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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Journal of Animation & Games Studies JMM (Jurnal Masyarakat Mandiri) Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) KOMPUTIKA - Jurnal Sistem Komputer Jurnal Sistem Cerdas Care : Jurnal Ilmiah Ilmu Kesehatan Jurnal Ilmiah Inovasi J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) JURNAL ILMU KOMPUTER INDONESIA Jurnal Teknologi Informasi dan Multimedia Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA International Journal of Healthcare and Information Technology
I Gede Wiryawan
Program Studi Teknik Komputer/Jurusan Teknologi Informasi, Politeknik Negeri Jember
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Public Health Other

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Pengaruh Prediksi Missing Value pada Klasifikasi Decision Tree C4.5 Arifianto, Aji Seto; Dewi Safitri, Kursita; Agustianto, Khafidurrohman; Wiryawan, I Gede
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022944778

Abstract

Pendekatan klasifikasi data bersifat supervised learning menuntut adanya dataset yang lengkap. Permasalahan yang muncul adanya missing value yaitu hilangnya nilai suatu atribut yang diakibatkan kesalahan dalam pengumpulan data, kesalahan saat memasukkan data, dan ketidakmampuan responden untuk memberikan jawaban yang akurat. Penelitian ini melakukan uji coba pengembangan rule decision tree C4.5 untuk data penyakit ginjal kronis. Dataset terdiri dari 400 record, 24 atribut dan 1 kelas target. Karakteristik data yang digunakan meliputi 11 data bertipe numerik dan 14 data bertipe nominal. Jumlah data kelas positif penyakit ginjal kronis 250, sedangkan negatif ginjal kronis 150. Total data yang tidak lengkap (missing value) 1012 records. Perlakuan pertama dibangun rule dengan menghitung entropy dan gain pada 360 data training yang terdapat missing value diperoleh 21 rules. Kemudian pada perlakuan kedua diterapkan prediksi missing value menggunakan rumus mean dan modus sebelum pembetukan rule tree, didapatkan 24 rules. Mengukur akurasi kedua rules tree C4.5 dilakukan menguji 40 data test, hasilnya 90% untuk rule dengan missing value dan 95% untuk dataset yang telah diprediksi nilainya. AbstractThe supervised learning approach to data classification requires a complete dataset. The problem that arises was the existence of missing value, namely the loss of the value of an attribute due to errors in data collection, errors when entering data, and the inability of respondents to provide accurate answers. This study conducted a trial on the development of the C4.5 rule decision tree for chronic kidney disease data. The dataset consisted of 400 records, 24 attributes and 1 target class. The data characteristics included 11 numeric data and 14 nominal data types. The number of positive data for kidney disease was 250, while the number of negative for kidney disease was 150 and the total of missing value was 1012 records. The first treatment was building a rule by calculating the entropy and gain on 360 training data where missing value was obtained, it was 21 rules. Then in the second treatment, the prediction of missing value was applied using the mean and mode formula before the formation of the rule tree, obtained 24 rules. Researcher was measuring the accuracy of the two rules tree C4.5 is done by using 40 data-testing, the result is 90% for rules with missing value and 95% for datasets whose value has been predicted.
SENAM VITALISASI OTAK: UPAYA EFEKTIF MENGUATKAN FUNGSI KOGNITIF, MENURUNKAN HIPERTENSI DAN EMOSIONAL PADA LANSIA Wiratma, I Wayan; Maba, I Wayan; Wiryawan, I Gede; Vipriyanti, Nyoman Utari
Care : Jurnal Ilmiah Ilmu Kesehatan Vol 9, No 3 (2021): EDITION NOVEMBER 2021
Publisher : Universitas Tribhuwana Tunggadewi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jc.v9i3.2304

