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All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer 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 Economics, Econometrics & Finance Education Engineering Social Sciences

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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.
SISTEM REKOMENDASI KATEGORI PASAR ABALONE (PREMIUM, REGULER, REJECT) UNTUK PASAR KONSUMEN MENGGUNAKAN DATA MINING Delvita Aulia Artika; Bunga Dwi Febrianti; Sirus Daniel Nababan; Arnita; Fanny Ramdhani
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 2 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i2.3993

Abstract

Abalone (Haliotis spp.), known as "kerang mata tujuh" in Indonesia, is a highly valuable marine commodity widely sought after in the premium culinary sector, yet its quality classification is still commonly performed manually and inefficiently. This study aims to develop a market category recommendation system for abalone (Premium, Regular, Reject) based on data mining to assist in quality assessment in an automated, objective, and accurate manner. The methods used include data segmentation with the K-Means algorithm based on the physical characteristics of abalone, then classification using Decision Tree to determine market categories. The dataset comes from the UCI Machine Learning Repository and has gone through pre-processing stages such as normalization, label encoding, and outlier handling before model training. The results show that the built classification system can predict market categories with 99% accuracy, along with high precision, recall, and F1-scores across all classes, indicating the system's strong potential for implementation in the fisheries industry to support fast, data-driven decision-making. This study combines K-Means segmentation and Decision Tree classification to develop an automatic and accurate abalone market category assessment system to replace subjective manual processes
ANALISIS PENGARUH JENIS PEKERJAAN DAN SALDO TERHADAP RESPON PADA KAMPANYE BANK MENGGUNAKAN ALGORITMA DECISION TREE Aldo Bonifasius Simbolon; Dean Siregar; Sybil Auzi; Febe Gracia Sembiring; Arnita; Fanny Ramadhani
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 2 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i2.3994

Abstract

In the era of digitalization, data has become a crucial asset in strategic decision-making within the banking sector. Financial institutions face challenges in understanding customer preferences and behavior more personally when conducting product promotion campaigns. This research aims to analyze and uncover the influence patterns of job type and account balance on customer responses to bank campaigns using the Decision Tree algorithm. A data-driven analytical approach is needed to uncover hidden patterns from collected customer information. The Decision Tree algorithm was chosen for its ability to provide predictions and present decision-making logic in an easily understandable tree structure, as well as its flexibility in handling both categorical and numerical data. The simplified dataset focuses on the 'job' and 'balance' features, which are then processed using label encoding for categorical features. The model was trained using the fit method and evaluated using Accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model's overall Accuracy reached 92%. For the 'yes' response class, recall reached 97% and precision 88%, indicating the model's good ability to identify potentially positive customers. Interpretation of the results from the Decision Tree structure explicitly indicates that job type and customer balance have different patterns of inclination towards marketing campaigns, where customers with high balances tend to respond positively. This research is expected to contribute to the development of a more accurate, effective, and efficient data-driven decision support system for marketing activities in the banking sector, based on the revealed influence patterns.
KLASIFIKASI STATUS MAHASISWA MENGGUNAKAN ALGORITMA XGBOOST PADA DATASET AKADEMIK Muhammad Rizki Andrian Fitra; Neysa Talitha Jehian; Thania Dealva Arsyad; Ayman Human Sukma; Arnita; Fanny Ramadhani
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 2 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i2.3997

Abstract

Monitoring student academic status is an important aspect of supporting the effectiveness of higher education systems. This study aims to classify student status—Dropout, Enrolled, and Graduate—using the XGBoost algorithm on an academic dataset from Kaggle consisting of 4424 entries and 37 features. The research was conducted using a quantitative approach and computational experiment methods. The stages included data pre-processing, training and testing data split (80:20 ratio), model training, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The results showed that the XGBoost model was able to classify student status with an accuracy of 79.9%. Further evaluation revealed that the model performed well on the majority class (Graduate), but had low performance on the minority class (Enrolled), especially with recall of 0,48. This indicates challenges in dealing with imbalanced class distributions. Therefore, the use of data balancing techniques must be implemented, thus the accuracy by the model reached 82,65%, for further refining and improving its performace, hyperparameter tuning is recommended for it. The results of this study are expected to assist educational institutions in detecting potential dropouts earlier and supporting data-driven decision making.
PREDIKSI PENJUALAN ONLINE MENGGUNAKAN RIDGE REGRESSION BERBASIS DATA HISTORIS Ayu Amelia Pertiwi; Sarah Putri Syaifullah Nasution; Arnita; Fanny Ramadhani
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 2 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i2.4015

Abstract

The development of digital technology has increased online purchases, changed consumption patterns, and created a need for accurate sales predictions for strategic decision-making. However, prediction accuracy is often hindered by multicollinearity issues among numeric features and the risk of overfitting in conventional linear regression models. This research aims to address these issues by applying Ridge Regression based on L2 regularization to predict total sales using historical e-commerce data. The methods used include data collection (541,909 records), preprocessing (removal of outliers and missing values), and modeling with scikit-learn, evaluated using MAE (73.25), RMSE (129.80), and the coefficient of determination (R²). The results indicate that the model is capable of predicting total sales with adequate accuracy even though there are deviations in extreme value transactions. Practically, the implementation of Ridge Regression has proven effective in reducing overfitting and enhancing prediction stability, making it a reference in inventory planning and marketing strategies. This finding reinforces the need for adaptive machine learning approaches in sales data analysis in the digital era.
Co-Authors Adidtya Perdana, Adidtya Aldo Bonifasius Simbolon Ayman Human Sukma Ayu Amelia Pertiwi Bunga Dwi Febrianti Dean Siregar Delvita Aulia Artika Erika Nia Devina Br Purba Fanny Ramdhani Febe Gracia Sembiring Hermawan Syahputra Lasker Pangarapan Sinaga Miftahul Janna Muhammad Rizki Andrian Fitra Neysa Talitha Jehian Ramadhani, Fanny Said . Iskandar Sarah Putri Syaifullah Nasution Sirus Daniel Nababan Susiana Sybil Auzi Thania Dealva Arsyad Yusfi Syawali Zulfahmi Indra, Zulfahmi