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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) G-Tech : Jurnal Teknologi Terapan
Basit, Muhammad Abdul
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering

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Analisis Postur Kerja di UKM Ismail Ali Menggunakan Metode NBM, REBA, dan OWAS Basit, Muhammad Abdul; Ismiyah, Elly
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6727

Abstract

In Gresik Regency, there are SMEs such as Ismail Ali that sell products such as cement. In lifting and arranging cement, manual material handling is carried out. As a result, workers are at risk of back injury whose activities are not ergonomically appropriate, musculoskeletal disorders can arise. From these activities, the NBM analysis was carried out to determine the source of pain obtained on a Likter scale of 3 in the high risk level, the REBA method by involving the angle of the worker's posture obtained a score of 11 in the very high category, and the OWAS method by examining the effect of the load on the posture obtained a score of 3 in the high category. This research shows that lifting and arranging cement in Ismail Ali SMEs has a high risk of musculoskeletal disorders, seriously affecting health and work productivity. The results of the analysis using the NBM, REBA, and OWAS methods, found that non-ergonomic work positions cause serious injuries if not addressed immediately. Therefore, industries need to implement corrective measures such as scheduled breaks, stretching, improved work postures, worker rotation to reduce the risk of injury, increase efficiency, and prevent human error due to fatigue.
OPTIMIZATION OF SOFTWARE DEFECT PREDICTION USING CNN AND ADABOOST: ANALYSIS AND EVALUATION Basit, Muhammad Abdul; Setyanto, Arief; Hidayat, Tonny
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6405

Abstract

This study focuses on enhancing software defect prediction (SDP) by integrating Convolutional Neural Networks (CNN) with the AdaBoost algorithm. The PROMISE dataset was employed in this research, and data balancing was achieved using the SMOTE Tomek technique. With the help of AdaBoost, we were able to increase the prediction accuracy after building a complex CNN model to extract features from the da-taset. The AdaBoost model's hyperparameters were fine-tuned using GridSearch to find the best values for enhanced model performance. For the studies, we used StandardScaler to normalize the data after splitting it into training and testing groups with an 80:20 ratio. The ex-perimental results show that compared to the baseline method, SDP's accuracy is significantly improved when CNN, AdaBoost, and GridSearch hyperparameter tweaking are used together. Accuracy, pre-cision, recall, F1 score, MCC, and AUC were some of the measures used to assess the model's performance.
Co-Authors Arief Setyanto Elly Ismiyah TONNY HIDAYAT