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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) JCRS (Journal of Community Research and Service) Jusikom: Jurnal Sistem Informasi Ilmu Komputer International Journal of Applied Sciences and Smart Technologies JURNAL MUARA PENDIDIKAN JURNAL TUNAS PENDIDIKAN Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Inovasi Pendidikan Sains dan Terapan VISA: Journal of Vision and Ideas Jurnal PhysEdu (PHYSICS EDUCATION) Jurnal Elementaria Edukasia Adidaya : Jurnal Aplikasi Pendidikan dan Sosial Budaya Jurnal Informatika dan Rekayasa Perangkat Lunak
Juanta, Palma
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Public Health Social Sciences Other

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LITERATURE REVIEW: EFEKTIVITAS MUSIK LATAR DALAM MENINGKATKAN KONSENTRASI BELAJAR MAHASISWA Vigo Hantanto, Ken; Steven Hutabarat, Yusuf; Juanta, Palma; Wilfred Solo, Devin; Felicia, Felicia; Sebastian, Alvin; Gevira Sofa, Natasya
ADIDAYA : Aplikasi Pendidikan dan Sosial Budaya Vol 1 No 2 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/adidaya.v1i2.1577

Abstract

This study aims to evaluate the effectiveness of background music in improving students' learning concentration through a literature review study. The results of previous studies show that background music can improve concentration. The first article used the Pretest Posttest Control Group Design method and found a significant difference with a calculated t value (3.100) greater than the t table (2.145). The fourth article used Quasi Experimental and Wilcoxon Signed Rank Test, showing a significant difference (p = 0.000). The seventh article, using qualitative methods, reported 83.3% of students agreed that music helps focus. However, the eighth article noted background music can improve mood but also increase cognitive load. In conclusion, background music, especially slow-tempo and instrumental, is effective in improving study concentration. Variations in effectiveness are due to music preference and learning context. Students and educators are advised to use background music that suits individual needs.
COMPARING REGRESSION METHODS FOR ASSESSING AND PREDICTION THE IMPACT OF SALARY INCREASES ON EMPLOYEE PERFOMANCE Juanta, Palma; Djuli, Zachary; Tifanny, Tifanny; Sitanggang, Delima; Anita, Anita
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10098

Abstract

In today’s competitive digital era, data-driven decision-making is key to enhancing the efficiency of human resource management. One of the main challenges is objectively assessing the impact of salary increases on employee performance, which is often assumed to be a primary motivator but rarely proven quantitatively. This study conducts a comparative analysis of two data mining methods, Linear Regression and Decision Tree Regression, to assessing and predicting the impact of salary increases on employee performance. A case study was conducted at PT. Taipan Agro Mulia using the company’s internal historical data. The analysis shows that Linear Regression performed better with an R-Square value of 0.731 or 73.1%, indicating that 73.1% of the variation in employee performance can be explained by salary increases. In comparison, Decision Tree Regression achieved an R-Square value of 0.700 or 70.0%. Additionally, Linear Regression recorded lower prediction errors (MAE = 4.78; MSE = 38.60; RMSE = 6.21) than Decision Tree (MAE = 5.61; MSE = 66.41; RMSE = 8.15). These findings demonstrate that data analysis approaches can serve as a strong foundation for formulating strategic salary policies aimed at improving employee performance
Enhancing Entrepreneurial Skills and Pancasila Student Profiles through Digital Learning Tools in Science Education Juanta, Palma; Festiyed, Festiyed; Diliarosta, Skunda; Lufri, Lufri; Yohandri, Yohandri; Moyo, Kgomotso
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.518

Abstract

This research examines the effect of project-based learning (PjBL) assisted by ethnoscience animation videos on students problem-solving skills and their Pancasila character profile in science education. The study employed a quasi-experimental design to evaluate the impact of independent variables (PjBL and animated videos) on dependent variables (problem-solving skills and character development). The research objectives were to assess the effectiveness of the PjBL model in improving problem-solving skills and to determine its role in developing Pancasila character dimensions in students. The study involved students from classes VII-1 and VII-6 at Dr. Wahidin Sudirohusodo Middle School as the sample. Data collection included test instruments to measure problem-solving abilities and non-test instruments for assessing character dimensions of the Pancasila student profile. Results showed significant differences in the average scores of problem-solving skills between the experimental and control classes, with a significance value (2-tailed) of 0.00 < 0.05. Similarly, for the Pancasila student profile dimensions, the significance value was also 0.00 < 0.05, indicating a significant improvement in the experimental group. These findings confirm that the PjBL model assisted by ethnoscience animation videos is valid, practical, and effective for improving both problem-solving skills and character development. In conclusion, integrating PjBL with ethnoscience animation videos addresses the challenges in science education by aligning with skill-based and culturally relevant learning approaches. The model supports students cognitive and character development, making it suitable for application in the Society 5.0 era.
Enhancing Customer Experience and Business Innovation through Digital Platforms in Southeast Asia Juanta, Palma; Setiawan, Sandy; Hetilaniar; Callula, Brigitta
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/1zrz4w08

