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FAKTOR-FAKTOR YANG MEMPENGARUHI KUALITAS PELAYANAN PERPUSTAKAAN SMAN 6 KOTA TANGERANG SELATAN DENGAN MENGGUNAKAN METODE ANALISIS FAKTOR Rahmat, Usep; Aditama, Aditama; Basir, Choirul
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 3 No 1 (2021)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v3i1.9295

Faktor-faktor yang bisa mempengaruhi kualitas pelayanan terhadap kepuasan pelanggan dapat terbentuk dari proses reduksi berbagai faktor. Dalam penelitian ini, peneliti akan meneliti kondisi yang ada pada Perpustakaan SMA Negeri 6 Kota Tangerang Selatan yang ada di Komplek Pamulang Permai 1 Pamulang Tangerang Selatan Banten. Metode yang akan digunakan adalah metode Analisis Faktor dengan teknik Principal Component Analysis menggunakan software SPSS 22. Ada beberapa dimensi kualitas pelayanan yang dapat mempengaruhi perilaku pelanggan dengan melakukan pengukuran menggunakan instrument SERVQUAL terhadap persepsi pelanggan pada kualitas Tangibles, Reliability, Responsiveness, Assurance, dan Emphaty (Parasuraman, 1988). Pengukuran tersebut selanjutnya akan memberikan penilaian seberapa memuaskan tingkat pelayanan tersebut sesuai harapan pelanggan. Populasi dalam penelitian adalah siswa yang melakukan peminjaman buku di Perpustakaan SMAN 6 Kota Tangerang Selatan dan untuk sampel penelitiannya berjumlah 100. Maka dengan menggunakan metode analisis faktor melalui aplikasi SPSS diketahui bahwa faktor-faktor yang mempengaruhi tingkat kepuasan pelanggan terhadap kualitas pelayanan Perpustakaan SMAN 6 Kota Tangerang Selatan adalah faktor sarana fisik, kehandalan, empati, dan jaminan dengan nilai korelasi masingmasing sebesar 0,725; 0,502; 0,618; 0,850. Sehingga faktor Assurance merupakan faktor yang paling mempengaruhi tingkat kepuasan pelanggan pada layanan perpustakaan

Pemanfaatan Informasi BMKG untuk Peningkatan Kapasitas Mitigasi Gempa dan Tsunami Berbasis Komunitas Susanto, Agung Budi; Winarni, Winarni; Basir, Choirul
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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

Indonesia merupakan wilayah rawan bencana gempa bumi dan tsunami karena berada pada pertemuan lempeng tektonik aktif. Di Jawa Barat, keberadaan Sesar Lembang berpotensi memicu gempa bumi dengan magnitudo menengah yang dapat berdampak signifikan pada wilayah padat penduduk. Sementara itu, wilayah pesisir selatan Jawa berada pada zona subduksi aktif yang berpotensi memicu gempa besar dan tsunami. Rendahnya tingkat pemahaman dan kesiapsiagaan masyarakat menjadi salah satu faktor meningkatnya risiko korban saat bencana terjadi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitas mitigasi gempa dan tsunami berbasis komunitas melalui pemanfaatan informasi resmi dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Metode pelaksanaan meliputi sosialisasi, pelatihan kebencanaan, pendampingan penggunaan aplikasi informasi kebencanaan, penyusunan peta evakuasi berbasis partisipasi masyarakat, serta simulasi evakuasi mandiri. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan kesiapsiagaan masyarakat dalam menghadapi ancaman gempa dan tsunami. Pendekatan berbasis komunitas dan penggunaan sumber informasi resmi dinilai efektif dalam mendukung upaya pengurangan risiko bencana. Kata kunci: mitigasi bencana; kesiapsiagaan masyarakat; gempa bumi; tsunami; BMKG

A Comparative Analysis of Support Vector Machine and Artificial Neural Network Methods for Predicting Vocational High School Student Graduation Didin Sahrudin; Ferhat Aziz; Choirul Basir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5742

Identifying which students may struggle in examinations early on is a critical challenge in vocational schools. This study aims to create and compare two machine learning models to predict the graduation status of Vocational High School (SMK) students majoring in Software and Game Development (PPLG). This prediction is based on their Competency Skills Test (UKK) scores. We used data from 310 students and tested two methods: Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results are very clear: the SVM model performed exceptionally well, achieving an accuracy of 99%. SVM was able to recognize both 'Competent' and 'Not Yet Competent' students in a balanced manner. Conversely, the ANN model's performance was poor, with an accuracy of only 66%. This occurred because the ANN failed to learn and simply guessed that all students would pass. This research concludes that SVM is a highly effective method to be used as an early warning system. With this system, schools can more quickly assist students who are at risk of failing. SVM achieved 99% accuracy with perfect precision for the Competent class and full recall for the Not Yet Competent class. ROC-AUC and PR-AUC indicated excellent separability and strong minority-class detection. ANN achieved only 66% accuracy, predicting all samples as Competent. Learning curves revealed stagnation and failure to learn minority class patterns. Additional baseline models (Logistic Regression, Random Forest) were tested, with SVM outperforming all others consistently. Statistical significance testing using McNemar's test confirmed that SVM provides significantly better classification performance than ANN (p < 0.01).

