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All Journal JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) INOVTEK Polbeng - Seri Informatika
Fauzan, Mochammad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Pengaruh Tekanan Injeksi terhadap Flow Length Material Polypropylene (PP) dengan Ketebalan Produk 1MM Widjaja, Hartono; Fauzan, Mochammad
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 5 No 1 (2023): Volume: 5 | Nomor: 1 | April 2023
Publisher : Pusat Penelitian, Pengembangan, dan Pemberdayaan Masyarakat (P4M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v5i1.111

Abstract

Injection molding is the injection of melted plastic material into a mold to form a product. A good formulation of the injection process is affected by several factors, factors that affect the injection process include the plastic melt temperature, product design, injection speed and injection pressure. This study examines the parameters that affect the flow length of the polypropylene (PP) material. This material was chosen because it is commonly used in life, such as food and beverage containers. This study aims to determine how far the flowability of PP plastic with a product thickness of 1 mm is based on the table of reasonable design values for the L/t ratio in the book Molding Simulation: Theory and Practice. This research method goes through several steps including, creating a cavity layout to form the research specimens, analysis of specimen molds, trial molds with calculated parameters and changing injection pressure parameters gradually, measuring and assessing the resulting flow length of the injection trial process, concluding the experimental results. The results of this study are expected to be taken into consideration when designing a product made from PP so as to minimize failures in the mold making process.
Computational Analysis of Student Stress on Social Media using Support Vector Machine and Latent Dirichlet Allocation Fauzan, Mochammad; Ashaury, Herdi; Ramadhan, Edvin
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8jcvxk45

Abstract

This study develops a two-stage machine-learning framework to identify academic stressors among Indonesian university students using Twitter data. A Support Vector Machine (SVM) classifier was trained on manually annotated tweets and benchmarked against Naïve Bayes, logistic regression, and random forest, achieving an accuracy of 0.91 and a macro F1-score of 0.914, outperforming all baselines. Tweets classified as stress-related with ≥75% confidence were subsequently analyzed using Latent Dirichlet Allocation (LDA), which generated six coherent stressor categories. The framework reveals both structural academic pressures and culturally specific patterns, including references to “dosen killer” and emerging mental-health vocabulary. Contributions include the first Indonesia-focused stressor map derived from large-scale social media discourse and the integration of confidence filtering to enhance topic quality. While results demonstrate the feasibility of social-media–based stress detection, limitations remain regarding temporal drift, annotation bias, and demographic representativeness. Future research should incorporate real-time streaming pipelines, multimodal annotation, and longitudinal evaluation to enhance robustness and early-warning potential.
Co-Authors Ashaury, Herdi Edvin Ramadhan Widjaja, Hartono