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All Journal Jurnal TIMES Conference on Innovation and Application of Science and Technology (CIASTECH) JURNAL PENDIDIKAN TAMBUSAI JSI (Jurnal sistem Informasi) Universitas Suryadarma Proceedings of Annual Conference for Muslim Scholars Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Teknologi Kimia Unimal INOTEK Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal Teknik Terapan (J-TETA) JUSIFOR : Jurnal Sistem Informasi dan Informatika Journal of Artificial Intelligence and Digital Business Adopsi Teknologi dan Sistem Informasi Nuras : Jurnal Pengabdian Kepada Masyarakat Eastasouth Journal of Effective Community Services JUSTIFY : Jurnal Sistem Informasi Ibrahimy COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Mutiara: Multidiciplinary Scientifict Journal Jurnal Cendekia Mengabdi Berinovasi dan Berkarya Abdi Samulang : Jurnal Pengabdian Kepada Masyarakat SmartComp Jurnal Pengabdian Sosial Jurnal Ilmiah Sistem Informasi JUAN: Jurnal Pengabdian Nusantara ZONA: Jurnal Pengabdian Masyarakat JAR'S (Journal of Advanced Research in Informatics) Jurnal Pengabdian Masyarakat Ekonomi dan Bisnis Digital Jurnal Ilmiah Sistem Informasi JUKEMAS : Jurnal Pengabdian Kepada Masyarakat (JISCO) Journal of Information System and Computing Jurnal Pengabdian Masyarakat Multidisiplin
Fatah, Zaehol
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Journal : jisco journal of information system and computing

 1 
Klasifikasi Berita Hoaks Di Media Sosial Menggunakan Algoritma Naive Bayes dan RapidMiner Karimah, Ummul; Fatah, Zaehol
JISCO : Journal of Information System and Computing Vol 3 No 2 (2025): Jurnal of Information System and Computing
Publisher : UIN Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/jisco.v3i2.4028

Abstract

The development of information technology and social media has made the distribution of information easier, but it has also increased the prevalence of fake news or hoaxes. This research aims to classify hoax and non-hoax news on social media using the Naïve Bayes algorithm with the assistance of the RapidMiner application. The data used is secondary data obtained from the Kaggle website and processed thru text preprocessing stages including tokenization, stopword removal, stemming, and TF-IDF weighting. The classification process was carried out using the Cross Validation method to measure model performance. The research results show that the Naïve Bayes algorithm has an accuracy of 90.20%, and precision values of 92.25% for the hoax class and 88.33% for the non-hoax class, with recall values of 87.78% and 92.62% respectively. These values indicate that the built classification model can easily identify hoax news. Thus, the Naïve Bayes algorithm has proven to be effective and efficient for use as a method for detecting fake news on social media. Keywords: Naïve Bayes, RapidMiner, Classification, Hoax News, Text Mining
Prediksi Resiko Penyakit Menggunakan Algoritma Random Forest sebagai Upaya Pencegahan Kesehatan Masyarakat Firdaus, Alvina Jelita; Fatah, Zaehol
JISCO : Journal of Information System and Computing Vol 3 No 2 (2025): Jurnal of Information System and Computing
Publisher : UIN Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/jisco.v3i2.4029

Abstract

Chronic diseases influenced by lifestyle factors are a crucial public health issue, while predictive models are often limited by class imbalance and a lack of clinical interpretability. This research aims to build an accurate and transparent disease risk prediction model based on lifestyle factors. The method used is hybrid classification, combining the Random Forest algorithm with the SMOTE (Synthetic Minority Oversampling Technique) technique to effectively address the initial data imbalance (3:1 ratio) in the Health Lifestyle Dataset. This balanced data was then split 80:20 for testing. The test results show the model achieved an aggregate accuracy of 74.43%, with strong precision (79%) for the risk class, indicating prediction reliability. Feature Importance analysis provides significant clinical insights, identifying Daily Water Intake (water_intake_l) and Sleep Duration (sleep_hours) as the most dominant predictive factors, even surpassing physiological factors. The conclusion indicates that this hybrid approach is effective as an early screening instrument, with the main advantage being the transparency of lifestyle variable interpretation, which directly supports data-driven prevention strategies
Penerapan Algoritma Decision Tree untuk Klasifikasi Kelulusan Mahasiswa Berdasarkan Faktor Akademik dan Sosial Dofiyanto; Fatah, Zaehol
JISCO : Journal of Information System and Computing Vol 3 No 2 (2025): Jurnal of Information System and Computing
Publisher : UIN Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/jisco.v3i2.4030

