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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat)
Putu Sugiartawan
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering 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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors Agus Qomaruddin Munir Christina Purnama Yanti I Gede Iwan Sudipa I Gusti Made Ngurah Desnanjaya I Kadek Adiana Putra I Kadek Agus Dwipayana I Wayan Sudiarsa Made Marthana Yusa Ni Made Maharianingsih Putu Wirayudi Aditama Rizkita Ayu Mutiarani Yusril Eka Saputra