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All Journal Jurnal Sistem dan Manajemen Industri REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Pelitabangsa Jurnal Lentera Pengabdian VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Sulaeman, Asep Arwan
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Transportation Other

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Pelatihan Penggunaan Media Sosial untuk Pemasaran di ASM Insulindo Butsianto, Sufajar; Sulaeman, Asep Arwan; Siswandi , Arif; Setyawan, Wisnu
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 1 (2025): Juni 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i1.117

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan mahasiswa serta civitas akademika ASM Insulindo dalam memanfaatkan media sosial sebagai sarana pemasaran yang efektif di era digital. Di tengah pesatnya perkembangan teknologi informasi, media sosial seperti Instagram, TikTok, dan Facebook telah menjadi platform strategis dalam memperluas jangkauan bisnis dan membangun brand awareness. Namun, pemanfaatannya masih belum optimal di kalangan mahasiswa yang memiliki potensi besar sebagai digital marketer. Melalui pelatihan ini, peserta dibekali dengan pengetahuan dasar mengenai digital marketing, strategi konten kreatif, penggunaan fitur-fitur iklan media sosial, serta analisis performa pemasaran digital. Metode yang digunakan meliputi ceramah, diskusi interaktif, praktik langsung, dan studi kasus. Hasil kegiatan menunjukkan peningkatan signifikan dalam pemahaman peserta terhadap teknik pemasaran digital serta kemampuan membuat dan mengelola konten promosi secara mandiri. Diharapkan pelatihan ini dapat menjadi langkah awal dalam menciptakan wirausahawan muda yang adaptif terhadap perkembangan teknologi digital.
Breast Cancer Classification Using Naïve Bayes and Random Forest Algorithms Gurning, Riris Naomi; Sulaeman, Asep Arwan; Afandi, Dedi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6609

Abstract

Breast cancer is one of the leading causes of death among women in Indonesia. Therefore, early detection is crucial to improving the chances of successful treatment. This study was conducted to evaluate the performance differences between the Naïve Bayes and Random Forest algorithms in classifying breast cancer data. The dataset used was sourced from Kaggle, and the entire data processing and model analysis process was performed using RapidMiner software. Data was split into 80% for training and 20% for testing to ensure optimal model evaluation. Evaluation was conducted using accuracy, precision, and recall metrics. The findings of this study indicate that Random Forest is capable of producing more effective classification performance than Naïve Bayes. Random Forest achieved an accuracy of 99.27%, recall of 99.27%, and precision of 99.30%. Meanwhile, the Naïve Bayes algorithm only achieved an accuracy of 83.78% with recall and precision of 83.80% each. The superiority of Random Forest is believed to stem from its ensemble approach, which can handle data complexity and reduce the risk of overfitting, thereby providing more accurate and stable prediction results. Based on these results, Random Forest is considered more suitable for use in machine learning-based early breast cancer detection systems. This study is expected to serve as a reference for the development of medical decision support systems and to encourage the use of classification technology in the field of health.
Co-Authors Abdul Halim Anshor Afriantoro, Irfan Agus Suwarno, Agus Antika Zahrotul Kamalia Arif Nuryono, Arif Butsianto, Sufajar Dedi Afandi Edora, E Febri Suratman Gurning, Riris Naomi Herindiyati , Herindiyati Hidayat, Ade Nurul Indra Setiawan Nawangsih, Ismasari Pasha, Vebina Sheila Putriani, Puput Raharjo, sugeng budi Rahman Hakim REZEKI, FITRI Setyawan, Wisnu Siswandi , Arif Tri Ngudi Wiyatno Widya, Adi Rusdi