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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) JUKI : Jurnal Komputer dan Informatika Journal of Soft Computing Exploration J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Applied Science and Technology Research Journal Nemui Nyimah
Bagaskara, Radhinka
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Other

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Penggunaan Algoritma Gaussian Naïve Bayes & Decision Tree Untuk Klasifikasi Tingkat Kemenangan Pada Game Mobile Legends Putro, Yoga Naufal Ray; Afriansyah, Aidil; Bagaskara, Radhinka
JUKI : Jurnal Komputer dan Informatika Vol. 6 No. 1 (2024): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v6i1.472

Abstract

The development of technology and the internet has increased the popularity of online games, such as Mobile Legends. However, in competitions, players often experience defeat due to various factors, including player skills, team strategies, and the right hero selection. The right hero selection is very important to increase the chances of winning. Therefore, the Mobile Legends Professional League (MPL) has become a focus for competitive teams around the world. This study aimed to determine the classification of victory in MPL matches based on draft pick. Gaussian Naïve Bayes and Decision Tree were used as classification algorithm models in this study. The process in this study included cleaning data, data transformation (labeling), handling imbalanced data, scaling, splitting, and hyperparameter. The evaluation stage used confusion matrix, correlation data, and AU-ROC curve. The results of this study showed that the Decision Tree method had better performance than Gaussian Naïve Bayes in classifying data using the confusion matrix. The AUC (area under the receiver operating characteristic curve) analysis showed that the decision tree had better performance than Gaussian naive Bayes in predicting positive and negative data. This is indicated by the higher AUC value for the Decision Tree, which is 0.67 compared to Gaussian Naïve Bayes which is 0.48. Classification models with higher AUC values can more accurately distinguish between positive and negative data. In this study, the Decision Tree had a higher AUC value than Gaussian Naïve Bayes so the Decision Tree could more accurately classify victory and defeat data.
Rancang Bangun Sistem Inventaris pada UMKM Girimulyo, Kabupaten Lampung Timur Anggraini, Leslie; Bagaskara, Radhinka; Verdiana, Miranti; Afriansyah, Aidil; Idris, Mohamad
Nemui Nyimah Vol. 5 No. 1 (2025): Nemui Nyimah Vol. 5 No. 1 2025
Publisher : FT Universitas Lampung

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Abstract

We discuss the design and implementation of an Inventory Management System developed specifically for Javamart Girimulyo to enhance accuracy and efficiency in stock control. The primary objective of the system is to automate inventory tracking, monitor stock levels in real time, and generate detailed inventory reports, thus minimizing manual errors and improving overall inventory oversight. The development followed the Modified Waterfall methodology, which provided a clear and systematic process across stages of requirements gathering, system design, coding, testing, and deployment. Key functionalities include automatic stock updates upon item inflow and outflow, low-stock alerts, flexible item categorization, and secure access controls for administrators. The system also offers an intuitive graphical user interface and integrates with a MySQL database backend to ensure data reliability and consistency. Testing outcomes indicate that the system delivers the necessary performance and stability, confirming its suitability for deployment. By digitizing and streamlining stock management processes, this project offers a practical and scalable solution to support inventory operations at Javamart Girimulyo.
Analysis Comparison of Depression Levels Based on Gender and Academic Factors of Students Verdiana, Miranti; Nugroho, Eko Dwi; Anggraini, Leslie; Bagaskara, Radhinka; Yulita, Winda; Afriansyah, Aidil; Algifari, Muhammad Habib
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.7975

Abstract

This study aims to analyze the level of depression among university students by examining gender and several academic indicators. The dataset includes responses from 27,901 students across various regions, with variables covering age, gender, academic pressure, study satisfaction, work/study hours, CGPA, and depression status. The analytical methods applied in this study include the chi-square test to evaluate the association between gender and depression status, point-biserial correlation to examine the relationship between numeric variables and depression, and logistic regression to develop a prediction model. The chi-square test results revealed no significant relationship between gender and depression (p = 0.774), indicating that depression affects both genders. In contrast, academic pressure exhibited the strongest correlation with depression status (r = 0.47), followed by work/study hours (r = 0.209) and study satisfaction (r = -0.168). The Logistic Regression model constructed using the four most relevant variables demonstrated satisfactory performance, achieving 75.5% accuracy and 82.1% recall in identifying students experiencing depression. These findings highlight the critical role of academic-related factors—particularly academic pressure—in influencing students' mental health. Therefore, targeted academic support strategies are essential to mitigate depression risks in higher education environments.
Co-Authors Afriansyah, Aidil Akram Abdillah, Attar Algifari, Muhammad Habib Andika Setiawan, Andika Anggraini, Leslie Aprilianda, Mohamad Meazza Atmajaya, Gde KM Ba’its, Alfian Kafilah Drantantiyas, Nike Dwi Grevika Dwi Nugroho, Eko Eko Dwi Nugroho Eko Nugroho Faisal, Amir Febrianto, Andre Fernandes, Rivaldo Fitrawan, Mhd. Kadar Habib Algifari, Muhammad Harmiansyah Harmiansyah, Harmiansyah Idris, Mohamad Listiani, Amalia Mufidah, Zunanik Muhammad Qaessar Kartadilaga Putro, Yoga Naufal Ray Rahman Indra Kesuma Ramadhani, Uri Arta Ridwan, Naufal Taufiq Rizkita, Alya Khairunnisa Untoro, Meida Cahyo Verdiana, Miranti Yulita, Winda