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All Journal Al-Hijr: Journal of Adulearn World Journal International of Lingua and Technology Journal International Inspire Education Technology (JIIET) World Psychology Journal Markcount Finance Journal International Dakwah and Communication International Journal of Educational Narratives Rechtsnormen Journal of Law Sharia Oikonomia Law Journal Journal Emerging Technologies in Education Journal of World Future Medicine, Health and Nursing Darussalam: Journal of Psychology and Educational Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Social Entrepreneurship and Creative Technology
Jie, Lie
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : World Psychology

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Risk Driving Review from Big Five Personality Chrisnatalia, Maria; Jie, Lie; Jixiong, Cai; Wei, Zhang
World Psychology Vol. 2 No. 2 (2023)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/wp.v2i2.113

Abstract

Accidents that occur due to drivers who tend to drive with a high risk. Vehicle users tend to drive recklessly and pass-through traffic which is called the risk of driving, this is influenced by internal and external factors. The purpose of this study is to look at the risk of driving in terms of five personalities and which personality types tend to lead to driving. The technique used is quantitative, with a sample of 100 participants. The sampling technique in this study was purposive sampling. How to collect data using google forms. Data analysis technique with simple regression test. Based on the hypothesis test, it was found that there is a relationship between agreeableness and neuroticism personality types on the risk of driving and Constiusness, Openess to experience and Extraversion have no relationship with the risk of driving. These results mean that the proposed hypothesis is accepted.
Early Detection of Developmental Disorders Through Machine Learning Algorithm Judijanto, Loso; Zou, Guijiao; Zani, Benny Novico; Efendi, Efendi; Jie, Lie
World Psychology Vol. 3 No. 3 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/wp.v3i3.710

Abstract

Machine learning algorithms have the ability to analyze huge amounts of data and discover patterns that may not be visible to humans. Machine learning offers new hope for faster, more accurate, and cheaper screening for early detection of developmental disorders. This research was conducted with the aim of developing an effective and efficient machine learning algorithm for analyzing child development data. Apart from that, it is also to identify the most relevant features and indicators for the detection of early developmental disorders. The method used by researchers in researching the Detection of Developmental Disorders through Machine Learning Algorithms is to use a quantitative method. The data obtained by researchers was obtained from the results of distributing questionnaires. The distribution of questionnaires carried out by researchers was carried out online using Google From software. The results of data acquisition will also be tested again using the SPSS application. From the research results, it can be seen that this research is expected to produce a model that is not only accurate, but can also be implemented in the wider health system to provide maximum benefits for society. And can improve children's health by enabling faster detection and intervention. Ultimately, this may improve long-term outcomes for children with developmental disorders. From this study, researchers can conclude that with advances in information technology, machine learning-based applications can be accessed via mobile devices and online platforms, allowing initial screening to be carried out easily by parents and educators, even before consulting a medical professional. In recent years, machine learning (ML) technology has shown that it has enormous potential for application in various fields, including health and medical care.
Co-Authors Abdul Karim Adawiyah, Siti Lutfi Robi’atul Ali Mufron, Ali Amelia Amelia Amelia Putri, Alya Rizka Andni, Riyan Anur, Homuud Ardenny, Ardenny Arifah, Ayunda Nur Azahra, Yunisa Budi Nugroho Cale, Wolnough Cale, Woolnough Chrisnatalia, Maria DAMANIK, INDRA JAYANTI Deannopel, Nesha Kirani Demsky, Caitlin Efendi Efendi Ekaputri, Najla Tahani Fikri, Muammar Al Firdaus Yuni Dharta Guilin, Xie Hartin Kurniawati Honda, Zein Iksal Yanuarsyah Imun Maemunah, Imun Intes, Amina Jamilah, Cucu Jiao, Deng Jixiong, Cai Johanna, Anne Jonathan, Bouyea Judijanto, Loso Khaidir, Widya Magfiroh, Magfiroh Nafsi, Farichatun Nida, Rahma Sofia Nitin, Mahon Nofa, Yosi Nurussa'adaturrahmah, Nurussa'adaturrahmah Purba, Benarita Qian, Shodiq R, Muh Rafli Rahayu, Wedi Pratanto Ramadani, Safti Witria Reviandari Widyatiningtyas, Reviandari Samir, Katayama Sari, Roza Muspita Sa’diyah, Fatichatus Setyawan, Gogor Christstmass Sri Ariyanti Syamsu Rijal Syamsu, Muhajir Teresia, Vany Thomas, Defoort Trimasary, Ima Tuharea, Jumiati Vanchapo, Antonius Rino Xu, Shanshan Yuanyuan Wang, Yuanyuan Z, Sri Putri Rahayu Zahro, Zahro Zani, Benny Novico Zhang Wei Zou, Guijiao