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All Journal Conference on Innovation and Application of Science and Technology (CIASTECH) Scientific Journal of Informatics G-Tech : Jurnal Teknologi Terapan Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) As-Sidanah : Jurnal Pengabdian Masyarakat JUSTIFY : Jurnal Sistem Informasi Ibrahimy JEECS (Journal of Electrical Engineering and Computer Sciences) (JISCO) Journal of Information System and Computing NJCOM: Community Service Journal
Fatah, Zaehol
Fakultas Sains Dan Teknologi Universitas Ibrahimy
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Klasifikasi Berita Hoaks Di Media Sosial Menggunakan Algoritma Naive Bayes dan RapidMiner Ummul Karimah; Zaehol Fatah
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 Alvina Jelita Firdaus; Zaehol Fatah
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; Zaehol Fatah
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.
Seminar Desain Grafis Untuk Meningkatkan Keterampilan Siswa Di Smk Ibrahimy 1 Sukorejo Ali Muhajir; Ahmad Efendi; Zaehol Fatah
NJCOM: Community Service Journal Vol. 1 No. 2 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15854741

Abstract

Seminar tentang desain grafis ini bertujuan untuk memberikan pemahaman dan wawasan mendalam mengenai pentingnya desain grafis di masa kini. Di area digital saat ini, desain grafis tidak hanya berfungsi sebagai elemen estetika, tetapi juga berperan sebagai alat stategis untuk menyampaikan pesan, membagun edentitas merek, dan daya tarik informssi. Corel DRAW salah satu perangkat lunak yang banyak dipakai untuk desain grafis dalam dunia pendidikan dan dunia kerja. Kemampuan desain grafis menjadi penting bagi siswa SMK. Kegiatan ini di lakukan dengan metode seminar di SMK Ibrahimy 1 Sukorejo. Hasilnya, siswa mampu memahami dan menerapkan desain grafis untuk mengembangkan keterampilan mereka di dunia pendidikan maupun di dunia kerja.
Application of K-means Clustering Data Mining in Grouping Data of People with Disabilities Moh. Bahauddin; Zaehol Fatah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.6

Abstract

Data mining is critical in enabling organizations to derive reliable insights from data. Social welfare remains a significant challenge in Indonesia, particularly for people with disabilities, emphasizing the need for targeted strategies. However, developing research has not used natural characteristics according to disability problems. This study utilizes the K-Means Clustering algorithm to analyze and categorize the population of people with disabilities in East Java. The attributes include the type of disability, population size, and regional distribution. We employs a dataset from the East Java Central Bureau of Statistics, comprising 342 data points across eight attributes, including region, disability type, and year. The analysis involves data preprocessing, transformation, clustering, and evaluation using the Davies-Bouldin Index (DBI). The results identify two optimal clusters, achieving the lowest DBI score of 0.097, indicating high cluster quality. Cluster 0 represents regions with fewer people with disabilities, while Cluster 1 highlights areas with higher populations. These findings provide a foundation for developing more focused and inclusive welfare programs tailored to regional needs, enhancing the quality of life for people with disabilities.
Co-Authors Abdul Jalil Abu Dzarrin Al Ghifari Ahmad Efendi Ahmad Homaidi Ahmad Yogianto Akhlis Munazilin Ali Muhajir Alvina Jelita Firdaus Cindy Nur Dina Alifia Dofiyanto Irma Yunita Jarot Dwi Prasetyo Jarot Dwi Prasetyo Lukman Aliyasin Lukman Fakih Lidimilah Moh. Aqil Mukhtar Alfarera Moh. Bahauddin Neny Yuli Susanti Neny Yuli Susanti Nur Azizah Taufik Saleh Taufik Saleh Ummul Karimah Zainur Rohman