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All Journal TEKNIK INFORMATIKA JURNAL MANAJEMEN (EDISI ELEKTRONIK) International Journal Software Engineering and Computer Science (IJSECS) Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Awang Long, Zalizah
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Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Constraint Clustering for Promotion Application: Central Java Case Study Maulindar, Joni; Awang Long, Zalizah; Che Mustapha, Jawahir; Purnomo, Singgih
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2586

Abstract

Organizations utilize Talent Management to enhance competitiveness, improve employee quality, develop potential, and retain talent. Talent management also plays a crucial role in the career development of lecturers. Promotions in the academic ranks and positions of lecturers in Indonesia are essential to consider, as they significantly impact the quality of lecturers, the accreditation value of higher education, and the global rankings of universities. In this study, a questionnaire was administered to 406 respondents. The results revealed six clusters correlated with the challenges of applying for functional lecturer positions. Based on the cluster analysis, Cluster 0 (20%) exhibited minimal obstacles, Cluster 1 (27%) faced highly challenging obstacles, Cluster 2 (13%) experienced neutral obstacles, Cluster 3 (15%) encountered manageable obstacles, Cluster 4 (18%) dealt with easily surmountable constraints, and Cluster 5 (7%) experienced significant hurdles. Future research could explore implementing a new talent management model, particularly for lecturers who need help applying for functional positions.
Predicting Indonesia’s Micro Small Medium Entreprises Stock Price Purnomo, Singgih; Nurmalitasari, Nurmalitasari; Nurchim, Nurchim; Awang Long, Zalizah
Jurnal Manajemen (Edisi Elektronik) Vol. 16 No. 1 (2025): Jurnal Manajemen (Edisi Elektronik)
Publisher : UPT Jurnal & Publikasi Ilmiah SPs Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/jm-uika.v16i1.17762

Abstract

The impact of stock prices on new enterprises, notably Micro, Small, and Medium Enterprises (MSMEs), in Indonesia is significant. Given the significance of stock prices for MSMEs, engaging in stock price forecasting is crucial. Several stock price forecast-ing models exist, but only a limited number are suitable for predicting stock prices using limited samples, such as the stock prices of MSMEs in Indonesia. The limited sample size is because MSMEs are newly established enterprises accessing stock prices. This study aims to predict MSME stock prices in Indonesia, namely SOUL and TGUK. The forecasting model utilized is ARIMA. The results suggest that the ARIMA (0,1,1) model provides the most precise forecast for the stock price of SOUL MSMEs, while the ARIMA (1,1,2) model yields the most outstanding performance for TGUK. Investors can use the forecast results to identify profit-able investment opportunities or protect their portfolios from po-tential losses. Moreover, companies can employ stock price pre-dictions to evaluate their performance, develop financial plans, and allocate resources.
Challenges in The Academic Promotion Process: Perspectives From Faculty Members Maulindar, Joni; Awang Long, Zalizah; Che Mustapha, Jawahir; Purnomo, Singgih
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4127

Abstract

The academic promotion process faces various challenges that cause delays for faculty members in reaching higher ranks. This research aims to identify and analyze the factors contributing to delays in the academic promotion process. The research method used is a quantitative approach, with data collection techniques involving the distribution of questionnaires to faculty members who are currently undergoing or about to undergo the academic promotion process. The research results indicate that the lack of transparency in rules, policy changes, evaluation complexity, communication limitations, and institutional support all have a significant and equal impact on the challenges of academic promotion, with each factor having a coefficient of 0.2000. The R-squared and Adjusted R-squared values of 1.000 indicate that this model can explain the entire variation in academic promotion challenges. The high statistical significance of all coefficients suggests that these results are almost certainly not due to chance. Data analysis also shows that there is little autocorrelation in the model's residuals, and the residual distribution is nearly normal. These findings highlight the importance of transparency, policy consistency, effective communication, and institutional support in the academic promotion process. Improvements in these areas are expected to reduce the challenges faced by faculty members during the promotion process
Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data Nurmalitasari, Nurmalitasari; Awang Long, Zalizah; Nurchim, Nurchim
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.43929

Abstract

Logistic regression is a model commonly used for predicting data with large sample sizes. However, in real-world scenarios, many cases involve small datasets that need to be addressed using logistic regression. The aim of this research is to develop a hybrid logistic regression model to address issues with small sample sizes by combining the Newton Raphson and Super Cubic methods. This hybrid model is applied to predict student dropout at Universitas Duta Bangsa Surakarta. The performance of the hybrid model is evaluated using two main metrics: the convergence of the parameter approximation to measure the precision of parameter estimation, and the ROC curve to assess prediction accuracy. Experimental results show that the Hybrid Logistic Super Newton model outperforms the logistic regression Newton Raphson model, requiring only three iterations to converge, thus improving computational efficiency. Moreover, this model achieves higher accuracy, with an AUC of 0.8833. These findings suggest that the developed model has the potential to be applied in various fields, such as healthcare, finance, and others, offering an effective solution for accurate, real-time predictive analytics. Further research could focus on optimizing the model’s computational efficiency and exploring its application in other domains with small dataset challenges, such as healthcare and finance.
Challenges in The Academic Promotion Process: Perspectives From Faculty Members Maulindar, Joni; Awang Long, Zalizah; Che Mustapha, Jawahir; Purnomo, Singgih
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4127

Abstract

The academic promotion process faces various challenges that cause delays for faculty members in reaching higher ranks. This research aims to identify and analyze the factors contributing to delays in the academic promotion process. The research method used is a quantitative approach, with data collection techniques involving the distribution of questionnaires to faculty members who are currently undergoing or about to undergo the academic promotion process. The research results indicate that the lack of transparency in rules, policy changes, evaluation complexity, communication limitations, and institutional support all have a significant and equal impact on the challenges of academic promotion, with each factor having a coefficient of 0.2000. The R-squared and Adjusted R-squared values of 1.000 indicate that this model can explain the entire variation in academic promotion challenges. The high statistical significance of all coefficients suggests that these results are almost certainly not due to chance. Data analysis also shows that there is little autocorrelation in the model's residuals, and the residual distribution is nearly normal. These findings highlight the importance of transparency, policy consistency, effective communication, and institutional support in the academic promotion process. Improvements in these areas are expected to reduce the challenges faced by faculty members during the promotion process
Co-Authors Che Mustapha, Jawahir Maulindar, Joni Nurchim Nurchim Nurmalitasari Nurmalitasari Singgih Purnomo