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All Journal Jurnal Simetris Prosiding SNATIF Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal JUPITER (Jurnal Pendidikan Teknik Elektro) Jurnal Pendidikan Fisika dan Keilmuan (JPFK) Jurnal Pendidikan Matematika (Kudus) Jurnal Sisfokom (Sistem Informasi dan Komputer) DoubleClick : Journal of Computer and Information Technology DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Diklat Review : Jurnal manajemen pendidikan dan pelatihan Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] bit-Tech Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Generation Journal Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Budimas : Jurnal Pengabdian Masyarakat Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi Nusantara Science and Technology Proceedings Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Multicience Jurnal Ekonomi TIERS Information Technology Journal Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Teknik Informatika Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Digital Transformation Technology (Digitech) Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Pena Dimas: Jurnal Pengabdian Masyarakat SATIN - Sains dan Teknologi Informasi Nusantara Journal of Computers and its Applications Journal Of Artificial Intelligence And Software Engineering SmartComp
Hasanah, Herliyani
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Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit Alfiyan Tegar Budi Satria Tegar; Herliyani Hasanah; Intan Oktaviani
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2715

Abstract

The increasing use of social media among students has raised concerns regarding its impact on mental health, academic performance, and interpersonal relationships. This study introduces a Streamlit-based web application that predicts social media addiction levels using the Random Forest algorithm. The model incorporates variables such as daily usage hours, mental health scores, and conflicts caused by social media. The innovation of this approach lies in combining machine learning with interactive visualizations for real-time addiction prediction, providing a user-friendly, data-driven tool for early screening. Unlike traditional models that primarily rely on self-reported data or simple metrics, this method integrates multiple behavioral and psychological indicators to improve prediction accuracy. The model outperforms linear regression in all key metrics, achieving an R² value of 0.9903, which explains 99.03% of the variation in addiction scores. It also reports a low Mean Absolute Error (MAE) of 0.0370, Mean Squared Error (MSE) of 0.0244, and Root Mean Squared Error (RMSE) of 0.1561, highlighting its accuracy. Black-box testing showed an average error of just 0.354% in predictions and confirmed that the app’s features function effectively across devices. These findings emphasize the potential of this application as an effective tool for identifying students at risk of social media addiction, enabling timely interventions, and offering a foundation for future improvements through real-time data integration and advanced machine learning models.
Implementation of Simple Linear Regression for Predicting of Students’ Academic Performance in Mathematics Hasanah, Herliyani; Farida, Anisatul; Yoga, Pineda Prima
Jurnal Pendidikan Matematika Vol 5, No 1 (2022): Jurnal Pendidikan Matematika (Kudus)
Publisher : Universitas Islam Negeri Sunan Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jpmk.v5i1.14430

