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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinkron : Jurnal dan Penelitian Teknik Informatika MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Griya Journal of Mathematics Education and Application EduMatika: Jurnal MIPA Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Umum Pengabdian Masyarakat (JUPEMAS)
Panggabean, Suvriadi
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Physics Social Sciences

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TRAINING PENINGKATAN KOMPETENSI INDUSTRI UNTUK SERTIFIKASI PROFESI NETWORK ENGINEER SKEMA NETWORK+ BERSAMA PT. NUSANET DAN PT. WILEARNING INDONESIA Kiswanto, Dedy; Syahputra, Hermawan; Panggabean, Suvriadi
Jurnal Umum Pengabdian Masyarakat Vol 2 No 1 (2023): Jurnal Umum Pengabdian Masyarakat
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58290/jupemas.v2i1.109

Abstract

Training peningkatan kompetensi dan sertifikasi network+ ini merupakan bentuk pengabdian masyarakat yang dilaksankaan dengan kolaborasi bersama PT Nusatnet dan PT Wilearning Indonesia. Adapun jumlah peserta terdiri dari 6 orang laki–laki karyawan PT Nusanet yang bertugas sebagai engineer network lapangan sedangkan PT Wilearning menyediakan tempat dan seluruh sarana prasarana yang dibutuhkan selama kegiatan training dan sertifikasi dalam penyampaian TIM menggunakan metode pembelajaran blended learning. 100% peserta dapat dengan baik menyelesaikan seluruh rangkaian exam dan mampu mendapatkan skor melebihi syarat minimal. Skor terkecil yang didapatkan 724 dan skor tertingi 824, jika dilakukan rata–rata pada skor peserta sebesar 783. Artinya rata–rata peserta mampu menjawab dengan benar soal yang diberikan sebesar 92% dari total 90 soal yang diberikan.
Development of Edutainment-Based Mathematics Learning Media on Social Arithmetic Materials Pratiwi, Shanty Hanna; Panggabean, Suvriadi
EduMatika: Jurnal MIPA Vol. 2 No. 4 (2022): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v2i4.252

Abstract

This study is research on the development of edutainment-based mathematics learning media on social arithmetic material at the seventh-grade junior high school level and using macromedia flash 8. The purpose of this study was to determine how the development of edutainment-based learning media at the seventh-grade junior high school level, and how appropriate the learning media was. edutainment-based mathematics in mathematics learning, as well as to determine the responses and interest of students to the development of edutainment-based learning media at the seventh-grade junior high school level. The development model used in this study is the ADDIE development model which consists of five stages, namely Analysis, Design, Development, Implementation, and Evaluation. In the implementation stage, the researcher limited the trial to a small scale with 15 students. The results of this study obtained a validation level of 90% by material experts, 91% by media experts, 82.4% of students expressed interest, which means that the development of edutainment-based mathematics learning media is feasible and valid as a mathematics learning media.
Sosialisasi Internet Sehat untuk Kalangan Remaja pada Sekolah di Pedesaan Adidtya Perdana; Nurul Maulida Surbakti; Dian Septiana; Panggabean, Suvriadi
Jurnal Pengabdian Masyarakat Gemilang (JPMG) Vol. 2 No. 5: November 2022
Publisher : HIMPUNAN DOSEN GEMILANG INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/jpmg.v2i5.76

