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Rekomendasi Penentuan Prioritas Mustahik Pada BAZNAS Pontianak Menggunakan AHP-TOPSIS: Rekomendasi Penentuan Prioritas Mustahik Pada BAZNAS Pontianak Menggunakan AHP-TOPSIS wahyu, Wahyu; Siregar, Alda Cendekia; Pangestika, Menur Wahyu
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11058

Prioritas mustahik menimbulkan tantangan signifikan dalam administrasi zakat, karena prosesnya sering dipengaruhi oleh faktor subjektif dan kurangnya efisiensi. Studi ini bertujuan untuk merancang Sistem Pendukung Keputusan (SPK) yang dapat memfasilitasi BAZNAS Pontianak untuk memprioritaskan mustahik dengan pendekatan objektif, sistematis, dan transparan, sehingga alokasi zakat menjadi lebih tepat sasaran. Metodologi yang diterapkan meliputi AHP untuk menentukan bobot kriteria melalui perbandingan berpasangan, dan Teknik untuk Urutan Preferensi berdasarkan Kesamaan dengan Solusi Ideal (TOPSIS) untuk memberi peringkat berdasarkan kedekatan dengan solusi ideal. Sistem yang dikembangkan adalah aplikasi web berbasis framework Laravel, yang dievaluasi menggunakan teknik black-box dan MAPE. Evaluasi menghasilkan nilai MAPE sebesar 0,131%, menunjukkan akurasi sistem yang luar biasa tinggi. Temuan ini menegaskan bahwa sistem ini mampu meningkatkan transparansi dan objektivitas distribusi zakat, dan berpotensi untuk diimplementasikan oleh lembaga amil zakat lainnya.

Classification Of Stunting In Toddlers Using The Random Forest Method Sutarti Dwi Yanti; Menur Wahyu Pangestika; Alda Cendekia Siregar
International Journal of Health Engineering and Technology Vol. 5 No. 1 (2026): IJHET MAY 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v5i1.786

Monitoring and data collection related to stunting at several community health centers (Puskesmas) in Ketapang Regency play a crucial role in assessing optimal growth and development of fetuses and newborns. One of the recurring issues in Ketapang Regency is the inaccuracy and inconsistency in monthly stunting data collection. This study aims to design a stunting classification model for children under five using the random forest method and to evaluate its classification accuracy. The research follows several stages problem identification, literature review, data collection, data processing, testing, and drawing conclusions. The performance of the random forest method is assessed to determine the impact of each stage on the model’s classification ability. Evaluation metrics are derived from the confusion matrix. The confusion matrix results show a recall of 97%, precision of 96%, F1-score of 96%, and an accuracy of 91%, indicating that the random forest method performs excellently in classifying nutritional status.

Diagnosa Penyakit Jagung Menggunakan Dempster Shafer Tanaman Pangan Dan Hortikultural Kalimantan Barat Putri Nabila Aprilia; Menur Wahyu Pangestika; Alda Cendekia Siregar
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3833

Disease attacks on corn plants can reduce productivity, requiring a system capable of rapid and accurate diagnosis. This study aims to develop a web-based expert system for diagnosing corn plant diseases using the Dempster-Shafer method. The system was built using a knowledge base obtained from experts at the Food Crops and Horticulture Service of West Kalimantan Province. The variables processed included symptom data, disease type, belief value, and control solutions. The diagnosis process was carried out by combining the belief value for each symptom using the Dempster-Shafer combination rule to generate a confidence level for the disease. Testing was conducted by comparing the system's diagnosis results with expert diagnoses on 20 test data sets. The test results showed the system achieved 90% accuracy, indicating that the Dempster-Shafer method is effective in handling symptom uncertainty and is capable of producing accurate corn plant disease diagnoses.Keyword: Corn Plants; Expert System; Dempster Shafer AbstrakSerangan penyakit pada tanaman jagung dapat menurunkan produktivitas sehingga diperlukan sistem yang mampu membantu diagnosis secara cepat dan akurat. Penelitian ini bertujuan mengembangkan sistem pakar berbasis web untuk mendiagnosis penyakit tanaman jagung menggunakan metode Dempster-Shafer. Sistem dibangun menggunakan basis pengetahuan yang diperoleh dari pakar di Dinas Tanaman Pangan dan Hortikultura Provinsi Kalimantan Barat. Variabel yang diproses meliputi data gejala, jenis penyakit, nilai belief, dan solusi pengendalian. Proses diagnosis dilakukan dengan menggabungkan nilai belief setiap gejala menggunakan aturan kombinasi Dempster-Shafer untuk menghasilkan tingkat keyakinan terhadap penyakit. Pengujian dilakukan dengan membandingkan hasil diagnosis sistem terhadap diagnosis pakar pada 20 data uji. Hasil pengujian menunjukkan sistem memperoleh akurasi sebesar 90%, sehingga metode Dempster-Shafer efektif dalam menangani ketidakpastian gejala dan mampu menghasilkan diagnosis penyakit tanaman jagung secara akurat.Kata kunci: Desmpster Shafer; Diagnosis; Penyakit Jagung; Sistem Pakar

PERBANDINGAN METODE COSINE SIMILARITY DAN JACCARD SIMILARITY PADA SISTEM TEMU KEMBALI INFORMASI PENCARIAN ARSIP DOKUMEN Melda Melda; Sucipto Sucipto; Menur Wahyu Pangestika
NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Vol 13, No 8 (2026): NUSANTARA: Jurnal Ilmu Pengetahuan Sosial
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jips.v13i8.2026.2186-2192

