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All Journal INFOKUM International Journal Software Engineering and Computer Science (IJSECS)
Ineke Pakereng, Magdalena A.
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Computer Science & IT

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FORECASTING DRUG SALES AFTER THE COVID-19 PANDEMIC IN PT. GRATIA HUSADA FARMA USING ARIMA METHOD Airlangga, Radithya; Ineke Pakereng, Magdalena A.
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v10i5.1188

Abstract

Forecasting is the initial stage in production planning and serves as a foundation for subsequent production activities. The advent of the Covid-19 pandemic in Indonesia began in 2020, causing unstable sales. The ARIMA or Autoregressive Integrated Moving Average method is a medium-term forecasting method that generates relatively good prediction values compared to other methods. PT Gratia Husada Farma is a company active in the pharmaceutical field in Central Java. The sales data possessed by PT GHF is not yet optimally utilized and only serves as historical records. Through the obtained data, a sales forecasting analysis is conducted using the ARIMA method with parameters p, d, q of 1, 0, 1. Evaluation of the method using the RMSE formula results in a value of 2.229 (0.02229). This shows that the ARIMA method analysis has an acceptable error value, less than one of the critical values or alpha of 0.05.
Analysis of Gender Inequality in Artificial Intelligence-Based Recruitment Systems: A Systematic Literature Review (SLR) Kumalasari, Herdaning Sandra; Ineke Pakereng, Magdalena A.
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6746

Abstract

The increasing adoption of Artificial Intelligence (AI) in recruitment has raised concerns about algorithmic discrimination that may disadvantage certain groups, particularly women. This study analyzed gender inequality in AI-based recruitment systems by synthesizing evidence from both technical and ethical perspectives. A Systematic Literature Review (SLR) was conducted on studies published between 2020 and 2025, applying predefined inclusion and exclusion criteria, followed by screening, quality assessment, and thematic synthesis. The review retained 10 studies (n = 10) that met the eligibility and quality threshold. Historically imbalanced training data emerged as the most frequently reported driver of gender bias, often producing unfair screening, ranking, and selection outcomes. Fairness conclusions were found to depend strongly on how recruitment outcomes were defined and measured, and prior studies consistently called for multiple fairness metrics supported by auditing practices. The literature also identified mitigation strategies spanning data balancing, fairness-aware model evaluation, transparency and audit mechanisms, and human oversight in decision-making. Gender bias in AI-based recruitment is, at its core, a socio-technical problem that requires combined interventions across data governance, model evaluation, and organizational accountability, while research gaps remain for future empirical validation and responsible AI deployment.
Co-Authors Airlangga, Radithya Kumalasari, Herdaning Sandra