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"Considering the importance of liquid static holdup in liquid-liquid extraction columns, a novel experimental approach for predicting the mentioned parameter in a rotary sieved disc contactor has is presented in this research. One chemical system without mass transfer was used, in which distilled water and butyl acetate were employed as the continuous and the dispersed phase, respectively. The static holdup has been measured using the draining method. Based on the experimental results, one correlation was proposed to predict the static holdup as a function of stage position in the column and rotating speed in the form of Reynolds numbers and also the dimensionless mother drop size. Changes in static holdup caused by each factor have been discussed and graphically illustrated. It was revealed that an increase in mother drop size will cause the growth of static holdup, while the rise of rotating speed will decrease the amount of static holdup. Furthermore, it was proven that static holdups in upper positions in the column are less than those in the lower positions."

"A correlation has been proposed for mean drop diameter in a Rotating Sieved Disc Contactor (RSDC) considering drops break up, as well as drops coalescence with static holdup in the case of no mass transfer. The proposed correlation is a function of a number of stages, rotating speed in the form of Reynolds number, static hold-up and mother drop size. The effects of the last two terms have not been considered by other researchers. Therefore, the results are compared with two reported correlations to show how these two important terms influence the size of drops. Distilled water was used as a continuous phase and toluene was applied as a dispersed phase in the experiments. The absolute average relative error and standard deviation for the correlation were 14.74% and 10.08%, respectively."

"In this study, a fuzzy-based expert system called the Pain Intensity Prediction Expert System (PIPES) was developed to predict pain severity risk (PSR) in shoveling-related tasks. The primary objective was to develop a non-changing rating risk assessment ergonomics tool that both efficient and comparable with those obtained from human ergonomics experts in the field of application. PIPES used fuzzy set theory (FST) to make decisions about the level of pain associated with a selected worker base on the measured task variables, namely scooping rate, scooping time, shovel load, and throw distance as input and PSR as the result. Values obtained from variable measurements from a sand shoveling task were run with PIPES, and the results were compared with the workers’ self-reported pain (WSRP) intensity using a numeric rating scale (NRS) tool. The result of validation showed that there was a strong positive relationship between WSRP NRS and PIPES NRS, with a correlation coefficient of 0.70. The independent sample t-test for mean difference showed that WSRP had a statistically significantly lower level of NRS (4.35 ± 2.1) compared to PIPES (4.75 ± 2.2), t (38) = - 0.591, p = 0.558. With a significance level of 0.001 at 95% confidence, the groups’ means were not significantly different. The study developed an expert system, PIPES, which can be used as a computerized representation of ergonomics experts, who are scarce. PIPES can be applied to construction industries, sand mine locations, and any workplace where materials are manually moved using a shovel."

"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."

"Fase kuning lampu lalu lintas menimbulkan zona dilema yang mempengaruhi reaksi pengemudi dalam menentukan sikap untuk berhenti atau meneruskan perjalanan. Kendaraan yang berada dalam zona dilema dapat terlibat melakukan pelanggaran lampu lalu lintas karena terlambat merespon fase kuning lampu lalu lintas. Penelitian ini mengembangkan algoritma IBR dalam sistem pelacakan nirkabel diskrit (DWT system) untuk pemantau pergerakan kendaraan di persimpangan yang dilengkapi lampu lalu-lintas. Algoritma IBR dipergunakan untuk memprediksi pelanggaran lampu lalu-lintas. Prediksi dilakukan berdasar waktu pindai oleh tiga sensor pencacah kendaraan untuk mendapatkan besaran parameter kecepatan, percepatan. Parameter kecepatan dan percepatan dipergunakan untuk mendapatkan batas-batas zona dilema. Hasil simulasi menunjukkan bahwa algoritma IBR dapat meningkatkan keberhasilan prediksi pelanggaran lampu lalu lintas pada sisa nyala lampu kuning antara 1 sampai 4 detik. Diperoleh peningkatan ketepatan prediksi pelanggaran lampu lalu lintas mencapai 6,87% lebih tinggi dibanding dengan hasil penelitian sejenis yang dilakukan oleh peneliti terdahulu.

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Yellow phase of traffic lights cause a dilemma zone that affects the driver reaction to determine attitude to stop or to go on. The vehicles that are in a dilemma zone able to engage red light running since late response to the yellow phase of traffic light. This research develop a IBR algorithms on the discrete wireless tracking system (the DWT system) to monitor the movement of vehicles at intersections.

The IBR algorithm is used to predict the red light runnings. Predictions made based on detection time of the three vehicle counter sensors to obstain the magnitudes of speed and acceleration parameters. Velocity and acceleration parameters used to obtain the dilemma zone boundaries.

