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All Journal PROCEEDING IC-ITECHS 2014 JURNAL MEDIA INFORMATIKA BUDIDARMA
Tajuddin, Taniza
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Implementasi Teknologi Web Geospasial dan Decision Tree untuk Klasifikasi Sebaran Pengumpulan Zakat Susanti, Pratiwi; Asyhari, Moch Yusuf; Juwari, Juwari; Ahmad, Khairul Adila; Shamsuddin, Norin Rahayu; Tajuddin, Taniza
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7027

Abstract

As one of the five pillars of Islam, zakat significantly contributes to socio-economic development and growth in a region. Zakat collection in the city of Madiun has various challenges, including the motivation of muzakki as zakat payers. Zakat is an obligation for someone with assets or income exceeding basic needs to fulfill their living needs. One way to increase motivation to pay zakat is using geospatial web technology to map muzakki in the city of Madiun. Muzakki report data, which previously had the form of a graphic diagram, was changed to a more interactive geospatial form. Several essential advantages in its implementation include embedding precise coordinate points to facilitate the geospatial-based monitoring process, important notes and information about muzakki, and the classification of zakat collection, including collecting zakat in high, medium, and low amounts. This method can represent muzakki mapping more easily and clearly so that zakat institutions (BAZNAS) can quickly determine their policies to increase zakat collection.
SHIELD: Symptom-Based Hybrid Intelligent Early Learning for Disease Prediction Fazil Akashah, 'Asrul 'Azeem; Tajuddin, Taniza
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1677

Abstract

Traditional diagnostic approaches often face delays and inaccuracies, while standalone machine learning models fail to account for individual uniqueness. The SHIELD system leverages hybrid machine-learning models to enhance disease prediction based on patient symptoms. This study integrates Gradient Boosting, Decision Trees, and Random Forest models, combining their strengths using an ensemble voting approach. A comprehensive dataset from Kaggle, enriched with symptom severity mappings, enables accurate and personalized predictions. The system delivers practical outputs, including disease names, descriptions, and home remedies, through a user-friendly web interface. Achieving an accuracy of approximately 99.59% with the ensemble model, SHIELD demonstrates its potential to revolutionize early disease detection, aligning with global health objectives.
Co-Authors Ahmad, Khairul Adila Aminuyati Fazil Akashah, 'Asrul 'Azeem Moch Yusuf Asyhari Pratiwi Susanti Shamsuddin, Norin Rahayu