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All Journal TEKNIK INFORMATIKA Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Khatulistiwa Informatika Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Techno Nusa Mandiri : Journal of Computing and Information Technology Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Informasi dan Teknologi Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Sosial dan Abdimas INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Journal Medical Informatics Technology SWAGATI: Journal of Community Service
Sri Hadianti
Department Of Information System, STMIK Nusa Mandiri, Jl. Raya Jatiwaringin No 2, Cipinang Melayu, Makasar, Jakarta Timur 13620|STMIK Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Pilar Nusa Mandiri

 1 
PENYELEKSIAN JURUSAN TERFAVORIT PADA SMK SIRAJUL FALAH DENGAN METODE SAW Nurlela, Siti; Akmaludin, Akmaludin; Hadianti, Sri; Yusuf, Lestari
Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1096.159 KB) | DOI: 10.33480/pilar.v15i1.1

Abstract

SMK Sirajul Falah is a Vocational High School located in the Bogor area. However, the selection of the favorite majors in SMK Sirajul Falah is still qualitative so that the process of choosing the favorite majors become not accurate. This is what makes the need for a method that is able to manage the data of the selection of the favorite majors and generate a ranking of the calculation of the weight of the selection of the favorite majors. In the selection of these favorite majors, there is a method of Simple Additive Weighting (SAW) which can be used in quantitative problem-solving. The SAW method is used to compare each criterion with one another, so as to give the results of the favorite majors and provide an assessment of each department at the Sirajul Falah Vocational School.
PREDICTION OF SURVIVAL OF HEART FAILURE PATIENTS USING RANDOM FOREST Rahayu, Sri; Purnama, Jajang Jaya; Pohan, Achmad Baroqah; Nugraha, Fitra Septia; Nurdiani, Siti; Hadianti, Sri
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1665

Abstract

Human survival, one of the roles that is controlled by the heart, makes the heart need to be guarded and be aware of its damage. Heart failure is the final stage of all heart disease. The medical record tool can measure symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistical analyzes but to highlight patterns and correlations not detected by medical doctors. So technology assistance is needed to do this in order to predict the survival of heart failure patients. With data mining techniques used in the available history data, namely the Heart Failure Clinical Records dataset of 299 instances on 13 features used the Random Forest algorithm, Decision Tree, KNN, Support Vector Machine, Artificial Neural Network and Naïve Bayes with resample and SMOTE sampling techniques. The highest accuracy with the resample sampling technique in the random forest is 94.31% and the SMOTE technique used in the random forest produces an accuracy of 85.82% higher than other algorithms.
Co-Authors Achmad Baroqah Pohan Achmad Rifai Adilah Purnamasari Aditya Gusti Aditya, Diyar Akmaludin Akmaludin, Akmaludin Aldi Jakaria Arina Selawati Azhari, Azidan Allen Deva Aziz, Faruq Chairul, Yasrizal Chandra Nugraha, Chandra Daniati Uki Eka Saputri Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Fani Nurona Cahya Fatimah, Winda Astariyah Fiqi Pranastara Frieyadie Galih Widagdo Heckel Sungkar Iftina Salya Rosa Izni Nur Karimah Jajang Jaya Purnama Kodri, Wan Ahmad Gazali Kurniawantoro Kusumayudha, Mochammad Rizky Laela Kurniawati Lestari Yusuf Mayangky, Nissa Almira Mochamad Rizky Kusumayudha Mochammad Rizky Kusumayudha Muhamad Hasan Muhammad Faitullah Akbar Nanik Wuryani Nila Hardi Nugraha, Fitra Septia Nur Fiyah Nur Sulistyowati, Daning Nurfalah, Ridan Nurul Jannah Salsabila Pioni, Pioni Putra, Mahesa Rafly Pratama Ramadhan, Mohamad Ragil Resti Kartika Dewi Ridan Nurfalah Ridan Nurfalah Riski Febrianto Rizka Dahlia Rizky Ade Safitri Rosalina, Alya Rousyati Rousyati Rudini, Edwin Saputra, Ridwan Sari, Putri Permata Siti Nurdiani siti nurlela Siti Nurlela Sofiyatul Mu’minah SRI RAHAYU Sri Rahayu Sugiyanti Sugiyanti Sulistyowati, Daning Nur Widagdo, Galih Windu Gata WITRIANA ENDAH PANGESTI Yutri Amelia