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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri Vivabio : Jurnal Pengabdian Multidisiplin Jurnal Pengabdian Masyarakat Indonesia Computer Science (CO-SCIENCE) Jurnal Abdimas Komunikasi dan Bahasa Indonesian Journal Computer Science (ijcs) Journal Medical Informatics Technology SWARNA: Jurnal Pengabdian Kepada Masyarakat
Aziz, Faruq
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur Khasanah, Nurul; Eka Saputri , Daniati Uki; Aziz, Faruq; Hidayat, Taopik
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.5522

Abstract

Sleep disorders such as insomnia and sleep apnea can significantly affect quality of life and increase the risk of chronic diseases. Early identification and classification of sleep disorders are crucial in preventing further impacts. This study aims to compare the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying sleep disorders using the Sleep Health and Lifestyle Dataset from Kaggle, which contains health and lifestyle data relevant to sleep patterns. The Random Forest and KNN algorithms were applied to classify sleep disorders into the categories 'None', 'Sleep Apnea', and 'Insomnia'. Based on the study results, the Random Forest algorithm achieved an accuracy of 89.69%, with the best performance in the 'None' category, reaching a recall of 96.08%. Meanwhile, KNN achieved an accuracy of 87.02% with K=5. Although Random Forest demonstrated superior results, challenges were still found in detecting the 'Sleep Apnea' category, where recall only reached 74.55%, likely due to data imbalance. This study shows that the Random Forest algorithm is more effective in classifying sleep disorders compared to KNN. Future research steps include data balancing and exploring other algorithms such as XGBoost to improve the performance of sleep disorder detection.
Improving Early Detection of Cervical Cancer Through Deep Learning-Based Pap Smear Image Classification Merlina, Nita; Prasetio, Arfhan; Zuniarti, Ida; Mayangky, Nissa Almira; Sulistyowati, Daning Nur; Aziz, Faruq
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.576

Abstract

Cervical cancer is one of the leading causes of death in women worldwide, making early detection of the disease crucial. This study proposes a deep learning-based approach that has the advantage of leveraging pre-trained models to save data, time, and computation to classify Pap smear images without relying on segmentation, which is traditionally required to isolate key morphological features. Instead, this method leverages deep learning to identify patterns directly from raw images, reducing preprocessing complexity while maintaining high accuracy. The dataset used in this study is a public data repository from Nusa Mandiri University (RepomedUNM), which has a wider variety of data. This dataset is used to classify images into four categories: Normal, LSIL, HSIL, and Koilocytes. The dataset consists of 400 images evenly distributed, ensuring class balance during training. Transfer learning is applied using five Convolutional Neural Network (CNN) architectures: ResNet152V2, InceptionV3, ResNet50V2, DenseNet201, and ConvNeXtBase. To prevent overfitting, techniques such as data augmentation, dropout regularization, and class weight adjustment are applied. The evaluation results in this study showed the highest accuracy with a value of ResNet152V2 = 0.9025, InceptionV3 = 0.8953 and DenseNet201 = 0.8845. ResNet152V2 excelled in extracting complex features, while InceptionV3 showed better computational efficiency. The study also highlighted the clinical impact of misclassification between Koilocytes and LSIL, which may affect diagnostic outcomes. Data augmentation techniques, including horizontal and vertical flipping and normalization, improved the model's generalization to a wide variety of images. Specificity was emphasized as a key evaluation metric to minimize false positives, which is important in medical diagnostics. The findings confirmed that transfer learning effectively overcomes the limitations of small datasets and improves the classification accuracy of pap smear images. This approach shows potential for integration into clinical workflows to enable automated and efficient cervical cancer detection.
Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur Khasanah, Nurul; Eka Saputri , Daniati Uki; Aziz, Faruq; Hidayat, Taopik
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.5522