Prediction of Five Elements Imbalance and Acupuncture Point Recommendations Using Health-LLM Agent Method for Symptom Diagnosis Based on Traditional Chinese Medicine (TCM) Theory at Acumastery Clinic Iwan Muttaqin; Arya Adhyaksa Waskita; Choirul Basir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5775

Traditional Chinese Medicine (TCM) is a medical system that has been historically proven effective in diagnosing and managing various symptoms through the concepts of the Five Element imbalance, Yin-Yang, and acupuncture points. In the era of artificial intelligence, the utilization of Large Language Models (LLMs) specifically designed for the healthcare domain, referred to as Health-LLM Agents (AI-based health agents powered by LLMs), holds great potential in supporting TCM practices with greater efficiency and precision. This study aims to design and evaluate the performance of a Health-LLM Agent in predicting imbalances among the Five Elements (Wood, Fire, Earth, Metal, Water) based on patient symptoms, while also recommending appropriate acupuncture points for therapy. The methodology involves fine-tuning an LLM model with prompt engineering tailored to TCM terminology and principles, along with integrating symptom data in semi-structured text format. Evaluation is conducted using expert validation and classification metrics such as diagnostic accuracy, relevance of acupuncture point recommendations, and result interpretability. The findings indicate that the Health-LLM Agent achieves an 81% accuracy in predicting Five Element imbalances and receives 92% positive validation from TCM practitioners regarding acupuncture point recommendations. These results demonstrate that the Health-LLM Agent can serve as a promising tool to support the digitalization and personalization of TCM diagnosis through AI-based systems

Klasifikasi Jenis Sayuran Berbasis Citra Menggunakan Convolutional Neural Network dengan Penyediaan Informasi Kandungan Nutrisi: Image-Based Vegetable Type Classification Using Convolutional Neural Network with Nutritional Content Information Provision Intan Idaman Halawa; Choirul Basir
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2267

Banyak jenis sayuran memiliki tampilan yang mirip dalam hal bentuk, warna, dan ukuran, sehingga sulit dibedakan secara manual karena membutuhkan waktu lama dan rentan terhadap kesalahan. Selain itu, rendahnya pengetahuan masyarakat tentang nutrisi sayuran yang sesuai dengan kondisi kesehatan pribadi menjadi tantangan tersendiri. Penelitian ini merancang model klasifikasi sayuran berbasis citra menggunakan deep learning dengan metode Convolutional Neural Network (CNN) berbasis TensorFlow yang diintegrasikan dengan penyediaan informasi kandungan nutrisi secara otomatis. Dataset mencakup 15 jenis sayuran umum: brokoli, capsicum, kacang, kembang kol, kentang, kubis, labu botol, labu pahit, lobak, mentimun, pepaya, terong, tomat, waluh, dan wortel, dengan total 21.000 citra yang dibagi menjadi data latih (training set 15.000 citra), data validasi (validation set 3.000 citra), dan data uji (test set 3.000 citra). Model CNN dengan arsitektur sequential 8 lapisan dilatih dengan variasi parameter masukan, epoch, dan metrik evaluasi. Model terbaik menunjukkan training accuracy 92,8%, validation accuracy 95,83%, dan testing accuracy 95,67%. Evaluasi menggunakan confusion matrix menghasilkan akurasi keseluruhan 96%, presisi tertinggi 99%, recall tertinggi 100%, dan F1-score tertinggi 100%. Model yang diusulkan berpotensi dimanfaatkan untuk pelacakan gizi, kontrol kualitas makanan, dan pengembangan produk pangan.

