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"ABSTRAKPada masa sekarang ini, teknologi semakin berkembang dan terus berkembang dengan cepat. Terutama kebutuhan adanya teknologi prediksi yang memerlukan pengembangan lebih dalam lagi sehingga dapat menghasilkan teknologi yang dapat memprediksi masa depan Multi-Step Ahead MSA secara lebih akurat. Salah satunya untuk teknologi prediksi peramalan cuaca sistem Chaos yang dapat membantu masyarakat dalam mempersiapkan aktifitas yang akan dilakukan. Penelitian ini melakukan simulasi percobaan penerapan Jaringan Saraf Tiruan berbasis Radial Basis Function RBF pada sistem prediksi data Chaos, data Lorenz dan data Mackey-Glass. Berdasarkan hasil percobaan dapat dilihat dari nilai bahwa penerapan jaringan saraf tiruan berbasis Radial Basis Function RBF memiliki tingkat keakuratan yang baik untuk memprediksi lebih dari 100 langkah kedepan.

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ABSTRACT Recently, technologies have been growing and growing fast. Especially, the need of prediction technology that need to be developed more so that it could create a technology that is capable to predict the future Multi Step Ahead MSA more accurate. One of the applied field of this prediction method is for forecasting Chaotic System which help the society in order to prepare their activity that will be scheduled. This research performs simulation experiments in applying the Artificial Neural Network based on Radial Basis Function RBF of prediction system for chaotic data, Mackey Glass equation and Lorenz rsquo s system. As can be seen from the values of the experimental results, applying Artificial Neural Network based on Radial Basis Function results high accuracy for predicting more than 100 steps ahead. "

"[ABSTRAKPerkembangan teknologi yang semakin cepat menjadikan teknologi penting di berbagai sektor kehidupan, khususnya di bidang industri. Perkembangan zaman membuat tingkat permintaan akan suatu produk menjadi berubah sehingga industri harus meningkatkan kinerja produksinya.Teknologi yang digunakan merupakan teknologi automasi di mana di dalamnya terdapat pengendali. Pengendali yang digunakan oleh kebanyakan industri merupakan pengendali konvensional karena pengendali konvensional relatif murah dan efektif. Akan tetapi pengendali konvensional ini tidak dapat digunakan untuk sistem yang kompleks dan non linear. Pengendali konvensional, misalnya pengendali PID, tidak dapat mengatasi terjadinya perubahan karakteristik dari sistem secara otomatis. Untuk itu diperlukan sistem pengendali yang mampu mengatasi perubahan karakteristik secara otomatis dan dapat beradaptasi dengan dinamika perubahan sistem yang diakibatkan adanya perubahan kondisi lingkungan kerja. Sistem pengendali yang dianggap mampu untuk beradaptasi dengan perubahan karakteristik dari sistem secara otomatis adalah pengendali berbasis Neural Network. Dalam percobaan ini parameter yang digunakan untuk menentukan pengendali yang baik adalah adaptivity serta kecepatan respon pengendali.Pada hasil simulasi ini didapatkan bahwa pengendali berbasis Neural Network dengan metode Radial Basis Function Neural Network (RBFNN) lebih baik dan lebih cepat dalam menanggapi perubahan karakteristik sistem dibandingkan dengan pengendali Neural Network berbasis backpropagation.ABSTRACTDevelopment of technology has been rapidly increasing that make technology as an important aspect in many sectors of life, especially in industrial sector. The times have changed the demand of a product so that industry has to enhance its production capacity.Technology used in industry is automation technology which has controller inside. Controller used in industry mostly is conventional controller because it has low price and good effectivity. However, conventional controller can?t be used for complex and non-linear system. For example, PID controller, it can?t handle the changes of system?s characteristic automatically. PID controller has to be reset to handle the new system?s characteristic. Because of that, industry need a controller that has ability to handle the changes of the system?s characteristic automatically and adapt with the dynamics of system?s changes caused by external factor. Controller system that has been considered for the ability of handling the changes of system?s characteristic automatically is Neural Network based controller. In this experiment, the parameters used to determine good controller is adaptivity of the system also the speed of controller response.The result of the experiment shows that Neural Network with Radial Basis Function Neural Network (RBFNN) based controller has better response to the changes of the system?s characteristic than Backpropagation based Neural Network controller.;Development of technology has been rapidly increasing that make technology as an important aspect in many sectors of life, especially in industrial sector. The times have changed the demand of a product so that industry has to enhance its production capacity.Technology used in industry is automation technology which has controller inside. Controller used in industry mostly is conventional controller because it has low price and good effectivity. However, conventional controller can?t be used for complex and non-linear system. For example, PID controller, it can?t handle the changes of system?s characteristic automatically. PID controller has to be reset to handle the new system?s characteristic. Because of that, industry need a controller that has ability to handle the changes of the system?s characteristic automatically and adapt with the dynamics of system?s changes caused by external factor. Controller system that has been considered for the ability of handling the changes of system?s characteristic automatically is Neural Network based controller. In this experiment, the parameters used to determine good controller is adaptivity of the system also the speed of controller response.The result of the experiment shows that Neural Network with Radial Basis Function Neural Network (RBFNN) based controller has better response to the changes of the system?s characteristic than Backpropagation based Neural Network controller., Development of technology has been rapidly increasing that make technology as an important aspect in many sectors of life, especially in industrial sector. The times have changed the demand of a product so that industry has to enhance its production capacity.Technology used in industry is automation technology which has controller inside. Controller used in industry mostly is conventional controller because it has low price and good effectivity. However, conventional controller can’t be used for complex and non-linear system. For example, PID controller, it can’t handle the changes of system’s characteristic automatically. PID controller has to be reset to handle the new system’s characteristic. Because of that, industry need a controller that has ability to handle the changes of the system’s characteristic automatically and adapt with the dynamics of system’s changes caused by external factor. Controller system that has been considered for the ability of handling the changes of system’s characteristic automatically is Neural Network based controller. In this experiment, the parameters used to determine good controller is adaptivity of the system also the speed of controller response.The result of the experiment shows that Neural Network with Radial Basis Function Neural Network (RBFNN) based controller has better response to the changes of the system’s characteristic than Backpropagation based Neural Network controller.]"

