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All Journal Building of Informatics, Technology and Science Community: Jurnal Pengabdian Pada Masyarakat
Muttaqin, Muhammad Al Ghorizmi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Languange, Linguistic, Communication & Media

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Peningkatan Kapabilitas Perlindungan Diri Perempuan Desa Batursari Melalui Praktik Dasar Muay Thai Trisnapradika, Gustina Alfa; Saraswati, Eka Rizky Anggi; Muttaqin, Muhammad Al Ghorizmi; Prakoso, Dwi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 5 No. 1 (2025): Maret : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/16fnb325

Abstract

Maraknya tawuran antar gangster di malam hari membuat masyarakat menjadi resah untuk beraktivitas dengan bebas. Hingga di era saat ini, perempuan masih menjadi objek atau target utama tindak kriminalitas akibat stigma bahwa perempuan adalah kaum yang lemah.  Desa Batursari memiliki populasi sebesar 35.229 jiwa dengan sebaran jumlah penduduk perempuan sebanyak 17.625 jiwa sehingga memiliki kerentanan menjadi korban kriminalitas yang tinggi. Oleh karenanya, Tim Pengabdi berkolaborasi bersama Dinas Perempuan dan Anak (DP3AP2KB) Provinsi Jawa Tengah dan perangkat Desa Batursari untuk mengadakan kegiatan edukasi dan praktik dasar Muay Thai sebagai bentuk antisipasi terhadap terjadinya kriminalitas. Hasilnya, terjadi 67,7% peningkatan pengetahuan dan kapabilitas kaum perempuan masyarakat Desa Batursari dalam bidang perlindungan diri.
Optimasi Algoritma SVM dengan Teknik SMOTE dan Tuning Parameter pada Klasifikasi Balita Stunting Muttaqin, Muhammad Al Ghorizmi; Trisnapradika, Gustina Alfa
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8330

Abstract

Stunting in toddlers is a chronic nutritional problem that has long-term impacts on human resource quality, including cognitive development and vulnerability to diseases. Brebes Regency is one of the priority areas for stunting management in Indonesia. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm in classifying stunting status among toddlers by addressing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and parameter tuning. A total of 9,598 anthropometric samples collected from several community health center in Brebes were processed through stages of data cleaning, label encoding, outlier handling, standardization, and class splitting, and then divided into training data (80%) and testing data (20%). Two models were compared: the baseline SVM model and the optimized SVM model, which integrates SMOTE and parameter tuning through GridSearchCV. The results showed that the baseline model achieved an accuracy of 98.31%, but the recall for the stunting class was only 89.19%. After applying SMOTE and parameter tuning, the model’s performance improved, achieving an accuracy of 99.78% and a recall for the stunting class of 98.46%. This improvement demonstrates that the use of SMOTE and parameter tuning is highly effective in enhancing the model’s sensitivity toward the minority class. Therefore, this study shows that a comprehensive optimization approach can effectively support early detection of stunting, making it a valuable tool for more targeted health intervention planning.
Co-Authors Prakoso, Dwi Saraswati, Eka Rizky Anggi Trisnapradika, Gustina Alfa