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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Kappa Journal International Journal of Regional Innovation
Septem Riza, Lala
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Humanities Astronomy Computer Science & IT Economics, Econometrics & Finance Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization Fariza Abu Samah, Khyrina Airin; Athirah Ahmad, Nurul; Amilah Shari, Anis; Fakhira Almarzuki, Hana; Arafah, Zuhri; Septem Riza, Lala; Abdul Halim, Amir Haikal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4334-4343

Abstract

In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
The Application of Rasch Model to Analyse the Validity and Reliability of an Instrument for Reflective Thinking Skills on Topic of Wave-Particle Dualism Juandi, Tarpin; Kaniawati, Ida; Samsudin, Achmad; Septem Riza, Lala
Kappa Journal Vol 8 No 2 (2024): Agustus
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/kpj.v8i2.27049

Abstract

This study aims to analyse the validity and reliability of an instrument for assessing reflective thinking skills on the topic of wave-particle dualism in modern physics lectures using the Rasch model. The Rasch model was selected for its capability to provide a more in-depth analysis of item performance and respondent ability, as well as to identify misfitting or biased items. The research method employed is a descriptive quantitative approach, utilizing Winsteps software for data analysis. The sample consists of 36 students enrolled in modern physics lectures at a university in West Nusa Tenggara. The results indicate that the instrument has excellent item reliability (0.91) and excellent internal consistency (Cronbach's Alpha 0.86), although the respondent reliability falls into the weak category (0.62). The instrument's validity also meets the Rasch model's acceptance criteria, with infit MNSQ and outfit MNSQ values ranging from 0.5 to 1.5. Further analysis reveals that some items are misfitting and need revision to ensure fairness and consistency in measuring reflective thinking skills. These findings make a significant contribution to the development of more accurate and reliable assessment tools in physics education
Energy Management of a Low-Cost Power Meter using ESP8266 and PZEM-016 Syafri Syamsudin, Muhammad; Septem Riza, Lala; Rasim, Rasim
International Journal of Regional Innovation Vol. 4 No. 1 (2024): International Journal of Regional Innovation
Publisher : Inovbook Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52000/ijori.v4i1.97

Abstract

The burgeoning significance of the Internet of Things (IoT) lies in its capacity to configure interconnected environments and facilitate human-object interactions through collaborative services. This study proposes an efficient energy management approach leveraging cost-effective technologies like the ESP8266 microcontroller and the PZEM-016 Modbus RTU energy monitoring module. Tailored towards wireless connectivity, this solution is purposefully crafted for diverse sectors operating within constrained budgets, obviating the need for intricate infrastructure. A systematic deployment of the forward engineering research methodology is undertaken to discern the requisites and hurdles inherent in energy management. The amalgamation of ESP8266, PZEM-016, and the MQTT protocol, with RabbitMQ serving as a message broker, forges an efficacious framework for inter-device information exchange. The solution's instantiation entails the interconnection of power meter devices using the MQTT protocol, transmitting data in JSON format. The PZEM-016 sensor constitutes the crux, adeptly measuring voltage, current, frequency, and power with precision. Furthermore, the solution encompasses a prototype Smart Meter fortified with Wi-Fi connectivity to the internet, thus extending network coverage ubiquitously. Economic scrutiny reveals that the resultant power meter device costs less than 100 USD, competitively positioning it against analogous market offerings. This economically optimized design advocates for widespread adoption across multifarious sectors constrained by budgetary limitations, assuaging the complexities inherent in energy management through a trifecta of efficiency, reliability, and affordability.
Co-Authors Abdul Halim, Amir Haikal Achmad Samsudin Amilah Shari, Anis Arafah, Zuhri Athirah Ahmad, Nurul Fakhira Almarzuki, Hana Fariza Abu Samah, Khyrina Airin Ida Kaniawati Rasim, Rasim Syafri Syamsudin, Muhammad Tarpin Juandi