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Aldi Bastiatul Fawait Fawait
Universitas Widya Gama Mahakam Samarinda
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Klasifikasi tingkat depresi mahasiswa menggunakan algoritma decision tree Ahmad Fadilla; Rywalman Rante Pasang; Cecillia Listia Anggraini; Sherly Virgoila Pidang; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.28

Abstract

Depression is a mental health disorder commonly experienced by college students due to academic, social, and financial pressures. The high rate of depression among college students can impact academic achievement, social relationships, and overall quality of life. Therefore, a method is needed to quickly and accurately identify levels of depression. This study aims to classify college students' depression levels using the Decision Tree algorithm. The method used includes utilizing the Student Depression Dataset obtained from the Kaggle platform, data preprocessing, building a classification model using the Decision Tree algorithm, and evaluating model performance. The Decision Tree algorithm was chosen because it produces decision rules that are easy to understand and interpret. The test results obtained an accuracy value of 81.08%. This value indicates that the model is capable of classifying college students' depression levels quite well. Furthermore, the classification report results shows that the model has a precision value of 0.81 for both classes. The recall value for the depression class reached 0.88, indicating that the model successfully recognized most students experiencing depression. The research findings can assist educational institutions in early detection of students at risk of depression, allowing for more effective treatment and support.
Sistem Klasifikasi Kelayakan Penerima Beasiswa menggunakan Algoritma Categorical Naive Bayes Otniel Christovel Ganda; Ridho Aulia Tabliq Sidiq; Bernardo Damian De Ornay; Yovi Aldiyanto; Klara Bare Nuhan; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.29

Abstract

Scholarship selection is often done manually, which takes time and has the potential for errors in assessment. This study aims to build a prediction model for student scholarship eligibility using the Categorical Naive Bayes algorithm. The data used in this study consisted of 1,042 student data with eight attributes: distance from residence to campus, gender, organizational participation, student activity unit (UKM) participation, GPA, parents' occupation, parents' income, and number of dependents. The research method included data preprocessing, feature encoding, data splitting with an 80:20 ratio, model training using three Naive Bayes variants (GaussianNB, CategoricalNB, and ComplementNB), and model evaluation using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that the Categorical Naive Bayes model achieved the best performance with an accuracy of 74.16%, precision of 50.85%, recall of 54.55%, F1-score of 52.63%, and AUC-ROC of 80.25%. The most influential features in determining scholarship eligibility were the number of dependents, GPA, and organizational participation. This study concludes that the Categorical Naive Bayes algorithm can be used to predict scholarship eligibility reasonably well, although it still needs improvement to handle imbalanced data.
Implementasi Artificial Intelligence Dalam Pendidikan: Systematic Literature Review Tahun 2024–2026 Maslim Tammaling; Reyza Febrianto; Gregorivo Hizkia Brighita Totopandey; Ronald Alexandre; Marcello Fellix Febrian; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.30

Abstract

Artificial Intelligence (AI) has emerged as one of the most influential technologies in transforming educational practices across different levels of learning. The rapid advancement of Generative AI, adaptive learning systems, learning analytics, and intelligent tutoring systems has significantly changed the way students and educators interact with learning resources. This study aims to analyze the implementation of Artificial Intelligence in education through a Systematic Literature Review (SLR) of scientific publications published between 2024 and 2026. The study adopted the PRISMA 2020 framework to identify, screen, and select relevant literature. A total of 26 journal articles met the inclusion criteria and were analyzed using thematic synthesis. The results indicate that AI has been widely implemented in personalized learning, academic assistance, adaptive learning environments, learning analytics, and intelligent tutoring systems. The primary benefits identified include improved learning effectiveness, increased student engagement, enhanced accessibility to educational resources, and support for data-driven educational decision-making. However, several challenges remain, including academic integrity concerns, privacy and data security issues, algorithmic bias, and excessive dependence on technology. The review concludes that AI has substantial potential to improve educational quality when supported by ethical implementation, effective governance, and balanced collaboration between human educators and intelligent systems.
Co-Authors Abiyajid Bustami Agry Alfiah Ahmad Fadilla Aisyah Nursyam Alfian Ma’arif ALYA MASITHA Andi Hasyim Andrian Anye Anton Yudhana Apolonia Diana Sherly da Costa Arief Yanto Rukmana Arifin, Merlina Lidiana Asno Azzawagama Firdaus Asno Azzawagama Firdaus Aulita Az'Zahra Dhena Aldy Bayu Pamungkas Bernardo Damian De Ornay Cecillia Listia Anggraini Dadang Muhammad Hasyim Darmun, Darmun Dedi Zulkarnain Pulungan Dodi Irawan Dony Andrasmoro Edwin Pramudya Eko Prasetio Widhi Elsya Fauziah Fahmi, Miftahuddin Farida Arinie Soelistianto Ferdinandus Heru Moreno Christian Paran Feri Adriyanto Furizal Furizal Furizal, Furizal Gede Enos Karli Gregorivo Hizkia Brighita Totopandey Haviluddin Haviluddin Hendratri, Bhaswarendra Guntur Hendrikus Hang Himang Hersiyati Palayukan Hidayatus Sibyan Huda, Syafa'at Ariful Jamil, Muh Jamil, Muh Jamil Judijanto, Loso Kariyamin, Kariyamin Klara Bare Nuhan Kohar , Abdul La Jupriadi Fakhri La Jupriadi Fakhri Leo nakanisi aran Lisnawati Loso Judijanto M. Fajar Rizky Maghfiroh, Hari Mahmoud Ahmad Al-Khasawneh Marcello Fellix Febrian Mardiati Mardiati Maria Kristiana Damayanti Teting Maslim Tammaling Merlina Lidiana Arifin Merlina Lidiana Arifin Milkhatun Milkhatun, Milkhatun Muh. Jamil Muhamad Fuat Asnawi Muhammad Kunta Biddinika Nadia Keril Saputri Nazaruddin Insyroh Nelson Sompa Arifin Nelson Sompa Arifin Norsianalara Nursalim Nursyam, Aisyah Otniel Christovel Ganda Puteri Aprilani Rahmah, Sitti Rahmah Rahmawati Ramelan, Agus Rayner Alfred Reviandari Widyatiningtyas, Reviandari Reyza Febrianto Ridho Aulia Tabliq Sidiq Rizky Wardhani Ronald Alexandre Rosmasari Rosmasari, Rosmasari Rusdi Umar Rywalman Rante Pasang Saputra, Yudhi Fajar Saputri, Nadia Keril Sara Hussain Sherly Virgoila Pidang Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Rahmah Sopia daud Sri Nur Hidayati Sugiarto Sugiarto Sugiarto S Sulung Alfianto Akbar Sunardi, Sunardi Suwarno, Iswanto Syaifullah, Ahmad Syekh Budi Syam Thitus Gilaa Vann Sok Vinsensia Florince Seke Virasanty Muslimah Wartono, Tono wati, asiah Yana Mulyana Yazeed Al Moaiad Yazeed Al Moaiad Yohanes Andriano Teras Yovi Aldiyanto Yudhi Fajar Saputra Yudhi Fajar Saputra Yudhi Fajar Saputra Yudhi Saputra Yulindawati Yulindawati, Yulindawati Yusriati, Yusriati Zakaria Ahmad Dahlan Zhang Li