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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Pseudocode Journal of ICT Research and Applications SINTECH (Science and Information Technology) Journal WIDYA LAKSANA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science INSERT: Information System and Emerging Technology Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi TEKNOSIA
I Md. Dendi Maysanjaya
Universitas Pendidikan Ganesha
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Transportation Other

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PELATIHAN PENGGUNAAN E-LEARNING SCHOOLOGY BAGI GURU SMK SE-KECAMATAN GEROKGAK Maysanjaya, I Made Dendi; Pradnyana, I Made Ardwi; Listartha, I Made Edy; Pratiwi, Putu Yudia; Kusumadewi, Ni Made Ayu Mita; Walhidayah, Irfan; Yasa, I Gede Agus Sukariana; Cahyadi, Kadek Wawan
JURNAL WIDYA LAKSANA Vol 10, No 2 (2021)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.13 KB) | DOI: 10.23887/jwl.v10i2.24977

Abstract

E-learning merupakan terobosan dalam meningkatkan kualitas pembelajaran. Beragam jenis learning management system (LMS) telah dikembangan dan digunakan, salah satunya adalah schoology. Meski demikian masih ada guru, khususnya guru SMK di Kecamatan Gerokgak yang sama sekali belum pernah menggunakan LMS. Sementara ada beberapa guru yang sudah pernah menggunakan LMS, menyatakan bahwa LMS yang digunakan masih memiliki beberapa kelemahan dan cenderung tidak stabil. Berdasarkan permasalahan tersebut dirancanglah sebuah kegiatan pelatihan untuk guru SMK sebanyak 46 orang, dan berasal dari beberapa SMK di Kecamatan Gerokgak. Metode pengabdian yang dilakukan terdiri atas lima tahap kegiatan, yang terdiri atas penentuan lokasi, persiapan, pelatihan, evaluasi, dan pelaporan kegiatan. Dari hasil pengabdian yang dilakukan, sebanyak 95,7% menyatakan sudah bisa menggunakan fitur schoology dan merasakan kebermanfataannya, serta 73,9% menyatakan akan menggunakannya sebagai media pendukung proses pembelajaran.
Prediksi Hasil Tender Pengadaan Barang dan Jasa pada Bagian Pengadaan Barang dan Jasa Sekretariat Daerah Buleleng dengan Algoritma C5.0 I Gede Agus Krisna Perdana; Listartha, I Made Edy; Maysanjaya, I Made Dendi
INSERT : Information System and Emerging Technology Journal Vol. 5 No. 2 (2024)
Publisher : Information System Study Program, Faculty of Engineering and Vocational, Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/insert.v5i2.76837

Abstract

Pengadaan barang dan jasa adalah salah satu program pemerintah untuk memenuhi kebutuhan akan suatu barang dan jasa oleh suatu Kementrian, Lembaga, atau Perangkat Daerah dengan melalui sebuah metode dan proses agar mencapai kesepakatan harga, waktu dan lainnya untuk memenuhi tujuan dari pengadaan barang dan jasa. di Bagian Pengadaan Barang dan Jasa Sekretariat Daerah Buleleng, setiap tahunnya terdapat paket tender yang gagal karena berbagai faktor yang menyebabkan gagalnya tujuan pembangunan kota dan menjadi isu transparansi penggunaan anggaran pemerintah yang dapat berpengaruhnya pandangan masyarakat terhadap pemerintah. Oleh karena itu datanya perlu digali lebih dalam atau data mining dengan tujuan memprediksi hasil tender sebagai manajemen risiko dalam pengadaan barang dan jasa di BPBJ Sekretariat Daerah Buleleng untuk perencanaan pengadaan barang dan jasa yang lebih efektif dan efesien. Algoritma C5.0 adalah salah satu algoritma yang dapat memproses data hasil tender dengan memproses dataset ke dalam bentuk pohon keputusan yang membentuk aturan-aturan untuk membantu dalam pengambilan keputusan dalam pengadaan tender di BPBJ Sekretariat Daerah Buleleng. Dengan tambahan metode attribute selection dan oversampling, performa terbaik yang didapatkan dari hasil pengujian 3 (tiga) jenis k-fold cross validation yaitu pada 5-fold menghasilkan performa accuracy 0.703152633, precision 0.688464330, recall 0.761427203, dan AUC score 0.703194444, pada 7-fold menghasilkan performa accuracy 0.708044382, precision 0.706945844, recall 0.742024965, dan AUC score 0.708044382, dan pada 10-fold menghasilkan performa accuracy 0.741379310, precision 0.716926571, recall 0.799029680, dan AUC score 0.741343226.
Perbandingan Kinerja Algoritma Naive Bayes dan K-Nearest Neighbor dalam Menganalisis Sentimen Pengguna Game Free Fire Sudiasta Putri, Nyoman Dinda Indira; Maysanjaya, I Made Dendi; Sunarya, I Made Gede
Jurnal Pseudocode Vol 12 No 2 (2025): Volume 12 Nomor 2 September 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.12.2.53-59