A Comparative Analysis of Support Vector Machine and Artificial Neural Network Methods for Predicting Vocational High School Student Graduation Didin Sahrudin; Ferhat Aziz; Choirul Basir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5742

Identifying which students may struggle in examinations early on is a critical challenge in vocational schools. This study aims to create and compare two machine learning models to predict the graduation status of Vocational High School (SMK) students majoring in Software and Game Development (PPLG). This prediction is based on their Competency Skills Test (UKK) scores. We used data from 310 students and tested two methods: Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results are very clear: the SVM model performed exceptionally well, achieving an accuracy of 99%. SVM was able to recognize both 'Competent' and 'Not Yet Competent' students in a balanced manner. Conversely, the ANN model's performance was poor, with an accuracy of only 66%. This occurred because the ANN failed to learn and simply guessed that all students would pass. This research concludes that SVM is a highly effective method to be used as an early warning system. With this system, schools can more quickly assist students who are at risk of failing. SVM achieved 99% accuracy with perfect precision for the Competent class and full recall for the Not Yet Competent class. ROC-AUC and PR-AUC indicated excellent separability and strong minority-class detection. ANN achieved only 66% accuracy, predicting all samples as Competent. Learning curves revealed stagnation and failure to learn minority class patterns. Additional baseline models (Logistic Regression, Random Forest) were tested, with SVM outperforming all others consistently. Statistical significance testing using McNemar's test confirmed that SVM provides significantly better classification performance than ANN (p < 0.01).

Prediction of Five Elements Imbalance and Acupuncture Point Recommendations Using Health-LLM Agent Method for Symptom Diagnosis Based on Traditional Chinese Medicine (TCM) Theory at Acumastery Clinic Iwan Muttaqin; Arya Adhyaksa Waskita; Choirul Basir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5775

Traditional Chinese Medicine (TCM) is a medical system that has been historically proven effective in diagnosing and managing various symptoms through the concepts of the Five Element imbalance, Yin-Yang, and acupuncture points. In the era of artificial intelligence, the utilization of Large Language Models (LLMs) specifically designed for the healthcare domain, referred to as Health-LLM Agents (AI-based health agents powered by LLMs), holds great potential in supporting TCM practices with greater efficiency and precision. This study aims to design and evaluate the performance of a Health-LLM Agent in predicting imbalances among the Five Elements (Wood, Fire, Earth, Metal, Water) based on patient symptoms, while also recommending appropriate acupuncture points for therapy. The methodology involves fine-tuning an LLM model with prompt engineering tailored to TCM terminology and principles, along with integrating symptom data in semi-structured text format. Evaluation is conducted using expert validation and classification metrics such as diagnostic accuracy, relevance of acupuncture point recommendations, and result interpretability. The findings indicate that the Health-LLM Agent achieves an 81% accuracy in predicting Five Element imbalances and receives 92% positive validation from TCM practitioners regarding acupuncture point recommendations. These results demonstrate that the Health-LLM Agent can serve as a promising tool to support the digitalization and personalization of TCM diagnosis through AI-based systems

An Exploration of the Optimal Solution for the Tuberculosis Transmission Model considers the Post-recovery Treatment using Optimal Control Theory Sanubari Tansah Tresna; Choirul Basir; Usep Rahmat; Enggar Prasetyawan
Jurnal Matematika UNAND Vol. 15 No. 2 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.2.224-236.2026

Tuberculosis, also known as TB, is among the most communicable diseases. It is strenuous to detect TB infection early, so the number of cases increases over time. Consequently, the cost of treating the TB-infectious sufferer is getting higher. However, since the recovered people from TB can become infected again, the post-treatment intervention needs to be conducted. Many mathematical models have been developed to study TB transmission among people. However, the study of cost-effectiveness analysis is not well-studied. Therefore, we are proposing a reformulated model of TB transmission into an optimal control model by considering the post-treatment intervention to reduce the cases of re-infected TB. The numerical simulations are performed to figure out the projection of its population dynamics under TB transmission. Next, we calculate the Average Cost-Effectiveness Ratio (ACER) and Incremental Cost-Effectiveness Ratio (ICER) to explore the most cost-effective strategy.

Pengembangan Model Super Resolution Lightweight Berbasis Hybrid untuk Lingkungan Komputasi Terbatas Ade Soekarno Putra Santoso; Kahfi Heryandi Suradiradja; Choirul Basir
Journal of Creative Student Research Vol. 4 No. 4 (2026): Agustus: Journal of Creative Student Research
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsr-politama.v4i4.6537

Computational resource limitations, particularly the use of CPU-based systems without GPU acceleration, require super-resolution models that can achieve a balance between reconstruction quality and computational efficiency. This study proposes a lightweight hybrid super-resolution model designed for resource-constrained computing environments. The proposed architecture integrates lightweight feature extraction, residual learning, and an efficient image reconstruction module to enhance image resolution while maintaining low computational complexity. Model performance is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), computation time, CPU utilization, and memory consumption under different scaling factors. Experimental results show that the proposed model achieves competitive reconstruction performance while maintaining computational efficiency compared with several lightweight benchmark models. These findings indicate that the proposed hybrid architecture is suitable for deployment in resource-limited computing environments. Future work may focus on evaluating and optimizing the model on edge computing platforms, including smartphones and embedded systems, to validate its performance under real-world operating conditions.

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