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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Penelitian Sains Publikauma : Jurnal Administrasi Publik Universitas Medan Area Jurnal Pengabdian Pada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Keperawatan Silampari TEMALI : Jurnal Pembangunan Sosial Dialektika: Jurnal Ekonomi dan Ilmu Sosial Diseminasi : Jurnal Pengabdian kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Rekayasa Elektro Sriwijaya Jurnal Teknologi BAKTI : Jurnal Pengabdian Kepada Masyarakat
Bambang Suprihatin
Jurusan Matematika, Fakultas MIPA, Unsri
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Faktor Determinan dan Motivasi Peserta KB Melalui Program KB Keliling untuk Memilih Metode Kontrasepsi Jangka Panjang Ovi Hendrika; Maryadi Maryadi; Bambang Suprihatin
Jurnal Keperawatan Silampari Vol 6 No 1 (2022): Jurnal Keperawatan Silampari
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (218.6 KB) | DOI: 10.31539/jks.v6i1.4420

Abstract

This study aims to determine what factors influence the selection of MKJP (Long Term Contraception Method) through the mobile family planning program in Prabumulih City. This research method is a quantitative analysis using secondary data from the 2021 Family Data Collection (PK21). The results showed that the Nagelkerke R Square value was 0.117 and Cox & Snell R Square 0.010, indicating that the independent variable's ability to explain the dependent variable is 0.010 or 1 percent. Ninety-one percent of other factors outside the model define the dependent variable. In conclusion, the element that has a significant influence on the selection of long-term contraceptive methods is the factor of access to family planning services. Keywords: Determinant Factors, Mobile Family Planning, Motivation
Algoritma Extreme Gradient Boosting (XGBoost) dan Adaptive Boosting (AdaBoost) Untuk Klasifikasi Penyakit Tiroid Anita Desiani; Siti Nurhaliza; Tri Febriani Putri; Bambang Suprihatin
Jurnal Rekayasa Elektro Sriwijaya Vol. 6 No. 2 (2025): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jres.v6i2.145

Abstract

Thyroid disease is a disease of the thyroid gland that can interfere with daily activities. Early detection of thyroid disease can have an important impact in optimizing the development of early detection systems that are more effective and accurate in detecting the disease. Data mining approaches can be used to solve this problem by utilizing various available algorithms, such as Adaptive Boosting and Extreme Gradient Boosting. This research aims to improve the development of early thyroid disease prediction by comparing the two algorithms by utilizing the percentage split method. This research provides results if the Adaptive Boosting algorithm provides an accuracy value of 97%. In class 0, the precision and recall values are the same at 98%, while in class 1 it is 80% and 90%. Meanwhile, testing using the Extreme Gradient Boosting algorithm gives an accuracy value of 98%. In class 0, the same precision and recall values are 99%, while for class 1 it is 86% and 90%. Based on the comparison by considering the accuracy, precision, and recall values, as well as the performance of the two algorithms, it is concluded that the implementation of the Extreme Gradient Boosting algorithm has the best performance for thyroid disease detection.
Perbandingan Algoritma CART Dan AdaBoost Pada Klasifikasi Demensia Muhammad Arya All Fajri; M Aldi Saputra; Anita Desiani; Bambang Suprihatin; Herlina Hanum
FORMAT Vol 15 No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i1.002

