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Kapti
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Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics

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Analisis Perbandingan Metode SAW (Simple Additive Weighting), WP (Weight Product) dan SMART (Simple Multi Attribute Rating Technique) Untuk Pemilihan Domba Kurban M Lutfi MA; Kapti; Yeza Febriani
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6380

Abstract

In implementing urban worship, it is often difficult for shohibul qurban to determine the quality of urban animals because it has several criteria/requirements that must be met so that the animals are sacrificed according to sharia. This study aims to analyze the decision support system method for selecting qurban animals using the SAW (Simple Additive Weighting), WP (Weight Product), and SMART (Simple Multi-Attribute Rating Technique) methods. The results showed that the WP method has an accuracy rate of 99.998% so this method is the most feasible to use for the selection of sacrificial sheep when compared to the SMART and SAW methods with the calculation results in the level of suitability at 99.994% for the SAW method and 99.876% for the SMART method. Sheep 4 has the highest weight ranking of other sheep in all methods, scoring 0.923 in the SAW method, 0.1727 in the WP method, and 17.8 in the SMART method. Sheep 4 criteria is the ideal criteria for a sacrificial animal.
Implementation of the LSTM Deep Learning Model for Water Quality Parameter Prediction febriani, yeza; astuti, dwi; Putra, Yusuf Wahyu Setiya; Handayani, Riska Dwi; Fatkhurrohman; Kapti
TRANSFORMASI Vol. 22 No. 1 (2026): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/t21qyp02

Abstract

Efficient water quality prediction is crucial amid the threat of increasing pollution. Conventional methods have limitations in terms of cost, time, and coverage, requiring an innovative approach based on artificial intelligence. This study aims to classify drinking water suitability using Long Short-Term Memory (LSTM) architecture, which is known to be effective for sequential data. The research method includes data collection from Kaggle, data pre-processing such as normalization and missing value handling, and data division into 80% for training and 20% for testing. The proposed LSTM model was evaluated using accuracy, precision, recall, and F1-Score metrics. The results show that the LSTM model is capable of achieving an overall accuracy of 95.47%, with a precision of 0.9511 and a recall of 0.9547. Although there are some False Positive classification errors (predicting water as unfit when it is actually fit), the overall performance of the model is excellent and reliable for this classification task. The conclusion of this study is that the LSTM model can be an effective and accurate solution for predicting water quality, supporting early detection and real-time pollution control efforts.
Smart Supplier Selection Menggunakan Metode TOPSIS pada Sistem Pendukung Keputusan Berbasis Web Fatkhurrochman, Fatkhurrochman; Nela Septi Wulandari; Tri Yusnanto; Kapti; Muhammad Abdul Muin; Wahyu Priyoatmoko; Fatimah Nur Arifah
TRANSFORMASI Vol. 22 No. 1 (2026): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/nrb9vm59

Abstract

Supplier selection is an important process in supporting a company's operational activities. CV Semangat Baru still conducts supplier evaluations manually, causing the decision-making process to become less effective, time-consuming, and prone to errors. This study aims to design and develop a web-based decision support system to assist in selecting the best supplier using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research method used is software engineering with the Software Development Life Cycle (SDLC) Waterfall development model. The system was developed using PHP programming language and MySQL database. The criteria used in the supplier selection process include product quality, delivery timeliness, price, delivery distance, and supplier reputation. The TOPSIS method was applied to rank suppliers based on the highest preference value. The results showed that the system was able to support the supplier evaluation process in a more systematic, objective, and efficient manner. Based on Black Box testing, all system functions operated properly with a success rate of 100%, while user evaluation results obtained a feasibility score of 91.3%, categorized as highly feasible. Therefore, the web-based decision support system using the TOPSIS method can assist CV Semangat Baru in determining the best supplier more quickly and accurately.
Co-Authors Dwi Astuti Fatimah Nur Arifah Fatkhurrochman, Fatkhurrochman Fatkhurrohman handayani, riska dwi Muhammad Abdul Muin Muhammad Lutfi Mahasinul Akhlak Nela Septi Wulandari Tri Yusnanto Wahyu Priyoatmoko Yeza Febriani Yusuf Wahyu Setiya Putra