Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Abdi Masyarakat Indonesia Society: Jurnal Pengabdian Masyarakat Al-Irfan: Journal Of Arabic Literature and Islamic Studies
Syaifuddin, Akhmad
Unknown Affiliation
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Optimasi Akurasi Model Prediksi Magnitudo Gempa Bumi dengan Integrasi Clustering DBSCAN pada Ensemble Learning (Random Forest & XGBoost) Syaifuddin, Akhmad; Prabowo, Tito
TIN: Terapan Informatika Nusantara Vol 5 No 7 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i7.6522

Abstract

Earthquake prediction is crucial for risk mitigation, particularly in taking appropriate preventive measures in the face of disasters. The magnitude of an earthquake is influenced by various factors, including location, depth, and the history of seismic activity in a region. This study aims to develop an accurate earthquake magnitude prediction model by integrating clustering and ensemble learning techniques. Earthquake catalog data from BMKG Indonesia is processed and clustered using the DBSCAN algorithm based on geographical location. The prediction model is constructed using Random Forest and XGBoost, then integrated through stacking ensemble learning techniques. Evaluation results indicate that the stacking model delivers the best performance, with the lowest Mean Squared Error (MSE) of 0.108 and the highest R-squared (R²) of 0.892, compared to individual models. This accuracy improvement is attributed to stacking’s ability to combine the predictive strengths of Random Forest and XGBoost. The study demonstrates that integrating clustering and ensemble learning can enhance earthquake magnitude prediction models. However, further research is needed to explore more comprehensive data and features and to test model generalization in other regions.
Empowering SMEs: Implementing Generative AI for Enhanced Copywriting in Bantul's Kusuka Ubiku Syaifuddin, Akhmad; A'yun, Luthfia Qurrota
Society : Jurnal Pengabdian Masyarakat Vol 4, No 1 (2025): Januari
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i1.518

Abstract

Micro, Small, and Medium Enterprises (SMEs) play a critical role in bolstering Indonesia’s economy. Efficient business management in these business typically encompasses four main areas: Human Resources, Operations, Marketing, and Finance. In Bantul, the SMEs within the Kusuka Ubiku community face significant challenges in effective marketing due to limited resources and expertise in copywriting. To address this, our program offers assistance in implementing generative AI technology to enhance copywriting skills. The initiative is structured in three phases: 1) Problem Identification, where the specific marketing hurdles are assessed; 2)  Assistance in Developing a Copywriting, which involves practical sessions on using generative AI tools; and 3) Evaluation and Feedback, to measure improvements in marketing outreach and effectiveness. The outcome of this assistance is a set of SMEs equipped with advanced copywriting tools and techniques, enabling them to produce market-relevant copywriting, expand their reach, and enhance brand recognition. The initiative not only aims to provide technical skills but also seeks to foster innovation, ultimately contributing to the sustainable growth of local businesses. Through this program, we anticipate a transformative impact on the marketing capabilities of SMEs in the region.
Peningkatan Kualitas Administrasi Pendidikan melalui Implementasi Sistem Edu Berbasis ERP di SMP IT Insan Mulia Surakarta, Jawa Tengah Widoyono, Bambang; Saptono, Ristu; Rohmadi, Arif; Syaifuddin, Akhmad; Hendra, Brilyan; Anggoro, Rizal Dwi; Ibrahim, Muhammad Syafiq
Jurnal Abdi Masyarakat Indonesia Vol 5 No 6 (2025): JAMSI - November 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2130

Abstract

SMP Islam Insan Mulia Surakarta-Jawa Tengah, mengalami kendala administrasi akibat sistem manualnya, terutama dalam penerimaan siswa baru (PPDB), pencatatan pembayaran, dan pengelolaan bank soal. Kendala-kendala ini menyebabkan keterlambatan, kesalahan, dan inefisiensi, sehingga membatasi kualitas layanan. Untuk mengatasi hal ini, dalam program pengabdian masyarakat kami mengimplementasikan sistem EDU berbasis ERP sebagai solusi terintegrasi. Sistem ini menggunakan model waterfall untuk analisis, perancangan, implementasi, pelatihan, dan pengujian yang diterapkan selama tiga bulan. Tiga modul diimplementasikan: PPDB, pembayaran, dan bank soal, yang diuji coba kepada 23 peserta. Evaluasi menunjukkan hasil positif dengan efisiensi (4,08), efektivitas (4,08), dampak (4,38), kepuasan (4,28), dan kemudahan penggunaan (4,17) pada rentang skala 1-5. Program pengabdian ini tidak hanya menyelesaikan kendala administratif di SMP Islam Insan Mulia Surakarta, tetapi juga menghadirkan model implementasi sistem informasi berbasis ERP yang dapat direplikasi di sekolah lain. Digitalisasi administrasi melalui modul PPDB, pembayaran, dan bank soal terbukti meningkatkan efisiensi, transparansi, dan profesionalisme tata kelola pendidikan secara umum.
Empowering SMEs: Implementing Generative AI for Enhanced Copywriting in Bantul's Kusuka Ubiku Syaifuddin, Akhmad; A'yun, Luthfia Qurrota
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2025): Januari
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i1.518

