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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) SISFOTENIKA
Arnita
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Decision Support System Using the Analytical Hierarchy Process Method in Determining Credit Recipient Eligibility Erika Nia Devina Br Purba; Arnita; Hermawan Syahputra; Lasker P Sinaga; Adidtya Perdana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2256

Abstract

Banks play a fundamental role in improving public welfare by collecting funds through savings and redistributing them as credit. Although credit is the primary source of bank revenue, it carries significant risks if the feasibility analysis of prospective borrowers is flawed, potentially leading to non-performing loans that disrupt financial stability. BPR Nusantara Bona Pasogit 17 faces this challenge as it currently lacks an automated decision support system, resulting in assessments that are often inconsistent or subjective. This research aims to develop a web-based decision support system using the Analytical Hierarchy Process (AHP) method to determine credit recipient eligibility. Developed using PHP and MySQL, the system incorporates criteria management, AHP calculation processing, and automated eligibility ranking. Comprehensive validation through black-box and white-box testing confirmed that all functional components performed correctly with consistent "PASS" results. The AHP implementation produced a Consistency Ratio (CR) of 0.03797, indicating high reliability in decision-making. Criterion priority weights were identified as: Income (0.386), Character (0.219), Loan Amount (0.162), Collateral (0.103), Loan Term (0.07), and Age (0.06). System testing on 100 customer records resulted in a maximum eligibility score of 0.93501 and a minimum of 0.41839.
Flood Prediction for the Wampu River Basin Using the Simple Additive Weighting Method:A Case Study of the Wampu River in Bahorok Miftahul Janna; Said Iskandar; Arnita; Zulfahmi Indra; Susiana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2268

Abstract

Flood is one of the natural disasters that frequently occurs in the Wampu Watershed (DAS Wampu), especially in Bahorok District. Flood risk is influenced by several factors such as rainfall, slope gradient, land use changes, and river depth. The problem in this study is the absence of a decision support system that can objectively determine flood risk levels. This study aims to determine the criteria and weights of flood risk, apply the Simple Additive Weighting (SAW) method, and analyze the accuracy level of the SAW method in determining flood risk. The method used in this research is the Simple Additive Weighting (SAW) method through several stages including criteria weighting, decision matrix construction, data normalization, preference value calculation, and alternative ranking. The research data consists of 18 villages with four criteria: rainfall, slope gradient, land use change, and river depth. The results show the classification of flood risk levels into high, medium, and low categories based on the obtained preference values. Villages with the highest preference values indicate a higher level of flood vulnerability compared to other villages. The model evaluation results indicate that the SAW method has an accuracy level of approximately 90% in determining flood risk classification. Based on these results, it can be concluded that the SAW method can be used as a decision support system to determine flood risk levels and provide recommendations for priority flood mitigation areas in Bahorok District.
Deteksi Emosi Suara Berbahasa Indonesia Berbasis Web Menggunakan MFCC dan BiLSTM: Web-Based Indonesian Speech Emotion Recognition Using MFCC and BiLSTM Yusfi Syawali; Arnita
SISFOTENIKA Vol. 16 No. 2 (2026): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v16i2.662

Abstract

Deteksi emosi suara berbahasa Indonesia masih menghadapi tantangan karena keterbatasan dataset lokal dan belum banyaknya implementasi dalam bentuk sistem yang dapat digunakan secara langsung. Penelitian ini bertujuan mengembangkan sistem deteksi emosi suara berbahasa Indonesia berbasis web menggunakan ekstraksi fitur Mel-Frequency Cepstral Coefficients (MFCC) dan klasifikasi Bidirectional Long Short-Term Memory (BiLSTM). Dataset yang digunakan merupakan dataset hybrid yang terdiri atas 1.200 berkas audio dari 40 aktor dengan lima kelas emosi, yaitu netral, senang, terkejut, jijik, dan kecewa. Fitur suara diekstraksi menggunakan 40 koefisien MFCC yang dikombinasikan dengan Delta dan Delta-Delta, kemudian diklasifikasikan menggunakan model BiLSTM dengan attention pooling. Hasil evaluasi pada data uji menunjukkan akurasi sebesar 80,56% dan Macro F1-score sebesar 0,8060. Model juga diimplementasikan ke dalam sistem web Serambi Emosi. Hasil pengujian Black-Box menunjukkan seluruh fungsi berjalan valid, sedangkan evaluasi usability menggunakan System Usability Scale memperoleh skor 71,25 dengan kategori Good. Hasil ini menunjukkan bahwa MFCC dan BiLSTM berpotensi digunakan sebagai prototipe pendukung analisis kondisi emosional berbasis suara.
Co-Authors Adidtya Perdana, Adidtya Erika Nia Devina Br Purba Hermawan Syahputra Lasker Pangarapan Sinaga Miftahul Janna Said . Iskandar Susiana Yusfi Syawali Zulfahmi Indra, Zulfahmi