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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) IJOEM: Indonesian Journal of Elearning and Multimedia Jurnal Krisnadana
Putu Sugiartawan
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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KEGIATAN BIMBINGAN TEKNIS ANIMASI UNTUK PEMBELAJARAN DAN ANALISIS MEDIA SOSIAL PADA DINAS PERINDUSTRIAN DAN PERDAGANGAN PROVINSI BALI Christina Purnama Yanti; Putu Sugiartawan; Made Marthana Yusa; Putu Wirayudi Aditama; Rizkita Ayu Mutiarani
Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Vol. 2 No. 2 (2022): Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat)
Publisher : Yayasan Lavandaia Dharma Bali

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Abstract

Disperindag (Dinas Perindustrian dan Perdagangan) Provinsi Bali memiliki binaan IKM (Industri Kecil dan Menengah) yang bergerak di berbagai sektor. Media menjadi salah satu kebutuhan pokok dalam usaha meningkatkan minat audiens terhadap produk dan jasa yang ditawarkan. Para IKM membutuhkan media yang berbeda dan menarik perhatian dari yang lain. Media konvensional sudah mulai ditinggalkan dan beralih ke media yang memanfaatkan teknologi seperti media animasi media sosial yang lebih menarik, efektif dan efisien yang didukug dengan konten yang menarik. Diperlukan kegiatan bimbingan teknis kepada IKM binaan Disperindag Provinsi Bali yang diberikan oleh civitas akademik INSTIKI yaitu dosen yang terlibat dalam kegiatan ini yang dikemas menjadi Pengabdian Kepada Masyarakat (PKM). Melihat keperluan pihak Disperindag Provinsi Bali terhadap perkembangan teknologi yang bisa menjadi bekal para IKM binaan dalam mengembangkan produk dan jasa yang ditawarkan, peneliti melaksanakan kegiatan pengabdian berbentuk bimbingan teknis Animasi untuk Pembelajaran dan Analisis Media Sosial kepada para IKM binaan Disperindag Provinsi Bali. Kegiatan PKM ini dilaksanakan pada tanggal 20 April 2022 sampai dengan 23 April 2022 dengan melibatkan civitas akademik INSTIKI yaitu terdiri dari 5 dosen. Hasil yang diperoleh adalah tanggapan memuaskan dari para IKM dalam pemahaman materi yang disampaikan
Smart Farming Untuk Pengaturan Suhu Ruangan Pada Budidaya Jamur Tiram Berbasis Backpropagation Putu Sugiartawan; I Gusti Ngurah Desnanjaya
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 12, No 2 (2022): Oktober
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.78546

Abstract

The problem with mushroom cultivation is the difficulty of regulating the room temperature of mushrooms, especially oyster mushrooms. The optimal production of oyster mushrooms is at temperatures between 25 C - 27 C. To regulate or manipulate humidity and room temperature to water the kumbung or mushroom room. The watering process is carried out several times to stabilize the room temperature during the day.To overcome the watering that is done manually, Automatic Temperature Control and Monitoring of Oyster Mushrooms Based on GSM Sim800l Arduino Uno is made. This tool uses a DHT11 sensor, relay, 16x2 LCD, GSM Sim 800L, and Stepdown. The test was carried out in a mushroom kumbung measuring 10.7m long, 5.9m wide, and 3.5m high. Watering time is done by observing the data at room temperature. The data is then studied using a backpropagation. This method aims to identify the pattern of watering time so that the optimal watering time is produced. The test results show that the tool can monitor the temperature and humidity of the kumbung mushroom with the following values: temperature 27°C - 33°C and humidity 70% - 90%. The introduction of mushroom watering patterns with BPNN showed an error rate of 40%.
Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network I Kadek Agus Dwipayana; putu sugiartawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.85584

Abstract

The distribution of pests in rice plants results in significant losses in production and damage to rice plants for farmers, seen from data on the area of rice borer attacks in the province of Bali in Tabanan district. Therefore, by predicting the distribution of rice pests, we can know the pattern of pest attacks so that we can anticipate them because predicting can provide accuracy and error values through the test results. One of the prediction models is BPNN, where BPNN's advantages for solving complex problems are very suitable for use where large amounts of data are involved and many input/output variables, BPNN is also capable of modeling nonlinear relationships between input and output variables, which may be difficult to capture by this type of predictive model. other. Backpropagation includes supervised learning, which means it can learn from labeled examples and can make accurate predictions on new, unlabeled data. Split data using K-fold cross-validation serves to assess the process performance of an algorithmic method by dividing random data samples and grouping the data as many as K k-fold values.
Sistem Pengering Daun Kelor Berbasis Internet of Things dan Artificial Intteligence I wayan Sudiarsa; Putu Sugiartawan; I Gede Iwan Sudipa; Ni Made Maharianingsih; I Kadek Adiana Putra
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 13, No 2 (2023): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.89823

