In this study, we compare the performance of both hybrid and non-hybrid forecasting models, explicitly focusing on Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in predicting commodity prices within the volatile market of Central Java, Indonesia. The primary objective is to evaluate which hybrid and non-hybrid models provide the most accurate and reliable forecasts under various conditions. Analyzing daily price data from the SiHaTi platform, an official service provided by Bank Indonesia, the Hybrid ARIMA-LSTM model emerges as the most accurate, achieving a forecast accuracy of 92.5%, compared to the 78.3% and 84.7% accuracies of Linear Regression and ARIMA, respectively. These findings underline the potential advantages of combining machine learning with statistical methods to improve predictions in dynamic market conditions, providing invaluable insights for policymakers and market analysts. However, it should be noted that only one hybrid model was compared, and future research should explore multiple hybrid models to ensure a comprehensive evaluation of their effectiveness.

Purpose: Rubber is one of the plantation commodities that contributes positively to the trade surplus in the agricultural sector. Seeing the positive trend in global rubber consumption and production, demand is expected to continue increasing in the future. To enhance rubber productivity, rubber processing technology can be used to make it more efficient, thus increasing the amount of latex extracted from the sap and reducing waste materialDesign/methodology/approach: One technology that can be developed to increase the productivity efficiency of rubber plants is by using Artificial Intelligence. This technology is expected to be implemented in the rubber plantation sector, specifically in the automatic recognition of rubber leaves.Findings/result: The measurement and performance analysis of the rubber leaf image classification algorithm based on Artificial Intelligence has also been evaluated, showing near-perfect accuracy on training data (99.86%) and very good performance on validation data (97.43%), with a very low validation loss (0.0873), indicating that the model has learned well by the last epochOriginality/value/state of the art: The population in this study consists of image data from various tree leaves, including 10 types of rubber leaves and non-rubber leaves

Purpose: This research aims to design a facial wash recommendation system based on all skin types, namely normal, dry, oily, combination, and sensitive. This is to tackle the limitation of previous systems that were developed based on limited skin types which are normal, dry, and oily using Promethee II, Fuzzy Logic, and SAW methods.Design/methodology/approach: This research uses the Analytic Hierarchy Process (AHP) method and a combination of AHP and Simple Additive Weighting (SAW) to consider the importance values of each criterion. Four criteria data are used, namely price, rating, content, and availability, along with 70 alternative data of facial wash products.Finding/Result: Sensitivity testing was conducted on both methods, and the combination of AHP and SAW produced a higher sensitivity percentage, which is 67.51%, whereas the AHP method provided a lower sensitivity percentage of 59.26%.Originality/state of the art: The combination of AHP and SAW is an innovation in designing a facial wash recommendation system, and the research results demonstrate that the combination of AHP and SAW is a superior method for recommending facial wash products.

Purpose: This study aims to monitor the service quality of JMO applications from time to time by classifying JMO user reviews into the class of positive, neutral, and negative sentiments.Design/methodology/approach : The method used in this study is the random forest classification method. Data processing in this study uses feature extraction, TF-IDF and labeling with the lexicon-based method.Findings/result: Based on the research results, it was found that the highest frequency of classification was the positive class with 17571 reviews compared to the neutral class with 8701 reviews and the negative class with 3876 reviews with an accuracy evaluation value of 93%, precision 88%, recall 93%, and f1-score 90%.Originality/value/state of the art:This study uses 150737 reviews that have been pre-processed using the random forest method and TF-IDF and lexicon-based feature extraction.

ndonesia's electricity consumption per capita in 2022 will reach 1,173 kWh/capita sourced from the Ministry of Energy and Mineral Resources. This consumption rate increased by around 4% compared to 2021, as well as a new record high in the last five decades. This must be accompanied by the availability of energy from power plants, especially renewable energy, namely solar energy because this solar power plant is considered safer for the environment and has a minimal maintenance schedule. In addition, it requires maximum utilization of solar panels and a monitoring system in real time so that the reliability of the power plant is maintained, the Smart Solar Tracker and Energy Control Based on Internet Of Things (IoT) are the answer to this problem. This research uses PV (Photovoltaic) as a power source in the system accompanied by a tracker drive in the form of actuators and servo motors that move in the direction of the sun. This IoT is integrated with a database server so officers can monitor and control if the device is damaged. The IoT module in this research uses the ESP8266 which functions for device control and relay. In addition to reading the voltage and current, both incoming and outgoing, use the ACS 712 voltage sensor and current sensor, not only that, there is also an LDR sensor to read the position of the sun.

Fluid flow problems can be constructed using applied mathematical modeling and solved numerically using computational fluid dynamics (CFD). Nondimensional variables, stream functions, and similarity variables are used to simplify the governing equations from Newton's law, and thermodynamics law. These equations consist of continuity equations, momentum equations, and energy. Backward Euler method numerically solves the equations. The results show that the smaller the influence of the given Stuart number and Prandtl number, the fluid velocity and temperature will increase. Diamond nano fluid with water base fluid moves faster and experiences an increase in temperature faster than engine oil base fluid. this is due to the thermo-physical heat capacity of the water base fluid being greater than that of the engine oil.