Pengelolaan arsip dokumen pada instansi pemerintahan memegang peranan penting dalam mendukung pelayanan administrasi yang efektif. Namun, proses pencarian arsip yang masih dilakukan secara manual di Kantor Kecamatan Pontianak Selatan menyebabkan pencarian dokumen membutuhkan waktu yang lama dan berisiko menimbulkan kesalahan. Penelitian ini bertujuan merancang sistem temu kembali informasi berbasis web serta membandingkan kinerja metode Cosine Similarity dan Jaccard Similarity dalam pencarian arsip dokumen. Metode penelitian meliputi pengumpulan data melalui observasi, wawancara, dan studi literatur, dilanjutkan dengan tahapan preprocessing teks, pembobotan TF-IDF, implementasi algoritma, serta pengujian menggunakan confusion matrix dan User Acceptance Testing (UAT). Hasil penelitian menunjukkan bahwa metode Cosine Similarity memiliki performa yang lebih baik dibandingkan Jaccard Similarity dengan nilai accuracy 72,6%, precision 100,0%, recall 72,6%, F1-score 84,1%, dan waktu pemrosesan 0,091 detik. Sementara itu, Jaccard Similarity memperoleh accuracy 26,4%, precision 100,0%, recall 26,4%, F1-score 41,8%, dan waktu pemrosesan 0,709 detik. Dengan demikian, Cosine Similarity lebih efektif digunakan pada sistem pencarian arsip dokumen karena mampu menghasilkan tingkat relevansi dan efisiensi pencarian yang lebih baik.

Diagnosa Penyakit Jagung Menggunakan Dempster Shafer Tanaman Pangan Dan Hortikultural Kalimantan Barat Putri Nabila Aprilia; Menur Wahyu Pangestika; Alda Cendekia Siregar
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3833

Disease attacks on corn plants can reduce productivity, requiring a system capable of rapid and accurate diagnosis. This study aims to develop a web-based expert system for diagnosing corn plant diseases using the Dempster-Shafer method. The system was built using a knowledge base obtained from experts at the Food Crops and Horticulture Service of West Kalimantan Province. The variables processed included symptom data, disease type, belief value, and control solutions. The diagnosis process was carried out by combining the belief value for each symptom using the Dempster-Shafer combination rule to generate a confidence level for the disease. Testing was conducted by comparing the system's diagnosis results with expert diagnoses on 20 test data sets. The test results showed the system achieved 90% accuracy, indicating that the Dempster-Shafer method is effective in handling symptom uncertainty and is capable of producing accurate corn plant disease diagnoses.Keyword: Corn Plants; Expert System; Dempster Shafer AbstrakSerangan penyakit pada tanaman jagung dapat menurunkan produktivitas sehingga diperlukan sistem yang mampu membantu diagnosis secara cepat dan akurat. Penelitian ini bertujuan mengembangkan sistem pakar berbasis web untuk mendiagnosis penyakit tanaman jagung menggunakan metode Dempster-Shafer. Sistem dibangun menggunakan basis pengetahuan yang diperoleh dari pakar di Dinas Tanaman Pangan dan Hortikultura Provinsi Kalimantan Barat. Variabel yang diproses meliputi data gejala, jenis penyakit, nilai belief, dan solusi pengendalian. Proses diagnosis dilakukan dengan menggabungkan nilai belief setiap gejala menggunakan aturan kombinasi Dempster-Shafer untuk menghasilkan tingkat keyakinan terhadap penyakit. Pengujian dilakukan dengan membandingkan hasil diagnosis sistem terhadap diagnosis pakar pada 20 data uji. Hasil pengujian menunjukkan sistem memperoleh akurasi sebesar 90%, sehingga metode Dempster-Shafer efektif dalam menangani ketidakpastian gejala dan mampu menghasilkan diagnosis penyakit tanaman jagung secara akurat.Kata kunci: Desmpster Shafer; Diagnosis; Penyakit Jagung; Sistem Pakar

Continuous Sign Language Recognition for Quranic Recitation by Deaf People Using Deep Learning Yulrio Brianorman; Rinaldi Munir; Nur Ulfa Maulidevi
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1600

This study proposes a deep learning-based system for recognizing Quranic recitation in the sign language, aimed at enhancing accessibility for the Deaf Muslim community. A central contribution is the construction of a novel dataset comprising videos from three Deaf signers performing Surah Al-Fatihah and Surah Al-Ikhlas, guided by the 2022 official Quranic sign language standard introduced by Indonesia’s Ministry of Religious Affairs. The recognition task is framed as a continuous sign language recognition (CSLR) problem to handle unsegmented input sequences. Five pre-trained convolutional neural networks—EfficientNet, GoogleNet, MobileNetV2, ResNet18, and ShuffleNet—were evaluated as visual feature extractors. These were followed by a temporal encoder composed of 1D CNN and BiLSTM, with sequence alignment performed using the Connectionist Temporal Classification (CTC). The experimental results show that ResNet18 and MobileNetV2 achieved the best performance with Word Error Rates (WER) of 5.00% and 7.92% on the test set, respectively. A cross-participant evaluation was also conducted to assess model generalization, although the results revealed performance gaps likely due to signer variation and limited data. The study highlights the suitability of lightweight and residual architectures for CSLR tasks in religious contexts and provides a benchmark for future research on inclusive sign language technologies. In cross-participant evaluation, the model achieved a validation WER of 8.44% on seen signers and 50.46% on an unseen signer, reflecting generalization challenges commonly observed in low-resource CSLR settings. The proposed system lays the groundwork for AI-assisted Quranic education tools tailored to the Deaf Muslim population.

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