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All Journal Journal of Applied Data Sciences Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal of Applied Informatics Research
Dimas Novian Aditia Syahputra
Universitas Negeri Surabaya
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Optimization of Personalized Fashion Recommendations for H&M: A Collaborative Filtering Algorithm Approach with Temporal Time Interval Analysis Moch Deny Pratama; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44593

Abstract

This study presents a personalized fashion recommendation system for the H&M dataset, utilizing a cosine similarity-based collaborative filtering algorithm. This study investigates the effect of temporal segmentation on recommendation performance by conducting three experiments using datasets divided into two-week, one-month, and two-year time intervals. The experimental results show that the two-year interval achieves the best performance, producing a Mean Average Precision (MAP) of 0.02254 with a computational time of 2741.7 seconds. In contrast, the two-week interval achieves a MAP of 0.00915 in 1609.2 seconds, while the one-month interval produces a MAP of 0.00554 with a computational time of 3118.9 seconds. The main contribution of this study lies in the optimization of data structure transformation through dictionary-based modeling, which significantly improves training efficiency. These findings underscore the crucial role of temporal granularity in improving the accuracy and computational efficiency of collaborative filtering-based personalized fashion recommendation systems.
Enhancing Clickbait Headline Identification Performance Without Preprocessing Through Feature Reduction and Sentiment Analysis Moch Deny Pratama; Anisa Nur Azizah; Misbachul Falach Asy'ari; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi; Dina Zatusiva Haq
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44659

Abstract

This study addresses the challenge of identifying clickbait headlines without relying on conventional text preprocessing, which can be resource-intensive and may degrade contextual integrity. To enhance detection performance, we examine three feature extraction methods: TF-IDF, Word2Vec, and Headline2Vec, an embedding technique designed for short texts like headlines. These features are optimized using feature selection algorithms, including Pearson Correlation Coefficient (PCC), Neighborhood Component Analysis (NCA), and Relief, to reduce dimensionality and enhance relevant signal retention. Sentiment polarity is also integrated as a complementary feature. A comparative evaluation is conducted using several machine learning classifiers, namely Support Vector Classifier (SVC), Random Forest, LightGBM, and XGBoost, across all combinations of feature extraction and selection methods. Results show that the optimal configuration Headline2Vec with Relief and SVC achieves the highest accuracy at 94.40%, outperforming other approaches. This demonstrates the effectiveness of combining semantic vectorization and feature selection for clickbait detection in the absence of traditional preprocessing. The findings support the development of streamlined and scalable classification models capable of maintaining high accuracy while reducing preprocessing overhead, making the proposed method particularly suitable for real-time and large-scale content moderation and news verification systems.
Pembuatan Sistem Informasi Pendaftaran Santri Baru di Pondok Pesantren Putri KH. Ahmad Basthomi Dimas Novian Aditia Syahputra; Dodik Arwin Dermawan; I Gde Agung Sri Sidhimantra; M Adamu Islam Mashuri; Andi Iwan Nurhidayat; Binti Khalifah; Salamun Rohman Nudin Nudin; Ari Kurniawan
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): September 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i5.837

Abstract

Pondok Pesantren Putri KH. Ahmad Basthomi menghadapi tantangan administrasi akibat proses pendaftaran santri baru yang masih manual. Sistem ini tidak efisien, memakan waktu, dan rentan kesalahan data di tengah meningkatnya jumlah pendaftar setiap tahun. Sebagai solusi, sebuah program pengabdian masyarakat mengembangkan sistem informasi pendaftaran berbasis web. Sistem ini dirancang dengan fitur unggulan seperti unggah dokumen digital, validasi data terintegrasi, notifikasi otomatis, dan monitoring real-time melalui antarmuka yang ramah pengguna. Program ini juga mencakup pelatihan bagi staf administrasi dan sosialisasi kepada wali santri. Tujuannya adalah untuk mewujudkan manajemen administrasi yang lebih modern, efisien, dan akurat. Dengan implementasi ini, pesantren diharapkan dapat menjadi contoh pemanfaatan teknologi bagi institusi pendidikan keagamaan lain dalam menghadapi tantangan era digital.
Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Moch Deny Pratama; Faris Abdi El Hakim; Dimas Novian Aditia Syahputra; Dodik Arwin Dermawan; Asmunin Asmunin; Salamun Rohman Nudin; Andi Iwan Nurhidayat
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.969

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.
Co-Authors Adinda Putri Pratiwi Andi Iwan Nurhidayat Anisa Nur Azizah Ari Kurniawan Asmunin Asmunin Binti Khalifah Binti Kholifah Dina Zatusiva Haq Dodik Arwin Dermawan Faris Abdi El hakim I Gde Agung Sri Sidhimantra M Adamu Islam Mashuri Misbachul Falach Asy'ari Moch Deny Pratama Rifqi Abdillah SALAMUN ROHMAN NUDIN Salamun Rohman Nudin Nudin