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All Journal Jurnal INSYPRO (Information System and Processing) ARUS JURNAL SAINS DAN TEKNOLOGI
Wahyuni , Titin
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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Analisis dan Pemetaan Amil Zakat Berdasarkan Data Di Kecamatan Rappocini Pada Lazismu Makassar Rachman, Fahrim Irhamna Rachman; Wahyuni , Titin; Setiawan, Tommy Reynaldy; Anas, Lukman
Jurnal INSYPRO (Information System and Processing) Vol 8 No 2 (2023)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v8i2.41231

Abstract

Predicting timely graduation brings numerous benefits not only to students but also to the university LAZISMU is a national-level zakat institution dedicated to community empowerment through the productive use of zakat funds, waqf infaq and other charitable funds from individuals, institutions, companies and other agencies. Founded by PP. Muhammadiyah in 2002 was subsequently confirmed by the Minister of Religion of the Republic of Indonesia as a national amil zakat institution through Decree No. 457/21 November 2002. With the enactment of Law on Zakat number 23 of 2011, Government regulation number 14 of 2014, and Decree of the Minister of Religion of the Republic of Indonesia number 333 of 2015. LAZISMU as a national amil zakat institution has been reconfirmed through the Decree of the Minister of Religion of the Republic Indonesia number 730 of 2016. This study attempts to explain the distribution strategy by mapping Zakat funds to Mustahik and how the decision-making analysis process is actually carried out by the amil zakat institution Lazismu Makassar. The success of zakat management is judged by the productivity and welfare of mustahik. So it is very important to present this study so that it can provide examples, lessons, and solutions to the problem of how to distribute zakat and become a role model. This study also looks at the comparison of mustahik and muzakki in each region so that the number of amil in that region can be determined. This research is qualitative in nature and relies on extracting non-participant observational data from each process. Keywords: Flow of System, K-Means, Input, Validitas, Output.
Implementasi Convolutional Neural Network (CNN) untuk Klasifikasi Penyakit Akut Berdasarkan Gejala Sari, Selvi Permata; Wahyuni , Titin; Rahman , Fahrim Irhamna
Arus Jurnal Sains dan Teknologi Vol 3 No 1: April (2025)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajst.v3i1.1185

Abstract

Penelitian ini bertujuan untuk mengimplementasikan algoritma Convolutional Neural Network (CNN) dalam mengklasifikasikan penyakit akut berdasarkan gejala yang terdapat pada rekam medis pasien. Data yang digunakan dalam penelitian ini diperoleh dari Rumah Sakit Umum Daerah Provinsi Sulawesi Barat, dengan total 1004 data pasien yang mencakup berbagai gejala spesifik. Proses penelitian melibatkan tahap preprocessing data, termasuk pembersihan teks gejala, transformasi data menjadi format numerik, dan normalisasi data untuk memastikan kualitas input. Model CNN dibangun dengan arsitektur yang terdiri dari beberapa lapisan convolutional dan max pooling, serta dioptimasi menggunakan fungsi aktivasi ReLU dan Softmax. Evaluasi model dilakukan dengan membagi data menjadi set pelatihan dan pengujian menggunakan proporsi 80:20, 90:10 dan 70:30. Hasil pengujian menunjukkan bahwa model CNN mampu mencapai tingkat akurasi sebesar 93% pada data uji, dengan nilai precision, recall, dan F1-score yang tinggi di berbagai kelas penyakit. Temuan ini menunjukkan bahwa CNN efektif dalam mengidentifikasi pola gejala untuk klasifikasi penyakit akut. Penelitian ini diharapkan dapat berkontribusi dalam pengembangan sistem pendukung keputusan medis berbasis kecerdasan buatan untuk diagnosis penyakit akut secara lebih cepat dan akurat.
Co-Authors Anas, Lukman Rachman, Fahrim Irhamna Rachman Rahman , Fahrim Irhamna Sari, Selvi Permata Setiawan, Tommy Reynaldy