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All Journal SEIKO : Journal of Management & Business Journal of Information Technology and Cyber Security
Wardhani, Shinta Kusuma
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Prediction of Women's Potential Type 2 Diabetes with Similarity Classifier Based on P-Probabilistic Extension Dewi, Ratih Kartika; Wardhani, Shinta Kusuma
Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.9945

Abstract

Diabetes is a chronic disease that occurs when the pancreas can’t produce enough insulin or when the insulin hormone can’t be used effectively by the body. The condition of the increased blood sugar, known as hyperglycemia, is a short-term impact that often occurs in uncontrolled diabetes. Meanwhile, the long-term impact of uncontrolled diabetes can cause damage to various body systems, especially blood vessels and nerves. Early detection of diabetes in individuals who are susceptible to diabetes is the main key to control diabetes issues. Artificial intelligence can help this issue. Early diabetes detection with artificial intelligence can predict whether a person in the next 5 years has the potential to suffer from diabetes type 2 or not, based on six variables including 2-hour plasma glucose concentration in the oral glucose tolerance test, diastolic blood pressure, fold thickness triceps, body mass index, diabetes pedigree function, and age. The prediction was built by using similarity classifier based on p-probabilistic extension, trained with the Pima Indian Diabetes dataset with women as research subjects. The contribution of this research is to select representative features in the Pima Indian diabetes dataset then implement them with similarity classifier based on P-Probabilistic Extension. The aim of this study is to compare similarity classifier algorithm with K-nearest neighbor as classifier that widely used in Pima Indian diabetes dataset. The test scenario is carried out by dividing 70% of the training data and 30% of the testing data, then the accuracy for the Pima Indian diabetes data will be compared with K-nearest neighbor and the similarity classifier. Accuracy shows a success value of 75.38%, so the similarity classifier that is built can be used to predict potential diabetes with better performance than K-nearest neighbor.
Strategi Optimalisasi Pemberdayaan Potensi Perikanan Air Tawar Dengan Menggunakan Ecosystem Mapping (Studi Kasus Pada Platform Digital MinaKita) Wardhani, Shinta Kusuma; Tricahyono, Dodie
SEIKO : Journal of Management & Business Vol 7, No 2.1 (2024)
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v7i2.7531

Abstract

Penelitian ini dilakukan untuk mengetahui aktor yang berperan, bagaimana hubungan antar aktor, peta hubungan industri dengan pendekatan ecosystem mapping, dan bagaimana strategi optimalisasi pemberdayaan. Metode penelitian yang dipakai ialah ecosystem mapping menggunakan model Ecosystem Pie Model (EPM) dengan bantuan in-depth interview terhadap informan terkait. Penelitian ini menggunakan teknik value analysis untuk menangkap analisis secara menyeluruh sehingga diperoleh kebutuhan dan hubungan antar aktor melalui penangkapan nilai. Strategi optimalisasi dirancang dengan gagasan pengelompokan elemen dengan pemodelan Business Model Canvas (BMC). Hasil penelitian memperlihatkan bahwa terdapat beberapa aktor yang fungsinya tidak berkaitan dengan aktor lainnya, sehingga tidak berdampak pada ekosistem bisnis digital MinaKita, yaitu: Dinas Perindagkop dan UMKM, Asosiasi, dan Universitas. Sebaliknya, Tengkulak dan Dinas Komunikasi Informatika adalah dua aktor yang paling berpengaruh dan paling berisiko untuk diajak bekerjama. Namun, apabila keduanya dapat dirangkul maka akan memberikan manfaat yang tinggi, yaitu memperluas saluran distribusi dan dukungan regulasi pemerintah. Melalui analisis pada resource, activities, value addition, dan value capture akan tercipta peluang nilai yang saling berkaitan untuk menciptakan alur bisnis yang efektif dan efisien melalui kolaborasi antar aktor. Penelitian ini juga menyarankan kerjasama seluruh anggota ekosistem bisnis digital platform MinaKita untuk mampu mengoptimalkan potensi perikanan air tawar, khususnya di Kabupaten Klaten. Kata Kunci: Platform Digital, Ecosystem Pie Model, Ekosistem Bisnis.
Co-Authors Dodie Tricahyono Ratih Kartika Dewi