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All Journal Jurnal Teknologi Informasi dan Multimedia International Journal of Social and Education (INJOSEDU)
Shafirah Fitri
Institut Teknologi dan Bisnis Bina Sarana Global
Computer Science & IT Social Sciences

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BODY LANGUAGE IN BUSINESS NEGOTIATIONS: STRENGTHS AND WEAKNESSES Melyana R Pugu; Nyi Dewi Puspitasari; Shafirah Fitri
INTERNATIONAL JOURNAL OF SOCIAL AND EDUCATION Vol. 1 No. 1 (2024): April
Publisher : Pondok Pesantren Baitul Quran

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Abstract

This research discusses the influence of body language in business negotiations, with an emphasis on analyzing the strengths and weaknesses of using body language as a non-verbal communication tool. The research was conducted through a literature review method, which involved collecting data from various relevant journal articles, books and other publications on the subject. The results show that body language consistently plays a vital role in supporting the effectiveness of business negotiations, where non-verbal cues such as eye contact, gestures, and facial expressions can reinforce verbal messages and foster trusting relationships. However, the study also revealed that cultural differences and potential misunderstandings can be a drawback in the application of body language, often leading to conflict and inaccurate perceptions of prosperity. The recommendations from this study conclude that training and awareness on multi-cultural body language is an important aspect that needs to be integrated in business negotiation practices in order to maximize the strengths and mitigate the weaknesses of body language.
Perbandingan Metode Naïve Bayes dan Random Forest dalam Memprediksi Penyakit Diabetes Melitus pada Klinik Citra Sejati Mohammad Radja Alyfa Amri; Egi Permana; Pramana Anwas Pachadria; Shafirah Fitri
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.747

Abstract

Diabetes mellitus is a chronic disease with a steadily increasing prevalence in Indonesia and is one of the leading causes of death, particularly in urban areas. Early detection of this disease is crucial to prevent serious complications such as heart disease, kidney failure, and vision impairment. In the era of digital transformation, machine learning techniques offer great potential to support early and automated diagnosis with higher accuracy. This study aims to develop a diabetes prediction system based on medical record data using two machine learning algorithms: Naïve Bayes and Random Forest. The dataset was obtained from Klinik Citra Sejati, consisting of 266 patient records with seven clinical features: age, gender, leukocytes, platelets, hematocrit, erythrocytes, and erythrocyte sedimentation rate (ESR). The models were implemented using Python programming language and the Scikit-learn library. Performance evaluation was carried out using the confusion matrix and classification metrics such as accuracy, precision, recall, and F1-score. Furthermore, ROC curve analysis and 95% confidence interval calculation were used to assess the stability and reliability of the predictions. The results showed that the Random Forest algorithm achieved an average accuracy of 89.97% with an AUC of 0.93, while Naïve Bayes achieved an accuracy of 85.97% with an AUC of 0.72. Based on these results, Random Forest is considered more effective for diabetes classification and is recommended as the primary algorithm for the development of clinical decision support systems based on local medical data.
Co-Authors Egi Permana Melyana R Pugu Mohammad Radja Alyfa Amri Nyi Dewi Puspitasari Pramana Anwas Pachadria