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All Journal SISFOTENIKA Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Riset Informatika
Siti Masturoh
Information System, Faculty Of Information Technology Universitas Nusa Mandiri
Computer Science & IT

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COMPARISON OF ACCURACY MEASUREMENTS IN MOTION SENSORS AND HEART RATE MEASUREMENTS USING ANALYTICAL HIERARCHY PROCESS METHODS Tomi Lifti Novier; Nurmalasari Nurmalasari; Widi Astuti; Siti Masturoh; M. Rangga Ramadhan Saelan
Jurnal Techno Nusa Mandiri Vol 18 No 2 (2021): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v18i2.2547

Abstract

The use of motion sensors in measuring heart rate using smartwatch applications is currently a trend. Everyone is very helpful for measuring their own heart rate. This research is about the comparison of accuracy in motion sensors and measuring heart rate using the Analytical Hierarchy Process (AHP) method. Every technology and application in motion sensor measurement in heart rate measurement has almost the same features and uses as Xiaomi, Samsung, and Apple Inc. From the calculations carried out by the researcher, it shows that the field/stadium that is the most chosen by the community (respondents) is by Random Sampling, with the acquisition of a value of 0.490 aka 49.00%. The second is Treadmill with a value of 0.294 aka 29.40%. the overall value is 0.216 aka 21.60% The alternative that is most chosen by the community (respondents) is the field/stadium. The Analytical Hierarchy Process method can make it easier for prospective technology users to be able to measure the accuracy of motion sensors and detect heart rates, the AHP method makes product decisions based on criteria and alternatives contained in the hierarchy, the results of the study are Apple Inc. as the respondent's choice for technology that is trusted to measure better accuracy on the motion sensor and measure heart rate.
Clustering of Clean Water Needs in Indonesia for the 2012-2017 Period Using the K-Means Algorithm Daniati Uki Eka Saputri; Taopik Hidayat; Siti Masturoh
SISFOTENIKA Vol 12, No 2 (2022): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v12i2.1241

Abstract

The need for clean water is important to support all activities of human survival. Data from the Central Statistics Agency (BPS) in 2017 showed the highest number of clean water distribution in each province was only 72.04%. These data indicate that access to clean water to meet daily needs is still far from sufficient. This study aims to classify the need for clean water for the period 2012-2017 using the K-Means algorithm. The data source was obtained from the official BPS website, namely data on the volume of clean water distributed to each province in Indonesia in 2012-2017. The process of replacing missing values was carried out on the missing data, then the data were grouped into three clusters, namely low (C0) in 25 provinces, high (C1) in 4 provinces, and moderate (C2) in 5 provinces using the K-Means algorithm. The centroid value for the C0 cluster is 150588.24, the centroid data for the C1 cluster is 1939461, the centroid data for the C2 cluster is 857876.6. The results of the K-Means clustering were tested using the Davies Bouldin Index (DBI) Validation as many as 3 clusters with a value of 0.534, the cluster results were optimal because the DBI value was close to 0.
Classification for Papaya Fruit Maturity Level with Convolutional Neural Network Nurmalasari Nurmalasari; Yusuf Arif Setiawan; Widi Astuti; M Rangga Ramadhan Saelan; Siti Masturoh; Tuti Haryanti
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.541

Abstract

Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. During storage, the papaya fruit is initially green, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.
Classification for Papaya Fruit Maturity Level With Convolutional Neural Network Nurmalasari Nurmalasari; Yusuf Arif Setiawan; Widi Astuti; M. Rangga Ramadhan Saelan; Siti Masturoh; Tuti Haryanti
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1169.294 KB) | DOI: 10.34288/jri.v5i3.225

Abstract

Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. The papaya fruit is initially green during storage, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.
Co-Authors Daniati Uki Eka Saputri M. Rangga Ramadhan Saelan Nurmalasari Nurmalasari Taopik Hidayat Tomi Lifti Novier Tuti Haryanti Widiastuti Widiastuti Yusuf Arif Setiawan