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All Journal JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research JUTECH : Journal Education and Technology Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika JAMAIKA: Jurnal Abdi Masyarakat Jurnal Inovasi Pengabdian Masyarakat AMMA : Jurnal Pengabdian Masyarakat JRIIN :Jurnal Riset Informatika dan Inovasi APPA : Jurnal Pengabdian Kepada Masyarakat Jitu: Jurnal Informatika Utama Jurnal Sains dan Teknologi Informasi Spectrum: Multidisciplinary Journal PESHUM
Syaeful Machfud
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Klasifikasi Gender Berbasis Citra Wajah Menggunakan Clustering Dan Deep Learning Okky Prasetia; Syaeful Machfud; Rosyani, Perani; Bobi Agustian
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.581

Abstract

Gender classification based on facial images is a significant challenge in the field of computer vision, especially when dealing with unstructured data sourced from social media platforms. This study proposes an integrated approach combining facial image preprocessing, clustering methods, and deep learning to enhance the accuracy of gender classification. The dataset used was obtained from a Big Data Competition and consists of male and female face images sourced from Instagram. Preprocessing was performed using OpenCV for face detection and cropping. Subsequently, the data were clustered using K-Means and DBSCAN algorithms to reduce noise and redundancy. Gender classification was then conducted using a sequential learning model based on Inception_v3, enhanced with Agglomerative Clustering for feature refinement. The evaluation of the system demonstrated strong performance with an accuracy of 92.97%, F1-score of 0.89556, precision of 0.97727, and recall of 0.83069. These results confirm that the integration of clustering techniques and deep learning significantly improves the effectiveness of gender classification based on facial images, especially for open-source and non-curated datasets.
Implementasi Entreprises Resource Planning Berbasis Web dan Mobile Menerapkan Metode SCRUM Syahdan, Muhammad; Nanang, Nanang; Suryaningrat, Suryaningrat; Machfud, Syaeful
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.711

Abstract

Companies operating in the property sector have highly complex business processes involving multiple divisions, such as engineering, marketing, legal, and finance. However, many property companies still manage their data manually and in a fragmented manner, leading to various risks such as data entry errors, communication failures, and other inefficiencies. This study aims to implement an integrated web- and mobile-based Enterprise Resource Planning (ERP) system to support and streamline business processes in a property company, making them more efficient and effective. The development methodology used is Agile, with data collected through interviews, observation, and documentation studies. The system was developed using web and mobile technologies to provide users with flexible access. The implementation results show that the developed ERP system is capable of supporting and improving business processes in the property sector, facilitating real-time data tracking, and increasing operational efficiency. With this system, the company no longer needs to rely on manual data recording and can improve the accuracy of decision-making. This research demonstrates that a digitally based ERP system can be a strategic and effective solution for property companies in facing the challenges of the modern era.
ANALISIS DAMPAK PENERAPAN TEKNOLOGI ARTIFICIAL INTELLIGENCE TERHADAP EFEKTIFITAS PEMBELAJARAN BAGI SISWA SMK Machfud, Syaeful; Halawa, Hedwin Winata; Gea, Kurniaman; Rizky, Mochammad; Indriani, Rifdah
JUTECH : Journal Education and Technology Vol 6, No 1 (2025): JUTECH JUNI
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i1.4362

