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All Journal Venus: Jurnal Publikasi Rumpun Ilmu Teknik Jurnal Sistem Informasi dan Ilmu Komputer
Cecilia D.P.B Gabriel
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

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Sistem Pakar Mendiagnosa Penyakit pada Ikan Nila dengan Metode Vcirs Yuliana Sera Bora; Cecilia D.P.B Gabriel; Maria Wilda Malo
Venus: Jurnal Publikasi Rumpun Ilmu Teknik  Vol. 3 No. 1 (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.v3i1.710

Abstract

An expert system for diagnosing diseases in tilapia is one solution to help fish farmers detect and treat diseases that attack tilapia. In this study, an expert system was developed using the VCIRS (Voting Classifier for Integrated Rule Set) method to diagnose tilapia diseases based on the symptoms shown. The VCIRS method was chosen because of its ability to combine several classifiers to improve diagnostic accuracy. This system allows users, especially fish farmers, to enter symptoms observed in tilapia and obtain a diagnosis of possible diseases and appropriate treatment recommendations. The evaluation results showed that this system has a good level of accuracy in diagnosing tilapia diseases, by providing fast and accurate results, and making it easier for fish farmers to make decisions related to fish health. This expert system is expected to increase the productivity of tilapia cultivation by reducing the mortality rate of fish due to diseases that are not detected quickly.
Sistem Pendukung Keputusan Penentuan Kelayakan Pensiunan Pegawai ASN dengan Model MADM Arnoldus Lamber Gai; Cecilia D.P.B Gabriel; Agustina Purnami Setiawi
Venus: Jurnal Publikasi Rumpun Ilmu Teknik  Vol. 3 No. 1 (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.v3i1.711

Abstract

Decision Support System (DSS) is a system used to assist decision makers in determining the best alternative based on certain criteria. Determining retirement eligibility for ASN (State Civil Apparatus) employees is one of the important aspects in human resource management, where this decision affects the future of employee careers, their welfare, and state budget management. This study proposes the development of a decision support system for determining retirement eligibility for ASN employees using the Multi-Attribute Decision Making (MADM) method. The MADM method is a decision-making technique that considers several criteria and alternatives to produce optimal decisions. In this study, the criteria used to determine retirement eligibility include retirement age, length of service, work performance, health, and other factors relevant to the applicable pension policy. The method used in this system includes several steps, namely identifying criteria, assigning weights to each criterion, and evaluating alternatives based on these criteria using calculation techniques such as AHP (Analytical Hierarchy Process) or TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The results of this evaluation will provide recommendations on whether an employee is eligible for retirement or can still continue his career. The developed system is expected to facilitate the decision-making process regarding ASN employee retirement by providing objective and transparent analysis, so that the decisions taken are more appropriate and in accordance with applicable policies. Thus, this system can improve efficiency and effectiveness in human resource management in government agencies.
Jaringan Saraf Tiruan untuk Memprediksi Jumlah Pertumbuhan Penduduk di Dinas Kependudukan dan Pencatatan Sipil Kabupate Sumba Barat Daya Rolan Semis Dangga; Cecilia D.P.B Gabriel; Karolus Wulla Rato
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4247

Abstract

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.
Klasifikasi Penyakit Demam Berdarah Dengue dengan Menggunakan Algortima K-Mens Fredi Gaji; Cecilia D.P.B Gabriel; Karolus Wulla Rato
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4251

Abstract

Dengue fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of the Aedes aegypti mosquito. This disease is a major health problem in many tropical countries, including Indonesia. Identification and classification of DHF patients is very important to prevent further spread and to provide appropriate medical treatment. In this study, the classification of DHF disease is carried out using the K-Means algorithm, which is one of the methods in machine learning used to classify data based on similarity of features. This study aims to apply the K-Means algorithm in classifying DHF cases based on data on symptoms that appear in patients, such as high fever, joint pain, skin rashes, and others. The data used includes patient medical records that record various clinical and demographic parameters. The K-Means algorithm is used to group the data into clusters that describe the severity category or potential risk of dengue disease. The results showed that the K-Means algorithm can be used to cluster DHF patients well, with the division of groups that can describe the severity of the disease. Evaluation was conducted using metrics such as silhouette and cluster validity to assess the effectiveness of the algorithm in performing classification. This model is expected to help medical personnel in decision-making, provide early warning, and improve rapid response to dengue cases.
Co-Authors Agustina Purnami Setiawi Arnoldus Lamber Gai Fredi Gaji Karolus Wulla Rato Maria Wilda Malo Rolan Semis Dangga Yuliana Sera Bora