"Jaringan Saraf Tiruan adalah salah satu metode baru yang dikembangkan untuk pemecahan berbagai masalah kompleks yang tidak dapat diselesaikan secara analitik. Salah satu pengembangannya adalah metode jaringan saraf pembelajaran Radial Basis Function, dengan metode inisialisasi bobot Nguyen-Widrow dan Orthogonal Least Square (OLS). Akurasi dan kecepatan pembelajaran yang dimiliki oleh Radial Basis Function (RBF) sangat menarik untuk diaplikasikan pada sistem kendali. Pemodelan Forward dan Invers sistem dilakukan dengan metode RBF dengan mengambil data sistem SISO Pressure Process Rig. Setelah dilakukan pemodelan, jaringan saraf tiruan akan diuji dengan Direct Inverse Test. Hasil identifikasi sistem dan identifikasi invers pada sistem Pressure Process Rig memiliki hasil yang baik. Begitu pula saat diuji coba dengan Direct Inverse Test, sistem kendali mempunyai performa cukup baik, namun tidak menutup kemungkinan adanya skema model lain yang dapat digunakan dalam pemodelan sistem.

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Artificial Neural Network is a newer field of study that could solve any complex problem that could not be done by analytical solution. Radial Basis Function (RBF) is one of the newer method of Artificial Neural Network with two distinct weight initialization method ; Nguyen-Widrow and Orthogonal Least Square (OLS) methods. RBF?s high recognition rate and very fast learning speed are interesting enough to be used in control system. RBF is used in forward and inverse identification in modelling Pressure Process Rig system. Direct Inverse Test is also done in order to make sure Radial Basis Function perform well in identifying a particular system. Radial Basis Function had a great perfomance in both forward and inverse system identification and also in Direct Inverse Test, but it is possible to have another learning scheme in system modelling."

"Fokus utama penelitian ini adalah merancang dan mengembangkan prototipe sistem registrasi IRS berbasis event-driven architecture serta mengevaluasi sistem tersebut dengan eksperimen chaos engineering. Implementasi sistem menggunakan Spring Boot framework, Apache Kafka sebagai event broker, dan Amazon Web Service (AWS) untuk infrastruktur. Pengujian dilakukan dengan melakukan API testing untuk menguji fungsionalitas sistem dan load testing untuk menguji reliability sistem. Terakhir, eksperimen chaos engineering dengan metode chaos monkey dilakukan untuk menguji resilience sistem. Hasil pengujian menunjukkan bahwa fungsionalitas sistem sebagai layanan IRS bekerja dengan baik. Sistem dapat tetap bekerja di bawah tekanan 40.000 mahasiswa yang disimulasikan mengakses sistem bersamaan. Pada kondisi chaos di mana beberapa server dimatikan, sistem masih dapat berfungsi dengan baik dan mahasiswa masih dapat menggunakan layanan registrasi IRS tanpa masalah.

