ideal standards. Incorporating seashells caused high ash content (32.16–34.84%). Consequently, BSCF pellets are recommended as an Alternative Fuel and Raw Material (AFR) for the cement industry, where mineral residues can substitute for limestone in clinker production. Optimization requires increased compaction pressure and reduced particle size to improve product efficiency.
Support Vector Machine (SVM) - Based Optimization of Leukemia Cell Image Classification Wanti, Linda Perdana; Romadloni, Annisa; Muhammad, Kukuh; Supriyono, Abdul Rohman
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2974

Abstract

Leukemia is a type of blood cancer characterized by the uncontrolled proliferation of abnormal white blood cells that originate from the bone marrow. Early detection of leukemia poses a significant challenge in the medical field, as the conventional diagnostic process still relies on manual microscopic observation by hematologists, which is time-consuming and prone to subjective errors. This study aims to analyze the potential of the Support Vector Machine (SVM) algorithm in optimizing the classification of leukemia cell images based on morphological and texture features extracted from microscopic images. The test results show that the SVM model with the RBF kernel provides the best performance with an accuracy of 96.4%, a precision of 95.8%, a recall of 96.1%, and an F1-score of 96.0%, surpassing the results of linear and polynomial kernels. The analysis shows that the use of a combination of shape and texture features has a significant effect on improving classification accuracy.
Analisis Pengaruh Engine Temperature Terhadap Laju Konsumsi Bahan Bakar Mobil Hemat Energi Kategori Urban Etanol Laroybafih, Muhammad Bagus; Anis, Samsudin; Nuril, Miftakhun; Huda, Fitroh Miftahul; Nurhidayat, Akhlis Rahman Sari; Laksana, Nur Akhlis Sarihidaya
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2976

Abstract

A crucial global issue is the world's dependence on fossil fuels as its primary energy source. An alternative solution to this problem is to use fuels such as ethanol. Using ethanol as fuel requires comprehensive optimization of the vehicle system, including the engine's temperature control. This research aims to determine the effect of engine operating temperature variations on fuel efficiency thru 4 engine temperature variations: 70, 80, 90, and 100 degrees Celsius, using two approaches: static and dynamic. The test results show that engine temperature affects fuel consumption and engine operating stability. Based on the experiments conducted, it was concluded that a temperature of 90°C is the most optimal working temperature, with an increase of 9.91% in static testing and 14.74% in dynamic testing (compared to the temperature with the lowest efficiency). It can be concluded that 90°C is recommended as the optimal working temperature for ethanol engines in the development of energy-efficient vehicles.
Perbandingan Kinerja Model Deep Learning Convolutional Neural Network (CNN) dan Multilayer Perceptron (MLP) untuk Klasifikasi Penyakit Diabetes Melitus Putri, Cindy Arlita; Utomo, Danang Wahyu
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2984

Abstract

Diabetes mellitus is a chronic disease with a continuously increasing number of sufferers. Early detection remains difficult because conventional methods often only recognize the disease at an advanced stage. This study evaluates the performance of the Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) in classifying diabetes using the NHANES dataset (2,278 samples; 21 positive for diabetes). The models were tested with k-fold cross-validation using the metrics accuracy, precision, recall, F1-Score, and ROC-AUC. Results show high accuracy and precision (0.99), an average recall of 0.67, and an F1-Score of 0.75. A paired t-test indicates that CNN is superior in some metrics with a p-value of 0.374, though the ROC-AUC difference is not significant. CNNs can capture complex patterns in health features such as glucose, BMI, and age, whereas MLPs remain reliable as a baseline. In conclusion, both CNN and MLP have the potential to be used for tabular data-based diabetes classification, with CNN showing a tendency to be more effective in detecting non-linear patterns in the imbalanced dataset.
Implementasi Stacking Ensemble Berbasis Cross Domain untuk Klasifikasi Diabetes Ijayanti, Selvi; Utomo, Danang Wahyu
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3000

Abstract

Diabetes mellitus is a chronic disease whose prevalence continues to increase and demands accurate early detection solutions that are adaptive to patient data diversity. This study implements the stacking ensemble method for diabetes risk classification with a cross-domain approach, integrating two popular datasets, namely the PIMA Indians Diabetes and NHANES. The experimental pipeline includes feature and label harmonization, missing value imputation using the median, standardization, and class balancing through oversampling. The base models used include Random Forest, Support Vector Machine, Decision Tree, and Multi-Layer Perceptron, with Logistic Regression as the meta learner in the stacking scheme. The evaluation was conducted systematically using stratified k-fold cross-validation and test split, as well as cross-domain scenarios to measure the model's cross-domain adaptation capabilities. In the adaptive domain scenario, the stacking ensemble achieved an accuracy of approximately 0.987% with a recall of 1.000% and an ROC-AUC of approximately 0.987%, while the accuracy of the single base learner reached an accuracy of 0.976% with a recall of 1.000% and an ROC-AUC of approximately 0.977%, thus demonstrating that the adaptive domain stacking approach provides consistently higher performance than the base model. These findings confirm the superiority of adaptive domain-based stacking in dealing with medical data heterogeneity and class imbalance issues, and reinforce its potential as a decision support system for early detection of diabetes in a wider population.
Arsitektur Hibrida IndoBERTweet - Convolutional Neural Network (CNN) untuk Klasifikasi Ujaran Kebencian Berbahasa Gaul di Media Sosial Margareta Valencia Suci Handayani; Muljono
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3026

Abstract

Detecting hate speech on Indonesian social media is challenging due to slang, abbreviations, and informal expressions that hinder automated text understanding. Traditional machine learning approaches often fail to capture contextual meaning effectively. This study aims to develop a hate speech detection system for Indonesian slang by evaluating contextual embedding IndoBERTweet combined with a Convolutional Neural Network (CNN) architecture. The research compares the performance of CNN and BiLSTM models using IndoBERTweet and FastText embeddings. A dataset of 1,477 labeled tweets categorized as Hate Speech, Abusive, or Non-Hate Speech was used. Evaluation metrics employed in this study consist of accuracy, precision, recall, F1 score, and AUC ROC. The results show that the IndoBERTweet + CNN model achieves the best performance, with 91.2% accuracy and a 91.1% F1-score, significantly outperforming FastText-based models. IndoBERTweet’s contextual embedding proves effective in handling the linguistic complexity and implicit meanings commonly found in Indonesian slang. These findings highlight the model’s strong capability for robust hate speech detection and open opportunities for its adoption as an automated content-moderation module that identifies and filters toxic narratives on social media platforms.

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