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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Paradigma Faktor Exacta
Ganda Wijaya
STMIK Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Pengaruh Media Terhadap Pengambilan Keputusan Dalam Menjalankan Program Keluarga Berencana Dengan Algoritma Decision Tree Ali Mustopa; Siti Khotimatul Wildah; Ganda Wijaya; Windu Gata; Sarifah Agustiani
Paradigma Vol 22, No 2 (2020): Periode September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (64.58 KB) | DOI: 10.31294/p.v22i2.8141

Abstract

Indonesia has become one of the countries with a diverse population so that it has the potential to experience social change, one of which is the influence of the media. Media is an information content that is almost a part of human life. One of the impacts of the media is in the health sector, one of which is in determining the Family Planning program. Family planning is one of the Indonesian government programs designed to reduce the speed of population growth. Since the implementation of the Family Planning Program in Indonesia many tools have been used to prevent pregnancy, namely contraception. Selection of a good contraction is certainly one important thing to plan. In determining good kotrasespi certainly there are influences from various things one of which is the media. Measurement of the influence of the media in determining the Family Planning program can be known by applying data mining. Research conducted with data mining uses a standard methodology called the Cross-Industry Center Process for Data Mining (CRISP-DM). The use of decissin tree in this study was done by comparing the same method by looking at the results of three models namely Split Validation, Cross Validation and Decision Tree Split. The results of Split Validation produce an accuracy of 90.50%, Cross Validation produces an accuracy of 91.58% and Decision Tree Split produces an accuracy of 89.83%. The best results are obtained by using cross validation where with the results of research on 1473 records the accuracy value is 91.58% and the AUC value is 0.690, where the results are obtained from the calculation of the True Positive (TP) 1328, False Negative (FN) ) 36, False Positive (FP) is 88 and True Negative (TN) 21. Exposure to the media is said to be good or influential if they do not have children and are Muslim and educate their husbands in junior high school with a low standard of living but the wife has a college education.  Keywords: Family Planning, Media Exposure, Data Mining, Decision Tree.
Prediksi Waktu Kelulusan Mahasiswa Menggunakan SVM Berbasis PSO Suhardjono S; Ganda Wijaya; Abdul Hamid
Bianglala Informatika Vol 7, No 2 (2019): Bianglala Informatika 2019
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (783.749 KB) | DOI: 10.31294/bi.v7i2.6654

Abstract

Waktu kelulusan dengan tepat waktu bagi mahasiswa sangatlah penting untuk menentukan pekerjaan dalam perkuliahan, maka dari itu perlu di prediksi kelulusan mahasiswa sebelum akhir semester dengan menggunakan model support vector machine yang memiliki keuntungan dalam membuat data menjadi optimal tetapi support vector machine memiliki kekurangan dalam pengoptimal parameter. Particle swarm optimization dapat memperbaiki kekurangan yang terdapat pada support vector machine dalam hal mengoptimalkan parameter. Dari hasil yang didapat dengan menggunakan model support vector machine berbasis particle swarm optimization dapat meningkatkan akurasi prediksi dari sebesar 85.81% menjadi 86.43%. dengan kenaikan sebesar 00.62%. Sehingga dalam memprediksi kelulusan mahasiswa dapat akurat dan secara optimal dalam mengukur parameter yang diperlukan
SISTEM BERBASIS CLOUD COMPUTING UNTUK IDENTIFIKASI RESEP DOKTER “BARSEP” Irwansyah Saputra; ANDI SARYOKO; GANDA WIJAYA; MEILYNDA TRISIANA; ASEP MULYANA; DANDI YUSBIAL BAYANI; DHARMA WINATA; VILSAFA KHOIRUNNISAK
Faktor Exacta Vol 13, No 4 (2020)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v13i4.7569

Abstract

A doctor's prescription is a doctor's written request to the pharmacist to prepare and give medicine to the patient. Prescriptions are made according to the needs of the patient after the doctor has examined and diagnosed the patient. However, doctor’s writing on a prescription that considered unclear can cause errors when compounding / preparing the drug and using prescribed drugs. In fact, the cure rate and life expectancy of patients is directly proportional to the administration of the right medicine. This study aims to prevent errors in the process of identification of prescription drugs by pharmacists. The technology used is cloud computing with the PHP 7.1.3 programming language, Laravel framework, and database storage using MySQL. BarSep application works by adding QR Code on recipe paper. The QR Code contains patient examination information including patient data, prescription drugs, and diagnoses, so that when the pharmacist scans the QR Code, the system will display all patient information that has been inputted by the doctor at the time of the examination. The results obtained from the implementation of the BarSep application at the Rapha Farma Pharmacy is BarSep applications effective for tackling errors in reading doctor's prescriptions that can save patients from medication errors. A doctor's prescription is a doctor's written request to the pharmacist to prepare and give medicine to the patient. Prescriptions are made according to the needs of the patient after the doctor has examined and diagnosed the patient. However, doctor’s writing on a prescription that considered unclear can cause errors when compounding / preparing the drug and using prescribed drugs. In fact, the cure rate and life expectancy of patients is directly proportional to the administration of the right medicine. This study aims to prevent errors in the process of identification of prescription drugs by pharmacists. The technology used is cloud computing with the PHP 7.1.3 programming language, Laravel framework, and database storage using MySQL. BarSep application works by adding QR Code on recipe paper. The QR Code contains patient examination information including patient data, prescription drugs, and diagnoses, so that when the pharmacist scans the QR Code, the system will display all patient information that has been inputted by the doctor at the time of the examination. The results obtained from the implementation of the BarSep application at the Rapha Farma Pharmacy is BarSep applications effective for tackling errors in reading doctor's prescriptions that can save patients from medication errors.                                                 
Co-Authors Abdul Hamid Agustiani, Sarifah Ali Mustopa, Ali Andi Saryoko ASEP MULYANA DANDI YUSBIAL BAYANI DHARMA WINATA Irwansyah Saputra MEILYNDA TRISIANA Siti Khotimatul Wildah Suhardjono S VILSAFA KHOIRUNNISAK Windu Gata