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All Journal Journal of Electrical Technology Jurnal Pengabdian Mitra Masyarakat Cosmic Jurnal Teknik
Mbera Mehuli
Universitas Islam Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology

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PELATIHAN CALISTUNG (MEMBACA, MENULIS dan BERHITUNG) SEBAGAI UPAYA PEMBERANTASAN BUTA HURUF DAN PENINGKATAN MINAT BELAJAR PADA ANAK USIA DINI DAN SISWA/I SD DI DESA PELAWI SELATAN, KECAMATAN BABALAN, KABUPATEN LANGKAT Edi Azwar; Mbera Mehuli; Antoni Antoni; Teddy Hardiansyah; Sri Hastuti Br Saragih
Jurnal Pengabdian Mitra Masyarakat Vol 2, No 1 (2022): Edisi September
Publisher : Universitas Islam Sumatear Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jurpammas.v2i1.6008

Abstract

Calistung is a skill that includes several abilities, namely the ability to read, write and count.  The ability to read, write and count is a very important initial capital for a child in the learning process. With the ability to read, write and count can be a good start for a child so that the child can learn other knowledge, can communicate his ideas and can express himself. Therefore, failure in mastering this ability will result in fatal problems both to continue education to a higher level and in social life in society.  Pelawi Selatan Village is one of UISU's partner villages in this village, there are still many children aged 4-9 years who cannot read and write and children aged 10-12 years who are not proficient in simple calculations, this is due to the lack of ability of parents to teach their children at home and the large number of educators who do not come from among educators. The methods used in this service activity are training and mentoring through tutoring and innovative and creative learning media such as learning while singing, abacuses and teaching media that are able to explore children's creativity. This service activity is one of the derivatives of the 2022 UISU KKNT work program which will be held on July 12 – August 19, 2022. This activity was carried out as many as 12 meetings from July 18 to August 10, 2022.  Through this activity, it was obtained to increase the ability of children aged 3-8 years in reading, writing and also in addition and subtraction, as well as an increase in the ability of children aged 9-12 years in calculating multiplication and division. And also obtained the enthusiasm of children in learning which indicates an increase in the demand for learning in children.
Model Machine Learning untuk Memprediksi Perilaku Konsumen sebagai Dasar Strategi Penargetan Ulang Iklan Antoni Antoni; Mbera Mehuli; Tri Andre Anu
Cosmic Jurnal Teknik Vol 2 No 4 (2025): November
Publisher : Ali Institute or Research and Publication

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Abstract

Penelitian ini mengkaji bagaimana machine learning dapat digunakan untuk memprediksi perilaku konsumen dan menyediakan dasar berbasis data dalam penyusunan strategi advertising retargeting. Dalam lingkungan periklanan digital yang semakin kompetitif, praktik retargeting generik yang memperlakukan seluruh pengunjung sebagai satu kelompok audiens sering kali menyebabkan pemborosan anggaran, kelelahan iklan (ad fatigue), serta rendahnya relevansi pesan, karena niat konsumen bersifat dinamis dan bervariasi menurut waktu, perangkat, sumber trafik, dan tahapan funnel. Untuk mengatasi permasalahan tersebut, penelitian ini menerapkan desain pemodelan prediktif kuantitatif dengan memanfaatkan data clickstream pengguna dan data peristiwa (event) e-commerce pada tingkat individu. Fitur perilaku direkayasa untuk menangkap indikator seperti recency, frequency, intensitas eksplorasi, durasi sesi, serta sinyal funnel (misalnya add-to-cart), yang kemudian diikuti dengan proses pembersihan data, pengodean, penskalaan, pembagian data latih–uji berbasis waktu guna mengurangi kebocoran informasi, serta penanganan ketidakseimbangan kelas. Algoritma Logistic Regression digunakan sebagai model dasar yang dapat diinterpretasikan untuk mengestimasi probabilitas terjadinya keluaran target (misalnya konversi) dalam rentang waktu tertentu. Kinerja model dievaluasi menggunakan metrik yang sesuai untuk data tidak seimbang, termasuk ROC-AUC dan Precision–Recall (PR-AUC), serta nilai presisi, recall, dan F1-score pada ambang operasional. Hasil penelitian menunjukkan kemampuan diskriminatif yang sangat kuat (ROC-AUC = 0,961) dan efektivitas tinggi pada kelas positif (PR-AUC = 0,913), yang melampaui garis dasar prevalensi sebesar 0,235. Keluaran probabilitas dari model memungkinkan segmentasi audiens yang terukur ke dalam kelompok niat tinggi, sedang, dan rendah, sehingga mendukung penerapan intensitas retargeting dan strategi pesan yang berbeda. Secara keseluruhan, temuan ini menunjukkan bahwa penilaian probabilitas berbasis machine learning dapat meningkatkan presisi operasional retargeting dibandingkan pendekatan yang hanya berbasis intuisi; namun demikian, dampak bisnis terhadap CPA dan ROAS tetap perlu divalidasi melalui eksperimen lapangan seperti pengujian A/B.
Application Of The Metaheuristic Algorithm To Optimize The K Value In K-NN In Grouping Factors Causing Stunting Antoni Antoni; Mbera Mehuli; Satria Yudha Prayogi
JET (Journal of Electrical Technology) Vol 11, No 2 (2026): : Edisi June
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i2.13792

Abstract

Stunting is a health problem that has a long-term impact on the quality of life of individuals and the development of a nation. Accurately identifying the factors that cause stunting is an important step in developing effective mitigation strategies. K-Nearest Neighbor (K-NN) is a machine learning algorithm that is widely used in data classification and grouping, but its performance is greatly influenced by the selection of optimal K value parameters. This research proposes the application of metaheuristic algorithms, such as genetic algorithms (GA) and Particle Swarm Optimization (PSO), to optimize the K value in K-NN in grouping factors that cause stunting. This method integrates the power of exploration and exploitation of metaheuristic algorithms to find K parameters that produce optimal accuracy. Based on the results of applying the metaheuristic algorithm, it was found that without optimization, K-NN only produces an accuracy of 63%, which shows the importance of choosing the right K value. The use of GA in K-NN optimization provides a substantial increase in accuracy, reaching 73%, thanks to its ability to explore the solution space effectively. Meanwhile, PSO also increases accuracy by up to 74%. It is hoped that the findings of this research will be a significant contribution in the development of a more accurate grouping model for analyzing factors causing stunting, so that it can support data-based decision making in an effort to address the stunting problem holistically.
Co-Authors Antoni Antoni Antoni Edi Azwar Satria Yudha Prayogi Sri Hastuti Br Saragih Teddy Hardiansyah Tri Andre Anu