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All Journal Jurnal Masyarakat Merdeka (JMM) Buana Information Technology and Computer Sciences (BIT and CS) Prosidia Widya Saintek Journal of Computing Theories and Applications
Syahroni Wahyu Iriananda
Universitas Widya Gama Malang
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation Other

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Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction De Rosal Ignatius Moses Setiadi; Ahmad Rofiqul Muslikh; Syahroni Wahyu Iriananda; Warto Warto; Jutono Gondohanindijo; Arnold Adimabua Ojugo
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
Pemanfaatan Kecerdasan Buatan Untuk Mendukung Pengembangan Bahan Pembelajaran Pada SMK Widyagama Malang Istiadi Istiadi; Fitri Marisa; Syahroni Wahyu Iriananda; Rangga Pahlevi Putra
JMM - Jurnal Masyarakat Merdeka Vol. 9 No. 1 (2026): MEI
Publisher : Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51213/jmm.v9i1.217

Abstract

Program pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi guru SMK Widyagama Malang dalam memanfaatkan kecerdasan buatan, khususnya Generative AI (GenAI), untuk pengembangan bahan ajar vokasional. Metode yang digunakan adalah pendekatan partisipatif melalui observasi, wawancara, workshop interaktif, dan pendampingan selama 2–3 minggu. Pelatihan mencakup penggunaan tools seperti ChatGPT, Canva AI, Diffit, dan MagicSchool. Hasil menunjukkan tingkat kepuasan peserta sebesar 86% serta peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan GenAI ke dalam pembelajaran. Secara keseluruhan, program ini efektif dalam mendorong peningkatan kompetensi digital guru, meskipun diperlukan durasi pelatihan yang lebih panjang untuk hasil yang optimal.
Application of XGBoost Algorithm in Sentiment Classification of MOBA Game Reviews on Google Play Store: Author's Country: Indonesia Daffa Yauzan Tusianto; Syahroni Wahyu Iriananda; Istiadi Istiadi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/5x0adm73

Abstract

In the rapidly evolving digital era, business applications like GoBiz play a crucial role in supporting the operations of Micro, Small, and Medium Enterprises (MSMEs). This study aims to analyze user sentiment toward the GoBiz app based on reviews on the Google Play Store by applying two machine learning algorithms: Extreme Gradient Boosting (XGBoost) and Random Forest. Two labeling approaches were used: score-based labeling, which refers to star ratings, and lexicon-based labeling using the VADER method. Data from 10,000 reviews were collected through web scraping and processed through preprocessing, labeling, TF-IDF feature extraction, model training, and evaluation. The evaluation results showed that the XGBoost algorithm excelled in score-based labeling with the highest accuracy of 86.81%, while Random Forest was more stable than the VADER approach with an accuracy of 84.98%. Both models performed well, but their effectiveness depended on the type of labeling used. This research contributes to the development of a sentiment classification system in digital business applications, and can be utilized by GoBiz application developers to improve service quality based on user perceptions.
LSTM-Based Classification of Indonesian Regional Song Lyrics by Language: Author's Country: Indonesia Muhammad Rizky Anandita Priatama; Aviv Yuniar Rahman; Syahroni Wahyu Iriananda
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 2 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/jh41mx81

Abstract

This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language. Unlike prior works that often focus on sentiment analysis or use unbalanced datasets, this research utilizes a balanced dataset consisting of 2,500 lyric segments from five regional languages: Javanese, Sundanese, Batak, Minangkabau, and Banjarese. A comprehensive preprocessing pipeline is applied, including case folding, text cleaning, tokenization, stopword removal, stemming, sequence padding, and label encoding to transform textual data into numerical representations. The model is evaluated using 5-fold cross-validation to ensure robustness and generalization across different data partitions. Experimental results show that the proposed model achieves an accuracy of 95.24%, precision of 95.36%, recall of 95.24%, and F1-score of 95.26%, indicating strong and consistent performance. These findings demonstrate that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages, enabling accurate classification despite similarities in vocabulary and structure. Furthermore, this study contributes to the advancement of natural language processing for low-resource languages and highlights the potential of deep learning approaches in supporting the digital preservation and automatic organization of Indonesian regional cultural content.
IMPLEMENTASI FUZZY AHP PADA SISTEM PENDUKUNG KEPUTUSAN KEDISIPLINAN SISWA SMK WIDYAGAMA MALANG Mohammad Yusron Kholish; Istiadi Istiadi; Syahroni Wahyu Iriananda
Prosidia Widya Saintek Vol. 5 No. 2 (2026)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kedisiplinan merupakan aspek penting dalam pendidikan yang memengaruhi kualitas belajar dan karakter siswa. SMK Widyagama Malang saat ini masih menggunakan metode manual yang rentan terhadap subjektivitas dan memakan waktu dalam mengevaluasi kedisiplinan siswa bermasalah. Penelitian ini bertujuan untuk mengimplementasikan metode Fuzzy Analytical Hierarchy Process (F-AHP) ke dalam sebuah Sistem Pendukung Keputusan (SPK) guna memberikan rekomendasi tindakan pendisiplinan yang lebih objektif dan presisi. Penelitian ini menggunakan sampel sebanyak 100 siswa dan 10 guru dengan metode pengumpulan data melalui observasi, wawancara, dan kuesioner. Kriteria utama yang digunakan meliputi kehadiran, keterlambatan, sikap, dan tugas. Pengujian sistem dilakukan menggunakan metode Blackbox Testing yang menunjukkan bahwa 100% fungsi sistem berjalan sesuai perancangan. Selain itu, pengujian akurasi yang membandingkan hasil rekomendasi sistem dengan penilaian manual guru pada 15 data uji menghasilkan tingkat kesesuaian sebesar 86,67%. Hasil ini membuktikan bahwa SPK berbasis F-AHP mampu menangani ketidakpastian dalam penilaian subjektif dan memberikan solusi yang efektif, efisien, serta konsisten untuk mendukung pihak sekolah dalam meningkatkan kedisiplinan siswa.
Co-Authors Arnold Adimabua Ojugo Aviv Yuniar Rahman Daffa Yauzan Tusianto De Rosal Ignatius Moses Setiadi Fitri Marisa Istiadi Istiadi Istiadi Istiadi Jutono Gondohanindijo Mohammad Yusron Kholish Muhammad Rizky Anandita Priatama Muslikh, Ahmad Rofiqul Rangga Pahlevi Putra Warto