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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 707 Documents
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Analisis Sentimen Aplikasi Easycash di Playstore Menggunakan Algoritma Support Vector Machine, Naive Bayes, dan Random Forest Classifier Rojakul Rojakul; Ainayah Syifa Hendri
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Easycash is an online lending platform that offers convenience and speed in the loan process, and is licensed and supervised by the Financial Services Authority (OJK). This study aims to analyze user sentiment towards the Easycash application on the Google Play Store by applying three machine learning classification algorithms, namely Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The research data was obtained through a web scraping process on 10,000 application user reviews. The analysis stages include data preprocessing, consisting of data cleaning, case folding, tokenization, stopword removal, and stemming, followed by sentiment labeling using a rule-based approach, and feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The dataset is divided into two parts, namely a training set of 80% and a testing set of 20% for model performance evaluation. The test results show that the Naïve Bayes algorithm has the best performance with an accuracy rate of 85%, followed by Random Forest at 84% and Support Vector Machine at 83%. WordCloud visualizations show that words like "easy," "fast," "limit," and "good" dominate positive reviews, while words like "pay," "bill," and "interest" frequently appear in negative reviews. Based on these results, the Naïve Bayes algorithm is considered the most effective in classifying user sentiment toward the Easycash app and can be used as a basis for evaluating and improving the quality of digital financial app services in Indonesia.
Analisis Percepatan Simulasi Tumbukan Airbag Kendaraan Berbasis Mikrokontroler Dengan Sensor ADXL345 dan FC-51 Yuniarto Agus Winoko; Ali Fauzan Kurnia; Ratna Monasari; Akbar Anugrah Ikhsani
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Delays or failures in airbag system activation can have fatal consequences for driver safety, thus requiring a collision detection mechanism with a high degree of precision and reliability. This study aims to design and evaluate an experimental testbed to analyze the characteristics of impact acceleration as the primary trigger parameter in airbag activation systems. The collision dynamics simulation was represented through the implementation of the sled test method using a data acquisition system that integrates an ADXL345 accelerometer, which had undergone precision validation via a six-position tumble test on 100 reference samples, and an FC-51 infrared sensor specifically calibrated for measuring the distance and velocity of objects. Based on vehicle dynamics literature, which refers to a deceleration range of 3g to 5g at speeds of 16–24 km/h, the threshold configuration for actuation is set at an acceleration of 40 to 46 m/s². The results of the instrumentation performance tests showed good data reading stability, with an average sensor noise fluctuation level of 3.5 m/s². A comparative statistical analysis using a two-sample t-test on two impact velocity variations demonstrated a mathematically highly significant difference in acceleration, with a p-value = 0.000. The airbag simulation developed demonstrates a high level of reliability in precisely identifying impact forces in accordance with threshold parameters. This study concludes that experimental methods are highly effective for characterizing airbag activation trigger parameters, but further research is needed to comprehensively evaluate airbag response performance.
Effect of Binder, Concentration, and Particle Size on Spent Coffee Grounds Briquette Akhlis Rahman Sari Nurhidayat; Ameliyana Rizky Syamara Putri Akhmad Yani; Ghaniyya Dzihni Jayanti; Cahya Widya Wati; Nur Akhlis Sarihidaya Laksana
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Spent coffee grounds are organic waste that have the potential to be developed as raw material for biomass briquettes. This study aims to examine the effect of binder type, mixture ratio, and particle size on the characteristics of spent coffee grounds briquettes. Testing included calorific value using the Bomb Calorimeter method based on ASTM D 240-02, as well as moisture and ash content using the Gravimetric method. Results show that binder type was the most dominant factor. Briquettes with guar gum binder produced the highest average calorific value of 17,043.5 J/g (specimen 1) and the lowest ash content of 1.27 %wt, far superior to combustion residue briquettes, which yielded only 10,352.5 J/g with 28.78 %wt ash content. Fine particle size (<200 mesh) was shown to improve calorific value, while increasing the proportion of spent coffee grounds in residue-based briquettes effectively reduced ash content. All specimens exhibited moisture content below 15 %wt.