Abstract

Demensia merupakan gangguan kesehatan ditandai dengan penurunan daya ingat, kemampuan kognitif, dan perilaku yang mengganggu aktivitas pada kehidupan sehari-hari. Masyarakat kurang mendapatkan informasi mengenai deteksi dini demensia yang disebabkan terbatasnya fasilitas kesehatan. Klasifikasi menggunakan data mining dapat membantu deteksi dini demensia. Penelitian ini bertujuan membandingkan algoritma CART dan AdaBoost untuk melihat metode yang paling efektif digunakan pada klasifikasi demensia. Pembagian data dilakukan menggunakan metode percentage split dan k-fold cross-validation. Percentage split membagi data menjadi dua bagian dengan 70% data pelatihan dan 30% data pengujian. K-fold cross-validation mengelompokkan data dengan 1 kelompok data menjadi data pengujian dan 9 kelompok data lainnya menjadi data pengujian yang dilakukan berulang pada setiap kelompok data sebanyak 10 kali. ADASYN digunakan untuk menyeimbangkan data pada setiap kelas. Hasil evaluasi kinerja pada kedua algoritma menunjukkan AdaBoost menggunakan ADASYN dan k-fold cross-validation memiliki nilai tertinggi untuk akurasi, presisi, recall, f1-score, dan ROC-AUC masing-masing sebesar 92.52%, 92.11%, 92.52%, 91.46%, dan 96.85%. Hasil ini menunjukkan bahwa algoritma AdaBoost sangat baik dalam memprediksi seluruh demensia dengan benar, mempertahankan keseimbangan antara presisi dan recall, dan membedakan tiga kelas demensia. Hasil penelitian menunjukkan keunggulan pendekatan ensemble learning dalam menangani variasi data dan meningkatkan stabilitas model klasifikasi demensia. Penelitian ini menunjukkan bahwa AdaBoost memiliki performa yang sangat baik dibandingkan CART pada klasifikasi demensia.
Pengembangan kreativitas siswa tunadaksa di SLB-D YPAC Palembang melalui handcraft Bambang Suprihatin; Andi Tenri Ajeng Nur; Yuli Andriani; Azhar Kholiq Affandi; Soya Febeauty Yama Otantia Pradini; Anita Desiani; Jessica Joseph Sen; Rosalinda Hizkia Amelia Manurung; Tiara Valentina Pakpahan
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 10, No 4 (2026): August (In Progress)
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v10i4.40388

Abstract

Abstrak SLB-D YPAC Palembang merupakan lembaga pendidikan bagi siswa berkebutuhan khusus yang mengalami hambatan fisik atau tunadaksa. Siswa tunadaksa memiliki keterbatasan pada fungsi motorik halus yang dapat memengaruhi kemandirian dan keterampilan mereka. Berdasarkan kondisi tersebut, dilakukan kegiatan pengabdian kepada masyarakat berupa pelatihan handcraft pembuatan bouquet snack sebagai sarana pembelajaran keterampilan yang kreatif, adaptif, dan produktif. Kegiatan pengabdian dilaksanakan di SLB-D YPAC Palembang pada bulan Agustus-September 2025 melalui tiga tahap, yaitu persiapan, pelaksanaan, dan evaluasi, dengan metode demonstrasi dan praktik langsung yang diikuti oleh delapan siswa tunadaksa serta didampingi oleh tim pengabdi dan guru. Evaluasi dilakukan melalui pre-test dan post-test yang dianalisis menggunakan metode Normalized Gain (N-Gain) untuk mengukur efektivitas pelatihan dalam peningkatan pemahaman dan keterampilan siswa setelah mengikuti pelatihan. Hasil analisis menunjukkan nilai N-Gain sebesar 0,58 yang termasuk dalam kategori sedang, sehingga pelatihan dinilai cukup efektif dalam meningkatkan pemahaman dan keterampilan siswa. Pelatihan ini juga memberikan dampak positif berupa peningkatan koordinasi motorik halus serta pengembangan soft skill siswa, seperti kemandirian, ketekunan, kemampuan bekerja sama, dan rasa percaya diri selama mengikuti kegiatan dan menyelesaikan pembuatan handcraft. Respon guru dan orang tua turut menguatkan bahwa kegiatan ini bermanfaat dan relevan dengan kebutuhan siswa tunadaksa. Secara keseluruhan, pelatihan handcraft ini dinilai efektif dalam meningkatkan kemampuan kreatif dan produktif siswa tunadaksa di SLB-D YPAC Palembang serta berpotensi untuk dikembangkan menjadi kegiatan pembelajaran keterampilan yang berkelanjutan di sekolah luar biasa. Kata kunci: bouquet snack; pelatihan handcraft; siswa tunadaksa. Abstract SLB-D YPAC Palembang is an educational institution for students with physical disabilities (tunadaksa), who often experience limitations in fine motor function that affect their independence and practical skills. Based on this condition, a community service program was conducted in the form of handcraft training on making snack bouquets to develop creative, adaptive, and productive skills. The program was implemented from August to September 2025 through three stages: preparation, implementation, and evaluation. The training used demonstration and hands-on practice methods and involved eight students with physical disabilities, assisted by the service team and teachers. Evaluation was conducted using pre-test and post-test assessments, analyzed with the Normalized Gain (N-Gain) method to measure effectiveness in improving students’ understanding and skills. The results showed an N-Gain value of 0.58, categorized as medium, indicating that the training was reasonably effective. In addition, the program positively impacted students’ fine motor coordination and supported the development of soft skills, including independence, perseverance, teamwork, and self-confidence during learning and task completion. Feedback from teachers and parents further confirmed that the program was beneficial and aligned with the needs of students with physical disabilities. Overall, this handcraft training is considered effective in enhancing the creative and productive abilities of students at SLB-D YPAC Palembang and has the potential to be developed into a sustainable skills-based learning activity in special education. Keywords: bouquet snack; handcraft training; tunadaksa student.
Perbandingan Kinerja Algoritma Random Forest dan Gradient Boosting dalam Klasifikasi Risiko Stunting pada Balita Diah Suci Ramadhani; Marisa -; Anita Desiani; Novi Rustiana Dewi; Bambang Suprihatin; Endro Setyo Cahyono; Lucky Indra Kesuma
Jurnal Teknologi Vol 26, No 2 (2026): Agustus 2026
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v26i2.9285

