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All Journal Gema Wiralodra Jurnal Media Informatika Journal of Artificial Intelligence and Digital Business Jurnal Manajemen, Akuntansi dan Bisnis
Sitti Mawaddah Umar
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Manajemen Sumber Daya Manusia (SDM) Upaya Peningkatan Kinerja Karyawan Di Era Globalisasi Sewang; Sitti Mawaddah Umar; Yusuf, Deddy; Kasim, Hasanudin
JUMABI: Jurnal Manajemen, Akuntansi dan Bisnis Vol 2 No 2 (2024): JUMABI : JULI
Publisher : CV. Eureka Murakabi Abadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56314/jumabi.v2i2.232

Abstract

Di era globalisasi, perusahaan menghadapi tantangan yang semakin kompleks dalam mengelola sumber daya manusia (SDM) untuk meningkatkan kinerja karyawan. Penelitian ini mengeksplorasi berbagai strategi manajemen SDM yang dapat diterapkan untuk meningkatkan kinerja karyawan di tengah perubahan global. Fokus utama terletak pada adaptasi terhadap teknologi baru, pengembangan keterampilan, dan peningkatan motivasi karyawan melalui program pelatihan dan pengembangan. Selain itu, pentingnya budaya organisasi yang inklusif dan fleksibel diuraikan sebagai faktor kunci dalam menciptakan lingkungan kerja yang produktif. Studi ini juga menyoroti peran kepemimpinan dalam memfasilitasi perubahan dan inovasi serta pentingnya komunikasi efektif dalam mempertahankan kinerja tinggi di era globalisasi. Temuan ini diharapkan dapat memberikan panduan praktis bagi manajer SDM untuk merancang dan menerapkan kebijakan yang mendukung peningkatan kinerja karyawan secara berkelanjutan.
The Behavior of Medical Record Officers at Mamuju Tengah General Hospital Marhawati, Marhawati; Umar, Sitti Mawaddah; Wahyuningsih, Sri
Gema Wiralodra Vol. 16 No. 1 (2025): Gema Wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v16i1.792

Abstract

The management of medical records at Mamuju Tengah General Hospital in 2016 was still conducted manually, with the completeness of medical records ranging from 60-80%. This study aims to describe the behavior of medical record officers at the hospital. Using a descriptive quantitative method, a sample of 38 medical record officers was selected using a saturated sampling technique. The research instrument used was a structured questionnaire that had been tested for validity and reliability. The type of data collected consisted of primary data obtained directly from respondents’ answers, and secondary data in the form of hospital documentation related to medical records management. Data analysis was performed using univariate descriptive statistics, presented in frequency distributions and percentages. The results showed that the knowledge of the officers (52.6%) and their actions (63.2%) were still inadequate. Additionally, the facilities and infrastructure in the medical records installation were also lacking (76.3%). However, the officers' perceptions of the standard operating procedures (65.8%) were considered good. It is recommended that the officers receive training, improve the facilities and infrastructure, and enhance the socialization of SOPs to improve the quality of medical record management.
Development of a Motor Vehicle Rearview Image Pattern Recognition System for Detection of Traffic Flow Violations on One-Way Roads: Image processing Sitti Mawaddah Umar; Justam, Justam
Jurnal Media Informatika Vol. 6 No. 3 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i3.6052

Abstract

This study aims to detect traffic violations, specifically motorcycles riding against the flow on one-way roads, by utilizing computer vision technology to recognize the rearview patterns of vehicles. The method employed involves applying the deep learning model Faster-RCNN for object detection, using image data captured from an IP camera mounted on a pole at a height of 2.5 meters with a 45-degree tilt angle to optimally monitor vehicles from behind. Image labeling was performed using the LabelImg application, while model training and classification were conducted using the TensorFlow framework. The developed system achieved a detection accuracy of 88%, demonstrating the effectiveness of this approach in identifying motorcycles violating traffic direction. These findings highlight the potential of computer vision as an automatic and real-time solution for traffic monitoring, which can help reduce dangerous violations and enhance road safety. Therefore, this research contributes significantly to the development of more advanced and efficient traffic violation detection systems.
Pemetaan Lanskap Emosional Di Twitter: Visualisasi Sentimen Netral, Positif, dan Negatif Dengan Word Cloud mawaddah, Sitti; Naswin, Ahmad; Sulkifli, Sulkifli
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3650

Abstract

Penelitian ini mengkaji penerapan teknik Word Clouds dalam ekstraksi sentimen pada platform Twitter, dengan fokus pada identifikasi kata dominan dalam cuitan yang dikategorikan sebagai netral, positif, dan negatif. Teknik visualisasi ini memungkinkan pemahaman yang lebih mendalam mengenai distribusi kata dan peranannya dalam representasi sentimen. Melalui Exploratory Data Analysis (EDA), penelitian ini berhasil memetakan karakteristik data, seperti tingginya kemiripan Jaccard untuk tweet netral dan pola distribusi panjang teks antara text dan selected_text. EDA juga mendasari penerapan aturan deterministik (rule-based gate) untuk menangani cuitan dengan kepastian tinggi dan membedakan kasus yang membutuhkan pendekatan berbasis model lebih lanjut, yaitu Named Entity Recognition (NER). Penerapan rekayasa fitur meta, termasuk pengukuran similarity Jaccard, selisih panjang kata, dan jumlah kata, berhasil meningkatkan presisi dalam pemetaan span sentimen pada teks mikro. Evaluasi model menggunakan similarity Jaccard menunjukkan performa yang sangat baik dengan akurasi 85%, diikuti oleh konsentrasi tinggi skor prediksi pada rentang 0,9–1,0. Temuan ini menegaskan bahwa pendekatan hibrida yang mengintegrasikan Word Clouds, analitik berbasis fitur, dan NER efektif dalam mengatasi tantangan kompleksitas bahasa informal di media sosial, seperti slang, elongation, dan ambiguity. Penelitian ini memberikan kontribusi signifikan dalam pengembangan metodologi sentiment extraction yang lebih presisi dan efisien untuk aplikasi Natural Language Processing (NLP) di platform sosial media
Co-Authors Hasanudin Kasim Justam, Justam Marhawati, Marhawati Naswin, Ahmad Raden Mohamad Herdian Bhakti Sewang Sri Wahyuningsih Yusuf, Deddy