Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System

Izzah, Abidatul; Widyastuti, Ratna

doi:10.26594/r.v2i2.548

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Setelah data didapat berupa logika samar yang akan dilanjutkan dengan pengolahan dengan menggunakan mesin inferensi. Hasil keluaran diubah kembali menjadi angka pasti dengan deffuzifikasi (defuzzyfication) seperti terlihat dalam gambar di bawah ini: [10].…”

Section: Metode Penelitianunclassified

Fuzzy Inference System for The Risks Pregnancy Detection

Khairul Fuady,

Zulisa

2023

Digitalzone

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Abstract One of the causes of the high Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR) in Aceh is the delay in handling cases of at risk pregnancies due to the lack of availability of easily accessible and well-documented information about conditions during pregnancy. So far, manual reporting through data recapitulation report was difficult to access quickly if there are cases maternal and infant mortality. Fuzzy logic can be used as an alternative for classifying pregnant women at risk with supporting data when examining pregnant women. Data related to pregnancy checks will be analyzed with a Fuzzy Inference System (FIS) to obtain information on pregnancy risks. The results of this study indicate that FIS can determine the risk of pregnancy more detailed range than using a manual scoring card. The results of the defuzzyfication value will describe the final decision related to pregnancy risk which can be categorized into low risk, high risk and very high risk. The problem solving steps in this study can be used for algorithms in the development of application programming for risky pregnancy early detection systems based on programming languages.

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Section: Metode Penelitianunclassified

Fuzzy Inference System for The Risks Pregnancy Detection

Khairul Fuady,

Zulisa

2023

Digitalzone

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“…Classifier evaluations are calculated using measures of accuracy, sensitivity, and specificity. Of the three measures obtained an accuracy of 94.33%, a sensitivity of 96.55% and a specificity of 84.21% (Izzah, A., and Widyastuti, R., 2016). Kusumadewi in 2004 to analyze connection of factors between lecturer and student.…”

Section: Previous Research Conducted By Annisa In 2015mentioning

confidence: 99%

Identification of Student Academic Performance using the KNN Algorithm

et al. 2020

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Students are an important asset in the world of education also an institution and therefore also need to pay attention to students' graduation rates on time. The ups and downs of the percentage of students' abilities in classroom learning is one important element for assessing university accreditation. Therefore, it is necessary to monitor and evaluate teaching and learning activities using the KNN Algorithm classification. By processing student complaints data and seeing the results of previous learning can obtain important things for higher education needs. In predicting graduation rates based on complaints, this study uses the K-Nearest Neighbor classification algorithm by grouping data k = 1, k = 2, k = 3 with the smallest value possible. In experiments using the KNN method the results were clearly visible and showed quite good accuracy. From the experiment it was concluded that if there were fewer complaints from one student it could minimize the level of student non-graduates at the university and ultimately produce good accreditation.

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“…Kelebihan dari metode FIS adalah dapat melakukan perhitungan non rigid (terenkripsi), sehingga kemungkinan ketidakpastian dapat dipertimbangkan [5]. Metode fuzzy Inference System Tsukamoto berbentuk IF-THEN yang direpresentasikan dengan menggunakan himpunan fuzzy dengan fungsi keanggotaan.…”

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E-Diagnosis Gangguan Kecemasan Menyeluruh Menggunakan Fuzzy Inference System (FIS) Tsukamoto

Daivan,

Saripurna,

Siambaton

2024

hello world j. ilmu kompʹût.

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Perkembangan teknologi saat ini telah banyak mengalami kemajuan. Banyak inovasi yang telah diciptakan dan dikembangkan mulai dari yang paling sederhana hingga yang paling kompleks. Dengan berkembangnya ilmu-ilmu teknik seperti informatika dan komputer, lahir suatu bentuk kecerdasan buatan (artificial intelligence) yang disebut Sistem Pakar. Sistem pakar adalah program komputer yang meniru proses berpikir dan pengetahuan pakar dalam memecahkan masalah tertentu. Sistem pakar dapat diterapkan pada berbagai bidang, salah satunya bidang kesehatan. Misalnya dalam mendiagnosis penyakit Gangguan kecemasan menyeluruh. Gangguan kecemasan menyeluruh (Generalized Anxiety Disorder) adalah suatu gangguan psikologis yang berhubungan dengan adanya gangguan mental. Gangguan kecemasan menyebabkan penderitanya mengalami gangguan kecemasan berlebihan, kegelisahan, mudah lelah, kesulitan berkonsentrasi, lekas marah, ketegangan otot, dan gangguan tidur. Tujuan yang akan dicapai dalam penelitian ini adalah mengembangkan sistem pakar yang dapat diakses langsung oleh pengguna demi memudahkan pengguna dalam mengidentifikasi penyakit gangguan kecemasan menyeluruh. Dalam penelitian ini metode pemecahan masalah yang akan digunakan adalah algoritma Fuzzy Inference System (FIS) yang merupakan salah satu algoritma yang digunakan untuk pendukung keputusan. Hasil penelitian yang di dapat berupa sistem yang dapat menghasilkan diagnosis awal yang dapat membantu sesorang dalam mendiagnosis gangguan kecemasan menyeluruh. Dengan membandingkan sistem yang telah dibuat dengan data diagnosis satu pasien yang telah dilakukan secara manual dengan pakar. Dengan kata lain sistem dapat melakukan diagnosis awal terhadap penyakit gangguan kecemasan menyeluruh.

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Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System

Cited by 4 publications

References 4 publications

Fuzzy Inference System for The Risks Pregnancy Detection

Fuzzy Inference System for The Risks Pregnancy Detection

Identification of Student Academic Performance using the KNN Algorithm

E-Diagnosis Gangguan Kecemasan Menyeluruh Menggunakan Fuzzy Inference System (FIS) Tsukamoto

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