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All Journal PROSIDING SEMINAR NASIONAL Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Sains Matematika dan Statistika Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) SOSIOHUMANIORA: Jurnal Ilmiah Ilmu Sosial dan Humaniora ASRO JOURNAL - STTAL ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Aplikasinya Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Indonesian Journal of Business Analytics (IJBA) Jurnal Pengabdian Mandiri Prosiding Seminar Nasional Official Statistics Jurnal Satya Informatika SAINTEK Emerging Statistics and Data Science Journal
Edy Widodo
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Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Social Sciences Other

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Optimasi Prediksi Diabetes Mellitus Menggunakan Komparasi Random Forest dan SVM dengan Analisis Pemilihan Fitur Berbasis SHAP Aswan Supriyadi Sunge; Dendy K. Pramudito; Abdul Halim Anshor; Edy Widodo
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Diabetes Mellitus merupakan masalah kesehatan di dunia yang sangat signifikan maka dari itu dibutuhkan prediksi dini dan akurat. Penelitian ini bertujuan untuk mengoptimalkan prediksi dengan membandingkan model Machine Learning (ML) dengan Random Forest dan Support Vector Machine, yang ditingkatkan dengan analisis SHAP (SHapley Additive exPlanations) untuk mencari fitur tertinggi atau berpengaruh. Penelitian ini menggunakan dataset yang terdiri dari 1000 data pasien dengan 14 fitur, dan 1 kelas. Preprocessing melibatkan pembersihan data dan duplicate, dilanjutkan dengan testing dan training data, dan hasil pengujian dengan model Random Forest mendapatkan akurasi 99%, sementara SVM mencapai 86%, lalu pengujian analisis SHAP mengungkapkan bahwa Age, Urea dan Kreatinin adalah fitur yang paling berpengaruh dari fitur yang lainnya. Hasil analisis perbandingan menunjukkan bahwa mengungguli dalam hal akurasi prediksi secara keseluruhan, dan ini sangat berkontribusi pada peningkatan metode prediksi yang optimal dan sebagai parameter klinis utama untuk diagnosis.
Penerapan Data Mining Menggunakan Metode Naïve Bayes Untuk Menentukan Faktor Yang Mempengaruhi Kelulusan Dan Ketidaklulusan Mahasiswa Di Universitas Pelita Bangsa Edy Widodo; Ditya Lambang Setyawan
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Data mining is the process of discovering patterns from datasets to generate information that can be used for prediction based on historical data. This study aims to analyze the factors influencing student graduation and non-graduation at Universitas Pelita Bangsa using the Naïve Bayes method. Data processing was conducted through manual calculations, Microsoft Excel, and RapidMiner, producing consistent evaluation results with an accuracy of 68.18%, precision of 33.33%, and recall of 16.67%. The findings indicate that the Naïve Bayes method can be effectively applied to predict student graduation factors with acceptable accuracy, making it a suitable approach for analyzing graduation data and supporting academic decision-making processes.
Sistem Pakar Diagnosa Kerusakan Komputer Dengan Metode Case Based Reasoning Pada Toko Click Komputer Karawang Edy Widodo; Anisa Devi Asmara
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Computer technicians often require a significant amount of time to diagnose computer damage, and in many cases, the process is delayed while they determine the appropriate solution. To address this issue, an expert system can be developed to provide faster and more accurate diagnoses of computer problems. This study applies the Case Based Reasoning (CBR) method, an artificial intelligence approach that solves new problems by referring to solutions from previous similar cases. By comparing current symptoms with stored cases in the system, appropriate recommendations can be generated efficiently. The system is developed using the PHP programming language, while MySQL is utilized as the database for storing case data and solutions. Through this implementation, the expert system is expected to assist technicians in identifying types of computer damage quickly, precisely, and accurately. As a result, it can reduce diagnostic time, improve work efficiency, and support technicians in delivering proper handling and effective solutions for computer-related issues.
Co-Authors Abdul Halim Anshor Abdul Kholiq Adindach Syadza Rizkia Afifi Indrasari Alfarid, Muhammad Ridho Alfarid Alhaqq, Faisal Hakim Akbar Ali Mashudi Alifyah, Hasna Alivia Ayudhi Asmaradana Amali, Amali Ami Mulya Pratiwi Andri Firmansyah Andrian Andrian Andriani Andriani Anisa Devi Asmara Anissa Karmila Islami Asdan Tri Madani Asep Arwan Sulaeman Aswan Supriyadi Sunge Ayu Mutmainnah Azanzi Bagus Pratama Bella Destia Butsianto, Sufajar Dendy K. Pramudito Dian Aditya Dian Bestriandita Dita Prima Tri Hapsari Ditya Lambang Setyawan Eko Heru Firdauz Elen Prihana Putri Fauziah , Sifa Febi Permata Putri Ginastuti, Ajrina Putri Ikhwan Syahtaria Indradewa, Rhian Latifah Nur Laila Lintang Amir Faiq Lintang Puspita Maulana, Rifki Ryan Muhamad Fatchan Muhamad Rizki Sahputra Muhammad Irfan Hayyi Muhammad Raihan Al Anshori Muhammad Sukriansyah Muhammad Tanaka Mulia Hidayahti, Rate Mulia Hidayahti Nanda Lailatul Humairoh Ninda Nurul Halim Pramudita, Zalfa Nindia Puspita Putri Nabilah Putri Ermayani Rafika Aristawidya Rias Monica Putri Ridhwan Mustajab Riefyal Arshyzha Mustain Riski Pratama Ramadhan Riyana Putri Rizki Maliki Zidni Rizky Normelia Sukarno Sukarno Surya Purnama Titian Dewi Fortuna Wulan Maghfirotul Fauziah Yunita Hilda