Abstract

Internet merupakan salah satu teknologi yang sangat berkembang sekarang ini. Namun penggunaan internet di kalangan remaja terutama pelajar sekolah menengah sering di salah gunakan sehingga dapat memberikan dampak negatif. Biasanya remaja maupun anak-anak menggunakan internet untuk membuka media sosial seperti facebook, twitter, instagram, dan lain sebagainya, bermain game online, dan yang lebih parahnya membuka situs-situs yang mengandung unsur pornografi. Hal ini tidak dapat dibiarkan terus terjadi dikalangan remaja maupun anak-anak. Untuk itu perlu dilakukan pembatasan terhadap penggunaan internet dengan menerapkan penggunaan internet sehat. Tujuan dalam melaksanakan pengabdian masyarakat ini adalah dengan memainkan peran internet dalam penyampaian informasi yang cepat agar nantinya siswa dapat menggunakan internet sebagai sarana pembelajaran yang tepat baik dan penggunaan internet yang tepat guna, dan tim juga memberikan sosialisasi secara interaktif. Metode pelaksanaan kegiatan yang dilakukan di ruangan kelas MTs Ar-Rahman Stabat adalah dengan memberikan ceramah, dan diikuti dengan contoh-contoh serta animasi agar para siswa dapat memahami lebih cepat. Hal ini dilakukan adalah untuk membangkitkan motivasi diri yang dimiliki oleh para siswa serta diiringi humor-humor singkat agar siswa-siswi tersebut tidak bosan dengan materi yang diberikan, pemberian materi diakhiri dengan sesi tanya jawab, dengan tahapan kegiatan yaitu Tahap Pra-pelaksanaan, Tahap Pelaksanaan dan Tahap Evaluasi. Hasil dari kegiatan pelatihan adalah Para siswa-siswi di MTs tersebut memahami dampak negatif dan positif dari penggunaan internet dan cara menghindari serta menyikapi dampak-dampak tersebut.
Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture Karo Karo, Ichwanul Muslim; Kiswanto, Dedy; Panggabean, Suvriadi; Perdana, Adidtya
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13110

Abstract

Hair diseases are common and can be caused by a variety of factors, including genetics, stress, nutritional deficiencies, as well as exposure to sunlight and air pollution. Accurate diagnosis of hair diseases is important for proper treatment, but can be challenging due to overlapping symptoms. The development of the healthcare world has widely utilized machine learning and deep learning approaches to assist in the healthcare field. This research aims to develop hair disease classification using Convolutional neural network (CNN). The CNN-based approach is expected to help health professionals diagnose hair diseases accurately and provide targeted treatment. This research involves an experimental design with three main stages: identifying the research problem, conducting a literature review, and collecting data. The research uses a dataset of hair disease images obtained from Kaggle, which are annotated and organized based on different hair disease types. After the image data is collected, the image dataset will go through the image preprocessing stage. Experiments were conducted using hair disease image data with 15 epochs on a CNN Deep Learning model with VGG-16 architecture, and resulted in an accuracy of 94.5% and a loss rate of 18.47%, with a testing epoch time of 9 hours 48 minutes. The results of this study show that CNN with VGG-16 architecture can successfully classify 10 types of hair diseases
Perubahan Sentimen Publik Terhadap Calon Gubernur Sumatera Utara Tahun 2024 Berdasarkan Data Twitter: Pendekatan Naive Bayes Panggabean, Suvriadi; Kiswanto, Dedy; Surbakti, Nurul Maulida; Azis, Zainal; Harahap, Tua Halomoan
MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Vol. 7 No. 2 (2024): Vol. 7 No. 2 September 2024
Publisher : Prodi Pendidikan matematika Universitas Islam Majapahit (UNIM), Mojokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36815/majamath.v7i2.3468

Abstract

Penelitian ini bertujuan untuk menganalisis perubahan sentimen publik terhadap calon gubernur Sumatera Utara pada pemilihan tahun 2024 menggunakan data Twitter. Fokus utama penelitian ini adalah dua kandidat, yaitu Edy Rahmayadi dan Bobi Nasution. Data yang digunakan mencakup 150 tweet per bulan dari periode Januari hingga Agustus 2024, yang diambil menggunakan API Tweet Harvest. Sentimen pada tweet tersebut diklasifikasikan menggunakan algoritma Naive Bayes ke dalam tiga kategori: positif, negatif, dan netral. Hasil analisis menunjukkan adanya fluktuasi sentimen publik yang signifikan setiap bulannya, terutama pada Juli dan Agustus 2024, yang mencerminkan perubahan persepsi publik terkait kedua calon gubernur tersebut. Penelitian ini diharapkan memberikan kontribusi dalam memahami opini publik melalui media sosial sebagai salah satu indikator dalam konteks pemilihan politik.
Prediksi Retur Produk Farmasi dan Klasifikasi Risiko Menggunakan Model ARIMA Felicia Eldora; Panggabean, Suvriadi
Griya Journal of Mathematics Education and Application Vol. 5 No. 2 (2025): Juni 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i2.611

