Extreme climate change in Indonesia, caused by air pollution from the use of fossil energy, has encouraged the development of renewable energy technology, including Wind Power Plants (PLTB). This technology is increasingly important for providing clean energy, especially in the maritime industry. The aim of this applied scientific work is to provide a battery charging solution when the emergency battery charger charging system on commercial ships experiences problems. To make it easier to monitor the power produced by wind power plants and also battery capacity via a smartphone application in real time. The method used in this applied scientific research research is experimental. The results of calculating the average power during testing at night show an average power of 10.22 Watt/min and an average current of 0.78 A. So it can be concluded that battery charging takes 11.53 hours. Meanwhile, the results of calculating the average power during testing during the day show an average power of 13.41 Watt/min and an average current of 1.02 A. So it can be concluded that charging the battery lasts for 8.82 hours. From the two data obtained, it can be concluded that charging is optimal during the day because the efficient charging time is 8.82 hours.

Along with the development of the industrial sector, companies are competing to create quality and affordable products. To support production, automation tools have emerged that help the production process so that it is faster. One of the machines that is widely used by large factories is a 3-phase induction motor. 3-phase induction motors are often used in driving equipment in industry because they have a simple construction, are relatively cheap, lightweight, have high efficiency, and are easy to maintain. This study utilizes the A3114 hall effect sensor device, PZEM-PP4T Sensor, 20x4 LCD, ESP32 module, Relay, Mitsubishi D700, MCP 4725 Sensor. The tests carried out showed that the performance of the sensor for measuring speed, voltage, and current on 3-phase motors has a high level of accuracy. Testing of motor speed shows a difference in speed value (Rpm) between the hall effect sensor and the tachometer, with an average accuracy percentage of 4,12%, indicating that the hall effect sensor has a fairly good level of accuracy. For voltage measurement, the Pzem sensor compared to the Multitester produces an average accuracy percentage of 0,90%, indicating that the pzem sensor is very reliable. Meanwhile, current testing shows a difference in value between the pzem sensor and the multitester with an average accuracy percentage of 0,98%, indicating a fairly high level of accuracy. Overall, this system can be relied on for measuring operational parameters of 3-phase motors because it has consistent and good accuracy. With this research, the 3-phase induction motor monitoring system which was originally done manually has changed to automatic and has a fairly high level of work efficiency.

Pada saat ini, teknologi informasi mengalami perkembangan yang sangat cepat. Pengguna dapat mengakses data atau informasi secara cepat, efisien, dan akurat. Pengolahan data merupakan proses untuk mengumpulkan, memproses, dan mengelolah data menjadi informasi yang dapat digunakan untuk pengambilan keputusan. PT. Subur Sedaya Maju Prabumulih, sebuah perusahaan yang bergerak di bidang jasa service, sewa-menyewa, dan perawatan alat-alat berat, telah menggunakan komputer sebagai alat bantu dalam pengolahan kas. Namun, bagian keuangan yang mengolah penerimaan dan pengeluaran kas tetap menggunakan Ms. Excel, yang memerlukan waktu dan tenaga yang relatif lama. Diperlukan adanya aplikasi yang dapat mengolah data penerimaan dan pengeluaran kas dengan cepat, mudah, dan akurat. Aplikasi tersebut harus terintegrasi dengan database agar dapat mempercepat pekerjaan bagian keuangan dalam membuat pelaporan bulanan dan update data kas masuk maupun kas keluar setiap hari

The rapid growth in vehicle ownership has led to increased traffic congestion, making the need for autonomous driving solutions more urgent. Autonomous Vehicles (AVs) offer a promising solution to improve road safety and reduce traffic accidents by adapting to various driving conditions without human intervention. This research focuses on implementing Deep Q-Network (DQN) to enhance AV performance in different driving modes: safe, normal, and aggressive. DQN was selected for its ability to handle complex, dynamic environments through experience replay, asynchronous training, and epsilon-greedy exploration. We designed a simulation environment using the Highway-env platform and evaluated the DQN model under varying traffic densities. The performance of the AV was assessed based on two key metrics: success rate and total reward. Our findings show that the DQN model achieved a success rate of 90.75%, 94.625%, and 95.875% in safe, normal, and aggressive modes, respectively. Although the success rate increased with traffic intensity, the total reward remained lower in aggressive driving scenarios, indicating room for optimization in decision-making processes under highly dynamic conditions. This study demonstrates that DQN can adapt effectively to different driving needs, but further optimization is needed to enhance performance in more challenging environments. Future work will focus on improving the DQN algorithm to maximize both success rate and reward in high-traffic scenarios and testing the model in more diverse and complex environments.