Abstract

Stunting in toddlers is a chronic nutritional problem that can impede physical growth and cognitive development; therefore, early detection is crucial to prevent long-term impacts. This study compared Random Forest and Gradient Boosting algorithms for classifying stunting risks in toddlers. The comparison of both algorithms was conducted to determine the model with better performance in classifying stunting status into four categories: severely stunted, stunted, normal, and tall. Testing was performed using the Percentage Split method (80% training data and 20% testing data) and 10-Fold Cross Validation. Model performance was measured based on accuracy, precision, and recall metrics. The results showed that the Random Forest algorithm produced better and more consistent performance compared to Gradient Boosting across both testing methods. In the Percentage Split method, Random Forest achieved an accuracy of 95.43%, meaning the model was able to accurately predict stunting status in a single test data split, whereas Gradient Boosting only achieved 85.68%, indicating a higher prediction error rate. In the 10-Fold Cross Validation method, Random Forest maintained an accuracy of 94.89%, meaning the model remained consistent despite being repeatedly tested using varied data subsets, while Gradient Boosting decreased to 77.47%, indicating that the model was unstable and sensitive to data variations. Additionally, Random Forest demonstrated stable precision and recall values above 90% across all stunting categories, particularly for the normal and tall categories. In conclusion, the Random Forest algorithm is more effective in classifying stunting risks in toddlers than the Gradient Boosting algorithm.
Co-Authors Agustina Bidarti Alga Mahida Ali Amran Andi Tenri Ajeng Nur Anita Desiani Azhar Kholiq Affandi Carolina Rahman Diah Suci Ramadhani Dian Cahyawati Dina Suzzete Sitorus Eka Susanti Endang Sri Kresnawati Endro Setyo Cahyono, Endro Setyo Erwin, Erwin Faishal Fitra Ramadhan Herlina Hanum Hermansyah Hermansyah Hermansyah Jessica Joseph Sen Listina Tri Dharmayanti Lucky Indra Kesuma M Aldi Saputra Marisa - Maryadi Maryadi Mega Tiara Putri Muhammad Arya All Fajri Muhammad Azwar Annas NEYSA ADIRATNA AGMI MASYITHAH Nidatri Mutiah Zatzah Novi Rustiana Dewi Ovi Hendrika Rifki Kurniawan Rosalinda Hizkia Amelia Manurung SINTA ELPATRIKA ABELIA Siti Nurhaliza Soya Febeauty Yama Otantia Pradini Sugandi Yahdin Tiara Valentina Pakpahan Tri Febriani Putri Yuli Andriani Yulia Resti Yusuf Hartono Zunaidah Zunaidah