Abstract

Free Fire is one of the most popular online games in Indonesia, yet it continues to receive a wide range of user reviews regarding gameplay experiences. These reviews reflect diverse user perceptions, including both praise and criticism, making sentiment analysis essential to understanding user satisfaction. This study aims to classify user sentiments toward Free Fire using a combined dataset collected from the Google Play Store and App Store, and to compare the performance of two text classification algorithms: Naive Bayes and K-Nearest Neighbor (KNN). The data were collected using web scraping techniques and manually labeled by expert validators. Text preprocessing involved cleansing, tokenizing, stopword removal, and stemming, followed by term weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The experimental results show that the Naive Bayes algorithm achieved the highest accuracy of 72.78%, while the KNN algorithm recorded a maximum accuracy of 45.91%. Based on these findings, Naive Bayes is proven to be more effective in classifying user sentiments related to Free Fire. The results of this study are expected to provide constructive insights for developers to improve the quality and user experience of the game.
Perancangan Dashboard Berbasis Business Intelligence untuk Optimalisasi Manajemen Persediaan di CV Bali Treasures Anak Agung Istri, Callysta Athalia; Maysanjaya, I Made Dendi; Mahendra, Gede Surya
Teknosia Vol. 19 No. 02 (2025): Vol. 19 No. 02 (2025): December 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/teknosia.v19i02.43478

Abstract

The global landscape is undergoing a digital transformation marked by the Fourth Industrial Revolution and the move toward society 5.0. Companies are expected to manage data efficiently. CV Bali Treasures, a percussion instrument manufacturer, still records wood-based production data manually using Microsoft Excel, complicating trend analysis and decision-making. This study implements a Business Intelligence (BI) dashboard using Microsoft Power BI to improve raw material inventory management. The methodology follows the BI Roadmap using data from the Processed Wood Inventory Mutation Report (LMHHKO) from March 2019 to September 2024. System evaluation was conducted through User Acceptance Testing (UAT) with the Black Box Testing method involving five users. The results show the dashboard effectively visualizes inventory data, supports data historical analysis, and speeds up decision-making. Users confirmed the dashboard met their needs and was easy to use. This BI implementation is expected to support digital transformation and enhance the company's efficiency and competitiveness.
Perbandingan Performa Algoritma Naive Bayes Dan Support Vector Machine Pada Analisis Sentimen Implementasi Program Kip-Kuliah Ni Putu, Ana Rainita; Maysanjaya, I Made Dendi; Mahendra, Gede Surya
Teknosia Vol. 19 No. 02 (2025): Vol. 19 No. 02 (2025): December 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/teknosia.v19i02.43479

