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Abstrak :
Di Indonesia, kereta api telah menjadi moda transportasi yang populer dan efisien, menawarkan kenyamanan dan kecepatan bagi pengguna. Dalam operasionalnya, salah satu tantangan utama adalah optimisasi, di mana kereta listrik muncul sebagai solusi yang efektif dengan biaya operasional rendah dan gaya traksi tinggi. Profil trayektori kecepatan yang mengindikasikan kecepatan yang diizinkan pada setiap posisi dapat membimbing pengemudi atau sistem operasi otomatis kereta (ATO) untuk mengoperasikan kereta dengan lebih efisien. Penelitian ini mengkaji pendekatan optimisasi untuk trayektori kecepatan Kereta Rel Listrik (KRL), dengan mempertimbangkan konsumsi energi sebagai ukuran kepuasan perusahaan kereta api dan waktu perjalanan sebagai ukuran kepuasan penumpang. Optimisasi kecepatan kereta dapat menggunakan algoritma Hybrid Evolutionary Algorithm (HEA). Penelitian ini mengusulkan metode multiobjektif untuk mengoptimalkan lintasan kecepatan kereta, dengan mempertimbangkan batasan kecepatan, strategi mengemudi yang meliputi fase accelerating, cruising, coasting, braking, serta adaptasi terhadap kondisi kemiringan, dan kurvatur lintasan. Selain itu, penelitian ini menunjukkan bagaimana Pareto front dari setiap generasi algoritma dapat digunakan untuk mengevaluasi dan memilih strategi operasi yang paling efektif. Dalam penelitian ini didapat bahwa hasil dari solusi yang didapat bisa mengurangi total energi sebesar 21.97% dan total waktu tempuh sebesar 5.11%. ......In Indonesia, trains have become a popular and efficient mode of transportation, offering comfort and speed to users. One of the main challenges in their operation is optimization, where electric trains emerge as an effective solution with low operational costs and high tractive force. A speed trajectory profile that indicates the authorized speed at each position can guide the driver or the automatic train operation (ATO) system to operate the train more efficiently. This study examines the optimization approach for the speed trajectory of Electric Rail Trains (KRL), considering energy consumption as a measure of railway company satisfaction and travel time as a measure of passenger satisfaction. Train speed optimization can utilize the Hybrid Evolutionary Algorithm (HEA). This research proposes a multi-objective method to optimize the train speed trajectory, taking into account speed limits, driving strategies including accelerating, cruising, coasting, and braking phases, as well as adaptation to track slope and curvature conditions. Additionally, this study demonstrates how the Pareto front of each algorithm generation can be used to evaluate and select the most effective operational strategy. In this research, it was found that the results of the solution obtained could reduce total energy by 21.97% and total travel time by 5.11%.

Abstrak :
Kanker adalah salah satu penyebab kematian utama di dunia,dengan jumlah kematian sekitar sepuluh juta kematian setiap tahun. Kanker hati menempati peringkat keenam untuk jenis kanker yang umum terjadi pada pria dan wanita. Menurut penelitian, pendeteksian dini penting untuk mencegah penyebaran kanker ke organ lain. Hal ini menyebabkan penggunaan machine learning di bidang medis untuk mengklasifikasikan data kanker agar manghasilkan diagnosis yang tepat. Namun ada kalanya dibutuhkan lebih dari satu algoritma untuk meningkatkan akurasi. Maka dari itu, penelitian ini bertujuan untuk menganalisis pengaruh Genetic Algorithm sebagai penyetelan hyperparameter untuk nilai akurasinya, Penggunaan Random Forest dengan Genetic Algorithm sebagai penyetel hyperparameter memberikan akurasi sebesar 85% dengan data testing 90%. Sementara untuk Random Forest saja, hasil akurasi tertinggi adalah 73% dengan data testing sebesar 40%. ......Cancer is one of the leading causes of mortality worldwide, with approximately ten million deaths each year. Liver cancer is the sixth most common type that occurs in both men and women. According to scientific studies, early detection is important to prevent the spread of this ailment to other organs. This led to Machine Learning in medical fields for classifying cancer data to produce an accurate diagnosis. However, there are times where just one machine learning algorithm is not giving a good accuracy score. Therefore, this study aims to analyze the effect of using Genetic Algorithm as hyperparameter tuning in terms of the accuracy level. The usage of  Random Forest with Genetic Algorithm as the hyperparameter tuning algorithm gives the accuracy of 85% with 90% data testing. Meanwhile, with Random Forest alone, the highest accuracy score is 73% with 40% testing data.

Abstrak :
Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms.