Garuda - Garba Rujukan Digital

TEKNOLOGI REKLAMASI LAHAN BEKAS TAMBANG BAUKSIT MENGGUNAKAN SEEDBALL TANAMAN ADAPTIF DAN KOMPOS DIPERKAYA FABA Putri, Aulya; Suwardi, Suwardi; Suryaningtyas, Dyah Tjahyandari; Oktariani, Putri; Widjaja, Hermanu; Randrikasari , Octaviana
RISALAH KEBIJAKAN PERTANIAN DAN LINGKUNGAN Rumusan Kajian Strategis Bidang Pertanian dan Lingkungan Vol 11 No 1 (2024): April
Publisher : Pusat Studi Pembangunan Pertanian dan Pedesaan (PSP3) dan Ilmu Pengelolaan Sumberdaya Alam dan Lingkungan (PSL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jkebijakan.v11i1.53149

Bauxite is one of the most mined minerals in the world and plays an important role as a raw material for aluminum production. The implementation of bauxite downstream policy has an impact on the expansion of bauxite mining area and bauxite post-mining land. The expansion of mining activities can cause an increase in the impact of environmental damage, one of which is the emergence of critical land in post-mining areas. Bauxite post-mining land often experiences problems with soil physical and chemical properties, which cause soil unable to support growth and fulfill plant nutrition needs. Alternatives that can be implemented is the use of adaptive plant seedball and Fly Ash-Bottom Ash (FABA) enriched compost utilization as ameliorant material. Post-mining land reclamation technology is the key to achieve successful reclamation and support the optimization of sustainable bauxite downstream programs in Indonesia.

PENERAPAN METODE NEUROSAINS DALAM PEMBELAJARAN SEJARAH putri, Aulya; Ribawati, Eko
JEJAK : Jurnal Pendidikan Sejarah & Sejarah Vol. 2 No. 1 (2022): Kajian Pendidikan Sejarah dan Keilmuan Sejarah
Publisher : Universitas Jambi

Dalam perkembangan zaman seperti sekarang, dimana terknologi lebih maju dan diandalkan dari berbagai segi kehidupan. Ini juga berpengaruh terhadap siswa yang akhirnya menjadi lebih pasif. Maka, tujuan dari penulisan ini guna mengoptimalkan otak kanan siswa untuk menciptakan gagasan baru, kreativitas serta inovasi dalam proses pembelajaran. Metode yang digunakan adalah studi Kualitatif dengan desain deskriptif. Melihat kondisi maupun sistem pendidikan yang berjalan di negara tercinta ini lebih memfokuskan terhadap logika, kata-kata, matematika dan berbagai hal lainnya yang berhubungan dengan angka. Sehingga banyak anak yang terkadang sulit dalam menggembangkan sebuah ide dan kurangnya kreativitas dalam melakukan sesuatu dikarenakan kurangnya pemanfaatan otak kanan dalam pembelajaran di sekolah. Metode neurosains bertujuan untuk menyeimbangkan kerja kedua otak guna menumbuhkan keaktifan siswa dalam mengeskpresikan perasaan dan bisa berfikir kritis terhadap pembelajaran sejarah, sehingga kelas tidak lagi menjadi tegang dan siswa berani untuk berpendapat. Maka suasana yang menarik menjadi salah satu upaya dalam menarik perhatian dan mina peserta didik demi terwujudnya pendidikan yang baik. Diharapkan dalam penerapan metode neurosains ini bisa merubah sistem pembelajaran yang diterapkan di Indonesia hingga sekarang dan dapat digunakan secara terus menerus oleh para calon pendidik dibidang lainnya.

The Application of Z-Score and Zavgren Models in Managing Financial Distress at PT Garuda Indonesia (Persero) Tbk Damayanti, Resma; Putri, Aulya
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.826

As an archipelago, the aviation sector in Indonesia plays an important role, but PT Garuda Indonesia (Persero) Tbk. as one of the airline companies has experienced significant financial pressure. In the third quarter of 2023, the company recorded a net loss of US$ 72.07 million. This condition may put the company at risk of financial distress, a situation in which the company experiences financial difficulties before bankruptcy. This study uses the Altman Z-Score Model and the Zavgren Model to predict potential financial distress at PT Garuda Indonesia (Persero) Tbk. The analysis results show that from 2021 to 2023, the Altman Z-Score is consistently in the Bankrupt category, reflecting a high risk of bankruptcy, while the Zavgren model shows vulnerable conditions in 2021 but also indicates bankruptcy in 2022 and 2023. The results of this study are expected to provide early warning and assist management decision-making to reduce the risk of bankruptcy.

