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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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Prediksi Diabetes menggunakan Metode Ensemble Learning dengan Teknik Soft Voting Hilmi Hanif; Danang Wahyu Utomo
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2534

Abstract

Diabetes is a chronic disease characterized by high blood glucose levels due to the body's inability to produce or use insulin effectively. This disease is one of the serious global health problems, and it has a significant impact; therefore, early detection is very important. Efforts to overcome this challenge can be made by applying machine learning, which provides a new and effective approach. This study aims to predict diabetes with a higher accuracy level through the Ensemble Learning Soft Voting method. In addition, the data balancing technique using SMOTE is applied to overcome the problem of imbalance in the data set. This study also compares various classification models using Machine Learning algorithms, namely LightGBM, XGBoost, and Random Forest. The test results show that the Random Forest model achieves the highest level of accuracy at 97.20%. In comparison, the Ensemble Learning Soft Voting method that combines the three algorithms has increased the accuracy to 97.74%. This Ensemble Learning approach has proven effective in significantly improving predictions and performing better than a single model.
Deteksi Dini Gangguan Kesehatan Mental dengan Model Bert dan Algoritma Xgboost Rahmadika Putri Tresyani; Wahyu Utomo, Danang; Maldini, Naufal
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2535

Abstract

Mental health disorders are severe conditions that affect a person's thoughts, feelings, behavior, and well-being. Data from the World Health Organization (WHO) shows that more than 264 million people worldwide experience depression, one of the most common forms of mental health disorders. However, limited access to psychological services, such as lack of professionals and high costs, are major challenges in providing adequate support. Therefore, innovative technology-based solutions are needed for efficient and affordable psychological support. Efforts to improve research results to develop a mental health chatbot model by combining BERT (Bidirectional Encoder Representations from Transformers) and XGBoost (Extreme Gradient Boosting) models. The BERT model is used to understand the context of the conversation, while the XGBoost algorithm is used for text classification. The dataset used comes from Kaggle, which consists of 312 question patterns with several patterns or classes, namely 79 classes. The results of the program implementation test produced a percentage of 93.05% and output in the form of a program in the execution of the model on Google Colab..
Pengaruh Metode Penyayatan dan Kedalaman Penyayatan terhadap Dimensi dan Kekasaran Permukaan Kayu Olahan Rahmat, Bahtiar; Purwanto, Agung Ari; Fahrudin, Muhammad; Widiyanto, Wahyu
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2537

Abstract

The application of CNC technology in the furniture industry has already become familiar, in addition to its use in the metal and plastic industries. HMR (High Moisture Resistant) panels, which had better moisture resistance than MDF (Medium Density Fiberboard), had also started to develop. However, few studies have investigated the effects of variations in machining parameters on the cutting quality of HMR boards. This study aimed to test the effects of machining parameters (cutting method and cutting depth) on the dimensions and surface roughness of the workpiece. Four schemes of machining parameter settings, namely conventional and climb methods with cutting depths of 2 mm and 4 mm, respectively, were performed with three repetitions each. After testing, it was found that the cutting method, cutting depth, and the interaction between the cutting method and cutting depth had not significantly affected the length and width dimensions of the specimen. However, the cutting method significantly influenced the final surface roughness of the specimen. The conventional cutting method with a cutting depth of 2 mm produced the best surface roughness, measuring 26.47 µm.
Pengaruh Variasi Kampuh Las dan Arus Listrik Terhadap Kekuatan Sambungan Las (Metal Inert Gas) MIG Pada Aluminium AA6063 Asrul
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2548

Abstract

Aluminium has beneficial characteristics such as corrosion resistance, heat conductivity, electrical conductivity, and lightweight. However, when looking at steel materials, aluminum has unfavorable welding properties. The selection of welding amperage and additives compatible with the parent metal can influence welding joints. The purpose of this study was to determine the effect of the strength of MIG welding joints of AA6063 aluminum alloy on the variation of groove welding with electric current. The test data shows a value equal to 1. The highest tensile test value using X camphor at an electric current of 150 amperes is 14.05 kg/mm², and the lowest tensile stress value with K camphor at a current of 155 amperes is 6.75 kg/mm². 2. The test results of the highest hardness value with the V shank at a current of 160 amperes, namely 87.09 HRB in the weld metal area, then the HAZ area decreased in hardness value, namely 76.33 HRB, and the parent metal area increased again in value, namely 78.80 HRB and 3. These results show that welding with an X shank is better than a V shank and a K shank based on the electric current in the test results.
Improving Cervical Cancer Classification Using ADASYN and Random Forest with GridSearchCV Optimization Saputra, Resha Mahardhika; Alzami, Farrikh; Pramudi, Yuventius Tyas Catur; Erawan, Lalang; Megantara, Rama Aria; Pramunendar, Ricardus Anggi; Yusuf, Moh.
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2552

