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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Pedagogy and Education Science Jurnal Ilmiah Manajemen dan Kewirausahaan
Ferdinand, Felix
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Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pengaruh Harga, Kualitas Produk, dan Lokasi terhadap Keputusan Pembelian Mixue di The Hok Ferdinand, Felix; Saputra, Muhammad Haris; Tan, Fiusdry; Favian, Lucas; Tio, Michael
Jurnal Ilmiah Manajemen dan Kewirausahaan (JUMANAGE) Vol 3 No 1 (2024): Volume 3 Nomor 1 Januari 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jumanage.2024.3.1.1589

Abstract

Penelitian ini bertujuan untuk mennguji pengaruh Harga, Kualitas Produk, dan Lokasi pada Keputusan Pembelian Mixue di The Hok. Populasi di dalam kajian ini berupa anak muda seperti mahasiswa di Universitas Dinamika Bangsa, yang berumur 18-30 tahun. Pendekatan yang digunakan dalam kajian ini berupa pendekatan kuantitatif dengan 110 responden. Pengujian data ini menggunakan SPSS dan pengumpulan data dengan menyebarkan kusioner sebanyak 17 pertanyaan kepada peminat Mixue di The Hok Kota Jambi dengan objek Para Mahasiswa UNAMA. Temuan dari penelitian sebelumnya mengindikasi bahwa Harga memiliki dampak pada Keputusan Pembelian, Kualitas Produk memiliki dampak pada Keputusan Pembelian dan Lokasi memiliki dampak pada Keputusan Pembelian. Tanggapan dari penelitian terdahulu berupa memberikan masukan bagi gerai Mixue tersebut untuk dapat terus menjaga Kualitas serta dapat menambahkan pemahaman yang berkaitan dengan penting nya Harga, Kualitas Produk, dan Lokasi pada Keputusan Pembelian. Dengan adanya masukan untuk gerai Mixue tersebut diharapkan dapat meningkatkan dan mempertahankan Keputusan Pembelian yang optimal dan dapat menarik konsumen baru untuk memutuskan membeli Mixue di gerai tersebut.
Effect of the Powerpoint Presentation on Academic Achievement of Agricultural Students Taught Digestive System of Animals in Nigeria Emmanuel, Egunsola Abraham Olawuyi; Ferdinand, Felix; Ibanga, Isaac John
Journal of Pedagogy and Education Science Vol 3 No 02 (2024): Journal of Pedagogy and Education Science
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jpes.v3i02.523

Abstract

This study investigated the effect of using PowerPoint presentations (PPP) as an instructional medium on the academic achievement of agricultural students in secondary schools in Adamawa State, Nigeria. The study employed a pre-test, post-test, non-equivalent control group quasi-experimental research design, with 88 students from secondary school as the sample. The participants were divided into an experimental group taught with PPP and a control group taught using conventional methods. An achievement test on the digestive system of animals was administered before and after the intervention. The data were analyzed using mean statistics and t-tests. The findings revealed that students with PPP achieved significantly higher academic scores than those taught using conventional methods. Furthermore, the male students outperformed their female counterparts in both groups. The results highlighted the effectiveness of PPP in capturing students' interest and enhancing academic performance in agricultural science. The study's outcomes align with previous research, emphasizing the benefits of using modern educational media for instructional purposes. In conclusion, the study recommends a shift from conventional teaching methods to educational media like PPP to enhance learning outcomes in agricultural science. Incorporating PPP into teaching can engage students' senses and promote a deeper understanding of the subject matter. The results also indicate that gender plays a role in academic achievement, suggesting the need for targeted interventions to support female students in their learning journey. Overall, the study underscores the potential of technology-based instructional approaches to improve students' academic achievements in secondary schools.
Comparison of Support Vector Regression and Extreme Learning Machine Methods for Predicting Bitcoin Prices Ferdinand, Felix; Anthony , Ryan; Jason Winata, Tanjaya; Sutanto, Jason; Souwiko, Richard; Fernando, Christian
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10983

Abstract

Bitcoin can be used for transactions, mining, and investments. Transactions with Bitcoin are highly secure with the help of Bitcoin miner validation. Miners who validate transactions are rewarded with Bitcoins which then adds supply to the Bitcoin network. However, over time, these rewards will run out. The depletion of Bitcoin supply can affect the price of Bitcoin. In addition, investing in Bitcoin is very risky with the fluctuating price of Bitcoin. Therefore, it is necessary to predict the price. In this research, prediction is done using Support Vector Regression (SVR) and Extreme Learning Machine (ELM). The dataset for Bitcoin price (USD) comes from Yahoo Finance. The types of Bitcoin prices predicted are Open, High, Low, and Close prices. Across all series and both splits, ELM outperforms SVR. Under the 80/20 split, the average error of ELM is MAE 418.698 USD, RMSE 633.953 USD, R² of 0.987, versus SVR’s MAE 1061.449 USD, RMSE 1227.499 USD, R² of 0.955. A reduction of 60.6% (MAE) and 48.4% (RMSE). With the 60/40 split, ELM remains strong (MAE 550.783 USD, RMSE 850.656 USD, R² 0.989 while SVR deteriorates (MAE 1843.534 USD, RMSE 2093.542 USD, R² of 0.935, yielding 70.1% and 59.4% average reductions in MAE and RMSE, respectively. ELM consistently tracks both levels and day to day movements, with typical errors of only a few hundred dollars. These results indicate that ELM is the more reliable choice and is capable of capturing non-linearities for Bitcoin price prediction.
Co-Authors Anthony , Ryan Emmanuel, Egunsola Abraham Olawuyi Favian, Lucas Fernando, Christian Ibanga, Isaac John Jason Winata, Tanjaya Muhammad Haris Saputra Souwiko, Richard Sutanto, Jason Tan, Fiusdry Tio, Michael