Abstract

Micro, Small, and Medium Enterprises (SMEs) play a critical role in bolstering Indonesia’s economy. Efficient business management in these business typically encompasses four main areas: Human Resources, Operations, Marketing, and Finance. In Bantul, the SMEs within the Kusuka Ubiku community face significant challenges in effective marketing due to limited resources and expertise in copywriting. To address this, our program offers assistance in implementing generative AI technology to enhance copywriting skills. The initiative is structured in three phases: 1) Problem Identification, where the specific marketing hurdles are assessed; 2)  Assistance in Developing a Copywriting, which involves practical sessions on using generative AI tools; and 3) Evaluation and Feedback, to measure improvements in marketing outreach and effectiveness. The outcome of this assistance is a set of SMEs equipped with advanced copywriting tools and techniques, enabling them to produce market-relevant copywriting, expand their reach, and enhance brand recognition. The initiative not only aims to provide technical skills but also seeks to foster innovation, ultimately contributing to the sustainable growth of local businesses. Through this program, we anticipate a transformative impact on the marketing capabilities of SMEs in the region.
Istiraatijiyyah Taklim al-Lughah al-'Arabiyyah bi al-Al'ab al-Lughawiyah Syaifuddin, Akhmad
Al-Irfan : Journal of Arabic Literature and Islamic Studies Vol. 2 No. 2 (2019): September
Publisher : Sekolah Tinggi Agama Islam Darul Ulum Banyuanyar Pamekasan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58223/al-irfan.v2i2.677

Abstract

As we know, the teaching of the Arabic language has spread throughout Indonesia, whether in formal or non-formal institutions, and whether in schools, institutes, or universities. However, unfortunately, Arabic language teaching in our country still suffers from various problems. One of these problems is the low motivation of students in the learning process. The author found this low level of motivation among students in both schools and universities. After analyzing the situation, the author found that the problem of students’ motivation is largely concentrated in four aspects: learning drive and needs, hopes and aspirations for the future, and the learning environment. The author then concluded that most students are weak in these four aspects. Therefore, the author attempted to propose an Arabic language teaching strategy using language games, as language games play an important role in stimulating and maintaining learners’ motivation.
Optimizing E-commerce Personalization through Hybrid Decision Tree–Nearest Neighbor Recommendation Integration Syaifuddin, Akhmad; Saptono, Ristu; Rohmadi, Arif; Widoyono, Bambang; Hendrasuryawan, Brilyan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5418

Abstract

Single-method recommendation systems face critical limitations: content-based filtering suffers from overspecialization while collaborative filtering struggles with data sparsity and cold-start problems. This research introduces an innovative hybrid recommendation framework that synthesizes Content-Based Filtering (CBF) utilizing Decision Trees with Collaborative Filtering (CF) employing Nearest Neighbor algorithms. Our approach addresses the inherent limitations of singular recommendation methodologies by integrating product attribute analysis with collective user behavior patterns. We conducted comprehensive evaluations using a shopping behavior dataset comprising 3,900 consumer records with diverse demographic and product interaction data. Our findings reveal that an asymmetric hybrid configuration—weighted at 70% for CBF and 30% for CF—achieves optimal performance with a Root Mean Square Error (RMSE) of 0.7422. The system incorporates an interactive user interface that facilitates a natural shopping experience: browsing available items, receiving personalized recommendations, and providing explicit feedback on suggested products. Through feature importance analysis, we identified key product attributes that significantly influence recommendation quality, including size variations and specific color preferences. The hybrid approach demonstrates 42% greater category diversity and 37% more recommendation diversity compared to pure content-based filtering, while maintaining superior accuracy metrics. Our research contributes to understanding optimal hybrid architectures and provides practical insights for implementing effective personalization strategies in real-world e-commerce environments.
Co-Authors A'yun, Luthfia Qurrota Anggoro, Rizal Dwi Arif Rohmadi Hendra, Brilyan Hendrasuryawan, Brilyan Ibrahim, Muhammad Syafiq Prabowo, Tito Ristu Saptono Widoyono, Bambang