Analisis Kesehatan Vegetasi Multi-Temporal Berbasis WebGIS Menggunakan NDVI Sentinel-2 dan Penilaian Multi-Kriteria SAW Akner Yosha Ade Saputra; Nurul Anisa Sri Winarsih; Muhammad Syaifur Rohman; Danny Oka Ratmana
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

This study integrates Sentinel-2 NDVI, the Simple Additive Weighting (SAW) method, and WebGIS to analyze vegetation health dynamics across 19 sub-districts in Grobogan Regency (±1,975.87 km²). Data were sourced from four Sentinel-2 Level-2A images (September 14–October 9, 2025) and supporting meteorological parameters. Results indicate NDVI values ranging from 0.2424 to 0.6297. On September 14, 42.1% of the areas were classified as High. However, by September 19, 12 sub-districts experienced a significant decline anomaly (), leaving only 21.1% in the High category. The SAW assessment yielded scores between 0.2793 and 0.4918. Spearman's validation demonstrated a perfect correlation () between NDVI and SAW rankings. The observed temporal fluctuations correlated with rainfall and humidity. In conclusion, this integrated approach effectively evaluates vegetation anomalies to support precise land management decisions.
Electrical Energy Saving Analysis in Educational Buildings Using CLTD and ECI Methods Arrad Ghani Safitra; Lohdy Diana; Lovyta Putri Adianti; Naja Imama Mufidah
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

An energy audit is a systematic assessment conducted to evaluate energy consumption and identify opportunities for improving energy efficiency in buildings. This study evaluates electrical energy consumption in three computer laboratories of the D3 PENS Building using the Energy Consumption Intensity (ECI) and Cooling Load Temperature Difference (CLTD) methods. The analysis was conducted based on measurements of room dimensions, indoor environmental conditions, electrical energy consumption, and cooling loads. The ECI values obtained for laboratories JJ-204, JJ-101, and JJ-310 were 24.44, 18.17, and 19.91 kWh/m²/month, respectively, indicating relatively high energy consumption. The CLTD analysis indicated that the cooling load was affected by external and internal heat gains, particularly those generated by occupants and computer equipment. The combined application of CLTD and ECI provides an assessment of the relationship between cooling load and electrical energy consumption. Energy conservation measures were proposed through operational adjustments, air-conditioning maintenance, and refrigerant replacement. The replacement of R-22 with MC-22 was estimated to reduce electricity consumption by 15.40%, corresponding to potential monthly savings of IDR 476,887.95. The study was limited to three computer laboratories under normal operating conditions and did not involve dynamic building energy simulation.
Analisis Pengaruh CLAHE Preprocessing Terhadap Kinerja Algoritma VGG-16 Transfer Learning dalam Klasifikasi dan Prediksi Penyakit Mata Berbasis Citra Fundus Jose Julian Hidayat; Marlina
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Eye diseases such as cataracts, diabetic retinopathy, and glaucoma require early detection to prevent permanent vision impairment. Previous studies have applied Convolutional Neural Networks for fundus image classification; however, optimization of preprocessing using Contrast Limited Adaptive Histogram Equalization in VGG16-based transfer learning remains limited, requiring further evaluation of its impact. This study aims to analyze the effect of CLAHE on VGG16 performance for classifying four fundus image classes: cataract, diabetic retinopathy, glaucoma, and normal. The method compared VGG16 without preprocessing and VGG16 with CLAHE using pre-trained weights and fine-tuning on classification layers. Performance was evaluated using accuracy, precision, and recall. Results showed that accuracy increased from 0.8229 to 0.8520, with an improvement of 0.0291. Improvements were also observed in glaucoma recall and normal precision, indicating better recognition of low-contrast disease patterns. These findings demonstrate that CLAHE enhances contrast, highlights features, and supports deep learning model generalization for medical image classification. Unlike previous studies that primarily focused on improving model architectures, this study specifically evaluates the impact of CLAHE on the sensitivity and generalization capability of VGG16 in multiclass fundus image classification
Numerical Analysis of Seat Inclination Effect on Student Chair Structural Performance Using Finite Element Method Taufik Ramadhan Fitrianto; Bahtiar Rahmat; Nurhanifah Nurhanifah; Siska Anggiriani; Desy Mulyosari; Alfani Risman Nugroho
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Student chairs are commonly designed based on ergonomic dimensions without considering the structural implications of seat configuration. This study investigates the effect of seat inclination on the structural response of a mahogany student chair using the Finite Element Method (FEM). Two anthropometrically derived chair models—a flat seat and a 4° inclined seat—were developed based on student anthropometric data from the Polytechnic of Furniture and Wood Processing Industry Kendal. Static simulations were performed in Autodesk Inventor 2022 under a 2000 N vertical seat load and an additional backrest load equivalent to 40% of the seat load. Structural performance was evaluated using Von Mises stress, total deformation, and Factor of Safety (FoS). The flat-seat model produced a maximum stress of 16.94 MPa, a deformation of 1.118 mm, and a FoS of 2.755, whereas the 4° inclined-seat model yielded 23.25 MPa, 1.501 mm, and 2.007, respectively. Although the inclined seat increased stress concentration and deformation, both configurations remained structurally safe, indicating that a 4° seat inclination represents an acceptable trade-off between ergonomic improvement and structural reliability.