Abstract

The selection of a suitable classification model is important in text-based sentiment analysis, especially in conditions of unbalanced data distribution. Naive Bayes and Support Vector Machine (SVM) are two algorithms that are often used in classification, but the comparison of their performance on unbalanced data still needs to be further reviewed. This study aims to compare the performance of the two algorithms in classifying public sentiment towards the Indonesia Smart Card (KIP) Lecture Program. The implementation of the KIP Lecture Program still faces challenges in the accuracy of aid distribution. This situation raises discussions and various controversies among the public, especially on the X platform. The data used were 1,644 tweets, with a distribution of negative sentiment of 1,392 tweets and positive tweets of 252. To overcome the imbalance of data class distribution, the Synthetic Minority Oversampling Technique (SMOTE) method is used. Based on the evaluation results, before SMOTE was applied, SVM obtained 92% accuracy and 91% precision, 77% recall, while Naive Bayes obtained 79% accuracy, 68% precision, and 78% recall. After the application of SMOTE, SVM performance significantly improved with accuracy, precision, and recall reaching 99%, while Naive Bayes improved to 95% on all metrics. These results show that although SVM excels in higher accuracy, Naive Bayes shows a more stable performance on the data neither after nor after the balancing process is performed.
Klasifikasi Severity Level Diabetic Macular Edema Berbasis ResNet-50 Maysanjaya, I Made Dendi; Pratiwi, Putu Yudia; Indradewi, I Gusti Ayu Agung Diatri
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.9-13

Abstract

Diabetes is one of the most common diseases people suffer from today, and it can lead to complications such as blindness, heart disease, and kidney failure. The condition of blindness caused by this disease is known as diabetic retinopathy (DR). An ophthalmologist will use a fundus camera to examine the retina, looking for several clinical features, such as microaneurysms (MA), hemorrhages (HM), cotton-wool spots (CWS), and exudates. Based on these clinical symptoms, clinicians then determined the patient's level of diabetic macular edema (DME) severity. Although several studies have applied CNN-based architectures for diabetic retinopathy detection, limited attention has been given to the impact of dataset imbalance handling on DME severity classification, particularly using ResNet-50. This study highlights the significant impact of extensive data augmentation on classification performance in imbalanced DME datasets. Evaluate performance using the accuracy, precision, and recall metrics. We used the IDRiD dataset, which consists of 516 images split into a training set of 413 and a test set of 103. IDRiD divides the dataset into three classes, namely normal, moderate DME, and severe DME. In the preprocessing stage, we enhanced contrast using CLAHE and resized the images to 224x224 pixels. To address the imbalance, we applied 11 data augmentation methods. We experimented by comparing the performance of two models: one with and one without dataset augmentation. Based on the test results, the best performance was obtained with the model that included dataset augmentation, achieving an accuracy of 0.5961, a precision of 0.63, and a recall of 0.61, while the baseline model (without dataset augmentation) gained 0.4553, 0.36, and 0.34 for the accuracy, precision, and recall, respectively.
Co-Authors Ali Djamhuri Anak Agung Istri, Callysta Athalia Cahyadi, Kadek Wawan Dessy Seri Wahyuni Dharma Putra, I Gede Wira Driya, Putu Dhanu E. Elsa Herdiana Murhandarwati Gede Aditra Pradnyana Gede Saindra Santyadiputra, Gede Saindra Gede Surya Mahendra Hanung Adi Nugroho I Gede Agus Krisna Perdana I Gede Mahendra Darmawiguna I Gede Riyan Ardi Darmawan I Gede Rusdy Mahayana Putra I Gusti Ayu Agung Diatri Indradewi I Made Angga Darma Putra I Made Ardwi Pradnyana I Made Edy Listartha I Made Gede Sunarya Irfanda, Achmad Kadek Suwis Satria Atmaja Komang Jepri Kusuma Jaya Komang Pramayasa Kusumadewi, Ni Made Ayu Mita Made Satria Wibawa Made Windu Antara Kesiman Muhammad Ridwan Ni Putu, Ana Rainita Noor Akhmad Setiawan Putu Hendra Suputra Putu Setiari, Gusti Ayu Putu Yudia Pratiwi Saraya, Fashan Sudiasta Putri, Nyoman Dinda Indira Walhidayah, Irfan Widhia K.Z Oktoeberza Yasa, I Gede Agus Sukariana