Abstract

Predicting student academic performance is an interesting thing to research. Student academic performance can be used to determine the level of student mastery of the subject matter that has been delivered. This research uses academic and personal data of secondary students on mathematics subject scores in Portugal with 395 data records. The purpose of this research is to study how linear regression is applied in order to determine the predictive results of students' academic performance. Prediction evaluation is done by calculating attribute correlation with class, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results of this research test the accuracy obtained linearly with correlation results, namely class failure (d) has the smallest RMSE and MAPE with an RMSE value of 1.148 and a MAPE of 9.82% of students' academic performance in mathematics. The results of the data analysis show that the failure variable has a positive effect on G3, where the probability value of the F test, the significance value for the simultaneous failure effect on G3 is 0.006 <0.05 and from the analysis of the coefficient of determination it is known that the failures variable is significant to the dependent variable with a large influence 63,8 % (model 2). Memprediksi prestasi akademik mahasiswa merupakan hal yang menarik untuk diteliti. Prestasi akademik siswa dapat digunakan untuk mengetahui tingkat penguasaan siswa terhadap materi pelajaran yang telah disampaikan. Penelitian ini menggunakan data akademik dan data pribadi siswa sekolah menengah pada nilai mata pelajaran matematika di Portugal dengan 395 data record. Tujuan dari penelitian ini adalah untuk mempelajari bagaimana regresi linier diterapkan untuk menentukan hasil prediksi prestasi akademik siswa. Evaluasi prediksi dilakukan dengan menghitung korelasi atribut dengan kelas, Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian ini menguji akurasi yang diperoleh secara linier dengan hasil korelasi yaitu kelas gagal (d) memiliki RMSE dan MAPE terkecil dengan nilai RMSE sebesar 1,148 dan MAPE sebesar 9,82% terhadap prestasi akademik siswa dalam matematika. Hasil analisis data menunjukkan bahwa variabel kegagalan berpengaruh positif terhadap G3, dimana nilai probabilitas dari uji F nilai signifikansi pengaruh kegagalan simultan terhadap G3 adalah 0,006 < 0,05 dan dari analisis koefisien determinasi diketahui bahwa variabel failuers signifikan terhadap variabel dependen dengan besar pengaruh 63,8 % (model 2).
Co-Authors Abi Yudistira, Muhammad Abinaya Awalia Adristi Asyifa, Elga Adi Nugroho, Rezza Adi Putra Wiratama, Ryan Adji Rohman, Bagus Afu Ichsan Pradana Agustina Srirahayu Ahmad Tomi Alamsyah, Bintang Alfiyan Tegar Budi Satria Tegar Aljundi, Izudin Alvian Andrianto, Rahmad Amilia Ayu Lala Kusumaningtyas An-Najmutsaqib, An-Najmutsaqib Anik Sulistiyanti Anisatul Farida Ardi Lestari, Sofiana Ardian Saputra, Muhammad Ardina Juliani, Novita Arfian Panji Sjifa Arfika Putri Risandi Arfiyan, Muhammad Ariya Budi Santoso Arlin Govinda Putra Artika Rahmawati, Dinda Arya Kusuma, Aldy Athallah Maulana Faiq, Muhammad Atina, Vihi Atsir, Ega Muhammad Azzahra, Afifah Bachtiar Estu Saputro Bahtiar Rifai, Afin Begti Rizal Nugroho, Nanda Bima Priambada, Garneza Bimantara, Anugrah Buana Perdana, Rendi Burhanudin, Latif Carlos Ximenes, Egidio Chris Hendrato, Jehian Christian Putra Pratama, Michael Claudia Swastikawati Daffa Rizki Putra Noordi Darmaji, Farid Dewi Anggraini, Puspita Dimas Dewanto, Ignatius Divia Putri, Radika Dwi Hartanti Dwi Refa, Arjuna Edy Kurniawan Eka Kurniawan, Geraldi Eko Purwanto Ester Anugrayningtyas Fajar Wahyu Hanafi Fany Lestari Farid Darmaji Fathi Zakka, Danang Febriano Dos Santos, Lissandro Fitria Eko Nurjanah Galih, Rhezka Mahendra Hanan Assyauqi, Wildan Handreas Satria, Vieri Hartanti, Dwi Ibrahimovic, Ridho Ilham Amani Saiful Haq Ilyas Nurulloh, Muhammad Intan Oktaviani Intan Oktaviani Karininna, AllisyaYunda Kasetyaningsih, Sufia Widi Lestari, Fany Lila Aprilia, Arlinda Marcelina, Indi Marcelino Jonatan Mashar, Muhammad Ali Maulana Firmansyah, Iqsal Maulid Ardiyan, Afrilza Maulindar, Joni Moh Muhtarom Muh Amin Faiz Nur Ridho Muh. Ali Mashar Muhamad Aldiyansyah Muhammad Dion Febrian Tino Muhtarom, Moh Mulindar, Joni Nada Febriani, Nada Naoki Priyanto, Braverindo Nastainu Huda Alriefna, Muhammad Nastiti, Faulinda Eli Nofikasari, Indah Novemy Triyandari Nugroho Nugroho Arif Sudibyo Nurchim Nurchim Nurmalitasari Nurmalitasari Nurmalitasari Nurmalitasari Oktaviani, Intan Pangestu, Arnan Dwi Pasha Alghifary, Haikal Perdana Artania, Ipung Permatasari, Hanifah Pipin Widyaningsih Prasetyo, Adi Tri Pratama, Hafiz Primadinata, Dimas Purwanto, Moch. Edy Putri Ristiyani, Asyira Raihan Ardiansyah, Muhammad Rijalul Hakim, Hanan Rizky Fadrilah, Septian Rohmad Mansa, Abel Rudi Susanto Safa Ariesta, Syahada Saktiawan, Galang Saputra Jati, Nugroho Saputro, Khoirul Adi Sarah Raihan, Alexadria Septiano Ozora, Kevin Setya Pradhana, Wahyudi Soenoto, Danudiraja Sri Sumarlinda Sufia Widi K. Sulistyani, Widya Suwandana, Alung Syaiful, Ido Syakuro, Abdan Syiffa Yofika Ailsa, Asy Tino, Muhammad Dion Febrian Tri Djoko Santosa Ulung Septiaji, Dimas valentino, deva Viesta Marbun, Randy Viesta Marbun Wahyu Aji, Jatmika Wahyudi, Risqi Widya Sulistyani Wiji Lestari Wiji Lestari WIJI LESTARI Wijiyanto Wira Hadikusuma, Ananda Yoga, Pineda Prima Yuana Alifya, Alexsa Yusuf Arifin, Burhan Zakharia, Ade Zakiya Nafis, Muhamad Zulfia Zahra, Kerin