Optimalisasi Aliran Turbin Aksial yang Menghasilkan Daya dalam Sistem Siklus Rankine Organik Berdasarkan Studi Numerik Haolia Rahman; Fitri Wijayanti Wijayanti; Gun Gun Ramdlan Gunadi; Albait Malikul Izzat
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Low-capacity Organic Rankine Cycle (ORC) turbines play an important role in utilizing low- to medium-temperature heat sources. This study aims to investigate the effects of working-fluid inlet temperature and turbine rotational speed on the power output and efficiency of an axial ORC turbine, as well as to determine the operating conditions that provide optimal performance. A Computational Fluid Dynamics (CFD)-based numerical simulation was conducted using SolidWorks Flow Simulation. The turbine analyzed in this study was a single-stage axial turbine equipped with eight half-round blades and operated with R134a as the working fluid. Simulations were performed at an inlet pressure of 2 MPa with inlet temperatures of 80°C, 90°C, and 100°C, and rotational speeds of 1500 rpm and 3000 rpm. The evaluated parameters included mass flow rate, enthalpy change, torque, input power, output power, and turbine efficiency. The simulation results showed that increasing the rotational speed from 1500 rpm to 3000 rpm improved the turbine power output. The highest output power of 14,064 W was achieved at 100°C and 3000 rpm, while the highest efficiency of 10.68% was obtained at 80°C and 3000 rpm. These findings indicate that rotational speed has a significant influence on turbine performance, whereas higher inlet temperatures do not necessarily result in the highest efficiency. The results of this study provide a useful basis for optimizing the design and operating conditions of axial ORC turbines for low-temperature heat recovery applications.
Audit Data Leakage dan Evaluasi Model Machine Learning untuk Prediksi Capaian Pembelajaran Lulusan dalam Outcome-Based Education Priaji Januardi; Rujianto Eko Saputro; Fandy Setyo Utomo
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Outcome-Based Education (OBE) implementation requires continuous evaluation of graduate learning outcomes (CPL). While machine learning models are widely used for predicting academic performance, most studies overlook fundamental issues like data leakage and class imbalance. This study evaluates the impact of data leakage audits on the performance of four models (Logistic Regression, Random Forest, XGBoost, and Deep Neural Network) in predicting CPL. The novelty lies in applying a structured audit framework prior to model comparison. Experiments utilized 27,262 academic records with stratified cross-validation. Audit results proved that proxy features caused unrealistic performance (Accuracy 0.999). After removing leaked features, Logistic Regression achieved the highest discrimination stability (AUC 0.772), while DNN recorded the highest F1-score. Wilcoxon tests confirmed no statistically significant performance difference among the models (α=0.05). In conclusion, on leakage-free OBE data, simple linear models remain highly competitive and suitable as the foundation for early warning systems.
Multi-Response Optimization of Spark-Ignition Engine Performance Using Methanol–Butanol Ethanol Fuel Blends through Response Surface Methodology Dwi Khusna Khusna; Gatot Setyono; Navik Kholili; Alfi Nugroho
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Improving combustion efficiency while reducing fuel consumption remains a major challenge in the development of spark-ignition (SI) engines. Oxygenated alcohol fuels have attracted considerable attention because of their potential to improve combustion quality and energy conversion efficiency. However, previous studies have mainly focused on single or binary alcohol blends, whereas comprehensive optimization of ternary methanol–butanol–ethanol (MBE) blends under different engine operating conditions remains limited. This study investigated the combined effects of MBE blend ratio (9–17 vol.%), engine speed (4000–9000 rpm), and ignition timing (15–25°CA) on SI engine performance using Response Surface Methodology based on a Face-Centered Central Composite Design (FCCD). Power, specific fuel consumption (SFC), and thermal efficiency (TE) were selected as response variables, and twenty experimental runs were conducted to develop quadratic prediction models and determine the optimum operating condition through desirability-based multi-response optimization. The developed models exhibited excellent predictive capability, with coefficients of determination (R²) exceeding 0.99 and statistically insignificant lack-of-fit values. Engine speed was identified as the most influential factor affecting all responses. The optimum operating condition was obtained at 16.21 vol.% MBE, 7015.42 rpm, and 24.31°CA, producing 6.32 kW power, 0.499 kg/kW·h SFC, 22.44% thermal efficiency, and an overall desirability of 0.973. These findings suggest that FCCD-based multi-response optimization is an effective approach for improving SI engine performance using ternary MBE fuel blends.

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