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All Journal CESS (Journal of Computer Engineering, System and Science) ITEj (Information Technology Engineering Journals) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) METIK JURNAL Jurnal Eduscience (JES) Jurnal Informasi dan Teknologi Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Inovasi Masyarakat Indonesia Jurnal Rekayasa Sistem Informasi dan Teknologi Pengabdian Pendidikan Indonesia (PPI) Portal Riset dan Inovasi Sistem Perangkat Lunak Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Rizki Suwanda
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Penerapan Metode Naïve Bayes Dalam Sistem Rekomendasi Pemilihan Program Studi Pendidikan Tinggi Berbasis Website Suwanda, Rizki; Anshari, Said Fadlan; Fhonna, Rizky Putra; Setiawan, Tulus
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15858

Abstract

Pemilihan program studi pendidikan tinggi merupakan keputusan penting yang berdampak pada arah karir dan pengembangan potensi siswa. Namun, banyak siswa mengalami kesulitan dalam menentukan pilihan yang sesuai dengan minat dan kemampuan akademik mereka. Dalam praktiknya, pemilihan program studi masih sering dilakukan secara subjektif tanpa dukungan data atau sistem yang dapat membantu proses pengambilan keputusan secara rasional dan terukur. Penelitian ini bertujuan untuk mengembangkan sebuah sistem rekomendasi berbasis metode Naïve Bayes yang mampu memberikan saran program studi kepada siswa berdasarkan data minat dan prestasi akademik. Metode Naïve Bayes dipilih karena mampu mengklasifikasikan data secara efisien dengan pendekatan probabilistik, meskipun asumsi antar atribut bersifat independen. Sistem ini diharapkan dapat menjadi alat bantu bagi siswa maupun pihak sekolah (seperti guru BK) dalam memberikan arahan akademik berbasis data. Tahapan penelitian dimulai dari studi literatur dan perancangan sistem, dilanjutkan dengan pengumpulan data berupa minat siswa dan nilai akademik, baik melalui dataset simulasi maupun data uji terbatas dari responden nyata. Data tersebut kemudian diproses dan digunakan untuk membangun model klasifikasi menggunakan algoritma Naïve Bayes. Selanjutnya, sistem diuji untuk mengukur akurasi dan efektivitasnya dalam memberikan rekomendasi program studi yang sesuai. Penelitian ini juga mencakup evaluasi sistem berdasarkan hasil klasifikasi serta analisis keterkaitan antara input (minat dan prestasi) dan output rekomendasi program studi.
The Implementation of Support Vector Machine to Analyze Compliance of Land and Building Taxpayers Nurul Nafisa; Rozi Kesuma Dinata; Rizki Suwanda
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Land and Building Tax (LBT) is an important source of revenue for local governments, supporting development and community welfare. However, low taxpayer compliance rates often pose a challenge in achieving the targets for Local Own-Source Revenue (LOSR). This study aims to develop a data-driven classification system to map areas with varying levels of LBT taxpayer compliance in Lhokseumawe City and to implement the Support Vector Machine (SVM) method to improve the accuracy of predicting taxpayer compliance. The research data was obtained from the Regional Financial Management Agency (RFMA) of Lhokseumawe City, encompassing LBT data from 2021 to 2023, with variables such as principal amount, penalties, total payments, due dates, and payment dates. This classification system divides taxpayers into two categories: Compliant and Non-Compliant. The results of testing the SVM model indicate that Banda Sakti sub-district has a compliance rate of 98%, Muara Satu has a compliance rate of 99%, Muara Dua has a compliance rate of 99%, and Blang Mangat has a compliance rate of 100%. The accuracy metrics from the implementation of the Support Vector Machine method for assessing land and building tax compliance show a Precision of 86%, a Recall of 100%, and an Accuracy of 86%. By applying the SVM method, it is hoped that there will be an increase in efficiency in the tax collection and management processes, thereby optimally increasing Local Own-Source Revenue (LOSR) and supporting better regional development.
Implementation of the Naïve Bayes Method in a Web-Based Fish Species Classification System Rizki Suwanda; Muhammad Fikry; Said Fadlan Anshari
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

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Abstract

The current fish resources are abundant, and the discovery of new species has increased the variety of fish in the ocean. These fish are categorized into three groups: demersal, pelagic, and reef fish, each with unique characteristics of their respective groups. The manual classification process for large datasets requires a long time and involves complex procedures. With the advent of data and information technology, it is now possible to recognize and identify several fish species found in the ocean, which can be classified into the three groups. To simplify this classification process, a web-based system has been developed to classify fish into these groups. The data to be processed in this research will be classified using the Naive Bayes method to address this issue. This technique utilizes large datasets to extract information that was previously unknown or inaccessible, and it can provide accurate information for various purposes. The data for this study will be collected from various internet references and direct data obtained from fish landing sites (TPI) in Lhokseumawe and North Aceh. Additionally, a literature review method will be used to complement the data analysis process. The development of the web-based system will be implemented to facilitate the classification of fish species based on the existing data.
Co-Authors ., Bustami Absa, Munzir Adek, Rizal Tjut Amanda, Sherly Dwi Ananda, Ria Anggara, Aji Ayu Rosmala, Ayu Rosmala Bayu Winata, Bayu Bustami Dahlan Abdullah Dendy K. Pramudito Desi Efendi, Syahril Erna Budhiarti Fadillah, Rizky Fanita Fanita, Fanita Fatih Dwi Laksana Haj, Shafly Ulya Halimatus Sakdiah, Halimatus Harapan, M. Khoiruddin Husna, Nur Fazri Judijanto, Loso Kamilah, Muna Laksana, Fatih Dwi Muchlis Abdul Muthalib Muhammad Daud Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Iqbal Muhammad Zakaria Muna, Ninal Munirul Ula Munzir Nazaruddin Ahmad Nur Mauliza Nurdin Nurdin Nurul Nafisa Rangkuti, Irsan Efendi Raudhatul Fazira Rivalzi, Tri Ananda Rizal Rizal Rizal Rizky Putra Fhonna Roslaini, Roslaini Rozi Kesuma Dinata Rozzi Kesuma Dinata Sagala, Wulandari Shakinahwati Said Fadlan Anshari Sartika, Tiara Syibral Malasyi, Syibral Taufiq Taufiq Tulus Setiawan Wardina Ningsih Yudhi Franata Zahra, Annisa Afrilia Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar