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All Journal Informatics for Educators and Professional : Journal of Informatics
Angga Dwi Firmanto
Politeknik Negeri Media Kretif
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Prediksi Kualitas Produk Manufaktur Semikonduktor Menggunakan Machine Learning Darusman Darusman; Aries Abbas; Angga Dwi Firmanto
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 1 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i1.3348

Abstract

The semiconductor manufacturing industry faces challenges in efficient and accurate product quality control. Traditional manual inspection methods are still used but have limitations in speed and accuracy. Therefore, the application of machine learning has become a potential solution to improve the prediction of semiconductor production outcomes. This study develops a predictive model using Logistic Regression, Decision Tree, Random Forest, XGBoost, Naïve Bayes, and Support Vector Machine (SVM) with various dataset partitioning techniques such as Normal Data (90:10), Oversampling (70:30), Undersampling (80:20 & 70:30), and Principal Component Analysis (PCA) (90:10). The dataset used is sourced from the Factory Manufacturing Semiconductor Test (FMST), comprising 1,567 samples and 591 features, with product quality test labels (Pass/Fail). The results show that XGBoost and Random Forest achieved the highest accuracy (0.95) on the normal dataset (90:10), while Naïve Bayes had the lowest performance (0.23) due to its limitations in handling datasets with a large number of features. The oversampling technique improved the performance of Decision Tree and Logistic Regression but reduced the accuracy of XGBoost due to the risk of overfitting. Meanwhile, undersampling was more effective for Decision Tree but decreased SVM performance. The application of PCA improved Logistic Regression accuracy to 0.81, proving that dimensionality reduction can enhance model efficiency. Further analysis shows that feature selection using F-score can optimize model performance by eliminating redundant features. This study concludes that XGBoost and Random Forest are the best models for predicting semiconductor manufacturing product outcomes, with broad potential applications in the industry.
Robot Pembersih Lantai Otomatis Berbasis Arduino Uno Darusman Darusman; Aries Abbas; Angga Dwi Firmanto
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 2 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Desember
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i2.3731

Abstract

The Arduino-based automatic floor cleaning robot is a system designed to assist in the floor cleaning process efficiently without human intervention. This system uses an ultrasonic sensor to detect obstacles and a dust sensor to measure and display the amount of dirt particles absorbed by the vacuum cleaner. The Arduino Uno is used as the main controller, regulating the robot's movement using DC motors and processing sensor data to be displayed on an LCD as a cleanliness indicator. This study employs a hardware programming method with an experimental approach, where the robot is tested in an environment with obstacles and dust particles as the main objects. Testing is conducted to evaluate navigation effectiveness and dust suction capability. The results show that the robot can operate automatically, avoid obstacles, and clean floors with a fairly high level of efficiency. This research contributes to the development of home automation technology by introducing a more efficient and autonomous floor cleaning robot solution. With this system, it is expected to improve room cleanliness without requiring direct human involvement.
Co-Authors Aries Abbas Darusman Darusman