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All Journal Pelita : Jurnal Penelitian dan Karya Ilmiah M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Jurnal Statistika dan Matematika (Statmat) Indonesia Berdaya International Journal Software Engineering and Computer Science (IJSECS) KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Tesseract: International Journal of Geometry and Applied Mathematics
Choirul Basir
Universitas Pamulang
Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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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

Abstract

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

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Abstract

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

Abstract

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

Abstract

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
Co-Authors Aditama Aditama Aditama Aditama Agung Budi Susanto Arya Adhyaksa Waskita Dewi Purnama Sari Didin Sahrudin DWI RAMADHANI Fauziyah Fauziyah Ferhat Aziz Gerry Sastro Ilmadi Ilmadi Imamatun Nisa Iwan Muttaqin Maolana, Cucu Nina Valentika Nursyirwan , Vivi Iswanti Rahmat, Usep Salma, Anca Almira Tabah Heri Setiawan Usep Rahmat Winarni Winarni Yosep, Herman Yuni Permata Sari