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Abstrak :
Bus arrival time is very important to passengers, not only at the origin terminal but also at every stop. If there is no predicted arrival time at a stop, the headway designed should match the bus frequency at the stop. The uncertainty in bus arrival time can hinder the headway from matching the bus frequency at the stops. Moreover, lack of information on the service route and actual arrival times at stops leads to difficulty for passengers in planning their trips. Observation surveys were conducted to collect data on the problems of bus arrival frequency and uncertain arrival times at a selected stop with multiple routes during off-peak hours in Putrajaya Malaysia. This paper proposes a method to estimate arrival times at bus stops using the adaptive neuro fuzzy inference system (ANFIS) and several models are proposed to predict arrival times using MATLAB Curve Fitting Tool. All the proposed models exhibited RMSE close to 0 and R2 close to 1.

Abstrak :
Bus arrival time is very important to passengers, not only at the origin terminal but also at every stop. If there is no predicted arrival time at a stop, the headway designed should match the bus frequency at the stop. The uncertainty in bus arrival time can hinder the headway from matching the bus frequency at the stops. Moreover, lack of information on the service route and actual arrival times at stops leads to difficulty for passengers in planning their trips. Observation surveys were conducted to collect data on the problems of bus arrival frequency and uncertain arrival times at a selected stop with multiple routes during off-peak hours in Putrajaya Malaysia. This paper proposes a method to estimate arrival times at bus stops using the adaptive neuro fuzzy inference system (ANFIS) and several models are proposed to predict arrival times using MATLAB Curve Fitting Tool. All the proposed models exhibited RMSE close to 0 and R2 close to 1.

Abstrak :
Providing travelers with accurate bus arrival time is an essential need to plan their traveling and reduce long waiting time for buses. In this paper, we proposed a new approach based on a Bayesian mixture model for the prediction. The Gaussian mixture model (GMM) was used as the joint probability density function of the Bayesian network to formulate the conditional probability. Furthermore, the Expectation maximization (EM) Algorithm was also used to estimate the new parameters of the GMM through an iterative method to obtain the maximum likelihood estimation (MLE) as a convergence of the algorithm. The performance of the prediction model was tested in the bus lanes in the University of Indonesia. The results show that the model can be a potential model to predict effectively the bus arrival time.

Abstrak :
Latar Belakang: Stroke merupakan masalah kesehatan global yang signifikan, dengan tingkat kematian yang bervariasi di berbagai wilayah. Negara-negera berkembang, seperti Indonesia, mengalami tingkat kematian cukup tinggi akibat stroke. Penanganan yang terlambat adalah salah satu penyebab kematian/kecacatan yang cukup sering terjadi dalam kasus stroke. Oleh karena itu, manajemen pra-rumah sakit yang efektif sangat penting untuk proses penyembuhan yang optimal. Dalam beberapa studi-studi sebelumnya, kesadaran keluarga terhadap stroke telah dibuktikan berperan penting dalam manajemen pra-rumah sakit. Studi yang kami lakukan sekarang bertujuan untuk mengevaluasi dampak kesadaran stroke keluarga lebih lanjut. Metode: Studi analitis komparatif ini menilai kesadaran keluarga terhadap stroke pada dua kelompok: kelompok dengan pasien yang tiba di rumah sakit dalam period emas (<4.5 jam dari awal muncul gejala) dan mereka yang tiba setelahnya. Studi ini, dilakukan di Rumah Sakit Cipto Mangunkusumo, menggunakan kuesioner kesadaran stroke yang didistribusikan kepada kerabat pasien. Kerabat yang menerima kuesionar berada di ruang gawat darurat, ruang perawatan, atau dikontak melalui panggilan telepon. Analisis statistik melibatkan independent samples t-test dan multivariable binary logistic regression untuk mengendalikan faktor selain kesadaran stroke. Hasil: Studi ini melibatkan 50 subjek, dengan 25 partisipan di grup periode emas dan pasca periode emas. Hasil independent t-test menunjukkan bahwa kerabat dengan kedatangan periode emas memiliki skor kesadaran stroke yang lebih tinggi dibandingkan dengan mereka yang tiba setelah periode emas (p = 0,007). Multivariable binary logistic regression menunjukkan bahwa kesadaran stroke dan waktu perjalanan dari tempat tinggal mempengaruhi waktu kedatangan ke rumah sakit secara signifikan (p = 0,035 untuk kesadaran stroke dan p = 0,016, untuk waktu perjalanan). Model ini mengklasifikasikan 78% kasus dengan akurat, dengan kesadaran stroke meningkatkan peluang kedatangan dalam periode emas sebesar 2,11 kali. Pengatahuan terhadap pusat panggilan darurat 112/119 berkorelasi positif dengan kedatangan dalam periode emas, meskipun sebagian besar subjek memilih transportasi pribadi. Tantangan dalam rujukan pasien paling besar bagi kerabat pasien adalah ketidaktahuan terhadap gejala stroke (28%) dan kesulitan transportasi (22%). Kesimpulan: Penelitian ini menyimpulkan adanya korelasi signifikan antara kesadaran stroke yang tinggi pada keluarga dan kedatangan dalam periode emas. Waktu perjalanan dari tempat tinggal adalah faktor tambahan yang mempercepat kedatangan pasien ke rumah sakit. ......Introduction: Stroke poses a significant global health challenge, with mortality rates varying across regions. Developing countries, such as Indonesia, experience high stroke-related mortality rates. A fairly frequent cause of high mortality/disability is over delayed stroke treatment. Effective prehospital management is therefore essential for optimal outcomes, as it emphasizes on fast treatment delivery. Family awareness has been suggested to play a pivotal role in prehospital management, as it can influence the rate of stroke symptom recognition and emergency responses. Evaluating the validity of this suggestion will be the main objective of this research. Methods: This comparative analytical study assesses family stroke awareness among two groups - those with patients arriving within the golden period (<4.5 hours after symptomatic onset) and those arriving beyond it. The study, conducted at Cipto Mangunkusumo Hospital, utilizes a stroke awareness questionnaire distributed to relatives in the emergency room, nursing rooms, or through phone calls. Statistical analyses include an independent t-test and multivariable binary logistic regression to control for potential confounding factors. Results: The study, involving 50 subjects, reveals that relatives with golden period arrivals exhibit significantly higher stroke awareness scores compared to those with post-golden period arrivals (p = 0.007). Multivariable binary logistic regression indicates that stroke awareness and travel time from residence significantly influence arrival times (p = 0.035 and p = 0.016, respectively). The model accurately classifies 78% of cases, with stroke awareness increasing the odds of golden period arrival by 2.11 times. Awareness of the emergency call center positively correlates with golden period arrivals, despite a majority opting for private transportation. Challenges in patient referral include relatives' unawareness of stroke symptoms (28%) and transportation difficulties (22%). Conclusion: This research establishes a significant correlation between higher family stroke awareness and golden period arrivals, emphasizing the crucial role of family education in improving prehospital stroke management. The study also suggests that reducing travel time from residence is an additional factor promoting timely hospital arrival.

