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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Terapan (J-TIT) Abdimasku : Jurnal Pengabdian Masyarakat GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Mandiri Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi)
Ellya Helmud
ISB Atma Luhur
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Physics Social Sciences Other

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Improving Puskesmas Staff Competency Through Office Application Workshop At Puskesmas Selindung Wishnu Probonegoro; Lili Indah Sari; Sujono; Ellya Helmud
GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 2 (2021): GANDRUNG: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Olahraga dan Kesehatan, Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/gandrung.v2i2.1338

Abstract

Puskesmas is a health service facility that organizes public health efforts and first-level individual health efforts, service agencies that provide services to the community, really need the speed of information services so that they can provide optimal services to the community. To provide optimal and maximum service to the community, the puskesmas needs to increase the competence of the staff at the puskesmas by conducting office application workshops for their staff. For the puskesmas staff in Selindung, there are already some who know how to use office applications such as word, excel and powerpoint. But in detail in the use and utilization of office applications in more detail, they still do not understand and are not optimal. This is what makes the author and the team collaborate with the Puskesmas Selindung to carry out a work shop for office applications for puskesmas staff. The purpose of this workshop is to increase the competence of Pusekesma staff in the use and use of computers, especially in office applications, including Microsoft Word, Microsoft Excel and Microsoft PowerPoint. Because these three applications are often used for data and information needs managed by the Selindung health center. The method used in this activity is a workshop and training model. That is by conducting lectures or giving material, and directly practised
Fish Disease Classification Using MobileNetV3Large Transfer Learning and Fine-Tuning Dela Fifi Lusiana; Ellya Helmud; Rahmat Sulaiman
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16246

Abstract

Fish diseases represent a major challenge in the aquaculture industry as this phenomenon frequently leads to significant economic losses. Manual disease identification requires specialized expertise and is time-consuming in the field. Therefore, this study aims to implement the MobileNetV3Large Deep Learning architecture to automatically identify eight types of fish conditions. This research dataset utilizes 2,400 digital images distributed evenly across eight fish condition categories. Each class consists of 300 image samples, including Bacterial Red disease, Aeromoniasis, Bacterial gill disease, EUS Disease, Fungal diseases Saprolegniasis, Parasitic diseases, White tail disease, and a Healthy Fish group. The dataset was sourced from https://www.kaggle.com/datasets/irfanulhuda/fish-disease-detection-dataset. These conditions include bacterial, fungal, viral, and parasitic infections, as well as healthy fish conditions. The research methodology applies Transfer Learning techniques combined with Fine-Tuning optimization on the last 70 layers. The methodology applies a transfer learning strategy with a data split of 80% for training, 10% for validation, and 10% for testing. This step was taken to adapt the model's weights to the visual characteristics of the fish disease images. The process was evaluated using the Adam optimization function and the Categorical Cross-Entropy loss function. Experimental results demonstrate highly superior model performance on the test data. The MobileNetV3Large model successfully achieved a test accuracy of 92.92% with a loss value of 0.2099. Furthermore, evaluation through the Confusion Matrix and ROC curves yielded an average AUC value of 1.00 across the majority of classes. This figure indicates that the model possesses exceptionally high discrimination capacity and sensitivity. In conclusion, the computational efficiency of the MobileNetV3Large architecture makes this system a highly potential solution. Researchers can implement this model on mobile devices to assist fish farmers in diagnosing diseases quickly and accurately directly at the aquaculture sites
Analisis Sentimen Evaluasi Pembelajaran Udemy Menggunakan Support Vector Machine Untuk Peningkatan Kursus Irwansyah Irwansyah; Ellya Helmud
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 4 (2026): Agustus 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i4.3968

Abstract

Learning evaluation plays an important role in improving course quality, yet user reviews are generally unstructured and difficult to analyze manually. This study aims to analyze user sentiment toward Udemy course evaluations using the Support Vector Machine (SVM) algorithm. The dataset consisted of 50 balanced English-language reviews (26 positive and 24 negative) selected from approximately 500 scraped reviews, with sentiment labels determined based on the rating categories provided by the source website. The research process included text preprocessing, TF-IDF feature weighting, SVM classification using a linear kernel, and 10-fold cross-validation. Model performance was evaluated using accuracy, precision, recall, and F1-score. The proposed model achieved an accuracy of 82% and an average F1-score of 0.82, indicating that the combination of TF-IDF and SVM effectively classifies user sentiment. These findings can assist course providers in evaluating user feedback and improving learning quality. Keywords: Sentiment Analysis; Support Vector Machine; TF-IDF; Business Intelligence; Learning Evaluation Abstrak Evaluasi pembelajaran berperan penting dalam meningkatkan kualitas kursus, namun ulasan pengguna umumnya berupa data teks tidak terstruktur yang sulit dianalisis secara manual. Penelitian ini bertujuan menganalisis sentimen pengguna terhadap evaluasi pembelajaran pada platform Udemy menggunakan algoritma Support Vector Machine (SVM). Dataset terdiri atas 50 ulasan berbahasa Inggris yang seimbang (26 positif dan 24 negatif) dari sekitar 500 hasil scraping, dengan label sentimen ditentukan berdasarkan kategori rating pada situs sumber. Tahapan penelitian meliputi preprocessing teks, pembobotan TF-IDF, klasifikasi SVM berkernel linear, dan validasi menggunakan 10-fold cross validation. Kinerja model dievaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan accuracy sebesar 82% dan rata-rata F1-score sebesar 0,82, sehingga kombinasi TF-IDF dan SVM efektif mengklasifikasikan sentimen pengguna serta dapat mendukung peningkatan kualitas pembelajaran. Kata Kunci: Analisis Sentimen; Support Vector Machine; TF-IDF; Business Intelligence; Evaluasi Pembelajaran
Co-Authors Ade Septryanti Anisah Ari Amir Alkodri Bambang Adiwinoto Bambang Adiwinoto Cindy Aprilia Claudio Khelvin Dela Fifi Lusiana Delpiah Wahyuningsih Devi Irawan Dhita Heru Pambudi Dian Novianto Fifin Fifin Fitri Yani Fitriyani . Franli Chandra Thian Harrizki Arie Pradana Irwansyah Irwansyah Kirana, Chandra Lili Indah Sari Lukas Tommy Marini Marini Maya Saftari Parlia Romadiana Rahmat Sulaiman Sujono Supardi Tedy Suwanda Wishnu Aribowo Probonegoro Wishnu Probonegoro Yohanes Setiawan Japriadi