Abstract

Drying Moringa leaves is needed to reduce the water content so that the Moringa leaves become fresh and can be used for the following process. Drying Moringa leaves to change the water content from 80% to 9.2% requires ideal heating conditions because the heating speed must not damage the nutritional content in the leaves. Developing an existing drying system using IoT to monitor humidity and temperature to increase the drought stability of the Moringa leaves produced. By using IoT, it is hoped that drying conditions can be watched from anywhere and recorded so that if undesirable things happen, it will be easier to track the history of the drying process that has taken place. This system is also connected to a recommendation system using an Artificial Neural Network (ANN). This system will provide recommendations for the best conditions for Moringa flour production because various external factors influence the drying of Moringa leaves. Utilization of the ANN model can recognize data patterns in seasonal time series. The results of implementing the Moringa leaf drying machine can reduce the time by 120 minutes faster than the previous tool
Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction Putu Sugiartawan; Yusril Eka Saputra; Agus Qomaruddin Munir
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 4 (2023): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90149

Abstract

The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training.
Accrual-Based Accounting Information System For Financial Compliance Monitoring I Nyoman Darma Kotama; Putu Sugiartawan; I Dewa Ayu Sri Murdhani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

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Abstract

Small and medium-sized enterprises (SMEs) often face challenges in implementing efficient and accurate financial reporting systems, primarily due to the limitations of manual accounting processes. These challenges lead to errors, delays, and compliance issues, which hinder timely decision-making and financial transparency. This research proposes the development and evaluation of an accrual-based Accounting Information System (AIS) designed to address these issues by automating financial reporting and compliance monitoring. The motivation behind this study is to improve the financial management practices of SMEs by providing a reliable system that ensures accurate financial reporting and real-time compliance monitoring. The main contribution of this research is the design of an AIS that integrates key financial functions, such as transaction processing, accrual calculations, and compliance checks, to streamline financial operations. Evaluation results from case studies indicate that the system significantly reduced reporting errors by 50%, enhanced compliance accuracy by 25%, and decreased report generation time by 40%. Despite these successes, challenges remain in system integration with legacy accounting software and handling complex financial transactions. Future work will focus on enhancing the scalability of the system, integrating advanced machine learning techniques for predictive financial analysis, and improving the integration process to allow for broader implementation in diverse business contexts. Additionally, the development of a mobile application to improve accessibility to financial reports and compliance alerts will be explored.
Classifying Indonesian Batik Motifs by Region Using Swin Small Transformer Architecture Ida Bagus Ketut Sukanegara; Aniek Suryanti Kusuma; Putu Sugiartawan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.314

Abstract

Batik plays a crucial role in Indonesian cultural heritage, with regional motifs encoding local philosophies, identities, and socio-historical contexts while also sustaining creative industries and tourism. Automated classification of batik by region can support documentation, education, and authentication, yet remains challenging due to visually overlapping patterns, high intra-class variability, and subtle inter-regional differences. Building on recent advances in Vision Transformers, this study investigates the Swin Small Transformer architecture for classifying Indonesian batik motifs into five regional categories: Jawa Barat, Jawa Tengah, Jawa Timur, Madura, and Yogyakarta. The proposed framework employs the swin_small_patch4_window7_224} model initialized with ImageNet-pretrained weights and fine-tuned on a curated regional batik dataset. The hierarchical shifted-window attention mechanism of Swin is leveraged to capture both local repetitive elements and broader compositional structures that characterize regional styles. Experimental evaluation on a held-out test set consisting of 80 images demonstrates outstanding performance. The model achieves perfect classification results with overall accuracy, macro-averaged precision, recall, and F1-score all reaching 1.0000. No misclassifications are observed across any regional category, indicating that the proposed architecture effectively learns discriminative representations of regional batik motifs. These findings suggest that hierarchical Vision Transformers can robustly model the nuanced visual cues underpinning regional identity in batik patterns and provide a strong alternative to conventional convolutional neural network approaches. Beyond batik classification, the proposed framework may be extended to other cultural-heritage textile applications, supporting digital preservation, educational initiatives, and large-scale documentation of traditional artistic assets.
Enhancing Rice Disease Classification Using CLAHE and Transfer Learning on Leaf Image Data Samuel Welson; Aniek Suryanti Kusuma; Putu Sugiartawan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.315

