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All Journal Venus: Jurnal Publikasi Rumpun Ilmu Teknik Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
V.H Valentino
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

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Implementasi Sistem Pakar dalam Kalkulasi Bantuan Korban Bencana Alam dengan Metode Help Victims of Natural Disasters Calculation Using Expert System Senna Hendrian; Muhammad Tri Habibie; Ade Kurnia Solihin; Umar Wirantasa; Wisdariah Wisdariah; Gerie Munggaran; V.H Valentino
Venus: Jurnal Publikasi Rumpun Ilmu Teknik  Vol. 3 No. 2 (2025): Venus: Jurnal Publikasi Rumpun Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/venus.v3i2.806

Abstract

Handling natural disaster victims requires a fast, precise, and fair aid distribution process. In this context, expert systems can be utilized as a decision-making tool in determining the type and amount of aid that should be given to victims. This article develops a desktop-based expert system using the Java programming language, which is able to calculate the type of aid based on the condition of the victim, the level of damage, and the number of affected family members. The method used is a rule-based expert system with if-then logic. The results show that this system can assist field officers in accelerating the calculation and distribution of aid.
Implementasi Klasifikasi Datamining dengan Algoritma C4.5 untuk Rekomendasi Pemilihan Fakultas Perguruan Tinggi Berdasarkan Minat dan Bakat Siswa SMK Senna Hendrian; V.H Valentino; Wisdariah, Wisdariah; Riezca Talita Trista; Dudi Parulian
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1159

Abstract

Selecting a faculty that aligns with students’ interests and talents is a strategic step in determining the success of higher education and future career paths. However, most vocational high school (SMK) students still face difficulties in identifying the most suitable faculty due to the lack of data-driven analysis. This study implements the C4.5 classification algorithm within data mining techniques to build an automatic and measurable faculty recommendation system. The dataset consists of attributes such as SMK major, interest level, aptitude test results, academic grade average, and gender, with the output being the recommended faculty. The C4.5 algorithm was chosen for its ability to generate a transparent and interpretable decision tree, which helps both guidance counselors and students understand the rationale behind the recommendations. The experimental results show that the constructed classification model achieved an accuracy rate of 88%, based on cross-validation testing using data from 12th-grade students. The implementation of this system is expected to serve as an objective tool in the faculty selection process and to promote a data-driven decision-making approach in secondary education environments.
Co-Authors Ade Kurnia Solihin Dudi Parulian Gerie Munggaran Muhammad Tri Habibie Riezca Talita Trista Senna Hendrian Umar Wirantasa Wisdariah Wisdariah Wisdariah, Wisdariah