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All Journal Jurnal Sistem dan Manajemen Industri REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) 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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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.
Twitter Sentiment Towards 2024 Jakarta Governor Candidates With Naïve Bayes Algorithm Abei, Fikri; Sulaeman, Asep Arwan; Suprapto, Suprapto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5358

Abstract

This study aims to analyze public sentiment towards candidates for the 2024 Governor of DKI Jakarta through the Twitter platform, with a focus on classifying positive and negative sentiment. Along with the rapid development of social media, Twitter has become the main channel for people to voice political opinions. Sentiment analysis was conducted using the Naive Bayes algorithm to classify the sentiment of tweets collected through crawling techniques during the campaign period. The data used includes user tweets, with features such as frequently occurring words, popular hashtags, and discussion topics related to each gubernatorial candidate. The results showed that the Naive Bayes algorithm provided the best performance in classifying sentiment data in the period August 1 to December 26, 2024, with the highest accuracy rate reaching 75% at a data ratio of 90:10. This research also identified challenges in sentiment classification, such as the presence of new terms in test documents that are not recognized by the training model. The findings are expected to provide a clearer picture of public perceptions of gubernatorial candidates and contribute to the analysis of political sentiment on social media
Penerapan Metode Waterfall pada Sistem Informasi Inventory Berbasis Website Ananda, Angga Thifal; Butsianto, Sufajar; Sulaeman, Asep Arwan
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5669

Abstract

PT. Toa Galva Industries is a company that has been established for more than 47 years, which still uses a manual inventory system such as recording goods data and data on goods in and out using ledgers, causing work to be less efficient and inaccurate. To find out what goods the company owns, the admin must check the goods files one by one so that they are prone to errors in the process. Therefore, it is necessary to create a website-based inventory information system application system that helps companies improve the efficiency and effectiveness of goods management. This system is designed to store data on incoming and outgoing goods and create stock reports quickly and accurately. The Waterfall method is used in designing this system because it has a clear and structured flow, where each stage must be completed before moving on to the next stage. And using the Black Box Testing method. The research results show that this system can help companies manage stock of goods effectively and efficiently, as well as provide fast and accurate reports on goods produced.
Co-Authors Abdul Halim Anshor Abei, Fikri Afriantoro, Irfan Agus Suwarno, Agus Ananda, Angga Thifal 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 Setyawan, Wisnu Siswandi , Arif Suprapto Suprapto Tri Ngudi Wiyatno Widya, Adi Rusdi