Abstract

Sleep disorders such as insomnia and sleep apnea can significantly affect quality of life and increase the risk of chronic diseases. Early identification and classification of sleep disorders are crucial in preventing further impacts. This study aims to compare the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying sleep disorders using the Sleep Health and Lifestyle Dataset from Kaggle, which contains health and lifestyle data relevant to sleep patterns. The Random Forest and KNN algorithms were applied to classify sleep disorders into the categories 'None', 'Sleep Apnea', and 'Insomnia'. Based on the study results, the Random Forest algorithm achieved an accuracy of 89.69%, with the best performance in the 'None' category, reaching a recall of 96.08%. Meanwhile, KNN achieved an accuracy of 87.02% with K=5. Although Random Forest demonstrated superior results, challenges were still found in detecting the 'Sleep Apnea' category, where recall only reached 74.55%, likely due to data imbalance. This study shows that the Random Forest algorithm is more effective in classifying sleep disorders compared to KNN. Future research steps include data balancing and exploring other algorithms such as XGBoost to improve the performance of sleep disorder detection.
ASSESSING SMPIT AJIMUTU GLOBAL INSANI WEBSITE QUALITY USING THE WEBQUAL 4.0 METHOD Saryoko, Andi; Aziz, Faruq; Eliyana, Instianti; Saputra, Elin Panca; Saputra, Bagas Eka
Jurnal Techno Nusa Mandiri Vol. 20 No. 2 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.6448

Abstract

Digitalization in the world of education encourages schools to have quality websites to provide online information and learning services. This study aims to measure the quality of the SMPIT Ajimutu Global Insani website using the Webqual 4.0 method, which involves three main dimensions: usability quality, information quality, and service interaction quality. This research method involves a survey of 50 respondents consisting of teachers, students, and parents of students. Data were analyzed descriptively using a Likert scale to evaluate the level of user satisfaction. The results showed that the information quality dimension had the highest score (4.2), followed by service interaction quality (4.0), while usability quality scored the lowest (3.8). These findings indicate that the website content is relevant, but navigation and interface design need improvement. Recommendations are given to improve the quality of the website, including optimizing interactive features and adding multimedia content. The implementation of the results of this study is expected to support the digital transformation of schools more effectively.
Prediksi Kelulusan Mahasiswa Menggunakan Algoritma C4.5 dengan RapidMiner: Studi Kasus Data Akademik Perguruan Tinggi XYZ Khasanah, Nurul; Saputri, Daniati Uki Eka; Hidayat, Taopik; Aziz, Faruq
Indonesian Journal Computer Science Vol. 4 No. 2 (2025): Oktober 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v4i2.9647

Abstract

Ketepatan waktu kelulusan mahasiswa merupakan indikator penting dalam menilai kualitas dan efektivitas pendidikan tinggi. Keterlambatan kelulusan tidak hanya berdampak pada akreditasi program studi, tetapi juga pada efisiensi manajemen akademik dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini bertujuan membangun model prediksi kelulusan mahasiswa menggunakan algoritma C4.5 berbasis pohon keputusan dengan dukungan perangkat lunak RapidMiner. Dataset yang digunakan terdiri atas 379 entri mahasiswa, yang mencakup atribut demografis (jenis kelamin, umur, status nikah), status mahasiswa, indeks prestasi semester (IPS 1–8), serta indeks prestasi kumulatif (IPK). Proses penelitian meliputi pengumpulan, pembersihan, transformasi data, pemodelan, dan evaluasi performa. Model diuji menggunakan pembagian data 70:30 serta validasi silang (10-fold cross-validation) untuk memastikan keandalan hasil. Hasil pengujian menunjukkan akurasi 97,81% dan nilai AUC 0,991, yang menegaskan kemampuan algoritma C4.5 dalam mengklasifikasikan status kelulusan secara tepat. Temuan ini menunjukkan peningkatan signifikan dibandingkan penelitian sebelumnya dengan algoritma Naïve Bayes (88,16%) dan K-NN (87,8%). Atribut yang paling berpengaruh adalah IPS3, IPS4, IPS5, status pekerjaan, dan umur mahasiswa. Penelitian ini berkontribusi pada pengembangan model prediksi yang tidak hanya akurat, tetapi juga mudah diinterpretasikan, sehingga dapat dimanfaatkan oleh institusi pendidikan dalam menyusun kebijakan intervensi akademik dini bagi mahasiswa berisiko terlambat lulus.
PKM PENDAMPINGAN DIGITALISASI SEKOLAH SMPIT AJIMUTU GLOBAL INSANI TAMBUN UTARA-BEKASI BERBASIS ARTIFICIAL INTELLIGENCE Saryoko, Andi; Aziz, Faruq; Elyana, Instianti; Rizky, Mhd; Junior, Mario Christian
Jurnal AbdiMas Nusa Mandiri Vol. 7 No. 2 (2025): Periode Oktober 2025
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v7i2.7399