Abstract

Pharmaceutical product distribution faces specific challenges, particularly in managing product returns that can affect logistics efficiency and service quality. This study aims to predict the return quantity of pharmaceutical products using the ARIMA (Autoregressive Integrated Moving Average) model and to classify bad goods risk based on the prediction results. The data used consists of monthly return records from a Pharmaceutical Wholesaler (PBF) for a products—Paracetamol Syrup—during the period from January 2023 to December 2024. The research methodology includes data preprocessing, ARIMA model identification and estimation, residual diagnostics, forecasting, and risk classification. The results show that the ARIMA(1,1,1) model provides sufficiently accurate forecasts for Paracetamol Syrup, with predicted returns over the next six months falling into the medium-risk category. These findings offer valuable insights for pharmaceutical wholesalers to anticipate potential losses due to damaged or expired products and to design distribution strategies that are more responsive to return patterns.
Impact of Cosine Similarity Function on SVM Algorithm for Public Opinion Mining About National Sports Week 2024 on X Mansyur, Abil; Karo Karo, Ichwanul Muslim; Firdaus, Muliawan; Simamora, Elmanani; Darari, Muhammad Badzlan; Habibi, Rizki; Panggabean, Suvriadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30605

Abstract

Public opinion on PON 2024 (National Sports Week in Indonesia) became a trending topic on X (formerly Twitter), reflecting both positive and negative sentiments. Understanding these sentiments is important for evaluating the event and preparing for the upcoming. However, baseline SVM algorithms using standard kernel functions are not optimized for text similarity and limit performance in sentiment analysis. This research proposes cosine similarity as a substitution for the kernel function on SVM, enhancing the sentiment analyzer's performance on public opinions about PON 2024. The approach leverages cosine similarity's strength in handling text-based data. The key contribution of this research is the integration of cosine similarity into the SVM algorithm as a replacement for kernel functions, improving performance in sentiment analysis. Additionally, this study offers a comprehensive comparison with baseline SVM and provides actionable insights for upcoming PON. The study collected 1,011 tweets related to PON 2024 using web scraping and the Twitter API, followed by labeling sentiments as positive, neutral, or negative. Several preprocessing techniques also were applied to prepare the data. Two models were developed: baseline SVM and another using SVM integrated with cosine similarity, both evaluated through accuracy, precision, recall, and F1-score. The baseline SVM achieved 85.1% accuracy, 85% precision, 83% recall, and 83.3% F1-score, struggling particularly with negative sentiment. Opposite, by integrating cosine similarity on SVM, the performance improved to 88.73% accuracy, 88.3% precision, 89.3% recall, and 88.3% F1-score—a boost of 3.3-6.3%. Additionally, the public opinion revealed that positive sentiments mostly focused on athlete achievements and medal awards, while negative sentiments highlighted issues like referee performance and specific sports (e.g., football). This approach can serve as a valuable tool for event organizers to identify public concerns and maintain positive aspects for the upcoming PON 2028.
Co-Authors Abil Mansyur, Abil Adidtya Perdana, Adidtya Azis, Zainal Darari, Muhammad Badzlan Dedy Kiswanto Dian Septiana Felicia Eldora Habibi, Rizki Hermawan Syahputra Ichwanul Muslim Karo Karo Nurul Maulida Surbakti Pratiwi, Shanty Hanna Simamora, Elmanani Tua Halomoan Harahap, Tua Halomoan