Abstrak :
Gempabumi yang terjadi akibat pelepasan energi di dalam permukaan bumi akan menghasilkan penjalaran gelombang seismik. Gelombang tersebut akan terekam oleh stasiun penerima yang nantinya dilakukan pemrosesan data sebagai kebutuhan interpretasi dari seismogram. Pada proses pengolahan data salah satunya yaitu penentuan waktu tiba gelombang. Penentuan waktu tiba dari gelombang primer dan sekunder masih dilakukan dengan cara manual oleh operator sehingga memiliki kekurangan seperti waktu yang lama, tingkat subjektivitas yang tinggi dan hasil akurasi yang rendah. Pada penelitian ini dilakukan inovasi dalam penentuan arrival time dengan pendekatan deep learning yaitu menggunakan algoritma Convolutional Neural Network (CNN) dan Long Short Term Memory (LSTM). Program yang dibuat dengan menerapkan kedua algoritma ini akan dilakukan pengujian terhadap data lain. Hasil uji pada program yang sudah dibuat kemudian dilakukan komparasi pada hasil picking dari IRIS Wilber. Uji yang dilakukan menggunakan data dari gempa Palu 28 September 2018. Hasil uji dari program komputer yang dibuat dengan perbandingan picking hasil IRIS Wilber memberikan rata-rata eror sekitar 0.005 dan komparasi waktu dari origin time memiliki perbedaan sekitar 2 detik. Program ini sudah menghasilkan hasil prediksi yang cukup akurat. ......Earthquakes that occur due to the release of energy in the earth's surface will result in the propagation of seismic waves. These waves will be recorded by the receiving station which will later be processed as a result of the interpretation of the seismogram. One of the data processing processes is determining the arrival time of the waves. The determination of the arrival time of the primary and secondary waves is still done manually by the operator so that it has drawbacks such as a long time, a high degree of subjectivity and low accuracy. In this study, innovation was carried out in determining arrival time with a deep learning approach, namely using the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) algorithms. Programs created by applying these two algorithms will be tested on other data. The test results on the program that has been made are then compared to the picking results from IRIS Wilber. The test was carried out using data from the Palu earthquake on 28 September 2018. The test results from a computer program made with a comparison of IRIS Wilber's picking results give an average error of around 0.005 and a comparison of the time from the origin time has a difference of about 2 seconds. This program has produced predictive results that are quite accurate.