Mean-Variance Optimal Portfolio Selection with Risk Aversion on Transportation and Logistics Sector Stocks Based on Multi-Criteria Decision-Making Putri, Aulya; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.895

The importance of the transportation and logistics sector to a country's economy, coupled with the growth of this sector in Indonesia, requires investment support for this sector to continue to grow. Therefore, stocks in the transportation and logistics sector are attractive for investment portfolio consideration. The optimal portfolio selection is to minimize the risk with the expected return. In the formation of an investment portfolio, the problem is how to determine the weight of capital allocation in order to get the maximum return while still considering the risk in each stock, by considering several criteria in decision making. This study was conducted to determine the best stock selection in the transportation and logistics sector listed on the Indonesia Stock Exchange, and determine the optimal weight in the investment portfolio. The method used is Multi-Criteria Decision Making (MCDM), namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) using 15 financial metrics as relevant criteria in stock selection. Furthermore, to determine the allocation weight to form an optimal stock portfolio using the Mean-Variance model with Risk Aversion. The stocks analyzed were 28 stocks in the transportation and logistics sector. The results of research based on MCDM selected 9 stocks, namely MITI, BIRD, HATM, TMAS, JAYA, PPGL, BPTR, ASSA, and RCCC. However, TMAS, PPGL, and BPTR stocks are not included in portfolio formation because they have a negative average return. Based on the optimization results, the allocation weights of the 6 stocks included in the optimal portfolio are BIRD (37.7%), JAYA (24.6%), MITI (12.9%), HATM (9.9%), ASSA (7.5%), and RCCC (7.4%). The results of this study are expected to be a consideration in making investment decisions.

Estimation Model of Pure Health Insurance Premiums in Southeast America Using Generalized Linear Model (GLM) with Gamma Distribution Putri, Aulya
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.873

Health insurance premiums are on the rise due to increasing medical costs, inflation, and the lingering effects of the COVID-19 pandemic. Accurate premium pricing is crucial for insurance companies to maintain financial stability and offer fair rates to policyholders. Generalized Linear Models (GLM) have been widely used in actuarial science for modeling insurance premiums. This study proposes the use of GLM with a Gamma distribution to model health insurance premiums. The Gamma distribution is suitable for non-negative and positively skewed data, which is characteristic of insurance claim amounts. By analyzing historical data from a Southeast United State insurance company, we aim to identify key factors influencing premium pricing and develop a robust premium model. The model will consider factors such as age, gender, BMI, number of children, and smoking status to predict individual risk profiles and determine appropriate premiums. Our findings indicate that age and smoking status are the most significant factors affecting premium rates. Older individuals and smokers tend to have higher premiums due to their increased risk of health issues. Gender and BMI, however, were found to have no significant impact on premium pricing in this specific dataset. Insurance companies can use the identified factors (age, smoking status, etc.) to create more precise risk profiles for their policyholders.

Estimation Model of Pure Health Insurance Premiums in Southeast America Using Generalized Linear Model (GLM) with Gamma Distribution Putri, Aulya
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i1.181

Health insurance premiums are on the rise due to increasing medical costs, inflation, and the lingering effects of the COVID-19 pandemic. Accurate premium pricing is crucial for insurance companies to maintain financial stability and offer fair rates to policyholders. Generalized Linear Models (GLM) have been widely used in actuarial science for modeling insurance premiums. This study proposes the use of GLM with a Gamma distribution to model health insurance premiums. The Gamma distribution is suitable for non-negative and positively skewed data, which is characteristic of insurance claim amounts. By analyzing historical data from a Southeast United State insurance company, we aim to identify key factors influencing premium pricing and develop a robust premium model. The model will consider factors such as age, gender, BMI, number of children, and smoking status to predict individual risk profiles and determine appropriate premiums. Our findings indicate that age and smoking status are the most significant factors affecting premium rates. Older individuals and smokers tend to have higher premiums due to their increased risk of health issues. Gender and BMI, however, were found to have no significant impact on premium pricing in this specific dataset. Insurance companies can use the identified factors (age, smoking status, etc.) to create more precise risk profiles for their policyholders.