Abstract

The rapid expansion of digital platforms across Southeast Asia has accelerated the emergence of interconnected digital ecosystems that integrate multiple services, partners, and data-driven interactions, fundamentally reshaping customer expectations and competitive dynamics in the region. This study aims to examine how ecosystem oriented digital platforms enhance customer experience and foster business model innovation by analyzing the influence of ecosystem capabilities, service integration, and value co-creation. Using a quantitative approach, data were collected from 378 active users of digital platforms across Indonesia, Malaysia, Vietnam, Thailand, and the Philippines, and analyzed using Structural Equation Modeling with Partial Least Squares (SEM–PLS) to evaluate relationships among key variables. The findings indicate that strong ecosystem capabilities significantly improve customer experience through seamless integration, personalization, and multi-stakeholder collaboration, while customer experience also mediates the relationship between ecosystem capabilities and business model innovation. Furthermore, ecosystem oriented platforms demonstrate greater agility and value creation potential compared to single-service platforms. These results underscore the strategic importance of digital ecosystems in driving superior customer experience and enabling sustainable business model innovation in Southeast Asia, offering valuable implications for digital firms, startups, and policymakers to strengthen regional digital transformation.
Comparison Random Forest and Logistic Regression in Predicting Motivation and Learning Outcomes of Junior High School Students Juanta, Palma; Pavithra, Valencia; Hutabarat, Nurija Sri Paska; Simatupang, Yehuda M. P.
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1510

Abstract

Student learning motivation and learning outcomes are important factors that influence educational success, especially at the junior high school level. Previous studies that primarily emphasize academic achievement prediction alone, this study simultaneously evaluates student motivation and learning outcomes as dual prediction targets. Moreover, while earlier research often applied only a single algorithm or focused on higher education datasets, this research specifically conducts a head-to-head comparison between Random Forest and Logistic Regression using junior high school data, thereby filling an important gap in secondary education predictive analytics. This study compares the performance of two machine learning algorithms, namely Random Forest and Logistic Regression, in predicting student motivation and learning outcomes based on data on learning habits, mental condition, attendance, sleep hours, family support, and academic grades. The study process included data pre-processing, normalization, separation of data into training and testing data, model training, and evaluation using accuracy, sensitivity, specificity, and AUC. Based on the study findings, Random Forest performed better with an accuracy of 0.91, sensitivity of 0.91, specificity of 0.94, and AUC of 0.94. Meanwhile, Logistic Regression obtained an accuracy of 0.84, sensitivity of 0.84, specificity of 0.90, and AUC of 0.95. These findings confirm that Random Forest is superior in its overall predictive ability, while Logistic Regression remains relevant due to its interpretability. This study aims to assist in the development of data-driven decision support systems in education to help schools identify students who require early intervention.
Co-Authors Abdillah, Satria Agitha Kembaren, Eykel Alexa, Hipatyah Alfadilla, Rayyan Amal Dinata, Prasetya Andrian, Danil Anggellino, Jasen Anita Anita Apriani Sijabat Bangun, Pery Chandria Banjarnahor, Jefri Callula, Brigitta Damanik, Bagus Defitra, Niko Delima Sitanggang, Delima Della Christin Zai, Stephani Djuli, Zachary Efrat Freditus Tampubolon, Mertfil Fa, Finley Fahrozy N Harahap, Mhd Rizky Felicia Felicia Ferdianto S., Diki Festiyed Festiyed Friska Telaumbanua, Vera Gevira Sofa, Natasya Gultom, Markus Lambok H. S, Samaria Chrisna Hetilaniar Hutabarat, Nurija Sri Paska Indra, Evta Jery Alfa Hutabarat, Bendedict Kelana, Nurullah Marina Laila, Ibrani Lufri Lufri Malau, Ridho Satrio Manalu, Nellie Epa Sihol Marito Marito Siagian, Shinta Moyo, Kgomotso Muhardi Saputra Nababan, Jennifer Jesica Nainggolan, Gabariel Nizam, Muhammad Fachrul Oloan Sihombing, Oloan Pavithra, Valencia Pooja, Netiya Purba, Windania Putri, Yulia Anjani Rahmat Izwan Heroza Ricky Ricky Saga Mo Lewio Dachi, Theodorus Salsabilla, Aischa Najwa Sebastian, Alvin Sesillya Nababan, Vita Setiawan, Sandy Siagian, Sinta Marito Sianturi, Authon Siloam Simatupang, Yehuda M. P. Simson, Erick Sinaga, Elvina Sinurat, Stiven Hamonangan Siti Aisyah Sitorus, Angelina Monica Sitorus, Sarah Tri Yosepha Skunda Diliarosta Steven Hutabarat, Yusuf Sumana, Raditia Sumantri, Akbar Tampubolon, Ferdinan Rinaldo Tampubolon, Johanes Joys Ronaldo Tifanny, Tifanny Vigo Hantanto, Ken Wijaya, Ryo Wilfred Solo, Devin Yerimadesi Yerimadesi Yerimadesi Yohandri Bow Zendrato, Beatrice Samala