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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Krisnadana Journal of Social Work and Empowerment
Ketut Jaya Atmaja
Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Education Environmental Science Social Sciences Other

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Pengenalan Aplikasi Pengolah Kata Microsoft Word pada Sekolah Dasar Ketut Jaya Atmaja; I Made Subrata Sandhiyasa; I Putu Yoga Endrawan; I Gede Ega Ariesta; Agustinus Sanaka Luan
Journal of Social Work and Empowerment Vol 5 No 2 (2026): Journal of Social Work and Empowerment - (Januari-Februari 2026)
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/jswe.v5i2.1105

Abstract

Dalam era digital saat ini, keterampilan menggunakan teknologi informasi menjadi sangat penting, termasuk bagi anak-anak sekolah dasar. Microsoft Word merupakan salah satu aplikasi pengolah kata yang paling umum digunakan dan memiliki berbagai fitur yang dapat membantu dalam proses belajar mengajar. SDN 2 Sembung Gede merupakan salah satu sekolah dasar negeri yang ada pada Desa Sembung Gede, Kecamatan Kerambitan, Kabupaten Tabanan. Sekolah ini terdiri dari 26 siswa. Oleh karena itu, pengenalan Microsoft Word kepada siswa SD akan memberikan mereka dasar yang kuat untuk keterampilan teknologi di masa depan. Dengan pelaksanaan kegiatan ini, diharapkan siswa SDN 2 Sembung Gede dapat memiliki kemampuan dasar dalam menggunakan Microsoft Word sehingga dapat menunjang proses belajar mereka sehari-hari. Kegiatan ini juga diharapkan dapat memotivasi siswa untuk lebih tertarik dalam mempelajari teknologi informasi.
Enhancing Sales Prediction Accuracy: A Hybrid Model of Single Exponential Smoothing and Golden Section Search Ketut Jaya Atmaja; Emmy Febriani Thalib; I Komang Surya Nata Darma Wiguna
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.1104

Abstract

Accurate sales forecasting is a component in inventory management, particularly for determining appropriate stock levels for upcoming periods. Forecasting inaccuracies may result in overstocking or understocking, leading to increased operational costs and decreased service quality to customers. In practice, many retail businesses still encounter difficulties in producing reliable sales forecasts due to fluctuating demand patterns and the use of forecasting methods with suboptimal parameter selection. Single Exponential Smoothing (SES) is widely used because of its simplicity and ease of implementation; however, its forecasting performance is highly dependent on the choice of the smoothing parameter (alpha), which is often determined using a trial-and-error approach. This study proposes a hybrid forecasting approach that combines Single Exponential Smoothing with Golden Section Search to enhance sales prediction accuracy. Golden Section Search is employed as a numerical optimization technique to systematically determine the optimal alpha value by minimizing forecasting errors measured using Mean Absolute Percentage Error (MAPE). The proposed approach is applied to sales data from XYZ as a case study using varying lengths of historical data, namely 3 months, 6 months, 12 months, 24 months, and 36 months. The results demonstrate that the proposed hybrid method is capable of producing forecasts with a good level of accuracy, particularly for short-term forecasting. The lowest MAPE value of 7.02% is achieved when using 3 months of historical data, indicating high responsiveness to recent demand changes. As the length of historical data increases, the model tends to become more stable but less responsive to trend fluctuations, resulting in higher error values. Overall, the proposed approach is effective in supporting inventory management decision-making by providing accurate and reliable sales forecasts.
Classification of Traditional Balinese Kites Using CNN for Cultural Preservation Ni Wayan Sumartini Saraswati; Eddy Hartono; Ketut Jaya Atmaja; Welda Welda; I Dewa Made Krishna Muku
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.v10i3.16181

Abstract

The digital preservation of cultural heritage has become increasingly important in sustaining local traditions amid rapid modernization. Balinese traditional kites represent a distinctive form of intangible cultural heritage with unique visual characteristics; however, their identification and classification are still largely based on subjective expertise. This research develops a Convolutional Neural Network (CNN)-based model for image classification to automatically recognize three primary types of Balinese traditional kites: Bebean, Janggan, and Pecukan. Beyond technical implementation, this research contributes to the development of a culturally specific visual dataset, addressing the limited representation of local heritage objects in mainstream computer vision research, which is predominantly based on global datasets of generic objects. A balanced dataset of 2,400 images was constructed and evaluated using 5-Fold Cross Validation to assess model stability and generalization capability. The proposed CNN model achieved an average validation accuracy of 91.5%, with balanced precision, recall, and F1-score across folds. Further evaluation on an independent test set of 282 images resulted in an accuracy of 87.94%, indicating a generalization gap of approximately 4%, which remains within an acceptable range. The results demonstrate that CNN-based classification can effectively support structured digital documentation of traditional kites. This study highlights the potential of computer vision not only as a technical tool, but also as a strategic approach to advancing data-driven cultural preservation and expanding AI applications within localized cultural contexts.
Co-Authors Agustinus Sanaka Luan Eddy Hartono Emmy Febriani Thalib I Dewa Made Krishna Muku I Gede Ega Ariesta I Komang Surya Nata Darma Wiguna I Made Subrata Sandhiyasa I Putu Yoga Endrawan Saraswati, Ni Wayan Sumartini Welda