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Sriyanto .
STIE ATMA BHAKTI Surakarta
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PREDIKSI PENYAKIT LIVER MENGGUNAKAN ALGORITMA RANDOM FOREST Kesuma, Martika; ., Sriyanto; ., Sutedi
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.499

Abstract

Diagnosing a disease using technology is no longer commonplace, with the continuous development of advances in the world of health, it can utilize technology in making decisions, especially detecting liver disease. Basically, the technology used can really help doctors compared to conventional manual analysis techniques which have been used to diagnose patient diseases. According to WHO (Word Heart Organization) in 2013, there were 28 million patients with liver disease in Indonesia. From this data, liver disease is referred to as one of the 10 diseases with the highest death rate. It would be very good if doctors could detect liver disease more frequently. quickly so that the patient can be immediately treated by a doctor. From the problems above that underlies the authors to conduct research on the classification of liver disease. In this study the authors wanted to predict liver disease using the Random Forest Algorithm. In selecting the right features and classifiers, the most important thing is to increase accuracy and computation in predicting liver disease. The researcher wants to know whether the Random Forest algorithm has a high accuracy value so that it can be a basis for using the Random Forest algorithm in predicting liver disease. Researchers used the Liver Disease Patient Dataset, in this research stage several steps were carried out starting from conducting Data Analysis, Exploratory Data Analysis, Preprocessing, Algorithmic Modeling, and Visualization. From this research stage, it can be seen the results of predicting accuracy using the Random Forest Algorithm. From the results of research conducted with the Random Forest algorithm, predictions were obtained with an accuracy value of 0.713326 with an f1 score of 81%.
PREDIKSI PENDAFTARAN PESERTA DIDIK BARU DENGAN METODE POLYNOMIAL REGRESSION, DAN K-MEDOIDS ., Noviana; Fartesa, Justi; Fauzi, Chairani; ., Sriyanto
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.528

Abstract

One school attempted to predict the acceptance of new students based on data from the previous year, but the results were inaccurate. The fluctuation in the number of new student admissions is a problem for SMK Negeri 2 Kotabumi in preparing class facilities, uniforms, books to support learning activities and determining steps and policies related to school promotion and targets for new student admissions in the following years. Predicting new student enrollment using the polynomial regression method, and K-Medoids, in processing student enrollment prediction data. The results obtained are Y values that are in accordance with the implementation results using python. For example, in 2018 the value Y = 0.0034x6 - 0.6194x5 + 46.754x4 - 1864.6x3 + 41412x2 - 485358x + 2E+06 = 1744.01 with R = 0.8779 accompanied by the same for each year, whereas for the K- Medoids method obtained in 2018 clustering 0 obtained 73 prospective students in the non-passing category and 19 in the pass category, while for 2019 to 2022 the number of cluster 0 is worth 0 and cluster 1 is worth 92 which means that all participants have passed
IMPLEMENTASI METODE REGRESI LINIER BERGANDA UNTUK ESTIMASI PENYAKIT GANODERMA DI PT NAKAU ., Kurniawati; Mawarni, Rima; ., Sriyanto; Aziz, RZ Abdul
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.538

Abstract

Sektor pertanian di Indonesia dibedakan menjadi tiga jenis yaitu perkebunan, sawah dan ladang. Dari ketiga jenis sektor pertanian, sektor perkebunan yang lebih banyak diminati dikarenakan pertanian jenis perkebunan cenderung memiliki nilai jual yang tinggi, pembudidayaan dalam skala besar, serta daya tariknya yang terus meningkat. Sektor tanaman perkebunan di Indonesia banyak didominasi oleh tanaman kelapa sawit, kakao, karet, tebu dan kopi, dari kelima tanaman ini kelapa sawit yang paling menguntungkan. PT. Nakau merupakan perusahaan yang tergolong dalam jenis perkebunan besar swasta (PBS) ini mulai melakukan proses penanaman kelapa sawit pada tahun 1999 sampai tahun 2011 dan melakukan tahap produksi ditahun 2004 hingga sekarang. Peneliti ini bertujuan untuk mengetahui perhitungan estimasi penyakit ganoderma menggunakan metode regresi linier berganda dengan aplikasi excel dan Rapidminer pada tahun 2021. Dan untuk menganalisa hasil perhitungan metode regresi linier berganda, sehingga dapat diketahui prediksi penyakit ganoderma dan dapat dilakukan perawatan sejak dini sehingga dapat mengoptimalkan produksi kelapa sawit pada PT Nakau. Hasil prediksi dari tahun 2016-2020 untuk tahun 2021 memiliki hasil sebanyak 1054,688 hasil perhitungan RapidMiner dan hasil perhitungan pada microsoft excel yang mendapat hasil 767,641 yang memiliki selisih 28 %, sehingga dapat diambil kesimpulan untuk estimasi penyakit ganoderma pada tahun 2021 sebanyak 768 samapai dengan 1.055 batang yang terserang penyakit ganoderma. Prediksi ini akan dapat membantu pihak PT Nakau dalam menanggulangi penyakit ganoderma yang akan menyerang tanaman sawit pada tahun 2021.
Co-Authors ., Noviana Aziz, RZ. Abdul Fartesa, Justi Fauzi, Chairani Kesuma, Martika Kurniawati . Mawarni, Rima Sutedi Sutedi