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Tempola, Firman
Universitas Khairun
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Transportation Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Deteksi Dini Resiko Tuberkulosis di Kota Ternate: Pelacakan dan Implementasi Algoritma Klasifikasi Abd Hakim Husen; Andi Sitti Nur Afiah; Soesanti Soesanti; Firman Tempola
Jurnal CoSciTech (Computer Science and Information Technology) Vol 3 No 2 (2022): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v3i2.3986

Abstract

Tingkat Kematian akibat virus Tuberkolosis masih cukup tinggi. sebagaimana dilaporkan di Kota Ternate Provinsi Maluku Utara bawah pada tahun 2018 sebanyak 452 kasus / 100.000 penduduk. Dengan tingkat kematian mencapai 23 orang / 100.000 penduduk. Tingkat kematian yang begitu tentu harus ada Langkah-langkah preventif sehingga dapat mengurangi resiko kematian akibat dari penyakit TBC. Untuk perlu dilakukan proses pelacakan kepada pasien suspek TBC di kota ternate. Pada penelitian ini menggunakan sampel dari wilayah kerja Puskesmas Kalumata Kota Ternate. Dengan jumlah sampel yang digunakan sebanyak 100 sampel didapat 47% pasien beresiko TBC. Dimana 70% didominasi kaum laki-laki. Selanjutnya data-data yang telah dianalisis oleh dokter, selanjutnya dilakukan proses klasifikasi dengan menggunakan dua metode klasifikasi yaitu metode Support Vector Machine (SVM) dan Jaringan Saraf Tiruan. Namun sebelum diterapkan metode klasifikasi, terlebih dahulu dilakukan proses imputasi untuk penanganan missing value. Dalam penelitian digunakan imputasi modu. Hasil pengujian yang dilakukan didapat akurasi tertinggi untuk metode SVM sebesar 92,5%, sedangkan ketika menerapkan jaringan saraf tiruan didapat akurasi tertinggi sebesar 91,66%. Namun saat diterapkan proses validasi dengan menggunakan k-fold cross validasi didapatkan rata-rata akurasi tertinggi yaitu 85,08 % dengan menggunakan 3-fold dan algoritma yang diterapkan adalah jaringan saraf tiruan
Implementasi Metode Fuzzy Tsukamoto Untuk Memprediksi Besarnya Pemakaian Listrik Rumah Tangga Tempola, Firman; Sujabtri Kabau, Haan; Fuad, Achmad; Rosihan; Lutfi, Salkin
Computer Science and Information Technology Vol 5 No 2 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i2.6682

Abstract

Many people are still confused about ordering the voltage level for electricity use in newly built homes, so that for households that previously needed a voltage of 900 VA, it was sufficient but used 1300 VA and vice versa, if the use of a large voltage of 900 VA is not in accordance with the number of customers' needs, then there will be a disturbance in the balance between the amount of voltage used and customer needs, but by applying the fuzzy method this can be predicted. The aim of this research is to predict the voltage of household electricity usage using the fuzzy Tsukamoto method. This method was chosen because it is flexible, and has tolerance for existing data and also has the advantages of being faster in computing, more intuitive, and also produces accurate prediction values. The results of implementing the fuzzy Tsukamoto method were tested for accuracy using the Mean Absolute Percentage Error (MAPE) method. The first test used 20 data as test data and the second test used 30 test data, so that the results obtained from the first test were 8.35% and the second test was 11.77%. Based on the table of capability values of the MAPE method forecasting model, the results of implementing the fuzzy Tsukamoto method can be used to predict the amount of household electricity usage with a percentage value < 20%, which is a good forecasting model capability.
Co-Authors Abd Hakim Husen Abdul Mubarak Abdul Mubarak Abidin, Abdullah Zainal ACHMAD FUAD Achmad Fuad1 Aina Musdholifah Akbar Riodano Laisouw Amal Khairan Amal Khairan Anas Anas Anas Anas, Anas Andi Sitti Nur Afiah Assaf Arief, Assaf Azim Hisyam Baksir Faisal F Taran Ibnu, Wirda Sintia Ishak, Gunawan Jamil, Moh. Kapita, Syarifuddin N Latif, Nurdewi Lilik Sumaryanti Moh. Jamil Mubarak, Abdul Muhammad, Miftah Putra, Surya Adi Pratama Ranjasmara, Muhammad Rendy Pratama Retantyo Wardoyo Rosihan Rosihan Rosihan Rosihan, Rosihan rosihan, rosihan Saiful Do Abdullah Salkin Lutfi Salkin Lutfi Samlan Mahdi Sirajuddin, Hairil Kurniadi Soesanti Soesanti Sujabtri Kabau, Haan Sukmawati, Hesti Sukril I Wahid Syarifuddin N. Kapita Tavares, Ofelia Cizela da Costa Umar, Kiki Amelia Wahid, Sukril I