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All Journal Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) BEES: Bulletin of Electrical and Electronics Engineering
Fitri Anggraini
STIKOM Tunas Bangsa, Pematangsiantar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Rancang Bangun Inverter Mengubah Arus Listrik DC ke AC Berbasis Arduino Uno Risky Binsar Pandapotan Simanjuntak; M Safii; Fitri Anggraini; Sumarno Sumarno; Indra Gunawan
Journal of Computer System and Informatics (JoSYC) Vol 2 No 4 (2021): Agustus 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v2i4.838

Abstract

At this time electrical energy is needed in terms of helping humans in carrying out their activities both in doing their daily work. In this case, it is impossible for there to be problems in periodic blackouts to save electricity resources carried out by PLN and to disrupt all human activities starting from the tools that require electrical energy. For this reason, it is necessary to anticipate by making an inverter which aims to make all the activities they do using electrical energy are not disturbed. This tool is assisted by using Arduino Uno as the main ingredient which later DC electrical energy, namely the battery, will convert electrical energy that we usually use AC electrical energy
Prototype Alat Pengecekan dan Penyortir Kesegaran Cabai Berdasarkan Warna Menggunakan Sensor Tcs230 Berbasis Arduino Elsa Indah Sari; Suhada Suhada; Fitri Anggraini; Dedy Hartama; Ika Okta Kirana
BEES: Bulletin of Electrical and Electronics Engineering Vol 2 No 1 (2021): July 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (594.381 KB) | DOI: 10.47065/bees.v2i1.762

Abstract

This study aims to create an arduino- based tool to determine the freshness of red chillies using the TCS230 color sensor to facilitate chilli traders, supermarkets and markets in choosing good and bad quality based on the color of chili. Fresh chili will be red while the bad ones are usually dull red, slightly brownish and yellowish. TCS230 converts the color of light to fermentation with a square signal output with a frequency that is proportional to the current. The working principle is that the chili passes through the TCS230 sensor from the display of this tool, it will be seen the amount of color based on the specified chili criteria will produce chili output through checking and sorting the chillies of good and bad quality.
Peramalan Jumlah Kasus Baru HIV Menurut Provinsi Menggunakan Machine Learning dengan Teknik Levenberg-Marquardt Irfani Zuhrufillah; Fitri Anggraini; Rizki Dewantara
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2172

Abstract

Early detection of HIV is a crucial step to reducing transmission and increasing the success of HIV treatment. The sooner HIV is detected, the sooner treatment can be carried out so that this infection can be controlled and does not develop into AIDS. Therefore, the purpose of this study is to forecast the Number of New HIV Cases in Indonesia based on 34 Provinces so that the government can obtain information early on to determine the right policy to suppress the increasing number of new HIV cases in Indonesia. This research proposes forecasting using a Machine Learning algorithm with the Levenberg-Marquardt technique. The research data is data on the number of new HIV cases by province obtained from the 2021 Indonesian Health Profile book issued by the Ministry of Health of the Republic of Indonesia. This research will be analyzed using three network architecture models, 3-15-1, 3-20-1 and 3-25-1. Based on the analysis of the three models used, the results show that the 3-15-1 model is the best because it produces a higher accuracy level than the other two models, which is 88%. It can be concluded that the Levenberg-Marquardt technique with the 3-15-1 model is quite suitable for forecasting new cases of HIV in Indonesia. Based on the prediction results, the number of new HIV cases by the province in Indonesia at the end of 2022 decreased significantly compared to 2021, which was 24668 compared to 36902 or reduced by around 12 thousand cases.
Pemanfaatan Machine Learning dengan Algoritma X-Means untuk Pemetaan Luas Panen, Produktivitas, dan Produksi Padi Irma Hakim; M. Rafid; Fitri Anggraini
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2654

Abstract

Rice plants are essential for the world, especially Indonesia because it is a rice-producing plant that is useful as a staple food for its people. A decreased harvest area, production, and rice productivity can affect food availability. Therefore, this research aims to classify and map the harvested area, production, and productivity of rice in Indonesia based on each province. The research data used in this paper is data on the harvested area (ha), production (tons), and rice productivity (Ku/ha) by Provinces in Indonesia for 2020-2022 obtained from the Indonesian Central Bureau of Statistics website. In this study, the algorithm used is X-Means Clustering with the help of the Rapid Miner application. The results of this study are in the form of grouping or mapping of harvested area, production, and productivity of rice, divided into 3 (three) regions, including 1. Harvested Area (divided into five groups: Very high Harvested Area consists of 3 provinces, High Harvested Area consists of 1 province, Medium Harvest Area consists of 3 Provinces, Low Harvest Area consists of 8 Provinces, and Very low Harvest Area consists of 19 Provinces 2. Rice Production Area (divided into five groups: Very high rice production consists of 3 provinces, Rice production High rice production consists of 1 province, Medium rice production consists of 3 Provinces, Low rice production consists of 8 Provinces, and Very low rice production consists of 19 Provinces 3. Regions of Rice Productivity (divided into five groups: Very high rice productivity consists of 6 provinces, High Rice Productivity consists of 13 provinces, Medium Rice Productivity consists of 7 Provinces, Low Rice Productivity consists of 4 Provinces, and Very Low Rice Productivity consists of 4 Provinces. This can be information for the Indonesian government, especially for the respective provincial governments, to be able to maintain the harvested area, production, and productivity of rice in Indonesia to remain stabel.
Co-Authors Dedy Hartama Dewantara, Rizki Elsa Indah Sari Ika Okta Kirana Indra Gunawan Irfani Zuhrufillah Irma Hakim M Safii M. Rafid Risky Binsar Pandapotan Simanjuntak Suhada Suhada Sumarno Sumarno