Abstract

This research aims to employ the C4.5 Decision Tree technique to classify the results of student graduation. This is achieved by taking into account both their scholastic performance and social factors. Scholastic performance indicators encompass the student's overall grade average, their academic status, and how often they attend classes, whereas social factors include their age, whether they are married, and their engagement in extracurricular activities. The information utilized was taken from an internal compilation of student information, which was refined and modified with the RapidMiner program. To ensure the correctness of the predictions, the categorization model was confirmed through the implementation of a 10-fold cross-validation strategy. The results of the tests demonstrated an 89.44% level of correctness, as well as a 91.38% level of precision and a 90.28% rate of recall, showing that the model functions at a level that is both remarkably successful and reliable. These discoveries reinforce the idea that the C4.5 Decision Tree algorithm is capable of accurately determining the patterns in student graduation through the integration of both scholastic and social elements. This can then act as a foundation for making scholastic decisions to improve the efficiency of the process of higher education.
Co-Authors ., Arwani A'yun, Qurrotul ABDUS SAMAD Abrori, Syariful Adi Susanto Adiba, Naila Afandi, Muhammad Dzikry Afifah, Fatma Nur Agustin, Riana Ahmad Afandi Ahmad Homaidi Aini, Imroatin Arifatul Aizah, Nur Akhlis Munazilin, Akhlis Al Qayyis, Zainul Arifin Alwi Alfiansyah, Noer Dian Alwi, As'ad Anam, Baitul Anisa, Halifatus Ardiansyah, Lukman Hakim Arsyad, M. Qoyis Auza’I Asrori, Muhammad Ilzam Atika, Komang Nitari Atreji, Reza Aziz, Hamdan Fauzi Nur Badriyah, Siti Sarifatul Badrus Sholeh Baijuri, Achmad Billah, Moch. Hegal Muktasim Candra, Ongky Ali Damayanti, Alfina Damayanti Diandhita, Safna Dofiyanto Dwi Norrepa Efendi, Ahmad Fadil Dwi fadhila, nor Fahimurridho, Ahmad Faidah, Mutmainnah Ilmiatul Fawaid, Heri Nur Febrian, Fikri Fernando, Ahmad Wahyu Firdaus, Alvina Jelita Firman Santoso Firmandala, Legi Octa Sofyan Ghani, Farhan Ahnaf Hakim, Syafiq Ilham Hakimah, Milla Halilatul Muallafa Hamdani Hamdani Hamdani, A. Haq, Muhammad Nabil Dhiya’ul Hasan Basri Hermawati, Rosa Holidiyah, Na'imatul Homaida, Nur Ida Ayu Putu Sri Widnyani Ikman, Ahmad Maulana Ikormi, Kamilul irma yunita Ismael, Kharisma Ayu Jundanuddin, Muhammad Karimah, Ummul khasanah, wardhatun Khoirunisak, Dewi Kintari, Putri Lazim, Farihin Lidimilah, Lukman Lidimilah, Lukman Fakih Lutfi, Zainul Lutfiana, Nurisma Maghfiro, Maidatul Mahdi, Aminullah Moh. Mahtum, Rohiqim Majid, Muhammad Sabilillah Marwan Marwan Maulana, Moh. Iqbal Muasaroh, Yurida Islahatul Muhammad, Aqil Furqoni Mulianingsih, Suci Muqtadir, Faidhul Nafi'ah, Lailin Naqibuzzahidin, Naqibuzzahidin Nazila, Jamilatun Nur Indahsari, Luluk Nurdiana, Alfi Nurhasanah, Yeni Nuril Maghfiratus Sholeha, Siti Prasetyo, Jarot Dwi Qori'ah, Arafah Amaliyah Qori'ah, Arifah Amaliyah Rahmawati, Nurul Qolbi Rofiq, Ahmad Tahqiq Safitri, Lulu Saleh, Taufik Samsul Arifin Saputra, Zuhrian Nur Sari, Iin Puspita Savira, Anggita Yusiana Shidqin, Lalu Habil Mudkhola Sholeha, Nur Faliatus Sinta, Sinta Masruroh Siti Khoiriyah Sobri, Miftahus Syakirin, Muhammad Irham Taufik Hidayat Tholibah, Wafrotut Uswatul Muftakiyah Vadilah, Nur Jihan Wilda, Sufil Zubairi, Ach. Zulkarnain, Bagus Maulana