Simulation results show that the IBR algorithm improves the success of violation prediction during the period of the yellow light between 1 to 4 seconds. The experiments also exhibits that the accuracy of the red light running prediction increases up to 6.87% is higher than previous research works that have been studied during this work."

"Mengembangkan onsite-EEW (Earthquake Early Warning) merupakan masalah yang menantang karena keterbatasan waktu dan jumlah informasi yang dapat dikumpulkan sebelum peringatan dikeluarkan. Pendekatan yang dapat dilakukan untuk mencegah bencana akibat gempabumi adalah dengan memprediksi tingkat percepatan tanah di suatu lokasi menggunakan sinyal gelombang-P awal dan memberikan peringatan sebelum puncak percepatan tanah yang besar terjadi. Dalam kondisi sebenarnya, keakuratan prediksi merupakan masalah yang paling penting untuk sistem peringatan dini gempabumi. Pada penelitian ini mengimplementasi metode berbasis kecerdasan buatan untuk memprediksi tingkat getaran gempabumi secara dini, ketika gelombang P tiba di stasiun seismik. Sebuah model CNN dibangun untuk membuat prediksi dengan menggunakan small window 3 detik awal gelombang P dari rekaman accelerometer. Model ini dibangun dengan dataset dengan input gelombang seismik dengan variasi 3,2 dan 1 detik data gempabumi di wilayah Jawa Barat 2017 hingga 2023 dengan pembagian 80% data latih,, 10% data validasi dan 10% data uji . Dari evaluasi model terbaik, skema yang diusulkan mendapatkan akurasi 99.30%±0.63% dengan data uji.

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Developing onsite-EEW (Earthquake Early Warning) is a challenging problem due to the limited time and amount of information that can be gathered before a warning is issued. A possible approach to preventing earthquake-induced disasters is to predict the level of ground acceleration at a site using early P-wave signals and provide warnings before large ground acceleration peaks occur. In actual conditions, the accuracy of prediction is the most important issue for earthquake early warning systems. This study implements an artificial intelligence-based method to predict the level of earthquake tremors early, when P-waves arrive at seismic stations. A CNN model is built to make predictions using a small window of the first 3 seconds of P-waves from accelerometer recordings. The model was built with a dataset with seismic wave input with 3,2 and 1 second variations of earthquake data in the West Java region from 2017 to 2023 with a division of 80% training data, 10% validation data and 10% test data. From the evaluation of the best model, the proposed scheme obtained an accuracy of 99.30%±0.63% with test data."

"ABSTRAKSistem prediksi kedalaman memar dan distribusi kandungan SSC pada jambu berdasarkan waktu penyimpanan dengan menggunakan sistem citra Vis-NIR pada panjang gelombang 400-1000 nm dapat dikembangkan menjadi sistem non-destruktif. Sehingga informasi tambahan yang di dapat tidak hanya dapat membedakan daerah memar namun juga memberikan informasi kedalaman memar dan kandungan SSC pada daerah memar. Sistem yang diusulkan dievaluasi dengan menggunakan 160 sampel jambu dibagi dalam dua kelompok set data, yaitu set data training n = 140 dan set data testing n = 20 . Proses memar pada jambu dilakukan secara manual dengan dijatuhkan bola besi dari ketinggian 200 dan 500 mm dan kemudian sampel dianalisis dengan rentang waktu 0,3,4,5, dan 6 hari setelah dimemarkan. Sistem citra Vis-NIR yang digunakan terdiri dari satu set perangkat, diantaranya workbench, slider, dua sumber cahaya halogen 150W dan kamera citra Vis-NIR yang terhubung ke PC melalui Camera Link. Perangkat lunak sistem terdiri dari pengukuran profil gambar reflektansi, ekstraksi fitur, pemilihan fitur pada data spektral dan spasial, model prediksi kandungan SSC, dan model prediksi kedalaman memar. Model Partial Least Square Regression PLSR digunakan untuk mengembangkan model prediksi pada data spektral semua panjang gelombang. Model PLSR digunakan untuk mendapatkan prediksi nilai kandungan SSC dan kedalaman memar. Hasil yang diprediksi dibandingkan dengan hasil pengukuran uji lab kandungan SSC yang diperoleh dengan menggunakan refraktometer dan kedalaman memar yang diperoleh dengan menggunakan sifat measurement instrumental. Dari hasil kinerja model prediksi didapatkan nilai RMSE pada data testing 0,06 dan koefisien korelasi dari data testing 0,99.Kata kunci : Memar; PLSR; citra Vis-NIR.