The problem of plastic waste in Indonesia is increasingly urgent as plastic consumption increases and sustainable management is lacking. This research aims to explore the potential of utilising plastic bottle waste as a creative and environmentally friendly product in the form of "ReBloom" pencil case. This research uses a descriptive quantitative approach, which collects data from respondents aged 18-25 years old in Surabaya to measure perceptions and interest in recycled products. The study results show that the majority of respondents are interested in environmentally friendly products at an affordable price. The business feasibility analysis revealed that ReBloom has a positive economic outlook with a break-even point that can be achieved within a certain production quantity. Besides contributing to the reduction of plastic waste, the product also raises public awareness of the importance of recycling and circular economy. Digital marketing strategies and collaboration with communities are proposed to increase market acceptance. This research confirms that sustainability-based innovation can be a promising business opportunity while contributing to environmental preservation.

Penelitian ini menerapkan dua metode regresi yaitu Decision Tree dan linear berganda untuk memprediksi Body Mass Index (BMI) berdasarkan variabel-variabel lainnya pada dataset Asthma. Metode Decision Tree merupakan salah satu cara data processing dalam memprediksi masa depan dengan cara membangun klasifikasi dan regresi model dalam bentuk struktur pohon. Analisis regresi linear berganda merupakan model regresi yang melibatkan lebih dari satu variabel independen. Analisis regresi linear berganda dilakukan untuk mengetahui arah dan seberapa besar pengaruh variabel independen terhadap variabel dependen. Untuk mengukur tingkat keakuratan model peramalan, digunakan suatu ukuran keakuratan MAPE (Mean Absolute Percentage Error). Hasil MAPE menunjukkan nilai rata-rata prosentase kesalahan mutlak dari nilai sebenarnya dengan nilai peramalan. Pada penelitian ini, metode regresi linear berganda menunjukkan hasil yang lebih baik dengan nilai MAPE berturut-turut sebesar 12,737%; 12,76%; 12,89%; dan 12,99% untuk proporsi data uji berturut-turut 10%, 20%, 30%, dan 40% sedangkan nilai MAPE dari metode regresi Decision Tree sebesar 12,758%; 12,79%; 12,92%; dan 13,13%. Apabila digunakan ukuran kebaikan yang lain seperti MAE dan RMSE akan memberikan hasil yang analog, sedangkan ukuran kebaikan R2 berkebalikan. Penelitian ini dapat dilanjutkan dengan membandingkan hasil yang diperoleh dengan menggunakan metode lain dalam machine learning seperti Support Vector Machine (SVM), random forest, Artificial Neural Network (ANN), dan lain-lain.

Infinitess store in its production activities has been using computers, but still limited to spreadsheets. As its business grows, the limitations of spreadsheets become increasingly evident. All data collection is still done manually, starting from recording customer orders, inventory data collection of raw materials, scheduling production processes, and monitoring production processes. This research aims to design a web-based screen printing production information system at Infinitees store to improve service and facilitate information availability in the production process. The method used in the design is Prototype method. For the design, HTML and PHP are used, as well as MySQL as the database. The expected result of this research is to build and design a web-based screen printing production information system to facilitate data collection processes and present information related to the production process easily and quickly.