Abstract

Digital transformation in education is inevitable; however, many schools such as SMPIT Ajimutu Global Insani still face challenges in adopting technology. The main issue lies in the manual learning evaluation process, which leads to teacher time inefficiency and limited question variation, ultimately affecting learning quality. This community service program aims to bridge the digital gap by implementing an Artificial Intelligence (AI)-based solution. The novelty of this initiative lies in the use of the Question Maker Application (QUMAA)—an innovative tool that automates question generation and includes a question difficulty analysis feature, offering a comprehensive approach to digitizing school evaluation systems. The program was carried out through a participatory method consisting of five stages: needs assessment, application development, intensive training and mentoring for teachers, implementation and evaluation, as well as monitoring and sustainability planning. The results showed that the learning evaluation process was successfully digitized, with teachers’ ability to understand and use AI increasing by more than 50%, and question preparation time reduced by up to 50%. This program not only facilitated technology transfer but also empowered teachers and strengthened the school institution. The implementation of the AI-based QUMAA proved effective in improving efficiency and quality in learning evaluation. Furthermore, this model can be replicated in other schools as a concrete contribution to achieving the SDGs in education, emphasizing the need for ongoing support and funding to sustain digital innovation in the education sector.
Pengabdian Masyarakat Mengembangkan Keberlanjutan dalam Pemeliharaan Situs Web Sistem Administrasi RW013 Cipinang Melayu Nurmalasari, Nurmalasari; Masturoh, Siti; Lusiana Pratiwi, Risca; Aziz, Faruq
Jurnal Pengabdian Masyarakat Indonesia Vol 4 No 3 (2024): JPMI - Juni 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpmi.2348

Abstract

Rukun warga merupakan lembaga masyarakat yang dibentuk melalui musyawarah pengurus rukun tetangga dalam rangka memberikan pelayanan untuk masyarakat yang diakui oleh pemerintah daerah. Setelah di rancang bangun sistem administrasi RW013 Cipinang Melayu, pengurus RW013 cipinang melayu masih mengalami kendala dalam pemeliharaan atau maintenance situs web karena keterbatasan informasi dan pengetahuan pengurus. Oleh karena itu kegiatan pengabdian masyarakat ini yaitu berupa pelatihan workshop untuk mengembangkan keberlanjutan dalam pemeliharaan situs web Sistem Administrasi RW013 Cipinang Melayu untuk para pengurus RW yang menjadi admin dalam pengelolaan situs web. Metode pelaksanaan dengan konsolidasi internal, kemudian dilanjutkan koordinasi dengan mitra. Pelaksanaan pengabdian masyarakat di hadiri oleh mitra pengurus RW013 cipinang melayu, dosen dan mahasiswa. Tujuan dari pengabdian masyarakat ini adalah peningkatan pengetahuan pengurus sebagai admin untuk mengelola sistem informasi berbasis web ini sehingga pelayanan yang diberikan dapat menjadi lebih efektif dan efisien.
Pemanfaatan Kecerdasan Buatan untuk Pembuatan Materi Ajar Membaca dan Menulis di RA Al Muttaqin Azzahro, Fatimah; Hidayat, Arif; Aziz, Faruq
Jurnal Abdimas Komunikasi dan Bahasa Vol. 5 No. 1 (2025): Juni 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abdikom.v5i1.8891

Abstract

Kegiatan pelatihan yang bertujuan untuk meningkatkan kompetensi guru PAUD RA Al-Muttaqin dalam memanfaatkan teknologi berbasis kecerdasan buatan (AI) guna mendukung proses pembelajaran anak usia dini. Pelatihan ini berhasil mencapai tujuh manfaat utama, antara lain peningkatan literasi digital, kemampuan membuat media pembelajaran mandiri, pengajaran yang lebih menarik dan interaktif, efisiensi dalam persiapan mengajar, peningkatan rasa percaya diri guru, terjalinnya kerja sama antara kampus dan lembaga pendidikan masyarakat, serta keterlibatan mahasiswa dalam kegiatan sosial. Evaluasi melalui kuesioner menunjukkan bahwa peserta merespons kegiatan ini secara positif, dengan 85% menyatakan puas atau sangat puas terhadap kualitas materi, 100% menyetujui relevansi dan aplikasinya, serta 90% menilai penyampaian materi sebagai bagus atau sangat bagus. Selain itu, kegiatan ini memperoleh eksposur melalui publikasi di media massa nasional, memperluas dampak dan diseminasi praktik pembelajaran inovatif berbasis teknologi. Hasil ini menunjukkan bahwa pelatihan tidak hanya meningkatkan kapasitas individu guru, tetapi juga memperkuat sinergi antara perguruan tinggi dan masyarakat dalam mendorong transformasi pendidikan berbasis digital. This program aimed to enhance the competencies of teachers at PAUD RA Al-Muttaqin in utilizing artificial intelligence (AI)-based technologies to support early childhood education. The program successfully achieved seven key outcomes: increased digital literacy among teachers, the ability to independently create educational media, more engaging and interactive teaching practices, improved efficiency in lesson preparation, greater teacher confidence, strengthened collaboration between the university and community educational institutions, and active student involvement in social engagement activities. Evaluation through post-training questionnaires revealed a highly positive response from participants, 85% expressed satisfaction with the quality of the material, 100% agreed with its relevance and applicability, and 90% rated the delivery of the content as good or excellent. Furthermore, the activity received national media coverage, extending the dissemination of innovative, technology-based educational practices. These results demonstrate that the training not only enhanced individual teacher capacity but also fostered synergy between higher education and the community to promote digital transformation in education
YOLO MODEL DETECTION OF STUDENT NEATNESS BASED ON DEEP LEARNING: A SYSTEMTIC LITERATURE REVIEW Saryoko, Andi; Aziz, Faruq
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6986

Abstract

Maintaining proper student neatness (uniform compliance, grooming standards, and posture) is essential for fostering disciplined learning environments. While traditional monitoring methods are labor-intensive and subjective, computer vision-based solutions leveraging You Only Look Once (YOLO) architectures offer promising alternatives. The objective of this study is to evaluate YOLO optimization techniques for student neatness detection, identify key challenges, and propose relevant future research directions. This systematic review evaluates 28 recent studies (2021-2024) to analyze optimization techniques for YOLO models in student neatness detection applications. Key findings demonstrate that attention-enhanced variants (e.g., YOLOv10-MSAM) achieve 87.0% mAP@0.5, while pruning and quantization methods enable real-time processing (50-130 FPS) on edge devices like Jetson Orin. The analysis reveals three critical challenges: (1) occlusion handling in crowded classrooms (10-15% false negatives), (2) lighting/background variability, and (3) ethical concerns regarding facial recognition. Emerging solutions include hybrid vision-language models for explainable detection and federated learning for privacy preservation. The review proposes a taxonomy of optimization approaches categorizing architectural modifications (attention mechanisms, lightweight backbones), data augmentation strategies (GAN-based synthesis), and deployment techniques (TensorRT acceleration). Future research directions emphasize multi-modal sensor fusion and domain adaptation for cross-institutional generalization. This work provides educators and AI developers with evidence-based guidelines for implementing automated neatness monitoring systems while addressing practical constraints in educational settings.
Co-Authors Adjat Sudrajat Andi Ibrahim Andi Saryoko Andini, Siwi Putri Arif Hidayat Azzahro, Fatimah Chairul, Yasrizal Daniati Uki Eka Saputri Daryati, Elisabeth Isti Dirsam, Winarlin Dwiyogo, Santos Winarso Dwiza Riana Eka Herdit Juningsih Eka Saputri , Daniati Uki Eko Priyono Eliyana, Instianti Erna, Maria Agustina Fatah, Teddy Al Fitri , Evita Fitri, Evita Handayani, Popon Herwanto, Teguh Hewiz, Alya Shafira Hidayat, Taopik Ida Ayu Putu Sri Widnyani Ida Zuniarti, Ida Instianti Elyana, Instianti Irmawati Irmawati Junior, Mario Christian Kodri, Wan Ahmad Gazali Kurniawantoro Kurniyawantoro, Kurniyawantoro Lawrence Adi Supriyono Linda Marlinda Lusiana Pratiwi, Risca Mayangky, Nissa Almira Ma’mun, Sukrul Muhamad Fatchan Nita Merlina, Nita Nugraha , Siti Nurhasanah Nugraha, Siti Nurhasanah Nur Sulistyowati, Daning Nurmalasari Nurmalasari Nurul Khasanah Nurul Khasanah, Nurul Panca Saputra, Elin Prasetio, Arfhan Pratiwi, Risca Lusiana Ramadhan, Aditya Rian Rizky, MHD Sabara, Edi Saeda, Ida Said, Fadillah Santoso Setiawan Saputra, Bagas Eka Saputra, Ridwan Sarah Khairunnisa Budiyanto Septian, Rendi Siti Masturoh Sri Hadianti Sulistyowati, Daning Nur Sutisna, Rian Taopik Hidayat Veithzal Rivai Zainal Wadu, Novita M Kana Wahyu Indrarti Widiyawati , Widiyawati Widiyawati Widiyawati Widyasih, Ajeng Sri YANTO YANTO