Gamifying Vocabulary Learning: Students’ Perceptions of Kahoot! in a Junior High School Context Putri, Aulya; Dewi, Desi Surlitasari; Shalehoddin, Shalehoddin
JELLT (Journal of English Language and Language Teaching) Vol 9 No 1 (2025)
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36597/jellt.v9i1.19327

This study explores students’ perceptions of Kahoot! as a tool for vocabulary learning in a junior high school context, focusing on how it fulfills their motivational needs and enhances its acceptance. A qualitative approach was employed, utilizing semi-structured interviews, observations, and closed questionnaires to collect data from 20 seventh-grade students who used Kahoot! for vocabulary learning. The data were analyzed using descriptive statistics for the Likert scale questionnaire and content analysis for qualitative responses. The findings reveal that Kahoot! creates an enjoyable, interactive, and engaging learning environment, fostering positive experiences. Its gamified features, including competitive elements, sustain students’ motivtion and encourage active participation. Furthermore, students reported improved vocabulary retention facilitated by Kahoot!’s repetitive and manageable design. The platform’s intuitive interface enhances ease of use, allowing students to focus on learning without technological challenges. This study highlights Kahoot!’s potential to enhance vocabulary learning through gamified, student-centered experiences.

The Application of Z-Score and Zavgren Models in Managing Financial Distress at PT Garuda Indonesia (Persero) Tbk Damayanti, Resma; Putri, Aulya
International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.826

As an archipelago, the aviation sector in Indonesia plays an important role, but PT Garuda Indonesia (Persero) Tbk. as one of the airline companies has experienced significant financial pressure. In the third quarter of 2023, the company recorded a net loss of US$ 72.07 million. This condition may put the company at risk of financial distress, a situation in which the company experiences financial difficulties before bankruptcy. This study uses the Altman Z-Score Model and the Zavgren Model to predict potential financial distress at PT Garuda Indonesia (Persero) Tbk. The analysis results show that from 2021 to 2023, the Altman Z-Score is consistently in the Bankrupt category, reflecting a high risk of bankruptcy, while the Zavgren model shows vulnerable conditions in 2021 but also indicates bankruptcy in 2022 and 2023. The results of this study are expected to provide early warning and assist management decision-making to reduce the risk of bankruptcy.

Mean-Variance Optimal Portfolio Selection with Risk Aversion on Transportation and Logistics Sector Stocks Based on Multi-Criteria Decision-Making Putri, Aulya; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.895

The importance of the transportation and logistics sector to a country's economy, coupled with the growth of this sector in Indonesia, requires investment support for this sector to continue to grow. Therefore, stocks in the transportation and logistics sector are attractive for investment portfolio consideration. The optimal portfolio selection is to minimize the risk with the expected return. In the formation of an investment portfolio, the problem is how to determine the weight of capital allocation in order to get the maximum return while still considering the risk in each stock, by considering several criteria in decision making. This study was conducted to determine the best stock selection in the transportation and logistics sector listed on the Indonesia Stock Exchange, and determine the optimal weight in the investment portfolio. The method used is Multi-Criteria Decision Making (MCDM), namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) using 15 financial metrics as relevant criteria in stock selection. Furthermore, to determine the allocation weight to form an optimal stock portfolio using the Mean-Variance model with Risk Aversion. The stocks analyzed were 28 stocks in the transportation and logistics sector. The results of research based on MCDM selected 9 stocks, namely MITI, BIRD, HATM, TMAS, JAYA, PPGL, BPTR, ASSA, and RCCC. However, TMAS, PPGL, and BPTR stocks are not included in portfolio formation because they have a negative average return. Based on the optimization results, the allocation weights of the 6 stocks included in the optimal portfolio are BIRD (37.7%), JAYA (24.6%), MITI (12.9%), HATM (9.9%), ASSA (7.5%), and RCCC (7.4%). The results of this study are expected to be a consideration in making investment decisions.

Estimation Model of Pure Health Insurance Premiums in Southeast America Using Generalized Linear Model (GLM) with Gamma Distribution Putri, Aulya
International Journal of Business, Economics, and Social Development Vol. 6 No. 1 (2025)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.873

Health insurance premiums are on the rise due to increasing medical costs, inflation, and the lingering effects of the COVID-19 pandemic. Accurate premium pricing is crucial for insurance companies to maintain financial stability and offer fair rates to policyholders. Generalized Linear Models (GLM) have been widely used in actuarial science for modeling insurance premiums. This study proposes the use of GLM with a Gamma distribution to model health insurance premiums. The Gamma distribution is suitable for non-negative and positively skewed data, which is characteristic of insurance claim amounts. By analyzing historical data from a Southeast United State insurance company, we aim to identify key factors influencing premium pricing and develop a robust premium model. The model will consider factors such as age, gender, BMI, number of children, and smoking status to predict individual risk profiles and determine appropriate premiums. Our findings indicate that age and smoking status are the most significant factors affecting premium rates. Older individuals and smokers tend to have higher premiums due to their increased risk of health issues. Gender and BMI, however, were found to have no significant impact on premium pricing in this specific dataset. Insurance companies can use the identified factors (age, smoking status, etc.) to create more precise risk profiles for their policyholders.

Title

Found 10 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 10 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 10 Documents
Search