Abstract

This study aims to analyse the application of Artificial Intelligence (AI) technology to the effectiveness of student learning at SMK Nusantara 1 Ciputat. Using a descriptive qualitative research design, the research subjects involved 30 students selected through purposive sampling. Data were collected through semi-structured interviews and observations, then analysed using thematic analysis method with data triangulation. The results showed that the application of AI technology was able to improve students' understanding of the basic concepts of AI, its types, and its practical applications. After the training, 80% of students understood the basic concepts of AI, an increase from 20% before the training. However, the main challenges faced include infrastructure limitations, such as internet connection, as well as the need for further training for teachers. Support from the school and improved infrastructure are important factors for more effective AI implementation. This research contributes to the vocational education literature in Indonesia by demonstrating the potential of AI as an interactive and adaptive learning tool, and providing recommendations to overcome implementation challenges. Further studies with a broader scope are recommended to explore the long-term impact of AI in vocational learning.
Implementasi Metode Naïve Bayes Untuk Prediksi Penjualan Catering Pada PT Negara Rasa Indonesia Gulo, Benifati; Machfud, Syaeful
Jurnal Riset Informatika dan Inovasi Vol 3 No 8 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Penelitian ini membahas implementasi metode Naïve Bayes untuk memprediksi penjualan catering pada PT.Negara Rasa Indonesia. Latar belakang penelitian didasari oleh permasalahan penjualan yang belum optimal akibat tidak adanya sistem prediksi penjulan yang terstruktur. Metode Naïve Bayes dipilih karena kesederhanaan, kecepatan, serta kemampuannya dalam mengklasifikasikan data dengan tingkat akurasi yang tinggi. Data yang digunakan dalam penelitian ini adalah data historis penjualan selama dua tahun terakhir, yang telah melalui proses cleaning, labeling, dan transformasi menjadi empat kategori penjualan, yaitu sangat laris, laris, cukup laris, kurang laris. Proses pengujian dilakukan menggunakan perangkat lunak RapidMiner dengan membagi dataset menjadi data latih dan data uji pada berbagai rasio 80:20, Hasil pengujian menunjukkan tingkat akurasi yang sangat tinggi, dengan nilai tertinggi mencapai 91,41% Temuan ini membuktikan bahwa metode Naïve Bayes dapat diandalkan untuk memprediksi penjualan katering, sehingga dapat membantu pengambilan keputusan dalam pengelolaan dan perencanaan penjualan yang lebih efisien di PT. Negara Rasa Indonesia.
Implementasi Penunjang Keputusan Pemilihan Biji Kopi Robusta dan Arabika Terbaik Menggunakan Metode Promethee pada Perkebunan Kopi Saputra, Devani Erik; Machfud, Syaeful
Jurnal Sains dan Teknologi Informasi Vol 5 No 1 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v5i1.8827

Abstract

Coffee beans are one of the most important commodities in the food and beverage industry, where the quality of the beans significantly determines the final product produced. Assessing high-quality coffee beans is a crucial factor in maintaining consumer satisfaction and preserving flavor consistency. This assessment process requires an accurate and objective method, as it involves various complex factors such as size, color, aroma, taste, and moisture level. A Decision Support System (DSS) serves as an effective solution to assist in complex, multi-criteria decision-making processes. The purpose of this study is to develop and implement a DSS based on the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to objectively assess and determine the best quality Robusta and Arabica coffee beans. The PROMETHEE method was chosen because it is capable of handling multiple evaluation criteria simultaneously and generating alternative rankings based on measurable preference levels. This study includes data collection, analysis of factors affecting coffee bean quality, and the design and implementation of the PROMETHEE method within the developed DSS. The results of the study indicate that the application of the PROMETHEE method in the DSS can provide accurate, consistent, and reliable recommendations in determining coffee bean quality. Therefore, this system can serve as an effective tool for decision-makers in evaluating coffee bean quality in a more systematic and objective manner.
PEMANFAATAN ARTIFICIAL INTELLIGENCE (AI) DALAM MENINGKATKAN MOTIVASI BELAJAR SISWA PUSAT KEGIATAN BELAJAR MASYARAKAT (PKBM) BINA INSAN KAMIL Machfud, Syaeful; Prasetia, Okky; Ibnurhus, Gigih Amrillah
JAMAIKA: JURNAL ABDI MASYARAKAT Vol 6 No 3 (2025): OKTOBER
Publisher : Universitas Pamulang

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Abstract

This community service program aims to enhance learning motivation and digital literacy of learners at PKBM Bina Insan Kamil through the utilization of Artificial Intelligence (AI) technology. The initial conditions indicated low learning motivation caused by work-related fatigue, limited learning methods, and minimal use of digital technology by tutors. Although most learners already owned digital devices such as smartphones, their digital literacy skills remained limited. Tutors also faced challenges in mastering AI-based learning applications, resulting in conventional and less engaging instructional practices. This program was designed through intensive training and mentoring on the use of ChatGPT, Canva AI, Duolingo AI, Gamma AI, and AI Generate as innovative and technology-driven learning solutions. The implementation stages included preparation, training, implementation assistance, and evaluation. The results demonstrated an increase in learners’ motivation, improved ability to utilize AI-based applications for learning, and enhanced tutor competence in developing more engaging digital learning materials. The application of gamification through Duolingo AI significantly increased learner participation and engagement. Furthermore, the digitalization of PKBM administrative processes positively impacted institutional management efficiency. Overall, this program successfully supported digital transformation in non-formal education and has strong potential for sustainable implementation. Keywords: artificial intelligence; learning motivation; digital literacy; PKBM; innovative learning
Analisis Keamanan Sistem Informasi Pendidikan Menggunakan Framework ISO/IEC 27001 dan Pendekatan Gap Analysis Zaki, Fuad; Machfud, Syaeful; Nurlaila, Farida; Nanang, Nanang
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9344