Abstract

Aging is a life process characterized by decreased cognitive function. The decline in cognitive function will affect the health and quality of life of a person and their participation in society. One of the efforts to improve cognitive function is to do brain vitalizing exercises. Research Objectives: To determine the effectiveness of vitalizing brain exercise on cognitive function, hypertension and emotional. Research Methods This research design is included in the pre-experimental research design with one-group pretest-posttest design. This study used purposive sampling. The total population is 100 people and is strictly selected according to the inclusion criteria, namely seniors aged 60 years and over, seniors who are willing to be respondents, elderly people who are included in dementia sufferers, hypertension and emotional disturbances according to the interpretation of the mini mental state examination, blood pressure and geriatric depression scale, leaving 40 respondents. Interventions were carried out 24 times for 30 minutes in 8 weeks. The data were tested using a non-parametric statistical test, namely the Wilcoxon Rank Test. Results: the results of the mini mental state examination obtained p value = 0.00, meaning p 0.05 so that Ha is accepted and Ho is rejected, the hypothesis of the geriatric depression scale obtained p value = 0.00, meaning p 0.05 so that Ha is accepted and Ho is rejected, the hypothesis of hypertension is p = 0.000, meaning p 0.05 so that Ha is accepted and Ho is rejected Conclusion Brain vitalization exercise is effective to reduce cognitive function (dementia), hypertension and emotional mental disorders . Suggestion 
Hand Gesture Detection Implemented based on Long Short-Term Memory (LSTM) Method I Gede Wiryawan; Taufiq Rizaldi; Pramuditha Shinta Dewi Puspitasari; Arvita Agus Kurniasari
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.526

Abstract

The Indonesian government encourages accessibility of information that is friendly to people with disabilities, one of which is through the development of information and communication technology. Efforts to increase accessibility of information and encourage independence of people with disabilities need to be supported by the right solutions. According to the Central Statistics Agency, there were 0.68% of the total population of Indonesia in 2019, this data shows that deafness is one of the highest disabilities in Indonesia. Efforts to increase accessibility of information and encourage independence of people with disabilities need to be supported by the right solutions. One potential solution is the development of a self-service system that is friendly to the deaf. This study aims to develop a self-service system that is friendly to the deaf and helps in obtaining information and services independently. The results achieved in this study are in the application of hand signal detection using the Long Short-Term Memory method which can overcome the problem of long-distance dependency and improve performance in recognizing complex hand signal patterns. The hand signal recognition feature can be improved by overcoming the problem of long-distance dependency with a maximum user distance of 1.25 meters, the system can still recognize hand signals well. It is hoped that in the future, more in-depth studies can be carried out on long-distance dependency for variations of other hand signal recognition methods, so that people with disabilities can more easily use the self-service system.
Co-Authors Abdillah, Fauzan Ade Bagus Pratama, Ade Bagus Agus Purwadi Agustianto, Khafidurrohman Aji Seto Arifianto Albaab, Mochammad Rifki Ulil Andis Trihariprasetya Arvita Agus Kurniasari Arvita Agus Kurniasari Aziz, Afris Nurfal Aziz, Afris Nurfal Baskara Utoyo, Deva Bekti Maryuni Susanto Beni Widiawan, Beni Dewi Puspitasari, Pramuditha Shinta Dewi Safitri, Kursita dony setiawan hendyca putra Ely Mulyadi Ely Mulyadi Ernanta, Dimas Mulya Perkasa Estin Roso Pristiwaningsih Eva Rosdiana FAUZAN ABDILLAH Hudori, Huda Ahmad Indah Qurrothul Uyun Khafidurrohman Agustianto Khoiriyah, Hani'atul Khoiriyah, Hani’atul Linda Dwi Wahyuningsih Maulida Dwi Agustiningsih Meydiantika Anggia Putri, Tri Farin Muhammad Yusuf Mukhamad Angga Gumilang Muknizah Aziziah Mulyadi, Ely Nugroho Setyo Wibowo Nurfal Aziz, Afris Nurillahilwafi, Nurillahilwafi Nyoman Utari Vipriyanti Oktaviani, Siska Aprilia Perwiraningrum, Dhyani Ayu Pramuditha Shinta Dewi Puspitasari Prawidya Destarianto Puspitasari, Pramuditha Shinta Dewi Putra, Dhony Manggala Rani Purbaningtyas, Rani Sari, Sella Putri Taufiq Rizaldi Taufiq Rizaldi Trihariprasetya, Andis Vyan Ary Pratama Wayan Maba Wiratma, I Wayan Yogiswara Yogiswara Yuwita, Kirana