Dalam penelitian ini dipresentasikan identifikasi karakteristik laju inflasi mtm maupun yoy kota-kota di Jawa Tengah dan DIY pada waktu sebelum pandemi dan pada waktu pandemi Covid-19. Data yang digunakan adalah laju inflasi mtm dan yoy dari bulan Januari 2018 sampai dengan bulan April 2022 dengan menggunakan metode statistik non parametrik. Karakteristik inflasi yang diperoleh adalah bahwa pada bulan Februari, Agustus, September dan Oktober, inflasi mtm cenderung rendah pada kota-kota di Jawa Tengah dan DIY serta kota Ambon namun pada bulan Mei laju inflasi mtm cenderung tinggi. Pada sisi lain laju inflasi cenderung tinggi pada bulan November, Desember dan Januari. Di samping itu, sebelum waktu pandemi dan pada waktu pandemi Covid-19, laju inflasi mtm kota-kota di Jawa Tengah dan DIY cenderung mempunyai keterkaitan satu sama lain. Pada waktu sebelum pandemi, laju inflasi yoy kota Surakarta dan kota Semarang cenderung tidak terkait dengan kota-kota lain di Jawa Tengah dan DIY pada waktu pandemi Covid-19 hanya kota-kota Cilacap, Kudus dan Surakarta yang cenderung terkait, namun pada waktu pandemi, data laju inflasi yoy pada kota-kota di Jawa tengah dan DIY cenderung terkait satu sama lain. Hasil yang juga diperoleh adalah bahwa tidak terdapat perbedaan signifikan distribusi laju inflasi mtm pada waktu sebelum pandemi dan pada waktu pandemi Covid-19 untuk kota-kota di Jawa Tengah dan DIY, namun terdapat perbedaan signifikan distribusi laju inflasi mtm pada waktu sebelum pandemi dan pada waktu pandemi Covid-19 untuk kota-kota di Jawa Tengah dan DIY.

Dalam klasifikasi, ukuran kebaikan suatu metode ditentukan oleh akurasi dan skor F1. Penelitian ini bertujuan untuk menentukan rata-rata akurasi dan skor F1 pada klasifikasi menggunakan metode KNN pada data Iris dengan bantuan program Python. Rata-rata akurasi maupun skor F1 ditentukan dengan membuat variasi parameter yang digunakan yaitu random state dari 1 sampai dengan 10000, k yang digunakan dalam metode KNN dari 1 sampai dengan 31 (tergantung prosentase data uji yang digunakan), q (definisi jarak yang digunakan yaitu jarak Manhattan, jarak Euclid, dan jarak Minkowski) serta persentase data uji yang digunakan. Dalam penelitian ini, digunakan persentase data uji 10%, 20 %, 30 %, dan 40 %. Jika digunakan k = 5 dalam metode KNN, variasi random state 1 sampai dengan 10000 dan digunakan data uji 20 % maka diperoleh akurasi rata-rata 96,6667 %. Demikian juga dapat diperoleh histogram dari akurasi tersebut. Histogram tersebut cenderung menceng ke kiri. Dalam penelitian ini, juga diperoleh hasil bahwa dengan melakukan variasi k dalam metode KNN juga dapat diperoleh akurasi maupun skor F1 yaitu bahwa makin besar data uji yang digunakan maka makin kecil nilai k untuk mencapai akurasi ataupun skor F1 maksimum. Lebih lanjut, dalam perubahan nilai p pada jarak Minkowski, apabila p membesar maka nilai maksimum rata-rata akurasi akan cenderung membesar namun nilai k cenderung mengecil.

This study aims to identify the characteristics of the MoM inflation rate in cities in Indonesia. In addition, it also compares the distribution of the monthly inflation rate for the 2009–2014 period with the 2014-2019 period in cities in Indonesia. The method used to identify the characteristics of the MoM inflation rate is by using summary of statistics, while to compare the distribution of the monthly inflation rate for the 2009–2014 period and the 2014–2019 period, the two-sample Kolmogorov-Smirnov test is used. Characteristics of the month to month (MoM) inflation rate in cities in Indonesia for the period October 2009 to March 2019 was presented. Furthermore, the comparison of the characteristics of the MoM inflation rate from October 2009 to September 2014 (period 1) and the period from October 2014 to March 2019 (period 2) was also shown. The cities of Banda Aceh, Kendari, Yogyakarta, Jakarta and Sorong had an average characteristic of inflation each month which tended to be low in February, March, April and September.Six cities had significantly different characteristics during  period 2 inflation rate compared to period 1.