Abstract

Cervical cancer is a leading cause of death among women, with over 300,000 deaths recorded in 2020. This study aims to improve the accuracy of cervical cancer diagnosis classification through a combination of Adaptive Synthetic Sampling (ADASYN) and Random Forest algorithm. The research data was obtained from the Cervical Cancer dataset in the UCI Machine Learning Repository with an imbalanced data distribution of 95% negative class and 5% positive class. ADASYN method was chosen for its ability to handle imbalanced data by focusing on minority data points that are difficult to classify. The Random Forest algorithm was optimized using GridSearchCV to achieve maximum performance. Results show that this combination improved accuracy from 96.5% to 96.8% and recall from 93.7% to 94.3%. Feature importance analysis identified key risk factors such as number of pregnancies, age at first sexual intercourse, and hormonal contraceptive use that significantly influence diagnosis. This research demonstrates the effectiveness of combining ADASYN and Random Forest in enhancing classification performance for early cervical cancer detection.
Perbandingan Random Forest dan K-Nearest Neighbors untuk Klasifikasi Body Mass Index Menggunakan SMOTE-ENN untuk Mengatasi Ketidakseimbangan Data pada Analisis Kesehatan Naufal Yogi Aptana; Ikhsan, Ali Nur; Maulana Baihaqi, Wiga; Ajeng Widiawati, Chyntia Raras
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2553

Abstract

This study aims to compare the Random Forest and K-Nearest Neighbors (KNN) algorithms in Body Mass Index (BMI) classification using the SMOTE-ENN method to address data imbalance. The dataset consists of 2111 entries with demographic and health attributes of individuals. Data imbalance poses a significant challenge that may affect the accuracy of machine learning models. The SMOTE-ENN combination was employed to improve data distribution, enabling models to recognize patterns in minority classes better. Key evaluation factors included both algorithms' accuracy, precision, recall, and F1-score. Results indicate that the Random Forest algorithm achieved higher performance with 100% accuracy than KNN with 96% after applying SMOTE-ENN. These findings highlight the unique contribution of SMOTE-ENN in handling imbalanced data, enhancing classification model quality, and significantly impacting machine learning applications in healthcare.
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Sari, Laura; Faiz, Muhammad Nur; Muhammad, Arif Wirawan
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Optimasi Algoritma K-Nearest Neighbors Menggunakan GridSearchCV untuk Klasifikasi Penyakit Diabetes Yaqin, Ainul; Kurniawan, Defri; Zeniarja, Junta
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2557

Abstract

Diabetes is a chronic disease that has a significant impact on global health, with prevalence increasing every year. Therefore, early detection is crucial to prevent further complications and save lives. The utilization of technology, such as machine learning, offers innovative solutions to improve the accuracy of predicting this disease. This research develops a diabetes prediction model using the K-Nearest Neighbors (KNN) algorithm with the Pima Indians Diabetes Database dataset. Given the class imbalance in the dataset, Random Over-Sampling technique was applied to balance the data distribution. The results showed that the KNN model optimized with GridSearchCV resulted in 88% accuracy, 89% precision, 75% recall, and 82% F1-score. This approach is expected to produce a more accurate and efficient model to support early detection of diabetes, and shows the great potential of machine learning technology in improving the effectiveness of disease diagnosis and control.
Rancang Bangun Angklung Elektrik dengan Mode Otomatis dan Manual Menggunakan Teknologi Mikrokontroler dan Smartphone Nur Hanafi, Ilham; Supriyono, Supriyono; Susanti , Hera
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2558

Abstract

Angklung is a traditional Indonesian musical instrument originating from West Java. Angklung is composed of two to four bamboo tubes tied with rattan ropes and played by shaking them. The existence of angklung is currently starting to be replaced by modern musical instruments. This research aims to produce angklung integrated with microcontroller and smartphone technology. The use of microcontroller technology allows angklung to be automated without changing the character of the original art. This research method uses a quantitative approach from the design stage to the final test. The research showed that angklung successfully played songs automatically and manually. The tone suitability test results reach 100%. The sound intensity test recorded an average of 86.9 dB in automatic mode and 88.2 dB in manual mode. The power consumption test shows power usage of 1,378 Watts in automatic mode and 1,461 Watts in manual mode.
Optimalisasi Sistem Informasi Layanan Keuangan dengan Metode First Come First Served (FCFS) Purwanto, Riyadi; Dwi Novia Prasetyanti; Cahya Vikasari; Rostika Listyaningrum
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2562

Abstract

One of the important things in financial management is financial services. Cilacap State Polytechnic (PNC) is a PTN as a Work Unit. Financial management at PNC refers to applicable government regulations, but financial service policies are the authority of the leadership as a strategy to create fast, effective, and accountable financial services. The budget usage policy stipulates that work units must propose budget usage based on the Fund Withdrawal Plan through a down payment form approved by the Financial Management Officer. The current problem is that down payment forms are often scattered or lost, so the response time for payments from finance is slow, and payment queues are often not in sequence. This results in activities being hampered. This research aims to create a prototype or development design for the Financial Services Information System at PNC. The FCFS method is used as a service scheduling algorithm based on arrival time to optimize financial services. In this way, the financial service process will be more organized, the response time for financial services will be faster, and the queue for service requests will be in line.

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