Abstract

Information system security forms a fundamental backbone for ensuring the continuity of digital services in the modern era, especially in educational environments that heavily rely on information technology. Educational institutions face serious challenges in maintaining data confidentiality, integrity, and availability due to limited resources, weak policy enforcement, and low user literacy in cybersecurity. This study aims to evaluate the implementation of educational information system security using the ISO/IEC 27001 framework and Gap Analysis approach. The research method employs a qualitative approach with international standard-based evaluation techniques, system observation, and interviews with system administrators. The findings show that out of 14 ISO/IEC 27001 control domains, only 3 domains (21.4%) are fully implemented: access control (A.9), communications security (A.13), and physical security (A.11). The highest security gaps are found in the information security incident management domain (A.16) with 0% implementation, business continuity management domain (A.17) at 15%, and compliance with policies domain (A.18) at 20%. The system has implemented HTTPS protocol, limited two-factor authentication, and Role-Based Access Control (RBAC), but lacks formal security policies, SIEM-based threat monitoring systems, automated backup procedures, and regular security training programs. The gap between actual conditions and ideal standards indicates the need for a holistic approach that integrates technical, managerial, and educational aspects to build a resilient, secure, and sustainable educational information system.
Klasifikasi Spesies Suara Burung Menggunakan YAMNet dan Random Forest untuk Konservasi Alam Syaeful Machfud; Simon Simarmata; Nur Rofiq
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9443

Abstract

Monitoring and identification of bird species is an important aspect of biodiversity conservation, but manual identification methods based on direct observation and expert listening still have limitations in terms of time, cost, and subjectivity. Other challenges arise due to variations in the quality of sound recordings, the presence of environmental noise, and the similarity in vocalization patterns between species that make it difficult to automate the classification process. This study aims to develop an automatic classification system of bird species based on acoustic signals by combining the YouTube Audio Event Network (YAMNet) model and the Random Forest algorithm. YAMNet is utilized to extract spectral log-Mel features that represent the frequency and temporal characteristics of bird sounds, while Random Forest is used as a classifier to determine species based on those features. The dataset used is the Sound of 114 Species of Birds till 2022, which includes species variation, recording duration, and complex acoustic conditions. The results showed that the features produced by YAMNet were able to form separation between species visually through Principal Component Analysis (PCA), although there was still overlap in species with similar vocalization characteristics. Evaluation using the confusion matrix shows that some species can be classified with a high degree of accuracy, while misclassification occurs mainly in species with similar frequency patterns. Receiver Operating Characteristic (ROC) analysis yields Area Under Curve (AUC) values of up to 0.98 in certain species, indicating the model's excellent discriminating ability. These findings suggest that the integration of YAMNet and Random Forest has the potential to be an efficient and reliable solution to support automated bird species identification systems in nature conservation.
Co-Authors Ahmad Basith Amrillah Ibnurhus, Gigih Arfan Yusuf Bobi Agustian Cahyono, Yono Chaerunisa Dimas Satrio Wicaksono Gea, Kurniaman Gigih Amrillah Ibnurhus Gulo, Benifati Halawa, Hedwin Winata HENDRI ARDIANSYAH Herdiansah, Febi Ibnurhus, Gigih Amrillah Indriani, Rifdah Junianto, Mochamad Bagoes Satria Khairudin Khairudin Muhammad Syahdan nanang nanang Nathan Sanjaya Nur Rofiq Nurlaila, Farida Okky Prasetia Perani Rosyani Prasetia, Okky Putri, Khailla Yaman Rizky, Mochammad Saputra, Devani Erik Simon Simarmata Suryaningrat, Suryaningrat Zaki, Fuad