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All Journal Jurnal Smart Keperawatan Engineering, Mathematics and Computer Science Journal (EMACS)
Annas, Taufiq
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics Nursing

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Hubungan Tingkat Stress Dengan Kepuasan Kerja Pada Masinis PT Kereta Api Indonesia Di UPT Crew Semarang Poncol Annas, Taufiq; Nurhayati, Susi
Jurnal Smart Keperawatan Vol 3, No 1 (2016): Juni 2016
Publisher : Sekolah Tinggi Ilmu Kesehatan (STIKes) Karya Husada Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34310/jskp.v3i1.459

Abstract

Tingkat  stres kerja berlebihan  dapat berdampak  negatif  terhadap  prestasi  kerja karyawan  PT. KAI yang pada akhirnya dapat merugikan perusahaan. Apalagi jika stres kerja tersebut berada dalam taraf tinggi tentu akan  memberikan  dampak  negatif.  Tujuan  penelitian  ini adalah  mengetahui  hubungan  antara  stress kerja dengan kepuasan kerja pada masinis PT Kereta Api Indonesia di UPT Crew Semarang Poncol. Jenis  penelitian  adalah  korelasi  dengan  pendekatan  cross  sectional.  Populasi  dalam  penelitian  ini adalah seluruh masinis PT Kereta Api Indonesia UPT Crew Semarang Poncol sejumlah 223 orang. Teknik sampling yang digunakan adalah accidental sampling dengan jumlah 143 orang. Hasil  penelitian  didapatkan  bahwa  tingkat  stres kerja yang dialami  oleh responden  sebagian  besar adalah tinggi yaitu sebanyak  67,1%. Kepuasan  kerja responden  sebagian  besar dalam kategori  tidak puas yaitu sebanyak 78 orang (54,5%). Ada hubungan antara tingkat stress dengan kepuasan kerja pada masinis PT Kereta Api Indonesia di UPT Crew Semarang Poncol.Berdasarkan  hasil tersebut  PTKAI diharapkan dapat menambah jumlah masinis dengan pola shift kerja yang longgar sehingga masinis memiliki waktu isirahat yang cukup untuk mengurangi stress akibat jadwal kerja yang padat. Kata kunci : Stres Kerja, Kepuasan Kerja. STRESS LEVEL RELATIONSHIP WITH JOB SATISFACTIONAT INDONESIAN PT TRAIN ENGINEERING AT UPT CREW SEMARANG PONCOL Excessive work stress levels can have a negative impact on employee performance at PT. KAI which in the end can harm the company. Moreover, if the work stress is at a high level, it will certainly have a negative impact. The purpose of this study was to determine the relationship between job stress and job satisfaction at PT Kereta Api Indonesia machinists at UPT Crew Semarang Poncol. This type of research is correlation with cross sectional approach. The population in this study were all engineers of PT Kereta Api Indonesia UPT Crew Semarang Poncol totaling 223 people. The sampling technique used is accidental sampling with a total of 143 people. The results showed that the level of work stress experienced by most of the respondents was high as much as 67.1%. Most of the respondents' job satisfaction was in the dissatisfied category as many as 78 people (54.5%). There is a relationship between stress levels and job satisfaction at PT Kereta Api Indonesia's machinists at UPT Crew Semarang Poncol. Based on these results PTKAI is expected to increase the number of drivers with a loose work shift pattern so that the machinists have sufficient rest time to reduce stress due to a busy work schedule. . Keywords: Job Stress, Job Satisfaction.
Advancing Indonesian Audio Emotion Classification: A Comparative Study Using IndoWaveSentiment Majiid, Muhammad Rizki Nur; Setiawan, Karli Eka; Pamungkas, Prayoga Yudha; Annas, Taufiq; Setiawan, Nicholas Lorenzo
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13415

Abstract

This study addresses the critical gap in Indonesian Speech Emotion Recognition (SER) by evaluating machine learning models on the IndoWaveSentiment dataset, a novel corpus of 300 high-fidelity recordings capturing five emotions (neutral, happy, surprised, disgusted, disappointed) from native speakers. The research aims to identify optimal classification techniques and acoustic features for Indonesian SER, given the language’s unique linguistic characteristics and the scarcity of annotated resources. Six models, Logistic Regression, KNN, Gradient Boosting, Random Forest, Naive Bayes, and SVC, were trained on 45 acoustic features, including spectral contrast, MFCCs, and zero crossing rate, extracted using Librosa. Results demonstrated Random Forest as the top performer (90% accuracy), followed by Gradient Boosting (85%) and Logistic Regression (75%), with spectral contrast (contrast2, contrast7) and MFCC1 emerging as the most discriminative features. The findings highlight the efficacy of ensemble methods in capturing nuanced emotional cues in Indonesian speech, outperforming prior studies on locally sourced datasets. Practical implications include applications in customer service analytics and mental health tools, though limitations such as the dataset’s-controlled conditions and fixed sentence structure necessitate caution in real-world deployment. Future work should expand the dataset to include regional dialects, spontaneous speech, and hybrid architectures like CNN-LSTMs. This study establishes foundational benchmarks for Indonesian SER, advocating for culturally informed models to enhance human-computer interaction in underrepresented linguistic contexts.
Co-Authors Majiid, Muhammad Rizki Nur Pamungkas, Prayoga Yudha Setiawan, Karli Eka Setiawan, Nicholas Lorenzo Susi Nurhayati