Authors:Yansen Theopilus, Johanna Octavia, Thedy Yogasara Pages: 1 - 11 Abstract: Exercising is a key activity to prevent diseases and strengthen our immune system. Unfortunately, people often find it difficult to have healthy behavior, especially for exercising regularly. There are some reasons for that problem, such as lack of exercise knowledge, lack of equipment, lack of available time and space, lack of exercise partner, and poor physical conditions. This behavior problem would make people vulnerable to illness. Therefore, this research aims to develop a wearable device to assist users’ exercise activity and encourage them to have exercise self-efficacy while ensuring a positive experience. The device development method used a combination of persuasive design, universal design, and rich experience design approach. The persuasive approach aims to design the product that can change the behavior problem into the behavior goal, while the universal design principle ensures that the device suits various user characteristics. We also use a rich experience design approach to generate a positive experience from negative emotions. This research has invented a smart exercise band that acts as an exercise instructor, partner, and reminder that recommends various personalized exercise programs according to the user characteristics (gender, age, physical condition, schedule, and other preferences). The device was evaluated using Task Completion Method, Retrospective Thinking Aloud, and Perceived Persuasiveness Questionnaire to assess the usability, persuasiveness, and experience. The evaluation results have shown that the product has good usability, effective persuasion, and positive experience. PubDate: 2021-12-28 DOI: 10.46923/ijets.v3i1.97 Issue No:Vol. 3, No. 1 (2021)
Authors:Kevin Grahadian, Ignatius A. Sandy Pages: 12 - 19 Abstract: Asymmetric travelling salesman problem (ATSP) is an optimization problem which a salesman needs to visit all the city. In ATSP case, range from city A to city B is not the same as city B to city A. The purpose from this problem is to find a route that conclude the shortest possible route for the salesman. In this experiment, Farmland Fertility (FF) Algorithm is used to find the solution for ATSP problem. FF is a metaheuristic that inspired from farming activity. FF sees farmers that farm to get a high-quality plant to sell with high price. The farmers usually divide their farmland into some sections, and they try to give that section special materials or treatments depend on the soils. There are 3 parameters that will be the focus in this experiment. This study will design the farmland fertility algorithm to solve ATSP and find which parameters affect the result. The 3 parameters are '''' that act as special materials to give to the worst section, '''' that act as special materials to give to other sections, and '''' is possible combining soils. ANOVA is used for 27 combinations parameter to implement into five ATSP benchmarks. From the result of ANOVA, '''' have a significant impact to the algorithm performance. After all the parameters are set, the algorithm is implemented to study cases from BR17, FTV33, FTV44. FTV55 and FTV70. This algorithm only can find the best known solution on BR17, while on other study cases this algorithm cannot find the best known solution. PubDate: 2021-12-28 DOI: 10.46923/ijets.v3i1.105 Issue No:Vol. 3, No. 1 (2021)
Authors:Enny Sela, Fredianto, Suhirman, Ardhi Wicaksono Santoso Pages: 20 - 28 Abstract: Medicinal plants have benefits for preventing or curing various diseases. The number of medicinal plants and the lack of knowledge about the types of medicinal plants make it difficult for people to distinguish the types of medicinal plants. This difficultness causes people to prefer to use chemical drugs rather than medicinal plants. This study develops a system of identification of medicinal plants. There are four steps to build the system: input leaf images, pre-processing, invariant moment feature extraction, and K-Nearest Neighbours (K-NN) pattern recognition. A 100 images samples images from 5 types of medicinal plants were involved in this study. The identification process of leaf image begins with the cropping, resizing process, and several morphological operations. Then feature extraction stage uses invariant moments. The final stage of pattern recognition uses K-NN. The result of this research is that the system can identify the types of medicinal plants. Using the Manhattan distance, the study archives the highest average accuracy. PubDate: 2021-12-28 DOI: 10.46923/ijets.v3i1.114 Issue No:Vol. 3, No. 1 (2021)