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The main focus of this research is to design and develop a prototype of an event-driven architecture based course registration service, and to evaluate the system with chaos engineering. The system was implemented using Spring Boot as its framework, Apache Kafka as the event broker, and Amazon Web Service (AWS) for infrastructure. The testing was done by implementing API testing for evaluating the system’s functionality and load testing to evaluate system’s reliability. Finally, a chaos engineering experiment was carried out to evaluate the resilience of the system. The result shows that the system can deliver its functionality as a course plan registry pretty well. The system was able to work under the pressure of 40.000 student simulated to access the system simultaneously. In the chaos condition where several server were taken down, the system still performs well and able to provide the service without any problem for the students."

"AIS sebagai alat yang diwajibkan digunakan kapal menurut SOLAS sebagai pencegah tabrakan antar kapal memiliki potensi yang lebih besar dalam penerapan ruang lingkup data analytics. Data posisi kapal dapat membantu menggambarkan perilaku kapal di lautan. Aplikasi data AIS bisa membantu mengoptimalkan operasional kapal. Penelitian ini akan menjelaskan tentang sebuah metode penerapan data AIS untuk menghasilkan prediksi waktu tunggu kapal. Algoritma Extreme Gradient Boosting (Xgboost) akan digunakan sebagai pendekatan melakukan prediksi dari data historis. Dengan xgboost, prediksi yang dihasilkan mendapatkan nilai RMSE sebesar 268.47 dan R2 sekitar 0.3 setelah dioptimalkan dengan hyperparameter tuning. Hasil prediksi ini dapat digunakan sebagai pertimbangan penerapan green steaming ataupun bahan evaluasi pelabuhan untuk mengembangkan pelayanannya.

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AIS as a tool, according to SOLAS, used as a prevention of collisions between ships has more significant potential in the application of the scope of data analytics. Ship position data can help describe ship behavior at sea. AIS data applications can help optimize ship operations. This research will describe a method of applying AIS data to generate predictions of ship waiting times. The Extreme Gradient Boosting (Xgboost) algorithm will be used to make predictions from historical data. With xgboost, the resulting prediction gets an RMSE value of 268.47 and an R2 of about 0.3 after being optimized with hyperparameter tuning. The results of this prediction can be used as consideration for implementing green steaming or evaluating port evaluation materials to develop their services."

"Demam Berdarah Dengue (DBD) merupakan salah satu virus yang menginfeksimanusia melalui gigitan nyamuk Aedes aegypti dan Aedes albopictus. Menurut laporanCDC, Indonesia yang masuk dalam level 1 dari 3 yaitu level tertinggi, frequent orcontinuous kasus DBD. Perkiraan lebih awal dan akurat dari persebaran insiden DBDdapat meminimalkan ancaman dan membantu pihak yang berwenang untuk menerapkanlangkah-langkah pengendalian yang efektif. Pada penelitian ini, prediksi angka insidenDBD menggunakan faktor-faktor cuaca yang mempengaruhi perkembangan nyamuk itusendiri, yaitu temperatur, kelembapan, dan curah hujan sebagai variabel prediktor.Variabel prediktor ditentukan berdasarkan nilai korelasi silang dari time lag variabelprediktor terhadap jumlah insiden DBD. Penelitian dilakukan dengan memanfaatkansalah satu metode dalam machine learning, yaitu gated recurrent unit dalammembangun model prediksi insiden DBD tersebut. Performa model yang digunakandievaluasi dengan Root Mean Squared Error dan Mean Absolute Error. Hasil penelitianini menunjukkan bahwa prediksi angka insiden DBD terbaik, diperoleh denganmenggunakan proporsi data training-test: 90%-10%.

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Dengue Fever (DF) is a virus that infects humans through the bite of Aedes aegypti andAedes albopictus mosquitoes. According to the CDC report, Indonesia is included inlevel 1 of 3, namely the highest level, frequent or continuous cases of DF. Early andaccurate estimates of the spread of dengue incidents can minimize threats and help theauthorities to implement effective control measures. In this study, the prediction of DFincidence uses weather factors that influence the development of mosquitoesthemselves, namely temperature, humidity, and rainfall as predictor variables. Predictorvariables are determined based on the value of the cross correlation of the time lagpredictor variable to the number of DF incidents. The study was conducted by utilizingone method in machine learning, namely the gated recurrent unit in building the DFincident prediction model. The performance of the model are evaluated by Root MeanSquared Error and Mean Absolute Error. The results of this study shows that the bestprediction model of DF incidence rate, obtained using the proportion of training-testdata: 90% -10%."