Evaluasi kinerja pada instansi pemerintah merupakan aktivitas analisis yang sistematis, pemberian nilai, atribut, dan pengenalan permasalahan, serta mempersembahkan solusi atas masalah yang ditemukan guna meningkatkan akuntabilitas dan peningkatan kinerja instansi pemerintah. Untuk pemerintahan di Indonesia, penilaian atas akuntabilitas kinerja merupakan sebuah cerminan organisasi dalam merepresentasikan kinerjanya, sehingga tidak mengherankan jika setiap instansi pemerintah berupaya semaksimal mungkin untuk meningkatkan kinerjanya sesuai dengan kriteria yang ditetapkan oleh tim evaluator. Baru-baru ini, MENPAN RB merevisi evaluasi AKIP ke dalam Peraturan MENPAN RB Nomor 88 Tahun 2021 dengan banyak perubahan yang cukup fundamental. Hal ini berdampak pada perubahan strategi organisasi untuk mengembangkan evaluasi evaluasi guna dapat memprediksi nilai akuntabilitas kinerja dengan bantuan teknologi informasi berbasis ERP, seperti contoh kasus yang terjadi pada Kementerian Kelautan dan Perikanan. Penelitian ini merupakan studi kasus bertujuan untuk mengetahui bagaimana penerapan ERP pada pelaksanaan evaluasi AKIP yang dijalankan oleh Inspektorat Jenderal Kementerian Kelautan dan Perikanan mulai dari dukungan infrastruktur dan jaringan teknologi informasi yang digunakan, tahap perancangan dan pengembangan perangkat lunak, serta implementasinya dapat digunakan pada pelaksanaannya AKIP pada Tahun 2022. seperti contoh kasus yang terjadi pada Kementerian Kelautan dan Perikanan. Penelitian ini merupakan studi kasus bertujuan untuk mengetahui bagaimana penerapan ERP pada pelaksanaan evaluasi AKIP yang dijalankan oleh Inspektorat Jenderal Kementerian Kelautan dan Perikanan mulai dari dukungan infrastruktur dan jaringan teknologi informasi yang digunakan, tahap perancangan dan pengembangan perangkat lunak, serta implementasinya dapat digunakan pada pelaksanaannya AKIP pada Tahun 2022. seperti contoh kasus yang terjadi pada Kementerian Kelautan dan Perikanan. Penelitian ini merupakan studi kasus bertujuan untuk mengetahui bagaimana penerapan ERP pada pelaksanaan evaluasi AKIP yang dijalankan oleh Inspektorat Jenderal Kementerian Kelautan dan Perikanan mulai dari dukungan infrastruktur dan jaringan teknologi informasi yang digunakan, tahap perancangan dan pengembangan perangkat lunak, serta implementasinya dapat digunakan pada pelaksanaannya AKIP pada Tahun 2022.

Purpose: This study aims to determine the efforts to minimize the occurrence of risks in enterprise systems and how far the framework is applied to an organization, as well as what steps must be applied in anticipation of it.Design/methodology/approach: This study uses a systematic review research method of literature published by international journals in the period 2016 to 2021 which is subscribed to by Diponegoro University.Findings/result: Most of the selected journals stated that in an effort to secure enterprise systems in an organization, they really consider several aspects in it, especially in terms of cost which is one of the biggest considerations in it, besides that support from policy makers must be needed to make guidelines in implementing framework (framework) regarding the limitations of Authentication access and interaction on a system.Originality/value/state of the art: the method applied will focus on discussing the realm of enterprise systems, specifically discussing framework management in an effort to minimize risks to enterprise systems.

In this study, the authors designed an algorithm based on deep learning that can automatically classify plastic waste according to Resin Identification Codes (RIC). The proposed algorithm is built through several stages as follows. In the first stage, image acquisition of plastic waste is carried out, which is the input of the designed algorithm. The acquired plastic waste image must display the resin code of the plastic waste to be classified. Furthermore, the acquired image is divided into two sets, namely training and testing sets. The training set contains images of plastic waste used in the training phase of the deep learning architecture DenseNet-121 to identify the resin code of each plastic waste image and classify it into the appropriate class. The training phase is run for 100 epochs, and at each epoch, the cross-entropy loss function is calculated, which expresses the performance of the deep learning architectures in classifying plastic waste images. In the next stage, a trained deep learning architecture is used to classify the plastic waste images from the test set. Classification performance in the test set is also expressed as the cross-entropy loss function value. In addition, the accuracy value has also been calculated, which shows the percentage of the number of plastic waste images successfully classified correctly to the total number of plastic waste images in the test set, which the best accuracy is equal to 85%.