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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Adijaya Multidisiplin
Agung Mulyo Widodo
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Rancang Bangun Sistem Informasi Inventaris Barang di Gudang Berbasis Web Pada PT.XYZ Ilham Banuaji; Nizirwan Anwar; Binastya Anggara Sekti; Agung Mulyo Widodo
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

PT. XYZ is a company engaged in the field of trading in goods, whose main activity is recording stock in and out of goods in warehouses, currently still using Microsoft Excel. Along with the development of the company, activity and the number of transactions also continues to increase, recording using Microsoft Excel is felt lacking because of frequent delays and othererrors or human errors. Another problem is regarding the inaccurate warehouse design, which makes it difficult to find goods. The purpose of this research is to overcome the problems that arise and expedite the process of recording data on goods entering or leaving the warehouse, searching for goods, making final reports, archiving, and data backup. In this study, the authors will use the prototype method which has three stages, namely Listen to Customer, Build and Revise Mockup, and Customer Test Drives mock-up. The results of this study are able to overcome the problems that occur in recording with Microsoft Excel and warehouse design. Utilization of a web-based inventory information system with this database is proven to be able to provide solutions to problems in inventory. It can be used as an integrated system for collecting, storing, searching for goods, and processing data, as well as reporting.
Penerapan Algoritma Pengklasifikasi Untuk Mengukur Kepuasan Pelanggan E-Commerce (Studi Kasus : Shopee) Syamsul Bahri; Agung Mulyo Widodo
Jurnal Adijaya Multidisplin Vol 3 No 01 (2025): Jurnal Adijaya Multidisiplin (JAM)
Publisher : PT Naureen Digital Education

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Penelitian ini bertujuan untuk mengukur tingkat kepuasan pelanggan Shopee dengan menggunakan beberapa algoritma machine learning, yaitu Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, dan Naive Bayes. Tingkat kepuasan pelanggan dikategorikan ke dalam lima tingkat: Sangat Tidak Puas, Tidak Puas, Netral, Puas, dan Sangat Puas. Data survei diperoleh dari 1.000 Responden, mencakupi lima variabel utama, yaitu kualitas produk, layanan pengiriman, kualitas pelayanan, harga dan promosi, serta pengalaman berbelanja. Analisis dilakukan dengan heatmap korelasi untuk memahami hubungan antar variabel, serta feature importance menggunakan Random Forest untuk menentukan kontribusi relatif setiap faktor terhadap kepuasan pelanggan. Hasil penelitian menunjukkan bahwa harga dan promosi memiliki pengaruh tertinggi, diikuti oleh kualitas pelayanan dan pengalaman berbelanja. Penelitian ini memberikan wawasan strategis bagi Shopee untuk meningkatkan kualitas layanan berdasarkan analisis data dan memperkuat daya saing di pasar e-commerce.
Loan Repayment Prediction Using XGBoost and Neural Network in Japan's Technical Internship Training Suhendry, Mohammad Roffi; Gerry Firmansyah; Nenden Siti Fatonah; Agung Mulyo Widodo
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14709

Abstract

Delayed repayment of financial aid among participants in Japan’s Technical Internship Training Program presents challenges for training institutions in managing funds efficiently. To address this issue, this study aims to compare the performance of two machine learning models: Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) in predicting the likelihood of delayed loan repayments. The research begins with data preprocessing, including handling missing values, normalization, and feature selection based on a correlation threshold of 0.06, where features with absolute correlation values below this threshold are excluded. Three models are tested: XGBoost Default, XGBoost optimized using GridSearchCV, and MLP. These models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The XGBoost Default model achieves the highest accuracy at 95% and precision of 95%, although its recall is slightly lower at 83%. Tuning XGBoost improves recall to 84%, albeit with a marginal reduction in accuracy to 94%. In contrast, the MLP model demonstrates the lowest performance, with an accuracy of 92% and recall of 74%, indicating limitations in identifying delayed repayments. XGBoost also outperforms MLP in terms of ROC-AUC, scoring 91% compared to MLP’s 86%. These findings suggest that XGBoost is the more effective model for this predictive task. The results have practical implications for training institutions, enabling better participant selection, reducing repayment delays, and supporting more effective financial aid management.
Metaheuristic-Optimized SVM for Stunting Risk Detection in Pregnancy Wibowo, Yudha; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14710

Abstract

Stunting is a chronic growth disorder that originates during pregnancy, making early risk detection crucial for effective prevention and long-term child development. This study introduces a stunting risk prediction model based on urine testing, employing a Support Vector Machine (SVM) algorithm enhanced through metaheuristic optimization. Three metaheuristic algorithms—Grey Wolf Optimizer (GWO), Simulated Annealing (SA), and Firefly Algorithm (FA)—were utilized to fine-tune the SVM hyperparameters (C and gamma). Clinical urine samples collected from pregnant women served as the dataset for model training and validation. The results indicate that the SVM model optimized using GWO achieved the highest prediction accuracy at 94.15%, outperforming both the default SVM (88.46%) and the models optimized using SA (94.12%) and FA (85.71%). Additionally, significant improvements were observed in precision, recall, and F1-score metrics, affirming the effectiveness of metaheuristic tuning in enhancing classification performance. These findings highlight the potential of integrating metaheuristic algorithms with SVM for robust medical prediction tasks, especially in the early detection of stunting risks. The proposed model offers a promising and non-invasive diagnostic approach that can be implemented in prenatal care settings, enabling timely interventions to mitigate stunting and improve maternal and child health outcomes.
Co-Authors Binastya Anggara Sekti Budi Tjahjono Gerry Firmansyah Ilham Banuaji Nenden Siti Fatonah Nizirwan Anwar Suhendry, Mohammad Roffi Syamsul Bahri Wibowo, Yudha