Abstract

Rice foliar diseases pose a major threat to global food security by reducing yield and grain quality, motivating the need for scalable, objective, and automated diagnosis solutions. This study investigates the impact of Contrast Limited Adaptive Histogram Equalization (CLAHE) and transfer learning on the classification of three common rice leaf diseases—Bacterial Blight, Brown Spot, and Leaf Smut—from RGB leaf images. Using a dataset of 2,342 images split into training, validation, and test sets (80:10:10), we design a controlled experimental pipeline comprising four scenarios: with/without CLAHE, and with/without transfer learning. CLAHE is applied as a preprocessing step to enhance local contrast and lesion visibility under heterogeneous illumination and cluttered backgrounds, while transfer learning leverages ImageNet-pretrained convolutional neural networks fine-tuned for rice disease recognition. Models are trained and evaluated using accuracy, macro F1, and weighted F1 on a held-out test set. The combined CLAHE + transfer learning configuration achieves the best performance, with an overall accuracy of 0.94 and macro and weighted F1-scores of 0.94, substantially outperforming non-enhanced and non-transferred baselines. Qualitative analysis indicates improved separability between visually similar classes, particularly Brown Spot and Leaf Smut, under challenging imaging conditions. These findings underscore the effectiveness of integrating contrast enhancement with transfer learning for robust, field-oriented rice disease classification and highlight a practical pathway toward reliable image-based decision support in precision agriculture.
Techno-Economic Feasibility Analysis of Rooftop Solar Power Plant Implementation Based on Electricity Consumption Patterns and kWh Export Schemes I Made Agus Sudiartha; Putu Sugiartawan; Aniek Suryanti Kusuma
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/kj2cw117

Abstract

This study aims to analyze the techno-economic feasibility of implementing rooftop solar power plants based on electricity consumption patterns and kWh export schemes. The data used comes from the Kaggle dataset "Surabaya Electric Consumption by Sector" and is processed using Google Colab. The research method uses a descriptive-comparative quantitative approach through electricity consumption pattern analysis, technical simulation of rooftop solar power plants, and a comparison of 65% and 100% kWh export schemes. The results show that the industrial sector has the highest electricity consumption, followed by households and businesses and hotels. Simulations show that increasing solar power plant capacity increases energy production and kWh exports, but nighttime electricity imports still occur because the system does not use batteries. The 100% export scheme provides better economic feasibility than the 65% export scheme, with more optimal savings, ROI, and payback period. The best scenario is a rooftop solar power plant with a capacity of 12.0 kWp with a 100% export scheme, because it produces the highest technical and economic benefits.
Enhancing Price Classification of Chili Using Gradient Boosting Machines Putu Sugiartawan; Ni Wayan Wardani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.233

Abstract

This study explores the application of Gradient Boosting Machines (GBM) to enhance the classification and prediction of chili prices. The research uses a comprehensive dataset collected from various sources, including local markets, online platforms, and agricultural databases, covering multiple attributes such as chili type, region, harvest season, weather conditions, and demand-supply dynamics. The GBM model outperforms traditional machine learning algorithms, achieving an accuracy of 87%, with a high area under the ROC curve (AUC) of 0.91. Feature importance analysis indicates that harvest season and region are the most significant factors influencing price variations. The findings suggest that the GBM model provides reliable price predictions and insights into price-driving factors, offering valuable tools for stakeholders in the agricultural market. The study emphasizes the need for broader data sources and advanced techniques, such as time-series forecasting and XGBoost, to further improve chili price prediction models. These insights can help optimize supply chain management, price forecasting, and decision-making for producers, traders, and policymakers.
Co-Authors Adi Surya Artayasa Agus Qomaruddin Munir Aniek Suryanti Kusuma Christina Purnama Yanti I Dewa Ayu Sri Murdhani I Gede Iwan Sudipa I Gusti Made Ngurah Desnanjaya I Kadek Adiana Putra I Kadek Agus Dwipayana I Made Agus Sudiartha I Nyoman Darma Kotama I Wayan Sudiarsa Ida Bagus Ketut Sukanegara Made Marthana Yusa Ni Made Maharianingsih Ni Wayan Wardani Putu Wirayudi Aditama Rizkita Ayu Mutiarani Samuel Welson Yusril Eka Saputra