Authors:Fredi Prima Sakti, Haidar Rahman, Ikrima Alfi, Ridwan Budi Prasetyo Pages: 29 - 35 Abstract: One of the main issue that Baron Techno-Park (hybrid power plant) is facing are the practices of finding a suitable maintenance strategy. Operation and maintenance (O&M) of wind turbines are heavily affected by weather condition, particularly wind conditions. Blade failures, such as blade breakages, can lead to catastrophic consequences. The causes of blade breakages in Baron Techno-Park is due to unpredictable high wind speed from different directions. A technique that this research propose to implement a maintenance strategy in order to create an efficient O&M and also prevent the breakage of the wind turbine blades, is by using the Artificial Neural Network (ANN). ANN performance is satisfactory with the wind speed error of 30.25 % and wind direction error of 13.74 %. Also, R2 has a highest prediction of 0.998. Analyzing the survival wind speed of 60 m/s which is specify in the wind turbine specification. Analyzing the prediction results. It is safe to say that during the month of July 2021, it is not necessary for a maintenance schedule. PubDate: 2021-12-28 DOI: 10.46923/ijets.v3i1.115 Issue No:Vol. 3, No. 1 (2021)
Authors:Wiji Nurastuti Pages: 36 - 42 Abstract: Era revolusi industri 5.0 atau Society 5.0 yaitu pemecahan masalah sosial dengan bantuan dari integrasi ruang fisik dan virtual terkait teknologi big data yang terkumpul atas Internet of things (IoT) yang mana diubah oleh Artifical Inteligence (AI) menjadi sesuatu yang bisa membantu khususnya masyarakat sehingga kehidupan menjadi lebih baik. Era Society 5.0 berdampak pada semua aspek dalam kehidupan bermasyarakat khusunya ekonomi dan pendidikan. Telah tercatat jumlah dari penduduk miskin secara nasional mencapai angka 27,54 juta jiwa pada bulan Maret 2021. Jumlah tersebut menurun sebanyak 0,01 juta orang dibandingkan dengan bulan September 2020. Namun angka tersebut mengalami peningatan sebanyak 1,12 juta orang dari bulan Maret 2020. Selain kasus kemiskinan yang terus mengalami penuruan dan peningktan, bidang pendidikan kerap terjadi diskriminasi di kehidupan masyarakat. Hal tersebut dipengaruhi atas munculnya distingsi yang dilihat dari sudut pandang masyarakat terhadap hak pendidikan perempuan dan laki-laki. Pendidikan khusnya perempuan pada era Society 5.0 untuk Generasi Z yaitu sebagai bagian dari suatu pertumbuhan, perkembangan, kemajuan zaman dalam teknologi serta digitalisasi untuk mendorong dan mendukung perempuan agar mampu berperan dalam segala aspek di masyarakat. Kemajuan teknologi pada era Society 5.0 menimbulkan banyak peluang dengan peningkatan akses untuk perempuan terhadap pendidikan, aktualisasi diri, dan karir. Namun di sisi menimbulkan beberapa tantangan yaitu dunia kerja yang semakin kompetitif, perubahan terhadap pola asuh anak, dan penyeimbangan peran. Salah satu cara mengatasi tantangan yaitu mengokohkan peran dan fungsi keluarga, relasi antar keluarga, melek media dan informasi, mengembangkan standar norma dan kultural, serta menerapkan pola komunikasi yang lebih demokratis. PubDate: 2021-12-28 DOI: 10.46923/ijets.v3i1.116 Issue No:Vol. 3, No. 1 (2021)
Authors:Joko Aryanto, Ahmad Tri Hidayat Pages: 43 - 47 Abstract: One of the implementation models of combinatorial optimization that has a high complexity is the preparation of lecture schedules. Scheduling optimization needs to be optimized based on the schedule reference constraints. This study aims to design an optimization model with dynamic scheduling slots every day. The method used is query re-optimization with schedule distribution in accordance with predetermined limits. The models designed include space models, lecture times, and schedules. The model uses data that is in sync with the schedule, such as classrooms (theory and practice), credit and course loads (odd and even), lecturer schedules (teaching) and course time slots, as well as lecture sessions per day. This research is able to produce an optimal class schedule with all combinations of plotted schedules. The results of plotting the schedule are as desired and there are no conflicting schedules. PubDate: 2021-12-28 DOI: 10.46923/ijets.v3i1.119 Issue No:Vol. 3, No. 1 (2021)
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