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ABSTRACTAbstract The prediction system of bruising depth in guava based on storage operation using Visual NIR image in the ranges 400 ndash 1000 nm ranges, which could be developed a nondestructive system to predicted the bruise depth of guava. The additional information gained not only the position of the bruised area but also provides depth bruising information. And then, the objective of the research was to develop a nondestructive method for predicting the profile mapping of soluble solid content on bruises guava. The soluble solids content SSC as the parameter fruits was determined and correlated with the bruises area.The proposed system was evaluated using 160 samples of guava were divided in two groups. All of the samples are prepared for the training n 140 and testing n 20 set data. Bruises were manually induced and samples were analyzed 0, 3rd, 4th, 5th and 6th days after bruising. Individual guavas were then subjected to impact test by a steel ball at one of the levels height of impact test, i.e.,200mm and 500mm. The system used consists of a set such as workbench, controllable slider, two halogen light sources and a Visual NIR imaging camera that is connected to PC via Camera Link. The software of system consists of reflectance image profile measurement, feature extraction, feature selection on spectral and spatial data, soluble solids content prediction model, and bruise depth prediction model. The partial least squares regression PLSR models was used to develop prediction models on full wavelengths spectral data. The prediction model is used to get value prediction of soluble solids content and bruising depth. The predicted results compared with the reference measurement result of soluble solids content which obtained using a refractometer and bruising depth which obtained using an optical properties. The full spectral data and parameter fruits were analyzed using the Partial Least Square PLS to obtained prediction model of bruising depth and SSC of bruises guava. The peformance of prediction model provided value of the root mean square error of testing set of 0.06 and the correlation coefficient of a testing set of 0.99. The results of our work indicate that there is a feasibility of implementing hyperspectral imaging technique on the nondestructive bruise depth prediction of guava and suitable in an industrial sorting system for fruit quality, which would be useful for postharvest handling of fruit. Keywords kelebaman bruising , non destructive, Profitability, hyperspectral image Vis NIR."

"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. "

"Pemanfaatan gas suar bakar merupakan salah satu peluang untuk meningkatkan produksi LPG guna memenuhi kebutuhan LPG yang makin meningkat. Pemanfaatan gas suar bakar tersebut selain dapat meningkatkan ketahanan energi, juga memiliki keuntungan ekonomis dan sosial melalui penghematan devisa negara dari berkurangnya impor LPG, serta penciptaan lapangan kerja. Pemanfaatan gas suar bakar sebagai bahan baku kilang LPG perlu mempertimbangkan aspek kelayakan teknis maupun ekonominya. Pada penelitian ini dilakukan analisis awal dari sisi teknis dan keekonomian untuk menilai kelayakan pembangunan kilang LPG berbahan baku flare gas di lapangan Tambun, Jawa Barat (10 MMSCFD); Pendopo, Sumatera Selatan (2.4 MMSCFD); Semoga, Sumatera Selatan (7.7 MMSCFD); dan Tuban, Jawa Timur (6 MMSCFD). Simulasi proses menunjukkan kilang di Tambun dapat menghasilkan produk LPG terbesar yaitu 73.3 ton per hari produk LPG. Analisis parameter keekonomian juga menunjukkan kilang Tambun memiliki indikator keekonomian terbaik yaitu IRR 75.02%, NPV sebesar 43.86 juta US$, dan payback period 1.34 tahun. Analisis sensitivitas terhadap kilang LPG di Tambun, Pendopo, dan Semoga menunjukkan bahwa parameter yang paling memperngaruhi keekonomian ketiga kilang tersebut adalah biaya investasi.

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Utilization of flared gas is an opportunity to increase Indonesia?s LPG production, to meet increasing LPG demand. Utilization of flare gas can strengthen Indonesia?s energy security, as well as economic and social benefit through reducing LPG import and creation of jobs and employment. Utilization of flared gas as LPG plant feed needed to be considered in technical and economical aspects.

This research is a preliminary technical and economical analysis to evaluate feasibility of LPG plants using flared gas as the feed in Tambun Field, West Java (10 MMCSFD); Pendopo Field, South Sumatera (2.4 MMSCFD); Semoga Field, South Sumatera (7,7 MMCSFD) and Tuban, West Java (6 MMSCFD).

Process simulation shows that Tambun LPG Plant can produce biggest LPG product, 73.3 tons per day. Economic parameters analysis also shows that Tambun Plant has the best economic indicator, which are IRR of 75.02%, NPV ofr 43.86 juta US$, and payback period of 1.34 years. Sensitivity analysis of Tambun, Pendopo and Semoga plants show that the most sensitive parameters impacted on plant economics is capital investment."